prepared for: marine and coastal management department of environmental affairs and tourism private...

An Economic and Sectoral Study of theSouth African Fishing Industry

Volume 1. Economic and regulatoryprinciples, survey results,

transformation and socio-economicimpact.

PREPARED FOR:

Marine and Coastal Management Department of Environmental Affairs and Tourism Private Bag X2 Roggebaai 8012

PREPARED BY:

RHODES

UNIVERSITY

Department of Ichthyology & Fisheries Science and Department of Economics and Economic History Rhodes University PO Box 94, Grahamstown 6140, South Africa Telephone +27 046 6228241 Fax +27 046 6224827 Email: [email protected]

Date: September 2003 Editors: Mather, D., Britz, P.J., Hecht, T. & Sauer, W.H.H.

Contributors: Dinty Mather, Peter Kimemia, Faith Mlumbi; N Notyawa, Lindsay Martin, Sue Murray, Philip Ndimande, Monieba Isaacs, Mafaniso Hara, Roy Bross, Tremaine Wesson and Anton Roelofse

Format of citation: Sauer, W.H.H., Hecht, T. Britz, P.J. & Mather, D. 2003. An Economic and Sectoral Study of the South African Fishing Industry. Volume 1. Economic and regulatory principles, survey results, transformation and socio-economic impact. Report prepared for Marine and Coastal Management by Rhodes University.

www.envirofishafrica.co.za/projects/ess.html

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 Contents

i

TABLE OF CONTENTS EXECUTIVE SUMMARY 1 1. BACKGROUND TO THE ESS AND METHODOLOGY 27

1.1. INTRODUCTION 27

1.2. DATA COLLECTION AND DATABASING 28

1.3. FISHERY PROFILE REPORTS 29

1.4. ESS ECONOMIC, SOCIO-ECONOMIC AND ANALYSES 29

1.5. ESS PROJECT TEAM 30

Appendix 1.1 TERMS OF REFERENCE 31

Appendix 1.2 DATA REQUIREMENTS 36 2. A SIMPLE ECONOMIC SYSTEM OF THE FISHING MICROECONOMY 43

SUMMARY 43

2.1. INTRODUCTION: A COMPLEX SYSTEM 47

2.2. THE FISHING INDUSTRY AS A MICROECONOMIC SYSTEM 49

2.3. THE COMPONENTS OF THE ECONOMIC SYSTEM 49

2.4. MARKET FAILURE AND THE ROLE OF MCM 56

2.5. THE REGULATORY SYSTEM 59

2.6. THE ESS IN PERSPECTIVE 59 3. THE ECONOMICS OF ALLOCATIONS 61

SUMMARY 61


3.2. THE FORM OF RIGHTS 62

3.3. THE STRUCTURE OF RIGHTS 63

3.4. QUANTA OF ALLOCATIONS 65

3.5. THE MARKET FOR RIGHTS 66

3.6. CONCLUSION 68 4. THE REGULATION OF COMMERCIAL FISHING IN SOUTH AFRICA: AN EXAMINATION OF THE CONSTITUTIONAL COMPATIBILITY OF THE REGULATORY SYSTEM 69


4.2. THE CONSTITUTION IN GENERAL 69


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4.3. ADMINISTRATIVE JUSTICE 72

4.4. THE MARINE LIVING RESOURCES ACT 76

4.5. COMMENTS ON THE ACT AND ITS REGULATORY SYSTEM 80

4.6. CONCLUDING REMARKS 88 5. SURVEY RESULTS: EMPLOYMENT, SKILLS AND INCOME 89

SUMMARY 89


5.2. EMPLOYMENT, SKILLS AND INCOME (primary sector vessels only) 90 5.3. EMPLOYMENT, SKILLS AND INCOME (primary sector including on-shore support) 98

5.4. EMPLOYMENT, SKILLS AND INCOME (secondary and tertiary sectors) 104

5.5. EMPLOYMENT, SKILLS AND INCOME (totals for the fishing industry) 104

5.6. CONCLUSION 108

6. SURVEY RESULTS: CLASIFICATION (size and shape) 110

SUMMARY 110 6.1. INTRODUCTION 115

6.2. CLASSIFICATION (criteria) 115 6.3. CLASSIFICATION (size and shape) 117 6.4. CLASSIFICATION (Micro, Small, Medium, Large and Very Large) 117 6.5. CLASSIFICATION (by fishery) 119

6.6. CLASSIFICATION (other schemes) 137

6.7. STRUCTURAL TRANSFORMATION 140

6.8. CONCLUSION 142

7. SURVEY RESULTS: UNDERSTANDING AND MEASURING TRANSFORMATION 145

SUMMARY 145


7.2. THE NEED FOR MANAGED TRANSFORMATION 147

7.3. DEFINITION 148

7.4. TRANSFORMATION IN THE SOUTH AFRICAN FISHING INDUSTRY (context) 149

7.5. MEASURING TRANSFORMATION 151

7.6. CALCULATING EMPLOYMENT TRANSFORMATION INDICATORS 154


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7.7. TRANSFORMATION INDICATORS BASED ON THE ESS SURVEY 155

7.8. CONCLUSION 173 8. SURVEY RESULTS: SOCIO-ECONOMIC BASELINE AND IMPACT 175 SUMMARY 175

8.1. INTRODUCTION 177 8.2. ESS SURVEY: EMPLOYMENT, SKILLS AND INCOME BY PROVINCE 177 8.3. SOCIO-ECONOMIC IMPACT 183

8.4. CONCLUSION 201

Appendix 8.1: Socio-economic baseline 203

9. USER CHARGES AND REVENUE COLLECTION 269

SUMMARY 269

9.1. DEFINING TERMS 270

9.2. CONSIDERATIONS FOR IMPLEMENTING CHARGES FOR SA COMMERCIAL FISHING 274

9.3. A MODEL FOR DETERMINATION OF CHARGES AND FEES BASED ON VESSEL COSTS 276

9.4. A PRAGMATIC ALTERNATIVE 277

APPENDIX 9.1. Model of the calculation of charges and fees 279 APPENDIX 9.2. Bross Model: Cost and revenue model for catch levy determination 285 APPENDIX 9.3. Calculation of possible income from SA commercial fisheries 288

10. FUTURE MANAGEMENT: THE NEED FOR INFORMATION 299


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An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 Executive Summary

1

EXECUTIVE SUMMARY

1. BACKGROUND TO THE ESS AND METHODOLOGY

INTRODUCTION

The introduction of constitutional democracy in South Africa required the redrafting of most Acts of

parliament to promote goals of social equity and redress of the consequences of past racial

discrimination. The introduction of the Marine Living Resource Act of 1998 required the State to

“restructure the fishing industry to address historical imbalances and to achieve equity within all branches

of the fishing industry”, but provided no specific strategic policy guidelines for achieving this end. The

initial burden of implementing this social agenda fell on government fisheries biologists and a series of

interventions with profound economic, legal and political consequences were embarked upon without any

proper framework for understanding the expected outcomes. The focus of “restructuring” was a series of

annual fishing rights allocation processes whereby fishing quota was redistributed away from historically

white, larger, companies to smaller, mostly black, new entrants into the industry. This process was far

from smooth and resulted in a number of legal challenges of government decisions, instability within

many fisheries, and a tremendous strain on the under-equipped manpower resources within the

Department of Environmental Affairs and Tourism’s Marine and Coastal Management Branch (MCM).

Under the leadership of the newly appointed Minister Valli Moosa in 2000, the problems were

acknowledged and steps were taken to stabilise the fishing industry while still promoting transformation.

Interventions included the creation of a Deputy Director General post responsible for Marine and Coastal

Management, a moratorium of rights allocations for one year to allow for proper administrative process,

the establishment of a contracted out Rights Verification Unit, and the appointment of a private legal team

to adjudicate the 2001 rights allocation process. The moratorium on rights allocations in 2001 aimed at

stabilising the fishing industry was supported by a position paper entitled “Draft Discussion Document For

The Fisheries Management Plan To Improve The Process of Allocating Fishing Rights”

(http://www.environment.gov.za/docs/2000/fishing_rights/index.html#Appendix8), which identified the need for an

economic and sectoral study of the fishing industry. This need arose from a lack of basic information on

the economics and socio-economics of the fishing industry, which was critical to informing realistic policy

and decision-making around rights allocation, industry restructuring and general fishery management

issues.

The Department of Environmental Affairs and Tourism requested the South African Network for Coastal

and Oceanic Research (SANCOR) to assist in putting a team together to undertake the study and this

resulted in a multi-disciplinary team of expertise led by Rhodes University being appointed by means of a

contract administered by the National Research Foundation (NRF) (Table 1).


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Table 1. Contributions of the ESS project team

Contribution Responsible Institutions and Individuals ESS Fishery Survey and Fishery Profiles

Anchor Environmental Consultants Capfish Fisheries and Oceanographic Support Services Oceanographic Research Institute Pisces Research and Management Consultants Policy Centre for Land and Agrarian Reform (PLAAS), University of the Western Cape Rhodes University, Department of Ichthyology and Fisheries Science SA Deep Sea Trawling Industry Association Business Partners

Barry Clarke, John Bolton, Deborah Vromans, Charlotte Heijnis Chris Heineken Dave Japp, Jan Wissema Bruce Mann, Rudi vd Elst, Shaun Fennessy Andrew Penney, Andrea Pulfrich Monieba Issacs and Mafaniso Hara Peter Britz, Ntobeko Bacela, Tom Hecht, Ané Oosthuizen, Loni Dräger and Warwick Sauer Roy Bross Tremaine Wesson and Anton Roelofse

ESS database management

Rhodes University

Larry Oellerman and Jan Wissema

Economic and socio economic analysis

Rhodes University, Department of Economics and Economic History

Dinty Mather, Peter Kimemia, Faith Mlumbi, N Notyawa, Lindsay Martin, Sue Murray and Philip Ndimande

Policy Centre for Land and Agrarian Reform (PLAAS), University of the Western Cape

Monieba Issacs and Mafaniso Hara

SA Deep Sea Trawling Industry Association

Roy Bross

Legal Analysis ESS report editing and production

Rhodes University, Faculty of Law Rhodes University

Clive Plasket Dinty Mather, Peter Britz, Tom Hecht, Larry Oellerman, Warwick Sauer and Lisl Griffioen

The main objectives of the study were to:

¶ Provide a synoptic report on each commercial fishery.

¶ Provide a description of the microeconomy of the fishing industry.

¶ Provide baseline economic and socio-economic data.

¶ Provide precise definitions of scale-groupings of vessels within each fishery.

¶ Quantify the allocating rights in minimum economic units (minimum viable quota) by scale-

grouping of vessels operating in each particular fishery.

¶ Analyse realistic options regarding fee structures – charges and/or levies and/or royalty taxes.

¶ Providing a measurable estimate of the level of transformation (as defined), including the

distribution of the wage bill to previously disadvantaged individuals.


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It was intended that the ESS would inform the 2001 rights allocation process, and establish a database

which could be kept up to date to provide ongoing information and analyses for policy, management and

research processes.

DATA COLLECTION AND DATABASING

In order to compile the required holistic profile of the fishing industry, the gathering of information and

subsequent databasing was undertaken in the following ways:

Fishing Industry Survey A questionnaire of the required information was compiled and tailored to each fishery sector. The survey

aimed to capture a 100% sample of rights holders and processing establishments. An expert familiar with

the fishery concerned was appointed to execute the survey and each rights holder was approached either

personally, by phone or by post. The proportion of rights holders and vessels captured in the survey was

high:- 1 483 returns, which was equivalent to 87% of rights holders, 91% of total allowable catch or effort

and 83% of vessels. A series of meetings were held with industry associations and representatives to

discuss the ESS objectives and methodology, and very good cooperation from industry was achieved.

In addition to the questionnaire survey, representative cost data on different classes of fishing vessel was

obtained by Business Partners and individuals with an in-depth knowledge of particular types of fishing

operations. MCM databases were used to obtain information on rights holders and vessels, but these

proved to be of very little value as the databases were poorly maintained and often out of date or

inaccurate. Thus, the survey had to rely primarily on the questionnaire survey data. Socio-economic data

on employment and income of coastal communities was obtained from the 1996 census data in order to

contextualise the contribution of the fishing industry to the coastal economy.

ESS Database The questionnaire survey data was entered into a Microsoft Access database and summary reports

extracted for the ESS economic and socio-economic analyses.

Fishery Profile Reports A synoptic overview report on each fishery was written by the sub-consultant responsible for the

questionnaire survey, and summary data extracted from the ESS database was added to these reports.

These reports were forwarded to the rights allocation teams appointed by MCM to make

recommendations to the Minister on the allocation of fishing rights. The fishery profile reports form

Volume 2 of the ESS reports and are available on the internet (www.envirofishafrica.co.za/projects/ess.html).

ESS Economic, Socio-Economic and Legal Analyses The ESS survey data, fishery reports, additional literature and secondary sources were used to generate

the analyses and perspectives which form Volume 1 of the ESS Report.


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The volume includes:

ü A contextualisation of the principles underlying the microeconomy of the South African fishing

industry, and the form and structure of fishing rights. ü A legal perspective examining the constitutional compatibility of the regulatory system with

respect to fisheries.

ü Analysis of the ESS survey results in terms of employment, skills and income.

ü A classification of the size and shape of the fishing fleets.

ü Definition and measurement of transformation.

ü An analysis of options for user charges and revenue collection.

ü The socio-economic contribution of the fisheries sector to coastal towns.

The summarised results of the ESS Volume 1 are presented below.

2. A SIMPLE ECONOMIC SYSTEM OF THE FISHING MICROECONOMY This section provides a conceptual framework for viewing the South African fishing industry as a

microeconomic system, and for analysing the economic mechanisms and consequences that will follow

any management intervention in the fishery.

The South African fishing industry can be viewed as a complex system comprising:

1. A dynamic biological system. 2. An economic system. 3. A system of legal procedures. 4. A social system.

The biological aspects of the system are fairly well understood as these have been the traditional focus of

fisheries management by the State. The focus of the ESS was primarily on the economic system. It

does, however, also provide insights into the legal system and the socio-economic impact of the fishing

industry.

It is useful to view the fishing industry primarily as a microeconomic system that is linked to the domestic

and national economy, is constrained by a system of legal procedures, and has an impact on the

economic well-being of certain individuals in society. Fundamental to the nature and behaviour of the

microeconomic system are biologically-based, binding constraints on harvest (production), namely the

Total Allowable Catch or Total Allowable Effort and other catch restrictions.


5

The easiest way to understand the economic system is to break it up into a number of interacting

components (Figure 1). These components comprise:

1. Three sets of infrastructure, namely the primary sector infrastructure (vessels), primary sector

support infrastructure (buildings and so on) and the secondary and tertiary sector infrastructure

(capital goods needed to process and market fish products).

2. Three markets, namely, the input market, the capital market and the output market.

3. Economic activity that occurs when there is resource flow between the markets and

infrastructure.

If markets work, prices will determine the rate of flow between the relevant markets and the fishing

industry infrastructure such that economic resources are optimally allocated. If markets don’t work to

allocate resources efficiently, “market failure” is said to occur, and enabling institutions may be needed.

A number of forms of market failure occur in primary sector fishing activities:

1. Markets do not efficiently allocate living marine resources (In economic terms: “a common

property resource problem”).

2. Markets tend not to provide an optimal amount of research required to maintain the long term

productivity of the resource (In economic terms: “a free-rider problem”).

3. Markets may fail in the allocation of scarce capital resources to harvesting activities (In economic

terms: “an asymmetric information problem”).

To ensure the sustainable utilisation of living marine resources, microeconomic management in the form

of State intervention by the Minister of Environmental Affairs and Tourism is necessary to correct for

these failures. An understanding of the microeconomy of the fishing industry and factors determining the

behaviour of its participants is essential to the formulation of appropriate interventions by the State. In

particular, the nature of fishing access rights awarded by the State profoundly influence the behaviour of

the fishing microeconomy, and therefore definition of the form and structure of fishing rights will to a large

extent determine the economic and social outcomes.


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MICROECONOMY OF SA FISHERY

PRIMARY SECTOR:HARVESTING INFRASTRUCTURE VESSELS AND SUPPORT SERVICES

SECONDARY AND TERTIARY SECTOR:INFRASTRUCTURE FOR PROCESSING AND MARKETING

International Economy

Domestic Economy

OUTPUT MARKETProducts

CAPITAL MARKET

INPUT MARKETLabour market

(Main socio-economic

impact)

STATE INTERVENTION IN THE PRIMARY SECTOR THROUGH MCM

•Sets outputs through research

•Allocates primary resources

•Promotes social transformation goals

•Enforces compliance

MARKET FAILS TO:

•Allocate living marine resources

•Do biological research

•Enforce compliance

Levies

Services

Figure 1. A simple economic system for South African commercial fishing: infrastructure and resource flows. Economic activity is indicated by arrows representing resource flows.

3. THE ECONOMICS OF ALLOCATIONS

The important characteristics in any system of rights are the form of rights, the structure of rights and the

quanta of allocations.

The form of rights is defined by the biologically based regulations that constrain catch, usually a

proportion the Total Allowable Effort (TAE) or Total Allowable Catch (TAC) of a particular resource. The

choice of options, usually decided by the resource manager, will determine to some extent how the

patterns of resource use and economic outcomes are established in the economic system.

The structure of rights will determine, to a large extent, who ultimately undertakes the primary economic

activities in the fishing industry. The structure of rights may be classified under the following criteria:

1. The attachment criteria will control who can hold rights and under what circumstances they are

held.

2. The transferability of the right governs the extent and under what circumstances the rights can

be traded or leased.

3. The length of tenure deals with the period for which the rights are awarded.


7

The quanta of allocations refers to how the State decides to divide the TAC or TAE between the various

participants in the fishing industry or the public at large.

The form of rights, structure of rights and quanta of allocations will necessarily set up the conditions for a

market for rights. For example, in the case of South African commercial fishing, the existing system of

rights has established a rational ‘paper permit’ market. A further problem occurs when paper permits with

uneconomic quanta of fish are activated, and in the absence of economic rent, an incentive to over-

exploit is created. From an ITQ (individual transferable quota) point of view, the system of rights leads to

the trading of rights such that the most efficient harvesters will inevitably hold the rights to exploit the

resource.

The concept of a minimum viable quota (MVQ) is embodied in the attachment criteria of the structure of

rights. For an MVQ to make practical sense, it dictates that rights are attached to vessels, as the fishing

vessel is the primary means of harvest in the primary sector. Currently rights in South Africa are awarded

to individuals and companies in varying quanta, and re-allocation of rights as MVQs would have profound

legal and economic consequences. However, the definition of a minimum viable quantum of fish required

to operate a particular vessel class remains a useful and an economically defensible method of analysis,

or abstraction.

The choices open to the policy maker when designing a system of rights will depend entirely on their

expected economic and social outcomes. The options chosen have to be “fair” in terms of the law, and

experience to date has shown that any attempt by the State to change the nature and structure of fishing

rights is likely to be tested in court. The following section therefore provides a legal perspective on the

regulation of commercial fishing.

4. THE REGULATION OF COMMERCIAL FISHING IN SOUTH AFRICA:

AN EXAMINATION OF THE CONSTITUTIONAL COMPATIBILITY OF THE REGULATORY SYSTEM

The system created by the Marine Living Resources Act 18 of 1998 in terms of which commercial fishing

is regulated was assessed against the touchstone of the Constitution of the Republic of South Africa Act

108 of 1996 (the Constitution), the supreme law, and its requirements for the realisation of both

substantive and procedural fairness. It was also assessed against the provisions of the Promotion of

Administrative Justice Act 3 of 2000, which was passed by Parliament to give effect to the fundamental

right to just administrative action.

THE CONSTITUTION IN GENERAL

Of prime importance for present purposes is the fact that the Constitution is based on the founding value

of constitutional supremacy and the rule of law. This value is directly enforceable. The Constitution

requires that every exercise of public power must be capable of rational justification.


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The founding value expressed in s1(d), and particularly accountability, responsiveness and openness, are

also of prime importance for purposes of constitutional control of public power.

ADMINISTRATIVE JUSTICE

The first important steps towards creating an equitable system of administrative law were taken when the

interim Constitution1 introduced a fundamental right to what it termed ‘administrative justice’.2 The final

Constitution contained a similar fundamental right to what it termed ‘just administrative action’. Section 33

provides:

‘(1) Everyone has the right to administrative action that is lawful, reasonable and

procedurally fair.

(2) Everyone whose rights have been adversely affected by administrative action has the

right to be given written reasons.

National legislation must be enacted to give effect to these rights, and must –

a) provide for the review of administrative action by a court or,

where appropriate, an independent and impartial tribunal;

b) impose a duty on the State to give effect to the rights in

subsections (1) and (2); and

c) promote an efficient administration.’

In terms of s33(3) of the Constitution, the rights to lawful, reasonable and procedurally fair administrative

action and to reasons for adverse administrative action are to be given effect to by national legislation.

That legislation has now been passed as the Promotion of Administrative Justice Act 3 of 2000. It states

in its preamble that its objectives are to ‘promote an efficient administration and good governance’ and to

‘create a culture of accountability, openness and transparency in the public administration or in the

exercise of a public power or the performance of a public function, by giving effect to the right to just

administrative action’.

The Act provides for certain minimum requirements of procedural fairness in s3. Section 3(1) states that

when administrative action ‘materially and adversely affects the rights or legitimate expectations of any

person’ that administrative action must, in order to be valid, be procedurally fair.

1 Constitution of the Republic of South Africa 200 of 1993. 2 See s24.


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While the Act acknowledges that what is fair depends on the circumstances of each case, s3(2)(b)

provides that the following are the minimum requirements of procedural fairness:

a) ‘adequate notice of the nature and purpose of the proposed administrative action;

b) a reasonable opportunity to make representations;

c) a clear statement of the administrative action;

d) adequate notice of any right of review or internal appeal, where applicable; and

e) adequate notice of the right to request reasons in terms of section 5.’

It is against the legal provisions that have been set out above that decisions of functionaries involved in

the regulation of the commercial fishing industry will be tested. By the same token, the regulatory

framework is to be tested against these principles with a view to determining whether it complies with

constitutional requirements and values.

THE MARINE LIVING RESOURCES ACT

The Act creates a regulatory system in terms of which permits may be issued to authorise the exploitation

of marine living resources.3 Such permits have a limited lifespan, which may not exceed one year, may be

issued subject to conditions and may only be issued after a prescribed fee has been paid.4

The foundation for much of the regulatory system is to be found in a range of powers granted to the

Minister. Indeed, for all intents and purposes the Minister is the only functionary who exercises any

power to speak of in terms of the Act.5

COMMENTS ON THE ACT AND ITS REGULATORY SYSTEM

The comments on the Act and the system that it creates to regulate the commercial exploitation of fish

are discussed under three heads of argument. These comments deal with problems associated with the

drafting of the Act and certain concepts that it uses; the powers of the Minister and the profile of the

administrative system that the Act creates; and the Act’s failure to give adequate substantive and

procedural guidance to decision-makers.

Drafting and Concepts The drafting of certain sections of the Act leaves much to be desired. In a constitutional state such as

ours, based as it is on the rule of law, this creates a problem because one of the requirements of the rule

of law is that the law must be certain.6

3 Section 13(1). Note that the Act creates a great deal of unnecessary confusion by using the term ‘permit’ at certain times,’right’ at others and ‘licence’ at yet others. The drafters of the Act evince some confusion, particularly in respect of their use of the term ‘permit’ and ‘right’. This will be dealt with below. 4 Section 13(2). 5 Note that the Minister is advised by the Consultative Advisory Forum for Marine Living Resources, established in terms of s5. 6 Jowell ‘The Rule of Law Today’ in Jowell and Oliver (eds) The Changing Constitution (3ed), 57, 62-64; Dawood v Minister of Home Affairs, 2000 (8) BCLR 837 (CC), para 47; De Lange v Smuts NO 1998 (7) BCLR 779 (CC), para 46.


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The Powers of the Minister and the Administrative System It has been noted above that the Minister is central to the regulatory system created by the Act.

The Minister is vested with virtually every power that the Act creates, including a power to consider

appeals from decisions taken by others. As those powers vested in others are rather limited, the Minister

presumably delegates a substantial proportion of his or her powers to those others, in terms of s79.

Those more familiar with the day to day administration of the Act will be able to assess how well this

system works but it appears, on paper, to have certain weaknesses because of the centralisation of

power in the hands of the Minister.

It must be questioned whether the centralisation of power in the hands of the Minister is an effective and

rational allocation of administrative functions. It may be preferable to design a more formal and

structured administrative system to deal with important decision-making functions, such as the allocation

of rights, their transfer and their cancellation or suspension, and to vest in the Minister the power of policy

formulation and to decide appeals.

Legislative Guidance and the Exercise of Discretion The final issue dealt with in this paper focuses on what may best be termed the underdeveloped nature of

the regulatory system, from both a substantive and a procedural perspective.

CONCLUDING REMARKS

This outsider’s view of the Act may serve as a spur to address what appears to be an underdeveloped

administrative system that regulates an industry that generates billions of rands: if there are credible

answers to some of the criticisms, those answers can perhaps be made explicit for the benefit of lay

persons (and their lawyers) who will come into contact with the Act; if there are no credible answers to

some of the criticisms, law reform, whether through amending the Act or promulgating regulations, may

resolve the problems.

It is important to bear in mind that failures to meet the standards of the Constitution, either in the design of

the regulatory system or in its operation, may have frighteningly severe consequences. If, for instance,

the allocation committee is not empowered to do what it does and the Minister is held not to have properly

applied his or her mind to the allocations, the entire process could be set aside on review: that is a

consequence of adherence to the principle of legality and the rule of law.

It is important for another reason that the regulatory system is properly designed. It is not an end in itself.

It exists in order to generate rational and fair decisions that further the policy that the Act is created to

give effect to, and to do so in the public interest. To achieve these results, the regulatory system is

required to deliver decisions that, in broad and general terms, balance the economic imperatives, the

environmental concerns and the goals of transformation and, ideally, harmoniously maximise all of the

above.


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It is only when these goals and how they are to be achieved are explicitly set out and fed into the

decision-making process that they can be achieved in the accountable, responsive and open way

contemplated by s1(d) of the Constitution, and that applicants for rights and the public more generally will

be able to assess whether the regulatory system functions properly or not.

5. SURVEY RESULTS: EMPLOYMENT, SKILLS AND INCOME

The results from the ESS survey with regard to employment, skills and income by fishery and by race are

presented in this part of the report. The racial makeup of employment number, employment income and

average yearly income per sector and for the fishing industry as a whole is presented in Table 2.

Table 2. Employment and income in the South African fishing industry.

Total Black White

Employment 16 854 14 344 2 509

Total income (R million) 644.3 485.0 159.3 Primary sector

Average income (Rand) R38 229 R33 812 R63 491

Employment 10 876 10 355 521

Total income (R million) 348.1 303.4 44.6

Secondary &Tertiary

Sector Average income (Rand) R32 006 R29 306 R85 670

Employment 27 730 24 699 3 030

Total income (R million) 992.4 788.5 203.9 Total

Average income (Rand) R35 788 R31 923 R67 305

The fishing industry is an important employer in that it pays a relatively high average individual yearly

income of approximately R36 000. The majority of employees in the fishing industry are “semi-skilled” and

black. This is elaborated on in the “Socio-economic Baseline and Impact” section below. The proportion

of blacks employed was related to skills level (Figure 2). The lowest representation of black people was

found in the “Professional/ managerial” category, where approximately 50% of employees are black. The

rate of absorption of Black people into skilled work in the fishing industry is related to the circumstances

of specific fishery concerned. In addition, as skills take time to acquire, non-discriminatory employment

practices will not necessarily immediately reflect a balanced skills profile between the Black and White

people. These issues are dealt with in the section entitled “Understanding and Measuring

Transformation”. The fisheries employing the highest number of people are (in order of size) the

traditional line fishery, the squid fishery, the hake trawl fishery, the west coast rock lobster fishery and the

tuna bait boat fishery (Figure 3). However, in terms of total salary and wages paid, the deepsea hake

industry is the biggest payer followed by the squid, linefish and pelagic fisheries (Figure 4). In general, the

small boat fisheries, which require lower skills levels than the capital intensive, larger vessel fisheries, pay

lower wages. The total salary and wage bill for South African commercial fisheries was approximately R1

billion in 2000.


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0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

% Black employment

Professional/Managerial 52.2% 46.5% 49.6%

Skilled 64.8% 68.0% 65.1%

Middle services 62.6% 77.2% 71.9%

Semi-skilled 92.1% 98.5% 94.5%

Unskilled 96.0% 98.6% 98.0%

Primary Secondary and tertiary

Fishing industry

Figure 2. Proportion of black employment by skills level in the South African fishing industry.

EMPLOYMENT BY FISHERY

0500

100015002000250030003500

Linefi

shSqu

id

Deeps

ea H

ake

WC Roc

k Lob

ster

Tuna B

aitbo

at

Hake H

andli

ne

Hake L

ongli

ne

Pelagic

Tuna l

ongli

ne

Insho

re Hak

e

SC Roc

k Lob

ster

Shark

Long

line

Prawn T

rawl

Abalon

e

Toothf

ish

Num

ber E

mpl

oyed

Total Black White

Figure 3. Employment numbers by fishery for the South African commercial fishing industry.


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1784419518

301503729037578

4352443638

671706952271358

173586

SHARK LONGLINEPRAWN TRAWL

TOOTHFISHSC ROCK LOBSTER

TUNA LONGLINEINSHORE HAKE

HAKE HANDLINEABALONE

HAKE LONGLINETUNA BAITBOAT

WC ROCK LOBSTERPELAGICLINEFISH

SQUIDDEEPSEA HAKE

Figure 4. Total salary and wages (R’000s) paid by the commercial South African fisheries.

6. SURVEY RESULTS: CLASSIFICATION OF VESSEL AND ENTERPRISE SIZE

The aim of this part of the ESS was to provide a classification system of the vessel-based primary fishing

sector. The ESS vessel survey data is summarised as an enterprise classification system in terms of

capital value and vessel size (Table 3). The following vessel based definitions of enterprise size were

developed for individuals who own vessels, who fish for a living and whose businesses are separate and

distinct entities:

¶ SME: If the vessel has a market value of less than R1 million (or a replacement value of R3.4

million) and is the only one owned by a single operator, or group of operators, the enterprise is

classified as a SME. Also, if the single proprietor, or group of proprietors, own vessels with a

market value less than R1 million (or a replacement value of less than R3.4 million), the

enterprise is a SME. The national Small Business Act (1996) allows a value of total gross assets

of less than R4.5 million in this category.

¶ Medium Scale Enterprise: If the vessel is the only one owned by a single proprietor, or group,

and it has a market value of less than R1.74 million (or a replacement value of R5.8 million) the

operation is a Medium Scale Enterprise. Similarly, if the single proprietor, or group of proprietors,

own vessels with a market value less than R1.74 million (or a replacement value of less than

R5.8 million), the enterprise is a Medium Scale one.

Caution, however, should be exercised when applying this definition across fisheries.


14

Table 3. Enterprise classification system based on the capital value of vessels surveyed by the ESS.

Class group or

Enterprise size

Size-groups of vessel

Number of

vessels surveyed

Size group: Average

market valuevessels

(R million)

Enterprise size: Average

market value vessels

(R million)

Estimated replacement

value (R million)

(factor 3.33) 3m - 5m 445 0.07 0.23

> 5m - 8m 299 0.17 0.57

> 8m - 12m 108 0.31 1.03 SME

>12m - 14m 60 1.01

0.19

3.36

>14m - 18m 173 1.22 4.06

>18m - 20m 86 1.53 5.09 Medium

>20m - 25m 79 1.74

1.42

5.79

>25m - 30m 30 3.60 12.0

>30m - 35m 18 5.28 17.58 Large

>35m - 40m 13 8.28

5.09

27.57

>40m - 50m 37 11.67 38.86

>50m - 60m 7 10.64 35.43

>60m - 70m 9 14.00 46.62 Very Large

>70m 7 20.30

12.91

67.60

The “size and shape” of the South African fishing fleet, as surveyed by the ESS, is represented by fishery

in Figure 5. Key economic data such as the number of vessels, vessel performance, vessel age, capital

value, employment numbers and income are included in the summary.

Other classification systems and fishery rankings are provided, namely, by the capital intensity of vessels,

by the contribution per fishery to total employment, by the contribution of the fishery to total employment

income and by the contribution of the fishery to total capital value.


15

Figure 5 Size and shape of the South African Fishing Fleet (A3 foldout)


16


17

7. SURVEY RESULTS: UNDERSTANDING AND MEASURING TRANSFORMATION

The final goal of managed transformation is a normal society where it should not be possible to

distinguish between race and gender based on economic and socio-economic characteristics. The

Marine Living Resources Act 1998 is used to distinguish:

1. Social transformation, that is, transformation in skills, employment, income, ownership and

control, and access rights.

2. Economic transformation deals with increases in productivity and other welfare enhancing

processes.

3. Structural transformation usually deals with the managed change from big to smaller business.

Social transformation in employment, skills and income is the focus of this part of ESS. Economic

transformation is not covered and structural transformation is dealt with in the previous section: Survey

Results: Classification (Size and shape) of Vessel and Enterprise Size.

Four indicators of social transformation are derived from the ESS survey data, namely:

1. The percent black employment.

2. The proportion of total income accruing to black people, termed ‘Follow the buck’.

3. The ratio of the average yearly income of black people to that of white people.

4. A weighted employment transformation indicator based on the number of black people employed

per skills group and the difference between black and white average yearly incomes per skills

group. The weighting assigned per skills group is proportional to the degree to which black

people are under-represented in terms of income and employment within that group.

These transformation indicators were derived for 1) the primary fishing sector (vessels only), 2) the

primary fishing sector (including on-shore support) and 3) the secondary and tertiary fishing industry

sectors.

Eighty five percent of people employed in the fishing industry are black (Figure 6) but, as indicated above,

black people are under-represented in the categories of work requiring higher levels of skill, education

and experience. Thus it is not surprising that only 77% of personal income in the fishing industry accrues

to black people (Figure 7). This disparity is further highlighted when the average income of black and

white people is compared (Figure 8), as average personal income of black people is 45% that of white

people in the industry.


18

scrl it dshpt

sqsll Ti wcrl hll tbb ab Tf

tll lf hhl

pl

tf50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%%

Bla

ck e

mpl

oym

ent

85% of People Employed in the Primary Sector are Black

Figure 6. Scatter plot of fisheries ranked by the percentage of black fishers. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector reference point. Tf: primary fishing sector (vessels only)).

scrl dsh itsll

pt tbb sq Ti hll ab w crl Tf

lfhhl

tll

tf pl50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

'Fol

low

the

buck

' ind

icat

or

77% of Income in the Primary SectorAccrues to Black People

Figure 7. Scatter plot of fisheries ranked by ‘follow the buck’ in employment. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support), Tf: primary fishing sector (vessels only)).


19

tf

sll

pl

lf tbb ab hhlTf

hllTi wcrl tll dsh

sq

it pt scrl20%

30%

40%

50%

60%

70%

80%

90%

100%Av

erag

e in

com

e in

dica

tor

tf

Black People in the Primary Sector Earn on Average 45% of what White EmployeesEarn

Figure 8. Scatter plot of fisheries ranked by average income indicators. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support), Tf: primary fishing sector (vessels only)).

The results of the weighted transformation indicators reflecting the degree of racial equity in terms of

income and employment are presented for the primary sector (Figure 9), and secondary and tertiary

sectors of the fishing industry (Figure 10). Based on the definition of transformation above, and assuming

an 80:20 black to white racial demographic, racial equity or the transformation goal would be achieved

reflected as an 80% score. In other words it would not be possible to distinguish the employment

characteristics of the fishing industry on the basis of race.

it

Tfpt scrl

dsh pl ab Ti sqtf wcrl sll

hll tbb tlllf

hhl

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

Wei

ghte

d em

ploy

men

t ind

icat

or

Capital Intensive FisheriesSmall Vessel and Line Fisheries

Figure 9. Weighted employment transformation indicators for fisheries, primary sector (vessels only). (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deepsea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including onshore support) Tf: primary fishing sector (vessels only)).

The fisheries weighted employment transformation indicator for vessels mirrors to some extent the scale

distribution of the fishing vessels employed. There is a broad parallel between the capital intensity of the

fishery (the size of vessel and type of gear used) and its ability to absorb skilled black fishers. This makes

sense as better skilled, higher paid personnel are required on the larger, more sophisticated fishing

vessels.


20

Abalone

Hake

Pelagic TspSquid

Rock lobster

Shark

Prawns Linefish20%

25%

30%

35%

40%

45%

50%

55%

60%W

eigh

ted

empl

oym

ent i

ndic

ator

Figure 10. Weighted employment transformation indicators for the secondary and tertiary sectors of the fishing industry. (Tsp: secondary and tertiary reference point).

The aggregated results for weighted black employment and income transformation indicators were as

follows:

1. Primary fishing sector (vessels only): 55.1%.

2. Primary fishing sector (including on-shore support): 45.3%

3. Combined secondary and tertiary sectors: 49.1%

The most important finding with regard to measuring transformation and providing indicators of one type

or another is that they should be used in conjunction with other information. Preferably they should be

employed as relative measures within fisheries, or secondary and tertiary sector groups. They will also

serve as benchmarks by which progress towards racial equity in income and employment can be

measured.

The vessel based classification system and transformation indicators provide some insight into the issue

of structural transformation (the restructuring of the scale of enterprise distribution usually from big to

small). The broad conclusions are as follows:

1. Black Economic Empowerment can be implemented at all levels and in all fisheries (both labour

and capital intensive) in the South African fishing industry, and structural adjustment (from big to

small) is not a prerequisite for achieving transformation. The capital intensive fisheries have a

greater capacity to absorb skilled black people, and for ownership transformation through the

capital markets. The scope for “internal transformation” in small vessel, owner-operator

businesses (eg. squid and hake handline) is limited, and strategies to promote black ownership

will have to be implemented to achieve racial equity within these fisheries.


21

2. The labour intensive nature of the small boat fisheries is not sufficient justification for structural

adjustment within the capital intensive fisheries employing larger vessels. Structural adjustment

would probably result in a net loss in welfare benefits.

3. As a general rule, if an abuse of market power by larger companies were suspected, it would be

better to use the appropriate instruments created for that purpose by the Competitions Act.

8. ESS SURVEY RESULTS: SOCIO-ECONOMIC BASELINE AND IMPACT This section places the demographic information of the South African fishing industry gathered by the

ESS survey, into a broader social picture. This was achieved by comparing ESS survey data with 1996

South African population census data for coastal towns or centres.

Socio-economic baseline tables were constructed for the commercial fishing harbour towns and

aggregated at a provincial and national level. Approximately of 71% of income and employment in the

fishing industry is based the Western Cape Province followed by the Eastern Cape Province with 11%

(Table 4). Average income is similar for all provinces except the Northern Cape, which employs a small

number of mostly part-time employees.

Table 4. The average individual income per province and the distribution of total fishing industry income and employment per province. Region % Income % Employment Average income

Northern Cape 0.5% 0.8% R 21,517

Western Cape 71.8% 71.0% R 35,473

Eastern Cape 11.0% 11.6% R 33,095

Kwazulu-Natal 1.9% 1.9% R 35,762

Unspecified 14.8% 14.7% R 35,193

TOTAL FISHING INDUSTRY 100.0% 100.0% R 35,227

The contribution to total employment is estimated at 4.8% of the total number of black people and 0.9% of

all white people working in commercial harbour towns in South Africa (Figure 11). The proportional

contribution of the fishing industry to the employment of black people in coastal towns by province is:

Northern Cape Province 13.0%, Western Cape Province17.8%, Eastern Cape Province 1.2%, and

KwaZulu-Natal Province 0.2%. The fishing industry pays comparatively high wages to black people who

are mostly “semi-skilled” workers (Figure 12). As a proportion of income earners among black people in

the South African harbour towns, the fishing industry contributes 1.2% to the low income group (between

R6 000 and R18 000 per year), 14.5% to the middle income group

(R18 000 to R42 000 per year) and 3.8% to the high income group (above R42 000) (Figure 12). The

fishing industry has an insignificant impact on white employment and white income groupings.


22

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

Num

ber o

f peo

ple

empl

oyed

Fishing Industry 20069 2339

Other employment 399042 249187 2432

Black White Unspec

4.8%

0.9%

Figure 11. The number of people employed in the fishing industry and in other occupations in the South African commercial harbour towns.

020000

400006000080000

100000

120000140000160000

180000200000

Num

ber o

f Bla

ck p

eopl

e ea

rnin

g in

com

e

Other 45558 187006 92979 51263

Fishing industry 2331 15734 2004

< 6 > 6 - 18 > 18 - 42 > 42 - 360

The Fishing Industry Pays Higher than Average Wages

Income R'000

Figure 12. The number of black people employed in the fishing industry and in other occupations in the South African commercial harbour towns by income category.

9. USER CHARGES AND REVENUE COLLECTION The role of Levies in the South African fishing microeconomy is graphically represented in Figure 1: A

Simple Economic System for South African Fishing as a flow of revenue from primary sector fishing

activities to MCM. This pays for a flow of services provided by MCM (Qt) to the fishing industry. It stands

to reason that being an intervention in the microeconomic system, it influences how the system behaves.

The calculation of fees and levies for fishing in South Africa has lacked a quantitative basis and one of the

tasks of the ESS was, therefore, to review the system of fees and levies and to suggest a rational

approach to their determination.


23

The basis of implementation of fees and levies was the “user-pays” principle, whereby there are certain

specialised and specific services that directly benefit the user and that only the State can provide due to

one form or another of market failure. It is important to note that the user-pays principle is not a fishery

specific cost recovery approach. Charges and levies are based on an “ability to pay” in the respective

fisheries and used to fund a portion of the total basket of the fishery services provided by the State. The

economics of the various fisheries dictate that some have a greater ability to pay than others and that

levies will be determined accordingly.

The different instruments available for revenue collection were defined, and evaluated in terms of

economic efficiency, equity and practicality. These included fees, royalty charges, levies and the

proportional user charge (PUC). It was advised that the proportional user charge coupled with a penalty

charge system should replace (preferably in 2003) the current pay-as-you-catch levy system in TAC

based fisheries. The PUC and penalty charge system still, however, requires a fair amount of additional

data, workshopping and some analysis to make it feasible.

A model for the determination of levies was developed by the ESS, based on an array of income

statements (convertible into a short-run Cobb-Douglas production and cost functions) for a functional

group of vessels, based on size or some other distinguishing characteristic. Because this method of

analysis is vessel based, it necessitated a calculation of a minimum viable quota (MVQ) per vessel class

based on vessel operating costs (including proposed levy charges) and income.

The ESS was faced with two unforeseen problems that made it unfeasible to implement this system in

2002, namely:

¶ Because fishing rights are not attached to vessels (see Part 3: The Economics of Allocations),

the concept of an MVQ becomes an abstract concept and was not used as a basis for rights

allocation. Furthermore, there was aversion by many established industry players to accept the

concept of an MVQ.

¶ The collection of standard and comparable income statements for each size group of vessel

proved to be beyond the scope of the ESS study.

Levy charges were, however, reviewed with primary regard to President Mbeki’s vision of an

economically integrated southern Africa. It is important to bear in mind that ‘a feature of deeper economic

integration beyond a mere customs union, would be the harmonisation of fiscal instruments’ (Catteneo,

2001). Simply, this would include harmonising, where possible, user charges on primary economic

activities to avoid a competitive bias. Comparison of levies revealed that South African fishing levies are

substantially lower than Namibia’s for equivalent fisheries. Levy recommendations for commercial

fisheries were then developed fishery by fishery assuming the current pay-as-you-catch system of

collecting levies and that 100% of TAC/TAE is levied. Additional revenue from over-catch both in directed

species and by-catch were not included in revenue calculations. In all cases it is assumed that the legal

liability, but not necessarily the final burden, for paying the levy falls on the rights holder.


24

10. FUTURE MANAGEMENT: THE NEED FOR INFORMATION

The general policy change of MCM from a “resource centred” approach to marine resource management

to a “people centred” one, has had huge implications for every facet of management associated with the

use of the coast. Management processes around the utilisation of living marine resources in South Africa

can best be described as being in a state of flux. The development of fishery specific management

policies continue to be constrained by a lack of information. This has severely hampered the ability of the

Department to implement processes to achieve the goals of the MLRA, specifically the restructuring the

fishing industry to address historical imbalances and to achieve equity within all branches of the fishing

industry.

The ESS was initially designed to provide a first realistic overview of the main commercial fishing sectors

and was envisaged to play a role in the development of a sound policy framework and the revised

process for the allocation of rights in 2002. The lack of clear policy guidelines meant that the terms of

reference for this study were of necessity based on the anticipated policy and management requirements

of the Department, particularly those relevant to the 2001/2002 rights allocation process.

Not all key questions were addressed as first envisaged, and in consultation with the Department, the

approach to specific issues was modified. For example, the idea of a Minimum Viable Quota per vessel

category was found to be too simplistic a concept to implement for many fisheries. Due to the late

commencement of the study, and legal opinion on the type of information presented to the allocation

teams, only the industry overview documents (ESS Volume 2) were forwarded for inclusion in the

allocation process. The detailed analysis and interpretation of the survey data (ESS Volume 1) was

subsequently presented to the Department of Marine and Coastal Management and the industry in a

series of workshops, and feedback was included in the final analysis.

One of the key results of the ESS survey was that it highlighted the almost complete lack of prior

information on the size and shape of the South African fishing fleets. The fact that the ESS survey

database, which contains a vast amount of recent information, provides a very useful foundation upon

which to build a future data gathering and management system, was discussed at length in the feedback

workshops. It was agreed that such a system (Fisheries Information System – FIS) should be developed

as a matter of urgency as the ESS survey data was dating, and continuity in data acquistion was

accepted as vital in enabling MCM to provide current information, and to track changes in the different

fishing sectors. The short term requirements of the FIS would be to support the development of the new

policy framework, and the monitoring and management of the four-year interim rights period, with a view

to the successful implementation of long term rights. Given that the new policy will have to include a

closer working relationship with the fishing industry, the FIS can play a valuable role in providing a

platform for information flow between the Department and the different fishery sectors.

A suggested outline for creating such a baseline FIS is set out below (Figure 13). A crucial aspect of

such a system will be to design a flexible framework able to be easily developed and expanded as the

new management systems are put in place and further data requirements are crystalised.


25

FIS Coordinator

Database Manager

Data typists

FIS Database

INITIAL INCOMING DATA: INITIAL INCOMING DATA: -- RIGHTS APPLICATIONSRIGHTS APPLICATIONS-- VERIFICATION UNIT VERIFICATION UNIT -- ESS DATAESS DATA-- PERMIT INFORMATIONPERMIT INFORMATION

FISHERIES MANAGEMENT FISHERIES MANAGEMENT SUMMARIES & DATA SUMMARIES & DATA REQUESTS FROM MCMREQUESTS FROM MCMFISHING INDUSTRY,FISHING INDUSTRY,MEDIA &MEDIA &OTHER LIASIONSOTHER LIASIONS

F isheriesF isheriesI nformationI nformationU nitU nit

Database Administ.

FUTURE DATA INPUTS: FUTURE DATA INPUTS: -- VMS DATAVMS DATA-- INDUSTRY REPORTING INDUSTRY REPORTING -- INDUSTRY SURVEYSINDUSTRY SURVEYS-- INPUTS FROM INPUTS FROM RESEARCH AND OTHER RESEARCH AND OTHER MCM DATABASESMCM DATABASES

Figure 13. Schematic outline of the proposed Fisheries Information Unit.

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 1. Background

26


27

1. BACKGROUND TO THE ESS AND METHODOLOGY 1.1. INTRODUCTION The introduction of constitutional democracy in South Africa required the redrafting of most Acts of

parliament to promote goals of social equity and redress of the consequences of past racial

discrimination. The introduction of the Marine Living Resources Act of 1998 required the State to

“restructure the fishing industry to address historical imbalances and to achieve equity within all branches

of the fishing industry”. The initial burden of implementing this social agenda fell on Government fisheries

biologists, and a series of interventions with profound economic, legal and political consequences were

embarked upon without any proper framework for understanding the expected outcomes. The focus of

“restructuring” was a series of annual fishing rights allocation processes whereby fishing quota was

redistributed away from historically white, larger, companies to smaller new, mostly black, entrants into

the industry. This process was far from smooth and resulted in a number of legal challenges of

Government decisions, instability within many fisheries, and a tremendous strain on the under-equipped

manpower resources within the Department of Environmental Affairs and Tourism’s Marine and Coastal

Management Branch (MCM).

Under the leadership of the newly appointed Minister Valli Moosa in 2000, the problems were

acknowledged and steps were taken to stabilise the fishing industry while still promoting transformation.

Interventions included the creation of a Deputy Director General post responsible for Marine and Coastal

Management, a moratorium of rights allocations for one year to allow for proper administrative process,

the establishment of a contracted out Rights Verification Unit, and the appointment of a private legal team

to adjudicate the 2001 rights allocation process. The moratorium on rights allocations in 2001 aimed at

stabilising the fishing industry was supported by a position paper entitled “Draft Discussion Document For

The Fisheries Management Plan To Improve The Process of Allocating Fishing Rights”

(http://www.environment.gov.za/docs/2000/fishing_rights/index.html#Appendix8), which identified the need for an

economic and sectoral study of the fishing industry. This need arose from a lack of basic information on

the economics and socio-economics of the fishing industry, which was critical to informing realistic policy

and decision-making around rights allocation, industry restructuring and general fishery management

issues.

The Department of Environmental Affairs and Tourism asked the South African Network for Coastal and

Oceanic Research (SANCOR) to assist in putting a team together to undertake the study and this

resulted in a multi-disciplinary team led by Rhodes University being contracted via the National Research

Foundation (NRF). The main objectives of the study were to:

¶ Provide a synoptic report on each fishery.

¶ Provide a description of the microeconomy of the fishing industry.

¶ Provide baseline economic and socio-economic data.

¶ Provide precise definitions of scale-groupings of vessels within each fishery.


28

¶ Quantify the allocating rights in minimum economic units (minimum viable quota) by scale-

grouping of vessels operating in each particular fishery.

¶ Analyse realistic options regarding fee structures – charges and/or levies and/or royalty taxes –

that MCM can use to capture economic rent.

¶ Provide a measurable estimate of the level of transformation (as defined), including the

distribution of the wage bill to previously disadvantaged individuals.

It was intended that the ESS would inform the 2001 rights allocation process, and establish a database

which could be kept up to date to provide ongoing information and analyses for policy, management and

research processes. The original Terms of Reference of the ESS are included in Appendix 1.1.

1.2. DATA COLLECTION AND DATABASING

In order to compile the required holistic profile of the fishing industry, the gathering of information and

subsequent databasing was undertaken in the following ways:

1.2.1 Industry Survey and Information Gathering

A questionnaire of the required information was compiled and tailored to each fishery sector. A list of the

generic information that was required from each fishery is presented in Appendix 1.2. The survey aimed

to capture a 100% sample of rights holders and processing establishments. An expert familiar with the

fishery concerned was appointed to execute the survey and each rights holder was approached either

personally, by phone or by post. The proportion of rights holders and vessels captured in the survey was

high:- 1 483 returns, which was equivalent to 87% of rights holders (Table 1). Meetings were held with

industry associations and representatives to discuss the ESS objectives and methodology, and very good

cooperation from industry was obtained.

In addition to the questionnaire survey, representative cost data on different classes of fishing vessels

was obtained by Business Partners and individuals with an in-depth knowledge of particular types of

fishing operations. MCM databases were used to obtain information on rights holders and vessels, but

these proved to be of very little value as the databases were poorly maintained and often out of date or

inaccurate. Thus, the survey had to rely primarily on the questionnaire survey data. Socio-economic data

on employment and income of coastal commuities was obtained from the 1996 census data in order to

contextualise the contribution of the fishing industry to the coastal economy.


29

Table 1.1. ESS Survey returns from the fisheries covered and percentage coverage of rights holders, quota or catch and vessels. 1.2.2 ESS Database The questionnaire survey data was entered into a Microsoft access database and summary reports

extracted for the ESS economic and socio economic analyses.

1.3. FISHERY PROFILE REPORTS

The synoptic overview report on each fishery was written by the sub-consultant responsible for the

questionnaire survey and summary data extracted from the ESS database was added to these reports.

These reports were forwarded to the rights allocation teams appointed by MCM to make

recommendations to the Minister on the allocation of fishing rights. The fishery profile reports form

Volume 2 of the ESS reports.

1.4. ESS ECONOMIC, SOCIO-ECONOMIC AND LEGAL ANALYSES

The above information and additional literature and secondary sources was used generate the various

analyses and perspectives which form the basis of the chapters in the volume. These include:

ü A contextualisation of the principles underlying the microeconomy of the South African fishing

industry and the form and structure of fishing rights rights. ü A legal perspective examining the constitutional compatibility of the regulatory system with

respect to fisheries.

Fishery Coverage by: ReturnsRightsholders Quota/Catch Vessels

Abalone 100% 100% 100% 47Deepsea 93% 99% 98% 52Hake HL 100% 97% 100% 43Hake LL - 70% 32% 105Inshore 100% 98% 98% 11Linefish - - 69% 586

MidWater 100% 100% 30% 14Pelagic 78% 83% 89% 125Praw n 100% - 88% 5

SCRL 58% 69% 69% 11Seaw eed 79% - - 11Shark LL 52% - 100% 12

Squid 100% 98% 100% 160Tuna BB 85% - 100% 85Tuna LL 73% - 100% 19

WCRL 98% 100% 75% 197AVG/TOTAL: 86.8% 91.4% 83.2% 1483


30

ü Analysis of the ESS survey results in terms of employment, skills, income.

ü A classification of the size and shape of the fishing fleets.

ü Definition and measurement of transformation.

ü An analysis of options for user charges and revenue collection.

ü The socio-economic contribution of the fisheries sector to coastal towns.

1.5. ESS PROJECT TEAM

The ESS project team comprised of a large multi-disciplinary and multi-institutional team which was

coordinated by the lead agent team at Rhodes University. The project would not have been possible

without substantial inputs from MCM staff who willingly gave up their time to assist in providing

information and access to databases.

Contribution Responsible Institutions and Individuals ESS Fishery Survey and Fishery Profiles

Anchor Environmental Consultants Capfish Fisheries and Oceanographic Support Services Oceanographic Research Institute Pisces Research and Management Consultants Policy Centre for Land and Agrarian Reform (PLAAS), University of the Western Cape Rhodes University, Department of Ichthyology and Fisheries Science SA Deep Sea Trawling Industry Association Business Partners

Barry Clarke, John Bolton, Deborah Vromans, Charlotte Heijnis Chris Heineken Dave Japp, Jan Wissema Bruce Mann, Rudi vd Elst, Shaun Fennessy Andrew Penney, Andrea Pulfrich Monieba Issacs and Mafaniso Hara Peter Britz, Ntobeko Bacela, Tom Hecht, Ané Oosthuizen, Loni Dräger and Warwick Sauer Roy Bross Tremaine Wesson and Anton Roelofse

ESS database management

Rhodes University Larry Oellerman and Jan Wissema

Economic and socio economic analysis

Rhodes University, Department of Economics and Economic History

Dinty Mather, Peter Kimemia, Faith Mlumbi, N. Notyawa, Lindsay Martin, Sue Murray and Philip Ndimande

Policy Centre for Land and Agrarian Reform (PLAAS), University of the Western Cape

Monieba Issacs and Mafaniso Hara

SA Deep Sea Trawling Industry Association

Roy Bross

ESS report editing and production

Rhodes University Dinty Mather, Peter Britz, Tom Hecht, Larry Oellerman, Warwick Sauer and Lisl Griffioen


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APPENDIX 1.1: ESS TERMS OF REFERENCE

1. Background Marine and Coastal Management (MCM) is currently in the process of implementing the provisions of the

Marine Living Resources Act (MLRA) with regard to allocation of long term rights. In so doing, it will

endeavour to promote the over-arching objectives and principles set out in section 2 of the MLRA, which

states that:

The Minister and any organ of State shall in exercising any power under this Act, have regard to:

¶ The need to utilise marine living resources to achieve economic growth, human resource

development, capacity building within fisheries and mariculture branches, employment creation and a

sound ecological balance consistent with the development objectives of the national government.

¶ The need to restructure the fishing industry to address historical imbalances and to achieve equity

within all branches of the fishing industry.

This necessitates an upgrading of knowledge at Departmental (MCM) level in respect of the economics of

fishing in South Africa. The Department needs to obtain a sound understanding of the overall economic

benefits of each fishery as well as reliable estimates of microeconomic parameters such as values of

production, costs, profits, employment etc. Such data are essential in order to ensure the implementation

of an appropriate fee structure to recover costs associated with management, compliance and research,

to improve on the process of allocating fishing rights, to develop an adequate decision making framework

for the various commercial fisheries and to enable the formulation of a coherent policy on transformation.

To achieve this, a study is urgently required to establish a sound analytical framework for economic

decision-making and which can be undertaken on a recurrent, regular basis.

2. Fishing industry classification: definitions and terminology

For purposes related to the allocation of rights, the application of a fee structure and generally for the

economic and sectoral study contemplated in this document, there is a need to establish a coherent set of

definitions regarding the terminology associated with the classification of South African commercial

fishing.

¶ Each commercial fishery will be stratified according to the functional characteristics of the various

classes of vessel used in that fishery. This will serve as a scale-grouping vessel definition system

useful for managerial and analytical purposes.

¶ The classes of fishing enterprise (big, medium, small and micro), or sectors will be carefully defined

with respect to the levels of vertical and horizontal integration in each fishery and for the South

African commercial fishing as a whole.


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¶ The process of transformation in the SA fishing sub-sector will be defined as political transformation,

economic transformation and structural transformation. The definitions may differ between fisheries.

3. Economic and sectoral study (ESS)

Economic and sectoral analyses of the SA fishing sub-sector should begin by firstly, identifying the

various fisheries that exist, secondly, establishing the number and the characteristics of all fishing vessels

operating in that fishery, thirdly, (where possible) stratifying each fishery into groups by scale, function

and ownership of vessels, and lastly by separating the fishery into sectors (class of enterprise).

A discrete economic analysis, using representative cost data, will be undertaken for the purpose of:

¶ Providing precise definitions of scale-groupings of vessels within each fishery, thus enabling a

division into sectors.

¶ Quantifying the concept of allocating rights in minimum economic units (viable quota) by scale-

grouping of vessel operating in each particular fishery.

¶ Similarly, quantifying the minimum viable quotas needed per sector.

¶ Analysing realistic options regarding fee structures – charges and/or levies and/or royalty taxes – that

MCM can use to capture economic rent from fishing activities.

¶ Determining options regarding the rate at which fees can be levied and thus also the amount of

income that MCM can raise from each scale-group and each sector.

¶ Measuring the relative shares of TAC/TAE by sector.

¶ Providing a measurable estimate of the level of transformation (as defined), including the distribution

of the wage bill to previously disadvantaged individuals.

The above necessitates the collection and compilation of a number of databases:

¶ A complete database of the functional characteristics of all vessels used for commercial fishing in

South Africa, including their distribution of rights, ownership (where possible) and employment

characteristics.

¶ A representative cost database for each scale-group of vessel in each fishery.

¶ Historic catch statistics (where possible).


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¶ Historic price/quantity data (where possible).

¶ A database that links fishing activities to processing operations. This database should include

processing capacity, ownership structures and employment characteristics.

The compilation of the databases, and in particular the representative data, should be undertaken in

consultation with industry and MCM. Data will be linked algorithmically and summatively to produce a

series of decision tables.

4. Outputs Fishing industry classification: In the light of relevant provisions in the MLRA, the study contemplated in

this document will:

¶ Revise the classification of fisheries within the SA fishing sub-sector as currently used by the

Department, including the terminology associated therewith.

¶ Where possible to determine and describe the parameters to be considered in order to classify

vessels into scale-groups (e.g. size and/or function) within each fishery.

¶ Classify each fishery by sector similar to the DTI’s big and SMME grouping system.

¶ Suggest a precise and functional definition of transformation within the SA fishing sub sector.

Economics and sectoral study (ESS): The study contemplated in this document will also provide a

functional analytical framework, which can be used to generate decision tables, to enable decision

makers to determine the economic impact of options available to them. The decision table will contain

the following:

FEES (due regard will be given to the viability of the vessels engaged in commercial fishing activities).

¶ An analysis of the current fee structure of MCM and a study of the distribution of revenue collected

between levies, leases and other fees that the MLRA makes provision for.

¶ Realistic options regarding different fee structures and rates that the MLRA makes provision for, and

an indication of the distribution of revenue collected from the various instruments.

¶ With regard to the above, determining the amount of income that MCM can raise from each sector.


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VIABLE QUOTAS (consideration will be given to the scarcity of the resource and the overall objective of

accommodating new entrants to the industry).

¶ Realistic options regarding the minimum viable quota allocation required for vessels in a scale-group,

in its respective fishery, to operate viably.

¶ Realistic options regarding minimum viable quotas to sectors.

VERTICAL AND HORIZONTAL INTEGRATION (due regard will be given to the fact that vertically

integrated enterprises add value to the product by branding and efficient fishing practice).

¶ Measuring concentration of ownership from a vertically and a horizontally integrated point of view.

¶ At the bottom end of the scale, highlighting the inefficiencies that result from sub-economic short term

quota allocations, particularly to Black new entrants.

TRANSFORMATION (transformation objectives will be analysed with selected potential socio-economic,

socio-political and legal implications along with the necessary development interventions required to

achieve policy option goals).

¶ Socio-political, socio-economic and legal indicators with respect to options and alternatives for

redistributing different proportions of TAC/TAE between sectors.

¶ Socio-political, socio-economic and legal indicators with respect to options and alternatives for

redistributing different proportions of TAC/TAE from White individuals to Black individuals.

The decision tables contemplated above will specify the input data (parameters) needed, data sources

and data accessibility. It will also address the constraints in terms of insufficient data existence, data

quality, or data accessibility, as well as proposals on resolving such problems.

The results of the fishing industry classification study will be presented in a written report and a tabulated

form.

The results of the economic and sectoral study will be:

¶ Consistent with the terminology and classification scheme devised in the fishing industry classification

study.

¶ Presented in the form of a series of decision tables.

¶ Attached to the decision tables will be appendices providing more detailed explanations of the

indicators and measures used.


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¶ A written report dealing with the methodology employed to derive the indicators and measures used

in formulating the decision tables.

The researchers will attempt to integrate the relevant MCM staff during the process of the study. The

purpose of this is to enable key MCM staff to completely understand, and make inputs into, the process,

methodology and analytics of the study. The aim of this output is to maximise the value the study to

MCM and to enable a continued use, adaptation and sophistication of the decision tables by MCM.

Finally, the study will inform a future process for further data acquisition, data refinement and an

increasingly complex, but more complete and sophisticated, analytical approach.


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APPENDIX 1.2: DATA REQUIREMENTS

To achieve the outputs required by the Terms of Reference the following instructions were given to the

sub-consultants:-

There are four tasks that needed to be completed:

¶ A report on the fishery including a scale distribution arrangement of the fleet.

¶ A vessel database that captures certain characteristics of ALL the vessels in the fleet.

¶ Time series data that plots landed prices to quantities sold of directed catch and by-catch since 1994.

The entry and exit of vessels into the fishery since 1994 is also included.

¶ A database on certain characteristics of the shore based activities.

As the information required is general, please attempt to provide as much additional detail and data as

possible that brings out the special circumstances of the fishery you are dealing with. However, it is

imperative that all data requested be provided for a 100% sample of vessels in the fishery and a 100%

sample of shore based activities.

1. Report on the fishery

This should be a comprehensive report on the fishery and should include all aspects that might capture its

special circumstances. All additional information that the consultant deems important should be included

with as much data as possible. For example:

¶ An historical perspective

¶ The importance of shore based activities to the well-being of the fishing activities

¶ Perceptions on transformation

Once the vessel data has been collected, the consultants have to use their experience in the fishery. You

must divide the fleet (for the specific fishery) into broad categories based on vessel characteristics in

order to determine the scale distribution of the fleet. Please provide a detailed justification/argument why

you (the consultant) believe that the fleet can be sub-categorised the way you have recommended. Also,

arrange the vessel database according to this scheme, namely, the group of larger vessels first followed

by progressively smaller groups – please demarcate your grouping on the spreadsheet.


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2. Vessel data

This data should be collected for every fishing vessel (i.e. a 100% sample of all commercial fishing

vessels) and arranged per fishery (it must also be possible to cross tab between fisheries). It must be for

the current period, that is, January 2000- January 2001.

The data should be collected on a vessel by vessel basis. It should be arranged in electronic format with

vessels in rows and the information pertaining to each vessel along columns – as demonstrated below.

Please ensure that all information below is captured for every vessel in the fleet (you can provide

additional data/information and this would be appreciated, but don’t leave any of the required data out).

Please provide the data in electronic format to Larry Oellermann and to Dinty Mather

([email protected]). It can be in Excel or Quatro-pro. A web based data input site is available at

venus.sabex.com/mcm. This site is application dependent and requires Internet Explorer to access it.

Please send a hard copy of your database to Larry Oellermann.

1. Vessel information 2. Specific information

1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 2.1 2.2 2.3

Key: 1. Vessel information 1.1 Vessel number 1.2 Vessel name 1.3 Construction year 1.4 Vessel length 1.5 Call sign 1.6 Mass of rights 1.7 Classification:[if multipurpose must state the fisheries involved and proportionate uses (see section on access rights)] 1.8 GRT If any other general vessel information is necessary or appropriate to the specific fishery add it here under sub-section data fields 1.9, 1.10 and so on. The consultant must point out, in a separate report, why they have added the field/s. 2. Specific information 2.1 Deck type 2.2 Trawler type 2.3 Hull material 2.4 Hull type 2.5 Power type 2.6 Horse power 2.7 Winch power 2.8 Lights kilowatt 2.9 Propellor type 2.10 Engine type (inboard/outboard) 2.11 Onboard storage 2.12 Processing and cooling


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Any data fields that are deemed unnecessary for this specific fishery must still appear under their codes and filled with a 00. The consultant must justify, in a separate report, why these fields are not necessary. Extra specific information regarding the vessels in the fleet that the consultant thinks an important consideration should be added under fields 2.13, 2.14 and so on. The consultant must point out, in a separate report, why they have added the specific field/s. 3. Harbour 3.1 Harbour registration 3.2 Harbour where landings occur 4. Crew 4.1 Officers: 4.1.1 designation (S - skipper, M - mate, etc) 4.1.2 race (B - Black, W - White) 4.1.3 sex (M - male, F - female) 4.1.4 education (licences, etc)

4.1.5 type of employment (F- full-time, P - part-time, C - commission based)

4.1.6 income (weekly) 4.2 Fishing crew: 4.2.1 race (B - Black, W - White) 4.2.2 sex (M - male, F - female) 4.2.3 education ()


4.2.5 income (weekly) race 4.3 Non-fishing crew: 4.3.1 designation (C - cook, Q - quality control, E - engineer) 4.3.2 race (B - Black, W - White) 4.3.3 sex (M - male, F - female) 4.3.4 education ()


4.3.6 income (weekly) 4.4 Shore based crew: 4.4.1 designation (O - off-loader, W - waalskipper, M - maintenance

crew) 4.4.2 race (B - Black, W - White) 4.4.3 sex (M - male, F - female) 4.4.4 type of employment 4.4.5 duration of employment per cycle 4.4.6 income (daily for O, weekly for W and M) For the shore skipper and maintenance crew be careful of double counting as these employees often are shared between a number of vessels. All other shore based employment will be dealt with under vertical integration. Place the casual labour, or off-loaders, fields before the shore skipper and maintenance crew. 5. Ownership of vessel 5.1 Individual: 5.1.1 Name of individual 5.1.2 Contact details 5.1.3 Race (B - Black, W - White) 5.1.4 Sex (M - male, F - female) 5.1.5 Percentage of ownership 5.2 Trust: 5.2.1 Name of trust 5.2.2 Contact details 5.2.3 Race of trustees (B - Black, W - White) 5.2.4 Sex of trustees (M - male, F - female) 5.2.5 Percentage of ownership


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For ownership by many individuals or trustees, list as follows: The example shows two vessels, one owned by two individuals and the other owned by a trust with three trustees. 5. Ownership of vessel

5.1 Individual 5.2 Trust

5.1.1 5.1.2 5.1.3 5.1.4 5.1.5 5.2.1 5.2.2 5.2.3 5.2.4

Joe Box 11 W M 80

Mary Box 12 B M 20

FF Box 1 B M

B F

W F 5.3 Company: 5.3.1 Name of company 5.3.2 Contact details

5.3.3 Names of shareholders (for small companies name the individual shareholders, for larger companies name parent company - look up in annual reports)

5.3.4 Percentage shareholding 5.3.5 Race of shareholder (B - Black, W - white – see example below)

5.3.6 Sex (M - male, F - female) An example of company ownership data is shown below. For a small company, e.g. FishCo, there are 3 shareholders of different races who own the vessel. For a larger company, e.g., PJ’s, 40% is owned by B. Rand (a Black empowerment listed company) and 60% owned by other shareholders, sex distribution unknown (00 in data field).

5. Ownership of vessel

5.3 Company

5.3.1 5.3.2 5.3.3 5.3.4 5.3.4 5.3.4

FishCo PE Mr A 10 B M

Mrs C 30 W M

Mr M 60 B M

PJ’s CT B. Rand 40 B 00

60 W/B 00 At times it will be important to provide additional information of ownership structures. Please provide as much detail as possible in written form. If more than one vessel is owned by the individual, trust or company, and with different fisheries, then this should be captured by a database organising system (measuring horizontal integration) 6. Access rights 6.1 Name of holder of rights 6.2 Contact details 6.3 Race of right holder (B - Black, W - White) 6.4 Sex of right holder (M - male, F - female) 6.5 Type of right (e.g. hake, squid) 6.6 Quantity (500 tons, 8 fishers) 6.7 Prices paid per Kg of quota/catch leased


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List the names of all rights holders for whom the vessel fishes. This should include all types of rights held by multi-species fishing vessels. The important thing here is that all allocated rights are accounted for and can be cross tabbed back to MCM’s lists. 7. By-catch and discards 7.1 By-catch 7.1.1 type (e.g. kingklip) 7.1.2 proportion to allowable catch (20%) 7.2 Discards 7.2.1 type 7.2.2 proportion to allowable catch Where there are different types of by-catch and discards, list these beneath each other similarly to the scheme shown under ownership. 8. Vessel value 8.1 Market value of vessel 8.2 Replacement value of vessel 9. Harvesting capacity 9.1 Cycle length (days) 9.2 Max onboard storage (kg’s) 9.3 Cycles per year 9.3.1 maximum cycles per year 9.3.2 average number of cycles per year 10. Product distribution 10.1 Name of company who buys the vessel’s catch (if sold to the general public,enter GP in this field and

00 in the following fields under section 10) 10.2 Proportion of catch sold to this company 10.3 Address of company 10.4 Does the company process the product? (Y - yes, N - no) 10.5 Does the company market the product? (Y - yes, N - no) 10.6 Does the company own the vessel? (Y - yes, N - no) 11. Nature of fishing and gear type As this is probably specific to each fishery, provide a classification for the database using the scheme above. Based on your knowledge of fishery please give a score between 1 and 10 (10 is the best) on the principles of sound ecological balance and environmental impact based on, among other things, gear type, fishing practice and an evaluation of by-catch and discard practice. 3. Time series data 1. A time series beginning 1994 to current should be compiled. This time series should be a brief

summary of the detailed vessel characteristics. Important data points are - name of vessel - distribution of access rights used on the vessel on a year to year basis - harvesting capacity of each vessel (see point 9 above).


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As the entry and exit of vessels in a fishery should have not changed significantly this is simply a matter of checking changes in the fleet. Where changes have occurred, it is important to place a date on removal or addition to the fleet. Where a vessel has been removed please try to get the vessel data outlined in section A (also give a reason why the vessel has been removed from the fleet - if transferred to another fishery should be able to cross tab this information). 2. Attempt to gather as detailed a time series, as far back as is reasonable, on prices for the product

(beach prices). It is important to link the prices to quantities at the time of sale. It would be useful if this could also be linked to the quality of the product. While doing this please also compile similar time series of consumer prices (if possible), export prices and any other commodity prices that you feel are important for this specific fishery.

Here again the consultant’s knowledge of the industry is very important. Please try to give a well argued and carefully reasoned opinion on the link, if any, between the scale of distribution classification (section D) and the quality of the product. Also give a backed up opinion on the possible link between quality and horizontal integration (where the processing/marketing company owns the vessel/s). 3. As above for by-catch prices 4. Shore based activities

The task is to attempt to link the on-shore activities (packing, processing and marketing) to the off-shore activities (rights allocations). Please provide, in a similar manner to the vessel database, a spreadsheet in electronic format to Dinty Mather and to Larry Oellermann. Also include a hard-copy to Larry. 1. For each company directly involved in on-shore activities, the consultant must determine the current

capacity (how much fish, and what species, do they need to remain viable) – should be able to cross tab this back to the vessel database.

1.1 Company name 1.2 Geographical location 1.3 Capacity

1.3.1 species 1.3.2 tons

For some large companies there may be a number of different plants, please provide separate information on each plant. For each species, provide separate entries. 2. How many people does the company employ

2.1 Total number (proportion full-time and casual workers) 2.2 Percentage Black employees 2.4 Percentage female employees 2.3 Percentage of Black managerial employees to White managerial employees

Continue to separate out the data per plant. 3. Proportion of Black shareholding (if a listed company, find out if any other company has a major

shareholding and attempt to determine the racial mix of that company’s ownership - usually published in annual company reports).

4. To what extent does each of the processing facilities add value to the product (pack, prepare and

pack, can, market and so on), that is, what is the ratio of value added to the landed price (e.g. R1 beach price, R5 sale price = 5:1). If the company owns the vessels, attempt to find out the internal pricing policy.

5. Percentage sold in local markets (inverse of exports) 6. Market value of plant and replacement value of plant.


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5. Representative cost data The representative cost data is to be collected from appropriate financial institutions and industry bodies. The costs should be in the form of income statements, or cash flow statements, of a representative vessel for each scale group in each fishery.

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 2. Fishing Microeconomy

43

2. A SIMPLE ECONOMIC SYSTEM OF THE FISHING MICROECONOMY SUMMARY This section provides a conceptual framework for viewing the South African fishing industry as a

microeconomic system. It provides a framework for understanding the economic mechanisms and

consequences that will follow any management intervention in the fishery.

The South African fishing industry can be viewed as a complex system comprising:

1. A dynamic biological system.

2. An economic system.

3. A system of legal procedures.

4. A social system.

The biological side of the system is fairly well understood. The focus of the ESS is primarily on the

economic system. It does, however also provide an insight into the legal system, and the socio-economic

impact of the fishing industry.

It is useful to view the fishing industry primarily as a microeconomic system that is linked to the domestic

and national economy, is constrained by a system of legal procedures and has an impact on the

economic well being of certain individuals in society. The important biological indicators – as binding

constraints from society’s point of view – are the Total Allowable Catch or Total Allowable Effort and other

catch restrictions.

The easiest way to understand the economic system is to break it up into a number of interacting

components. These components are illustrated on Figure 2.1 and comprise:

Three sets of infrastructure, namely the primary sector infrastructure (vessels), primary sector

support infrastructure (buildings and so on) and the secondary and tertiary sector infrastructure

(capital goods needed to process and market fish products).

Three markets, namely, the input market, the capital market and the output market.

Economic activity that occurs when there is resource flow between the markets and infrastructure.

If markets work, prices will determine the rate of flow between the relevant markets and the fishing

industry infrastructure such that economic resources are optimally allocated. If markets don’t work to

allocate resources - market failure - enabling institutions may be needed.


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A number of forms of market failure occur in primary sector fishing activities:

¶ Markets do not efficiently allocate living marine resources (common property resource problem).

¶ Markets do not provide an optimal amount of research (free-rider problem).

¶ Markets fail in the allocation of scarce capital resources to harvesting activities (asymmetric

information problem).

To ensure the sustainable utilisation of living marine resources, microeconomic management in the form

of State intervention by MCM is necessary to correct for these failures.


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INSERT FIG 2.1 – DIAGRAM (A simple economic system)


46


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2.1. INTRODUCTION: A COMPLEX SYSTEM

The purpose of this section is to provide a broad picture of the human environment under which the South

African commercial fishing industry operates. It will also indicate how the Economic and Sectoral Study

(ESS) aims to enable a better understanding of the system.

Very simply, the South African commercial fishing industry can be seen as an interaction between four

systems, namely a dynamic biological system, a dynamic economic system (how resources flow in the

social system), a system of legal procedures and a social system (captured by the socio-economic and

socio-political characteristics and changes). A brief description of these systems is provided below. The

ESS provides some input into describing and defining the economic system, the social system and the

legal system.

1. The dynamic biological system and the quantity of the living marine resource (LMR) that can be

taken, or harvested, in a given time period is normally determined by a yearly Total Allowable Catch

(TAC) or Total Allowable Effort (TAE). This system is comparatively well understood, being the

foundation stone of the sustainable use of LMRs. Biological information can be used in economic

analysis either as a binding input or output constraint (for example a TAC) or it can be integrated into

the biological system as bioeconomics7. Each approach has its advantages and disadvantages. For

the purpose of answering the difficult questions facing the South African fishing industry, for

example, the issue of redistributing fishing rights, the former approach is better.

2. The economic system and how it operates. At its most basic level, this would include the

theoretical modeling and measurement of price determination8, sensitivities of all prices to changes

in the system9, the relationships of resource flows between the different sectors of the economy and

the fishing industry10 as well as measuring where and how the market fails11 to allocate resources

efficiently through the interaction of demand and supply.

7 Bioeconomics attempts to model the interaction between the biological dynamics as a capital system (uses a discount rate to determine the rate of exploitation), the input market (average cost across a fishery) and the output market (price quantity relationships). The end result is always a lower total allowable catch or effort sometimes called a Total Economic Catch. In effect it treats the fishery as a cartel, which is inherently unstable, and often uses over-simplified market assumptions to model and estimate the input and output market. Bioeconomics provides very little insight into the more difficult problems facing today’s fisheries. 8 This would include all factor input prices (for example, the wage rate is the price of labour), the price of capital (or the discount rate) and output prices (the prices of fresh and processed MLR for each particular species). 9 Sensitivities to prices are called elasticities in the economic literature. 10 The economic sectors of the economy, according to the system of national accounts, comprise among other a primary sector (hunting, fishing, forestry and agriculture), a secondary sector (manufacturing and processing) and a tertiary sector (services). 11 This is called market failure in the economic terminology.

The commercial fishing industry comprises primary sector activities (harvesting), secondary sector activities (processing) and tertiary sector activities (marketing and other support services).

To distinguish the living marine resource from other resources like labour, capital and other factor inputs, it is termed the LMR.


48

The ESS can provide, to a limited extent, some indicators that might be useful in classifying this

system. The data available, however, is insufficient to analyse the economic system of price

determination.

3. The system of legal procedures and constraints that limit the actions of individuals, groups of

individuals and the State involved in fishing activities. Some insights into this will be provided by the

ESS.

4. The effect that the fishing industry has on the economic well-being of individuals (socio-economics)

being the impact on individual and household livelihoods and economic circumstances to changes in

the fishing industry is examined largely through the labour market. Strategies used by individuals, or

groups of individuals to cope with changes in the system are dealt with in the socio-political analysis.

The ESS provides a socio-economic baseline of South Africa’s commercial harbours and a socio-

economic impact of the commercial fishing industry.

5. Finally, the way in which the components of a complex system (namely, the biological system, the

economic system, the legal system, the socio-economic system and the socio-political system)

interact to characterise the fishing industry could be a longer-term objective. It is also necessary to

understand and measure the effect that exogenous changes12 and endogenous changes13 will have

on the complex system. This is a goal to work towards by instituting a more careful and long term

research agenda and information gathering exercises.

In summary, it would be impossible to achieve a complete understanding and measurement of the fishing

industry in the short time period available for the ESS. However, the ESS does provide a first picture and

sets the scene for a more comprehensive understanding of the complex system. Thus, it also establishes

a broad framework from which to develop models and to measure the important components of the

complex system.

12 Exogenous changes are changes that occur outside of the system. 13 Endogenous changes are changes that occur within the system.

Economic systems are often concerned with prices and how they are used to allocate economic goods and services. Examples of some prices that are determined in markets are given below Wage rate: price of labour (w) Discount rate: price of capital (r) Output price: price of final output (p) Input price: Price of inputs (pi)

The ESS provides a first picture and sets the scene for a deeper understanding of the complex system.


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2.2. THE FISHING INDUSTRY AS A MICROECONOMIC SYSTEM Primarily the fishing industry is a microeconomic system and should be viewed as such. This is not to

say it is not intimately linked with the domestic and the international economy. It is more convenient,

useful and methodologically sound, however, to initially view the industry in a disaggregated form.

Based on some rather restrictive assumptions14, the neoclassical theory of markets15 shows how

resources in an economy can be efficiently allocated using a system of market prices. It is traditionally

considered the starting point of microeconomic analysis and measurement. It also provides economists

with certain tools for analysis and an array of well-accepted terminology. However, it should be noted

that the ESS will at times employ, as simply as possible, a certain logic inherent in the neoclassical

system, and that it is not and cannot be methodologically bound – particularly without the evidence in the

form of primary and comprehensive time series databases.

Microeconomic analysis is also concerned with the circumstances under which the system of prices fails

to efficiently allocate goods and services – market failure – and how to correct for this given a minimum

intervention by the State16.

The usual method of analysis in microeconomic research is to firstly attempt to understand the system,

then to model it in such a way that the model can be applied to available data sets and econometric

analyses17. The purpose of this section is to provide a broad outline and brief description of how the

microeconomic system of the fishing industry might be viewed.

2.3. THE COMPONENTS OF THE ECONOMIC SYSTEM

It is an important first step to create a somewhat artificial construct that highlights the interacting

components of the economic system. It is also significant to indicate, where possible, where the market

is expected to work and where it is expected to fail. To go further, an understanding of the circumstances

under which the market fails and the logic behind correcting for that specific market failure, would enable

the policy maker to institute careful corrective interventions.

14 The strong form includes perfect information, homogenous products, lack of market power both from the producer and consumer side and freedom of entry into and exit from the market. 15 See for example, Arrow and Debreu (1954). 16 It further deals with the issues of Government failure and the inevitable trade-offs that result from a situation where both the market and the Government fail. This will be briefly touched on during the discussion. 17 “Econometrics is the branch of economics concerned with the empirical estimation of economic relationships” Intrigator, Bodkin and Hsiao (1996).

Microeconomics (or the economics of systems smaller than the national economy) is usually concerned with the modeling and measurement of markets and market behaviour in the allocation of scarce resources.

Market failure occurs where the market system fails to allocate scarce resources using a system of prices.


50

In addition, a good empirical study to define the relationships that exists between the several components

of the economic system would enable the policy maker to predict fairly accurately the impact that policy

interventions would have on the economic system.

The system is illustrated on Figure 2.1 consists of three sets of infrastructure (primary, secondary and

tertiary) served by three markets (input, output and capital markets). Economic activity consists of the

flow of economic resources between these interacting components. The arrows represent resource flows.

The role of MCM, as a corrector of market failure, is also indicated.

The components of this system are briefly explained below. Where the ESS fits in is highlighted and

indication is given to the appropriate part in the broader report. Central to the system are the three

markets. These markets are all connected in some way with the local and international economy and

with each other. The easiest way of understanding markets is through the interaction of demand and

supply to determine market-clearing prices. For example, in a market where the price is too high,

producers supply too much and consumers demand less resulting in surpluses. Eventually high cost

producers exit from the market driving prices and the quantity produced down until the quantity supplied

just equals the quantity demanded. This is the logic of partial equilibrium neoclassical economics where

an efficient allocation of resources, from society’s point of view (welfare), is based on price. One should,

however, note that markets seldom work well and a too simplistic view of them could easily result in bad

and socially damaging policy.

2.3.1 The Input, Capital and Output Markets The relevant prices that can be used to estimate the efficient allocation of resources are: prices in the

input market, prices for final demand and the price of capital.

Prices in the input market are mostly determined through the interaction of demand and supply. The

most important is the labour market.

¶ Well-behaved labour markets are seldom, if ever, the norm. It is useful, however, to think of the

supply of labour of a particular skill interacting with the demand for those skills to determine a wage

rate – namely the price of labour. The number of jobs available in this activity may be considered a

crude proxy for the demand for labour. The total of individuals who are willing and able to undertake

the task in the economy determines the supply of the labour. If for example, the demand for a

particular skill is greater than the supply (that is, a shortage) the wage rate is expected to rise in

order to attract more entrants into that labour market. A labour market study is beyond the scope of

the ESS. However, employment indicators have been collected (Part 5: ESS survey results:

employment, skills and income) and used as a measure of transformation (Part 7: Understanding

Markets seldom work well and a too simplistic view of them could easily result in bad and socially damaging policy.


51

and measuring transformation). Also, the employment numbers are used to estimate the socio-

economic impact of the South African fishing industry (Part 8: Socio-economic base line and impact).

¶ Similarly, the interaction of demand and supply for other inputs (examples are fuel, bait, nets and

other fishing gear) should determine market clearing prices. Each market has different

characteristics, for example, the price of fuel is determined in the international market. The fishing

industry cannot influence fuel prices – it is a price taker.

Prices for the final demand of LMRs are determined in the output market. Here the demand for LMRs

interacts with the supply to determine a price that under well working markets is expected to yield an

efficient allocation of the world’s LMRs for final consumption.

¶ The standard variables when estimating final demand are product prices and individual incomes.

However, often a strong seasonal demand for certain LMR products is evident and should be

factored into the estimation techniques. The international economy plays a relatively important role

here as well. As the South African domestic economy is considered an open economy (it is allowed

to interact with the world economy with a minimum of interference from government) market prices

are often determined in the world market for the product. This usually means that the South African

market cannot easily change prices by changing its demand and supply conditions – it has to accept

world prices. Also, the fact that LMRs tend to have many complementary products, for example

chicken, it is relatively sensitive to changes in price (elastic).

¶ From the supply side, it is firmly established that the biological characteristics place a binding

constraint on the quantity supplied from any fishing ground, for example a TAC or TAE. This would

be expected to result in an insensitive (inelastic) medium-run18 supply curve. However, it is also a

well-known fact that fishing effort can be timed to coincide with a seasonal market, thus a relatively

sensitive relationship between quantity supplied and price (more elastic) in the short-run19 is

expected.

The ESS does not provide an output market analysis. However, Volume 2: Fishery Profiles is expected

to give trends in this regard, at least by tracking landed value prices over a number of years.

The price of capital, or discount rate, is determined in the capital market by the interaction of supply of

money for investment, that is, savings and the demand for money for investments (borrowing). The

determination of discount rates applied to a fishery should also include the risk20 and uncertainty21

characteristics of that particular fishery. Being a high-risk industry with large uncertainty, the discount

rate (or price of capital) is expected to be high.

18 The medium-run is best considered as I year where TAC or TAE measures are imposed. 19 The short-run would the length of a high demand season. 20 Risk is a statistically measurable indicator; the higher the variance from expected income, the greater the risk. 21 Uncertainty includes all those things that cannot, at least easily, be measured statistically. For example, the variability in fish stocks due to environmental factors.


52

From a capital theoretic point of view (see for example Hotelling (1931) and Gordon (1954) as primary

reference sources) this means that fishing enterprises will harvest more rapidly in the current period

leading to a strong incentive to over-exploit generally. The ESS does not provide a capital theoretic

analysis. In Part 6: ESS survey: classification (size and shape) the results of the ESS survey for both the

market, and replacement, capital value of commercial fishing vessels is presented.

2.3.2 Infrastructure Also illustrated in Figure 1 are activity blocks. These show, in a simplified way, the infrastructure

applicable to the three economic sectors represented in the fishing industry. Flows to and from the

markets to the infrastructure blocks create economic activity.

The primary sector infrastructure includes the vessels and equipment used to harvest fish. The

vessels can further be arranged according to the species of fish they target and their sizes. The ESS

surveyed 15 fisheries and classified them into five different size categories. This is presented in Part 6:

ESS survey: classification (size and shape).

The primary sector fishing support infrastructure comprises largely the capital and land that is

required to keep the vessels engaged in harvesting activities active. It is therefore essentially a support

activity; strictly it is part of the primary economic sector.

The secondary and tertiary sector infrastructure comprises those capital and land based resources

that are employed in economic activity for the further processing, beneficiation and marketing of fish.

Various levels of vertical integration, horizontal integration and a combination of both (conglomeration)

characterise a large portion of the South Africa fishing industry. There are a number of arguments why

integration occurs and debates on whether or not this phenomenon creates economic inefficiencies.

Three likely arguments may serve to explain the logic of its occurrence.

1. Firstly, the most basic explanation is a factor market control one. The processing firm captures

market power over its factor input suppliers (in this case the raw material, viz fish) to keep down its

input prices and in this way minimise costs, the dual of maximising profits. In South Africa where

processing companies can hold rights to fish, the control of fishing activities is a natural

consequence.

To avoid unnecessary ambiguity, the economic activity that occurs as a result of harvesting a specific species of LMR is called a fishery. For example, hake longline directed fishing activities is called the hake longline fishery. A multi-species fishery can be similarly defined. Note: It is acknowledged that in South Africa a fishery is often called a sector. The point of re-definition is not to draw attention to any fault, but merely to be consistent with the system of national account and to avoid ambiguity.


53

2. The second explanation deals with the opportunistic interaction between owners of, or investors in,

co-specialised assets22. For example, a fishing vessel is a very specialised asset being confined,

almost exclusively, to fishing operations. Likewise, investments in fish processing equipment are

specialised. In other words, there is a mutual dependence that requires long term contracts to

maximise the value from both the primary and the secondary economic activities. As each is

dependent on the other, there are opportunities for either side to renege. If for example, the fishing

operations demand a higher price, this erodes the profits of the processing company and visa versa

if the processing company offers a lower than contracted price. The pragmatic solution to this

potential problem is to vertically integrate. This phenomenon is well documented in the literature

(see for example, Klien, Crawford and Alchian, 1978).

3. A third possible cause may result from a widespread and well-documented observation of the

dependence of price on quality. Here the market demands a certain quality that is assured through

branded products. The easiest and most efficient way for the marketing and processing company to

assure quality is to control harvesting operations. The logical solution again is to vertically integrate.

4. The unique history of the trawling industry in South Africa (see Hake Overview report in ESS Volume

2).

The possible explanation of the phenomenon of integration in the fishing industry would likely be a

combination of the three. Because rights to exploit fish in South Africa can be held by processing and

marketing companies, the incentive to vertically integrate is increased. A positive spin-off to vertical

integration is the assurance of value adding to the fish and the consequent increase in employment and

income generation. Vertical integration is perhaps the only mechanism by which economic growth can

realistically occur in the fishing industry.

Some indication of integration is presented in Part 5: ESS survey results: employment, skills and income;

Part 6: ESS survey: classification (size and shape); Part 7: Understanding and measuring transformation;

and in the profile of the deep-sea trawl fishery.

2.3.3 Resource Flows and Economic Activity

When resources flow into the fishing industry infrastructure, economic activity occurs. The characteristics

of the economic activity are determined by the way the markets interact with the fishing industry to

determine its costs and revenue structures. Similarly the markets determine the way the fishing industry

is organised. Resource flows in the South Afican fishing industry are indicated in Figure 1 by arrows

between markets and sectors in the fishing industry.

22 Co-specialised assets occur where the flow of returns from the investment in one type of asset is dependant on the investment in another specialised asset, see for example, Joskow (1985, 1988)

Vertical integration is a natural consequence in the South African fishing industry. It is, perhaps, one of the few available mechanisms for economic growth in the industry.


54

¶ Labour and other factors from the input market flow into the primary, secondary and tertiary sectors of

the fishing industry resulting in economic activity. Income flows out in the form of wages, salaries and

dividends. These make up part of the costs to the fishing industry.

¶ Investment finance flows into the fishing industry to either replace redundant or worn out equipment

or to invest in new equipment. Income flows out into the capital market in the form of loan

repayments and savings. Loan repayments are cost items in the fishing industry.

¶ Marketable final products flow out into the output market and income flows in as sales revenues to

the fishing industry.

¶ Finally income flows out of the fishing industry to government in the form of fees and levies to MCM,

levies to local government and taxes to Provincial and Central government. Non-market services

from the various levels of government and specific services from MCM flow into the system.

The interaction between markets, infrastructure and flows determines the profitability of the activities

undertaken by private individuals and companies. This determines the size and shape of the fishing

industry (see, Part 6: ESS survey: classification (size and shape)).

A good example of efficient resource use is demonstrated by the clustering of vessels of similar sizes in

each particular fishery and also in similar types of fisheries (for example, the clustering of longline type

vessel). This is clearly demonstrated in Part 6: ESS survey: classification (size and shape). It also

seems to indicate that the market system in allocating productive vessels in the primary fishing sector is

at least apparently efficient. It would be dangerous, however, to consider this fact as there is a potential,

and probable, capital market failure in the primary fishing sector. This is briefly discussed later in this part

of the ESS.

Based on an extensive body of literature and evidence, a future task is to model the expected dynamic

relationships that exist in the various markets, how and in what quantities they flow to their particular uses

in the fishing industry and how they react to various exogenous and endogenous changes. This could be

followed by empirical work based on good data to measure as precisely as is possible the exact

relationships. The purpose of this potential analytical work is to determine the effect that various changes

may have on economic activity in the fishing industry.

2.3.4 The Components and the Single or Multi-Species Fishery The basic components of a fishery briefly described above are also directly applicable to all single-

species fisheries and in a more complex way to multi-species fisheries. In other words, the three sets of

components specific to each fishery, or multi-species fishery, are expected to work together to determine

the set of prices under which scarce resources are allocated. Similarly, empirical studies measure the

relationships between the various components and the expected responses to exogenous and

endogenous changes.


55

However, when analysing a particular fishery it is important not to lose sight of the fundamental unit of

analysis, namely the fishing vessel and its characteristics. It is also important to understand through

modeling, measurement and analysis the specific economic sub-system under which these units operate.

It is not appropriate in the ESS to go into the theoretical underpinning of how fishing vessels might be

expected to behave in response to various characteristics inherent in the social and economic system.

Suffice it to say, that the nature of specific capital and co-specific capital investments in fishing vessels

will most likely lead to certain economic behaviour not captured in an analysis of markets. In a similar

way, the lumpiness of capital investments23 points to specific empirical analyses that among other things

give rise to the formation of quasi-rents24. In addition, the flow and acquisition of economic (and

biological) information coupled with opportunistic behaviour (which is to the advantage of the economic

agent) also must be understood and measured (if possible). The bottom line is that a simple analysis and

measurement of markets will not provide the necessary indicators to understand the economic system

inherent in each fishery or the fishing industry as a whole.

For future economic analysis and management purposes, the gathering of time series data on fishing

vessels and fleets is essential. The analysis presented in the following chapters is based on the ESS

survey data which provides a snap-shot for year 2000. The required time series data was lacking for most

fisheries and this is something which needs to be addressed through effective information gathering and

databasing by MCM.

2.3.5 The Fishing Industry and the Macro and International Economy

As with most economic activity, the fishing industry is affected by the national and the international

economy. In many instances the South African fishing industry has to accept world prices for its factor

inputs and some of its outputs. It is affected by the exchange rate through increased Rand prices for

products sold in the international market but also by increased factor prices bought in the international

market. Whether the US$/R decline in exchange rate benefits the local fishery depends in part on the

imported proportion of its factor inputs and the extent to which it exports its outputs.

23 Lumpiness in capital investments refers to the inability to model a smooth quasi-concave production function showing perfect substitutability between capital and labour inputs. 24 Quasi-rents refer to the portion of earnings in excess of the minimum amount needed to cover variable costs, in the face of specialised sunk capital assets like fishing vessels, needed to prevent a fishing entrepreneur from exiting the fishing industry [adapted from Milgrom and Roberts (1996: 269)]


56

2.3.6 Common Property in the Primary Sector: Market Failure The common property25 nature in the primary sector of the fishing industry means that strong incentives

exist for harvesters to over-exploit. In other words, the market system and prices do not result in an

efficient allocation of marine living resources26.

Market failure in allocating resources among competing users provides the justification for Government

intervention – therefore the inclusion of the MCM block in Figure 1. In addition, there are other forms of

market failure in the fishing industry that further strengthens the case for government involvement in the

fishery.

2.4. MARKET FAILURE AND THE ROLE OF MCM

The market fails particularly in the primary sector of the fishing industry. This gives strong justification for

government intervention. Because this intervention will benefit in the first instance the fishing industry

and not society in general, a user-pays principle to revenue collections is appropriate (see Part 9: User

charges and revenue collections: recommendations). In South Africa with its history of non-market racial

discrimination the State has an added duty to encourage all forms of transformation in society (see Part 7:

Understanding and measuring transformation) and non-market based interventions may be required to

achieve this societal goal.

2.4.1 Forms of Market Failure in Primary Sector Fishing Activities

Four basic forms of market failure (though the fourth is less obvious) in the primary fishing sector

activities justify Government intervention. These are market failure in research, market failure in

allocation, market failure in compliance and market failure in the flow of information to the financial sector

(capital market).

1. Market failure in research. The necessary biological research to determine the amount of living

marine resources that can be exploited on a sustainable basis has clear public good

characteristics27. That is, although there are direct benefits of biological research to private

individuals involved in the harvesting of fish, the benefits cannot be excluded from those who do not

25 A common property regime in this case refers to a limited number of users who can exclude others, outside of their group – or community – from exploiting the resource. This is distinct from an open access problem where nobody has rights or duties to the resource. 26 It is not necessary to elaborate further as it is safe to assume that most people with a stake in the fishery have a good understanding of this phenomenon, albeit from Hardin’s (1956) Tragedy of Commons, in which he attempted to prove that the USA government has just cause to regulate family size – this did occur, and was justified under a similar argument, in China during the cultural revolution. 27 Public goods are described by Hyman (1999:134) as “Goods with benefits that cannot be withheld from those who do not pay and are shared by large groups of consumers”

Poorly defined property rights in the primary fishing sector results in a strong incentive for economic agents to over-exploit, meaning that there is an inherent market failure.


57

pay for them. The incentive thus exists for a less than optimal funding of research activities due to

what is termed, in the economic literature, as the free-rider problem28.

2. Market failure in allocation. As mentioned before, the price system does not work to allocate

resources among competing users in the fishery29. The State thus allocates these resources in

various size packages (or quanta) to private individuals, or groups of individuals.

A brief description of the axiomatic principles of allocations applicable to primary sector economic

activities in the fishing industry is presented in Part 3: The economics of allocations.

3. Market failure in compliance. As the allocation of resources by the State does not remove the

incentive to over-exploit, the State has to undertake a continuous control function to ensure

compliance to quanta of effort allocated.

4. Market failure in information flow to the capital market. This is a less obvious, but extremely

important, form of market failure. In essence, the capital market when lending finance for investment

into primary harvesting activities cannot easily determine the future flow of harvestable resources

available to the borrower30 or to other competing users31. Through effort control MCM can create

better signals to the capital market and thus avoid, to some extent, the problem of over- (or under-)

capitalisation in the fishery. This is a rather complicated form of market failure and will not be

discussed further.

28 “A problem that exists when people seek to enjoy the benefits of a public good without contributing anything to the cost of financing the amount made available” Hyman (1999:665) 29 The fishery is defined in terms of the activities confined to the primary sector, namely, fishing and harvesting. 30 The capital market has no way of easily determining the future biological characteristics of the stock of LMRs, the extent of competing users for the same species and the number of vessels already exploiting that species. This problem is enhanced as capital improvements usually mean more efficient harvesting; this must speed up the rate of increase in competition for, and exploitation of, the fixed stock of resources. Needless to say, this sets up something of a vicious circle – evidenced by the FAO declaring overcapitalisation in the fishery as the most worrying factor in the sustainable use of the world’s fish stocks.

The public good characteristics of biological, and other research, means that the market provides a less than optimal amount of research – a form of market failure.

Living marine resources will not be allocated efficiently among competing users in a common property regime.

If there are competing users, the incentive to over-exploit remains.


58

The four basic forms of market failure indicate the important role MCM has to play in the fishing industry.

It is well known that MCM has this role to play; however, for it to fulfill its function effectively, an

empirically based understanding of the market failures is essential. In other words MCM needs to know

as precisely as is possible the potential impact its interventions will have on the economic activities and

social well-being of individual players in the industry. Also, the rules that govern the activities of MCM

must be unambiguously established in a regulatory system (see Part 4: The regulation of commercial

fishing in South Africa: an examination of the constitutional compatibility of the regulatory system).

2.4.2 The User-Pays Principle It should be clear that the State (MCM) has a role to play in the fishery and has strong grounds to coerce

payments for services rendered to those who benefit.

This usually means employing a ‘user-pays principle’ of earmarked taxation. The user is also expected to

pay for the administration and implementation of MCM involvement. It is, however, important to note in

the circumstances where MCM activities benefit society as a whole transfers from central government are

a more equitable form of government finance. These issues will be covered in Part 9: user charges and

revenue collections: recommendations.

2.4.3 Transformation A further role of the State, which is unique to South Africa, is the facilitation of broad based social

transformation. Bearing in mind that apartheid was founded on a non-price form of racial discrimination32

and that the market system is by no means the ideal mechanism to counter its effects, the State has to

get involved. This is a well-recognised truth. It is also a fact that when MCM, or any other Government

body, involves itself in the reallocation of economic resources to correct past inequities it will influence the

economic system.

31 Capital market failure can also hamper timeous investment to replace aging vessels. 32 Arguments have been advanced that apartheid did benefit the market system, and owners of capital in particular, through lower wages, have been advantaged. It is not, however, the purpose of the ESS to provide an opinion in this regard.

The capital market cannot easily determine the future flow of LMRs available to the borrower or to competing users. Therefore it cannot easily observe the degree of capitalisation in the primary sector. This can lead to market failure in the allocation of capital resources to the primary sector fishing activities.

Market failures in the primary sector of the fishing industry provide justification for the State to demand payments for services rendered, employing under normal circumstances the “user-pays principle”.


59

To maximise the impact of its transformation agendas and to disrupt as little as possible the economic

system (thus also efficiency) the instituting body should have some idea on how the economic, as well as

the socio-economic and socio-political systems are expected to work and respond. Some transformation

issues are covered in Part 7: Understanding and measuring transformation, but an analysis of the

efficiency impact of various transformation agendas is not discussed.

2.5. THE REGULATORY SYSTEM

The Constitution (1996), the Marine Living Resources Act (1998) and the Administrative Justice Act

(2000) are the important legal instruments that govern MCM’s relationship with the general public and the

fishing industry. These Acts all have economic and social consequences for the South African public.

For example, a disturbance in information flows33 results if the procedure for re-allocation of fishing rights

is not clear in the Marine Living Resources Act, or conflicts with either the Constitution or the

Administrative Justice Act. The consequence of which could be a misallocation of economic resources

and not achieving the necessary transformation agendas in an efficient way. This is discussed in Part 4:

the regulation of commercial fishing in South Africa: an examination of the constitutional compatibility of

the regulatory system.

2.6. THE ESS IN PERSPECTIVE

The above discussion indicates the complexity of the system under which the South African fishing

industry operates. It also points to the very important and necessary role of MCM in correcting for the

many forms of market failure that exist: particularly in the primary activities of the fishing industry. The

ESS throws some light on the system by providing in Volume 1: Economic and Regulatory Principles,

Survey Results, Transformation and Socio-Economic Impact, the following:

¶ Part 2, a cursory understanding of the systems that operate in the fishing industry and the role of

MCM.

¶ Part 3, a discussion on the economics of allocations.

¶ Part 4, an independent review of the constitutional compatibility of the regulatory system inherent in

the Marine Living Resources Act (1998). This provides constraints to the complex system.

¶ Part 5, the income, employment and skills levels of individuals directly involved in the economic

activities of the commercial in the fishing industry.

¶ Part 6, a classification of the primary activities in the fishing industry in terms of its size and shape.

This is important, particularly from the point of view that the most obvious forms of market failure

occur in this sector.

33 An example of this is the incidence of influence costs that have occurred in the fishing microeconomy by excessive lobbying; see for example Milgrom and Roberts (1990) for a discussion and analysis on this phenomenon.


60

¶ Part 7, indicators of transformation based on employment, income and skills characteristics of people

employed in the fishing industry.

¶ Part 8, a socio-economic baseline study of South Africa’s commercial harbours. A socio-economic

impact of the commercial fishing industry is determined by using employment and income data from

the ESS and Statistics South Africa. In addition, a first look at the socio-political strategies used by

Black new entrants is presented.

¶ Part 9, a discussion with recommendations on some of the revenue raising instruments available to

MCM.

¶ Part 10, the ongoing need for economic information to inform management policy and decision

making

Volume 2: Fishery Profiles provides detailed reports on each fishery with the purpose of highlighting the

particular circumstances under which they operate. Conclusions and recommendations are presented in

the ESS Executive Summary.

References

ARROW, K.J. & G. DEBREU, 1954. The Existence of an Equilibrium for a Competitive Economy.

Econometrica, 22, 265—290.

GORDON, H.S., 1954. Economic Theory of Common Property Resources. Journal of Political Economy,

62, 124—142.

HARDIN, G., 1968. The Tragedy of the Commons. Science, 162, 1243—248.

HOTELLING, H., 1931. The Economics of Exhaustible Resources. Journal of Political Economy, 39,

137—175.

HYMAN, D.N., 1999. Public Finance: A Contemporary Application of Theory to Policy. Dryden Press,

Orlando, Florida, USA.

INTRILIGATOR, M.D., BODKIN, R.G. & C. HSIAO, 1996. Econometric models, Techniques, and

Applications, Prentice-Hall, Englewood Cliffs, New Jersey, USA.

JOSKOW, P., 1987. Contract Duration and Durable Transaction-Specific Investments: The Case of Coal.

American Economic Review, 77, 168—185.

KLEIN, B., CRAWFORD, R. & A. ALCHIAN, 1978. Vertical Integration, Appropriable Rents, and the

Competitive Contracting Process. Journal of Law and Economics, 21, 297—326.

MILGROM, P. & J. ROBERTS, 1992. Economics, Organization and Management. Prentice-Hall,

Englewood Cliffs, New Jersey, USA.

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 3. Economics of Allocations

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3. THE ECONOMICS OF ALLOCATIONS SUMMARY The purpose of this part is to provide a brief overview of the considerations that should be kept in mind

when allocating rights to exploit living marine resources. Its place in the microeconomic framework of the

South African commercial fishing industry is highlighted on Figure 2.1: A simple economic system for

South African fishing: infrastructure and resource flows.

The important characteristics in any system of rights are the form of rights, the structure of rights and the

quanta of allocations.

The form of rights is defined by the biological regulations that constrain catch, namely TAE or TAC. The

choice of options, usually decided by the resource manager, will determine to some extent how the

patterns of resource use and economic outcomes are established in the economic system.

The structure of rights will determine, to a large extent, who ultimately undertakes the primary economic

activities in the fishing industry. The structure of rights may be classified under the following criteria:

1. The attachment criteria will control who can hold a right and under what circumstances they are

held.

2. The transferability of the right governs the extent and under what circumstances the rights can

be traded or leased.

3. The length of tenure deals with the period for which the right is awarded.

The quanta of allocations refer to how the State decides to divide the TAC or TAE between the various

participants in the fishing industry or the public at large.

The form of rights, structure of rights and quanta of allocations will necessarily set up the conditions for a

market for rights. For example, in the case of South African commercial fishing, the existing system of

rights has established a rational ‘paper permit’ market and, in the absence of economic rent, an incentive

to over-exploit. From an ITQ (individual transferable quota) point of view, the system of rights leads to the

trading of rights such that the most efficient harvesters will inevitably hold the rights to exploit the

resource.

The concept of a minimum viable quota (MVQ) is embodied in the attachment criteria of the structure of

rights. For it to make practical sense, it dictates that rights are attached to vessels – currently rights in

South Africa are awarded to individuals and companies. However, the definition of a minimum viable

quantum of fish required to operate a particular vessel class remains a useful and an economically

defensible method of analysis, or abstraction. For example, it can be used to determine an “ability to pay”

levies in a particular fishery.


62

The choices open to the policy maker when designing a system of rights will depend entirely on their

expected outcomes.

3.1. INTRODUCTION

The purpose of this part of the ESS is to provide a brief overview of the considerations that should be

kept in mind when allocating rights to exploit fish. These are:

¶ The explicit regulations that govern the harvesting activities of economic players involved in primary

sector fishing activities dictate the form that rights should take, for example a TAC or TAE.

¶ The structure of the rights lays out the rules and procedures that will govern potential markets, that is,

the buying and selling of rights, the incidence of control and ultimately the economic behaviour of

firms involved in the fishing industry.

¶ The quanta, or size of rights, allocated also has a direct bearing on the economic behaviour of fishing

firms in the industry.

Finally a brief overview of the system of rights applicable to the South African situation is discussed along

with an axiomatic explanation of expected results.

3.2. THE FORM OF RIGHTS

The form of rights pertains to the regulations under which fishing firms are allowed to operate.

Consequently, the biological sustainability of the resource must be the first determinant of the form of

rights. In the first instance, fishing effort can be regulated in terms of the following instruments:

¶ Total catch allowed in a given period of time (TAC), and/or

¶ The total effort allowed (TAE), either in the form of:

ü Fishing vessels or fishers, and/or

ü The length of the fishing season, and/or

ü Gear and equipment restrictions, and/or

ü Area bound restrictions (territorial user fishing rights – TURF).

If for example, there are a number of options available, the choice of instrument, or mix of instruments will

result in different economic outcomes and patterns of resource use. Essentially, it depends on what

policy makers or resource managers find important, namely whether to,

¶ Restrict the total amount harvested by the imposition of a TAC, and/or

¶ Retard efficiency by combining a TAC or TAE with other effort and gear restrictions and/or closed

seasons.


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Restricting the total amount harvested by the imposition of a TAC, and if efficiently policed, will impose,

where necessary, its own form of closed season. There might, however, be a tendency for fishing firms

to increase efficiency in order to lower costs by investing in capital-intensive equipment. Inevitably, this

overcapitalisation, or economic inefficiency, means that the length of the fishing period, if the proportion of

TAC is held constant over time, must of necessity decrease. This has resulted in pressure from fishing

lobbies in many countries to increase TAC. From a sustainable resource use point of view, it is obviously

not a desirable outcome. Similarly, effort restrictions by imposing, for example, a TAE and/or a closed

season, also lead to overcapitalisation problems and a subsequent ‘political’ pressure on the resource.

Assuming that fishing enterprises do not easily find new ways of becoming efficient, a combination of

instruments that restrict fishing efficiency is desirable. These have to be well policed and designed

according to good technological and scientific practice.

3.3. THE STRUCTURE OF RIGHTS

The structure of rights has important implications for economic behaviour and thus also economic

efficiency. To a large extent it will determine who ultimately undertakes the primary economic activity of

fishing, to whom income and economic rent from the resource accrue and the strengthening or

weakening of the incentives to overcapitalise and over-exploit. In other words, it may set up a market for

rights that will inevitably have spillover effects on sustainable resource use. The structure of rights may

be categorised according to attachment, tenure and transferability.

3.3.1 Attachment The attachment criteria will determine who can hold the right and under what circumstances they are

held. All rights must, however, inevitably move to the primary sector. Also, harvesting equipment of

some kind or other must be used to exercise that right. The attachment criteria can thus range between

two extremes, namely:

¶ The right is attached to a vessel, or harvesting equipment, used to exploit the resource. Here the

right holder is the same person, or firm, who owns the vessel, or harvesting equipment.

¶ There are no attachment criteria, meaning that anyone - either involved or not involved in the primary

activity of fishing - can hold rights.

The important elements involve how income is distributed between those who harvest and those who

hold the rights to exploit.

¶ A full attachment of rights ensures that all income derived from fishing accrues to the firm, or person,

who holds the right to exploit.


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¶ With no attachment criteria, and with transferability, the firm with the ability to harvest might not be the

one who holds the rights to exploit. In fact, if there is a separation of the right to exploit from the

means to harvest there will be a relatively risk free transfer of a portion of the income involved. This

is a classic case of the incidence of rent in an economic system, and was manifest as the “paper

quota” problem in South Africa.

The attachment criteria must therefore reflect, preferably in an explicit way, the goals of the policy maker.

In other words, if rights are not attached, there should be some good reason why the State sanctions a

relatively risk free transfer of rents from an operative group to a non-operative group.

¶ It might be argued that a firm involved in the secondary sector of the fishing industry needs to control

their input costs (being an operative in the fishing industry) by holding power over the harvesters in

the form of ‘owning’ the rights to exploit. This argument may only hold water if, for example, the

processing firm vertically integrates and thus also owns the means to harvest.

¶ Perhaps the most convincing argument is the pressing need for social transformation. However, it

should be explicit that the rents accruing from an unattached right are used within some limited time

frame to ensure participation in the industry34, preferably in the primary sector activities or within a

vertically integrated firm. For example, new entrants into the South African hake fishery, drawn mainly

from previously disadvantaged groups, were allowed be “paper quota” holders for a period of three

years to allow them to acquire capital and productive assets such as vessels.

The attachment of rights also brings to bear the incidence of levies and/or charges associated with

harvesting. With unattached rights, it would be in the interests of fairness to levy the rights holder and not

the firm that harvest the resource.

The attachment criterion is later re-examined when combined with the elements of transferability and

tenure.

3.3.2 Transferability The transferability of a right determines the extent to which and under what circumstances the right can

be transferred. Similarly, as with the attachment criterion, the transferability of a right may be considered

to fall within two broad extremes, namely, from non-transferable to completely transferable. Many forms

exist between the two extremes. An important clarification is that of limited transferability, or the ability to

lease the right but not transfer ownership. It stands to reason that this criterion combined with that of

attachment will determine how a market for rights is expected to behave.

34 Unattached rights are awarded to ‘new entrants’ giving rise to what is termed a ‘paper quota’ system. This is one instrument that is being used to transform the fishing industry in South Africa.


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3.3.3 Length of Tenure

The length of tenure deals with the period for which the right is awarded. It may range from a single

period to an inheritable right. The issue of tenure is particularly vague in light of the idea of ‘new property’

discussed in Part 4: The regulation of commercial fishing in South Africa: an examination of the

constitutional compatibility of the regulatory system. Assuming, however, that the State can justifiably

award and take rights away at will, depending on the length of tenure of the right, it will have the ability to

influence how the market for rights behaves as well as how and where other economic resources will flow

in the primary fishing sector. The State’s ability to have an impact on resource flows in the primary sector

is intimately linked to that of attachment and transferability. The short term (1 year) length of tenure of

rights during the recent period of restructuring of the South African fishery has undoubtedly had profound

economic consequences, for example, an under investment in the renewal of infrastructure and fleets and

an incentive to over-exploit fish stocks. The move by the State to award longer term rights (and allow no

more entrants into the fishery) is thus vital to achieving economic efficiency and sustainable utilisation of

fish stocks.

3.4. QUANTA OF ALLOCATIONS

The quanta of allocations refers to how the State divides the TAC or TAE between participants in the

fishing industry, or the public at large. It also deals with the issue of proportionality of TAC or TAE.

The issue of proportionality is, perhaps, best understood using the South African fishery as an example,

where rights holders are awarded only a proportion of the TAC or TAE. This means that if the TAC or

TAE increases or decreases then all rights holders experience a similar proportional change in their

allocations.

¶ Without the proportionality criteria, a fall in TAC, for example, would mean that some economic role

players would have to fall out of the system. Also, with an increase in TAC there would be pressure

to increase the number of role players. It would be safe to state that without proportionality, the

uncertainty arising from the physical environment coupled with the dynamics of the stock is made

more severe.

¶ Where effort is controlled by limiting the number of vessels through the implication of a TAE,

proportionality does not make sense. Other methods determined in the form of right have to be used

to control for the sometimes necessary reductions in effort. For example, the use of closed seasons

and stringent gear restrictions.

The more important issue in the quanta of allocations issued by the State to the various economic agents

is again linked to the attachment and tenure criterion. For example, a minimum viable quota (MVQ) only

makes sense when the rights are attached to a fishing vessel. This in turn is dependent on the value of

the resource being exploited, the entry-level capital investment, the proportion of variable costs to fixed

costs and the opportunity for economies of scale.


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Without an attachment criterion, the quanta of rights will in part determine the fluidness of the market for

rights. For example, if unattached fully transferable (or tradable) rights were allocated in very small

bundles relative to that amount needed to make a vessel profitable, it would be expected for the rights to

be traded relatively often until sufficient quantities are accumulated that make it worth while to harvest.

3.5. THE MARKET FOR RIGHTS

The interaction between the form of rights, the structure of rights and the quanta of allocations will of

necessity set the scene for the market for rights. To illustrate the point, a few examples will be detailed.

These examples are based on the assumption that the rights holder will strategically choose that option

that maximises individual benefit.

3.5.1 The Existing System of Rights Allocations in South Africa The system of rights allocations in South Africa bases itself on the premise that rights can be awarded,

and removed, from period to period by fiat of the State. The general system has emerged partly as a

result of previous policies during the apartheid regime and partly as a result of an urgent reallocation

agenda.

¶ The form of rights depends on the fishery for which the right to exploit is awarded. The strong

tradition of resource management in South Africa has resulted in a carefully planned and efficient

form of rights for each fishery.

¶ Until 2000, the rights were universally awarded under the following structure

- A one year tenure.

- Limited transferability. Namely, they can be leased but not traded for resale.

- Unattached. That is, anybody who is a South African citizen can hold rights to exploit marine living

resources.

¶ The quanta of allocations, which to some extent have an historical pattern, are not universally

consistent with the ability of the right holder to viably enter into primary fishing sector activities.

From the point of view of a ‘new entrant’ being allocated a quantum of rights (usually in sub-economic

bundles) that can be leased and are unattached, it makes sense to strategically choose a risk free flow of

rents. Strengthening this obvious choice, under these circumstances lending institutions would not

readily provide finance for investment purposes. Thus a market for ‘paper permits’ is firmly established.

This market is based on the rational behaviour of ‘new entrants’ to a system of rights. The market is

efficient, from an economic point of view, only if the rents are transferred to the rights holder. However, if

no rents exist and the fishing firms have to catch more to cover the additional costs incurred by leasing

rights, the market is not efficient and the sustainable utilisation of the resource is threatened.


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From the point of view of historic rights holders, it makes strategic sense to institute joint ventures with

new entrants if they have the perception that their future applications will be favoured35. In addition, these

arrangements may result in lower costs than leasing rights if the historic rights holder owns, or leases, a

fishing vessel. There are also added incentives to over-catch or under-report catches by a greater margin

than before and to utilise the incidental catch more efficiently and to a greater extent. If there are rents

from fishing, the incentive for over-catching and greater exploitation of ‘allowed’ incidental catch will be

marginal and the market for these rights will be efficient. However, in the absence of rents, the system of

rights must inevitably lead to over-exploitation of both the targeted and incidental catch species.

3.5.2 The General Idea of an ITQ The general idea of an Individually Transferable Quota (ITQ) in the system of rights described above is

that:

¶ The right should be of a form that allows the institution of a TAC.

¶ The structure of the rights must have the following characteristics.

- The rights must be unattached.

- The right must be tradable and divisible. That is, transfer of ownership of the

quantum of rights, or part thereof, must be possible.

- The rights must be of a long term, or multiple period, tenure.

¶ The rights must be proportional to the TAC. The quantum of rights awarded to individuals is

unimportant.

If the above system of rights holds, traditional economic logic dictates that the rights will be traded until

the most efficient harvesters of the resource hold them. That is, they will find their way through a market

of rights to their most efficient use. If approached from the point of view that markets seldom work

properly due to such things as bounded rationality, private information in strategic bargaining and so on,

the ITQ system of rights will fail to produce the desired results. There is a growing body of evidence in

the literature that corroborates this view.

3.5.3 MVQs Revisted A MVQ is dictated by the attachment criterion in the structure of rights. Regardless of the other criteria,

the attachment of a right to a fishing vessel necessitates that the vessel holds a quantum of rights that is

at least sufficient to make it economically viable, in other words a MVQ. This could include an expected

return in line with the relevant risk characteristics inherent in that fishery. On the other hand, the

existence of specific sunk capital, namely the fishing vessel, means that there may be quasi-rents. A

quasi-rent is that portion of revenue above variable costs that is just sufficient to keep the fishing

entrepreneur in the fishery.

35 Also, the larger vertically integrated firms that market branded products need to maintain their supply of LMRs to retain market share and product continuity.


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Evidence that supports the second argument is likely to result initially in a counter-intuitive result. That is,

in the presence of quasi-rents, and coupled with the ability to exercise stringent effort control by attaching

rights to fishing vessels, an efficient utilisation of both fish and other economic resources is likely. In

other words, under rather special circumstances the concept of an MVQ is an efficient one that could

result in the elusive goal of economic efficiency without overcapitalisation.

3.6. CONCLUSION

The aim of this section was to point out that the system of rights applied to a fishery will not only have an

effect on the market for rights, but in the absence of rents will influence economic activity and thus the

flow and distribution of real economic resources. The choices open to policy makers in this regard

depends entirely on their expected outcomes.

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 4. Constitutional Regulation

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4. THE REGULATION OF COMMERCIAL FISHING IN SOUTH AFRICA: AN EXAMINATION OF THE CONSTITUTIONAL

COMPATIBILITY OF THE REGULATORY SYSTEM 4.1. INTRODUCTION In Premier, Mpumalanga v Executive Committee, Association of State Aided Schools, Eastern

Transvaal36 O’Regan J spoke of the interaction between the affirmative steps needed to achieve

transformation, on the one hand, and the requirements of procedural fairness, on the other. She said:37

‘This case highlights the interaction between two constitutional imperatives, both

indispensable in this period of transition. The first is the need to eradicate patterns of

racial discrimination and to address the consequences of past discrimination which persist

in our society, and the second is the obligation of procedural fairness imposed upon the

government. Both principles are based on fairness, the first on fairness of goals, or

substantive and remedial fairness, and the second on fairness in action, or procedural

fairness. A characteristic of our transition has been the common understanding that both

need to be honoured.’

This document will examine the system created by the Marine Living Resources Act 18 of 1998 in terms

of which commercial fishing is regulated. This system will be assessed against the touchstone of the

Constitution of the Republic of South Africa Act 108 of 1996 (the Constitution), the supreme law, and its

requirements for the realisation of both substantive and procedural fairness. It will also be assessed

against the provisions of the Promotion of Administrative Justice Act 3 of 2000, which was passed by

Parliament to give effect to the fundamental right to just administrative action.

4.2. THE CONSTITUTION IN GENERAL

The founding values of the Constitution, which, in a sense, encapsulate the essential features of the

South African State, are set out in s1. This section provides:

‘The Republic of South Africa is one, sovereign, democratic state founded on the

following values:

a) Human dignity, the achievement of equality and the advancement of human rights and

freedoms.

b) Non-racialism and non-sexism.

c) Supremacy of the constitution and the rule of law.

d) Universal adult suffrage, a national common voters roll, regular elections and a multi-

party system of democratic government, to ensure accountability, responsiveness and

openness.’

36 1999 (2) SA 91 (CC). 37 At para 1.


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From these values a number of important provisions of the Constitution flow so as to give it a capacity to

control the exercise of all public power. In President of the Republic of South Africa v South African

Rugby Football Union38 the Constitutional Court set these out as follows:39

‘The constitutional goal is supported by a range of provisions in the Constitution. First, in

the Bill of Rights there is the right of access to information and the right to just

administrative action. Both these provisions require national legislation to be enacted by

3 February 2000 to give effect to these rights. Pending the enactment of that legislation,

the provisions of the interim Constitution apply. Secondly, all the provisions of the Bill of

Rights are binding upon the Executive and all organs of State. The Bill of Rights,

therefore, imposes considerable substantive obligations upon the administration. Thirdly,

chap 10 of the Constitution, entitled “Public Administration”, sets out the values and

principles that must govern public administration and states that these principles apply to

administration in every sphere of government, organs of State and public enterprises.

This chapter also establishes a Public Service Commission to promote the values of

public administration. Fourthly chap 9 of the Constitution establishes the office of the

Public Protector, whose primary task is to investigate and report on conduct in the public

administration which is alleged to be improper. Fifthly, the Constitution establishes the

office of the Auditor-General whose responsibility is to audit and report on the financial

affairs of national and provincial State departments and administrations as well as

municipalities.’

Of prime importance for present purposes is the fact that the Constitution is based on the founding value

of constitutional supremacy and the rule of law. This value is directly enforceable. The Constitutional

Court has held that the rule of law means that: no body or person may exercise public power or perform

public functions unless the authority to do so has been conferred by law;40 that when such functionaries

exercise power or perform functions they are required to do so in good faith and they may not

misconstrue their powers;41 that they are required to exercise powers rationally;42 that, to protect

fundamental rights, laws should be ‘pre-announced, general, durable and reasonably precise rules

administered by regular courts or similar independent tribunals according to fair procedures’;43 and the

rules must be stated in a ‘clear and accessible manner’.44

It is no accident that s1(c) forms part of the Constitution. It gives expression to the broader value that

underpins every constitutional state – that every exercise of public power must be capable of rational

justification.

38 2000 (1) SA 1 (CC). 39 At para 134. 40 Fedsure Life Assurance Limited v Greater Johannesburg Transitional Metropolitan Council 1999 (1) SA 374 (CC), para 58. 41 President of the Republic of South Africa v South African Rugby Football Union, supra, para 148. 42 Pharmaceutical Manufacturers Association of South Africa; in re: Ex Parte Application of the President of the Republic of South Africa 2000 (2) SA 674 (CC), paras 89 and 90. 43 De Lange v Smuts NO 1998 (7) BCLR 779 (CC), para 46, quoting the formulation of the rule of law of Mathews Freedom, State Security and the Rule of Law, 20. 44 Dawood v Minister of Home Affairs 2000 (8) BCLR 837 (CC), para 47.


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This idea was eloquently expressed by Ackermann J in S v Makwanyane45 albeit in the context of the

constitutionality of the death penalty and against the backdrop of another founding value, that of

equality:46

‘In reaction to our past, the concept and values of the constitutional State, of the

“regstaat”, and the constitutional right to equality before the law are deeply foundational to

the creation of the “new order” referred to in the preamble. The detailed enumeration and

description in s33(1) of the criteria which must be met before the Legislature can limit a

right entrenched in chap 3 of the Constitution emphasise the importance, in our new

constitutional State, of reason and justification when rights are sought to be curtailed. We

have moved from a past characterised by much which was arbitrary and unequal in the

operation of the law to a present and a future in a constitutional State where State action

must be such that it is capable of being analysed and justified rationally. The idea of the

constitutional State presupposes a system whose operation can be rationally tested

against or in terms of the law. Arbitrariness, by its very nature, is dissonant with these

core concepts of our new constitutional order. Neither arbitrary action nor laws or rules

which are inherently arbitrary or must lead to arbitrary application can, in any real sense,

be tested against the precepts or principles of the Constitution. Arbitrariness must also

inevitably by its very nature, lead to the unequal treatment of persons. Arbitrary action or

decision-making is incapable of providing a rational explanation as to why similarly placed

persons are treated in a substantially different way. Without such a rational justifying

mechanism, unequal treatment must follow.’

The founding value expressed in s1(d), and particularly accountability, responsiveness and openness,

are also of prime importance for purposes of constitutional control of public power. This has been

articulated by Froneman DJP in Carephone (Pty) Ltd v Marcus NO:47

‘[34] The particular conception of the State and the democratic system of government as

expressed in the Constitution determines the power to review administrative action and

the extent thereof (cf Craig Administrative Law 3ed at 3-40). Of importance in this regard,

for present purposes, is the constitutional separation of the executive, legislative and

judicial authority of the State administration, as well as the foundational values of

accountability, responsiveness and openness in a democratic system of government

(s1(d) of the Constitution). The former provides legitimacy for the judicial review of

administrative action (but nor for judicial exercise of executive or administrative authority),

whilst the latter provides the broad conceptual framework within which the executive and

public administration must do its work and be assessed on review.

45 1995 (3) SA 391 (CC). 46 At para 156. 47 1999 (3) SA 304 (LAC), paras 34 and 35.


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[35] When the Constitution requires administrative action to be justifiable in relation to the

reasons given for it, it thus seeks to give expression to the fundamental values of

accountability, responsiveness and openness. It does not purport to give courts the power

to perform the administrative function themselves, which would be the effect if justifiability

in the review process is equated to justness or correctness.’

4.3. ADMINISTRATIVE JUSTICE Writing in 1989, the present President of the Constitutional Court, Justice Arthur Chaskalson, said of

administrative law that it was the ‘interface between the bureaucratic state and its subjects. The day to

day lives of ordinary people are profoundly affected by the way those who hold power over their lives

exercise that power. Important steps towards the creation of a just society can be taken by opening up

the administrative process and developing an equitable system of administrative law’.48

The first important steps towards creating such a system of administrative law were taken when the

interim Constitution49 introduced a fundamental right to what it termed ‘administrative justice’.50 The final

Constitution contained a similar fundamental right to what it termed ‘just administrative action’. Section 33

provides:

‘(1) Everyone has the right to administrative action that is lawful, reasonable and

procedurally fair.

(2) Everyone whose rights have been adversely affected by administrative action has the

right to be given written reasons.

National legislation must be enacted to give effect to these rights, and must –

d) provide for the review of administrative action by a court or, where appropriate, an

independent and impartial tribunal;

e) impose a duty on the State to give effect to the rights in subsections (1) and (2);

and

f) promote an efficient administration.’

In the South African Rugby Football Union case, Chaskalson P held that the ‘principal function of s33 is to

regulate conduct of the public administration and, in particular, to ensure that where action taken by the

administration affects or threatens individuals, the procedures followed comply with the constitutional

standards of administrative justice. These standards will, of course, be informed by the common-law

principles developed over decades’.51

48 ‘The Past Ten Years: A Balance Sheet and Some Indicators for the Future’ (1989) 5 SAJHR, 293, 298 - 299. 49 Constitution of the Republic of South Africa 200 of 1993. 50 See s24. 51 Supra, para 136.


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In Ngxuza v Permanent Secretary, Department of Welfare, Eastern Cape Provincial Government52

Froneman J held:

‘Mention has already been made that the Constitution provides that the courts have a

policing role to ensure that public power is exercised in accordance with the principle of

legality. It also declares ours to be a democratic state (section 1). One of the foundations

of democracy is that those who are chosen to rule must be accountable to those they

govern. The Constitution recognises that as a founding value of our democracy (section

1(d)). It also recognises that modern societies need to be run by persons other than

those directly elected by the people. Those in the public administration must accordingly

also be subject to the foundational values of democracy, otherwise the promise of

democracy may become an illusion. So the Constitution states explicitly that public

administration must be governed by the democratic values and principles of the

Constitution, and states specifically that public administration must be accountable

(section 195, particularly section 195(1)(f)). The fundamental importance of accountable

public power is emphasised in the Bill of Rights chapter of the Constitution by providing

that everyone has the right to administrative action that is lawful, reasonable and fair

(section 33). And the courts are the final instruments to ensure the accountability of the

exercise of public power (sections 34 and 165). In this way the courts become an

indispensable instrument of democracy as far as the public administration of the country

is concerned …’

From the above it will be evident that the Constitution is based on what Professor Corder refers to as a

‘rights based conception of public law’ which seeks, as its main aim, to prevent abuse of power. For

administrative law, he argues that this means the following:53

‘The core values of this approach to democracy and the functioning of the State are

openness of action, participation in decision-making, justification for decisions made, and

accountability for administrative action. The importance of the constitutional requirements

of lawfulness, procedural fairness, reason-giving and justification for administrative action

to such a conception of democracy is self evident. So the particular form of democratic

framework within which we now operate is explicit and mandatory, and the principles of

administrative law must be revised and developed or created to give expression to such a

concept.’

In terms of s33(3) of the Constitution, the rights to lawful, reasonable and procedurally fair administrative

action and to reasons for adverse administrative action are to be given effect to by national legislation.

That legislation has now been passed. It is the Promotion of Administrative Justice Act 3 of 2000.

52 2000 (12) BCLR 1322 (E), 1328H – 1329B. See too Carephone (Pty) Ltd v Marcus NO, supra, para 19 in which Froneman DJP stated that the purpose of the administrative justice section of the Bill of Rights ‘is to extend the values of accountability, responsiveness and openness to institutions of public power which might not previously have been subject to those constraints’. 53 ‘Administrative Justice: A Cornerstone of South Africa’s Democracy’ (1998) 14 SAJHR 38, 41. See too van Wyk ‘Administrative Justice in Bernstein v Bester and Nel v Le Roux’ (1997) 13 SAJHR 249, 251. See further, Henderson ‘The Curative Powers of the Constitution: Constitutionality and the Ultra Vires Doctrine in the Justification and Explanation of the Judicial Review of Administrative Action’ (1998) 115 SALJ 346.


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It states in its preamble that its objects are to ‘promote an efficient administration and good governance’

and to ‘create a culture of accountability, openness and transparency in the public administration or in the

exercise of a public power or the performance of a public function, by giving effect to the right to just

administrative action’.

The Act provides for certain minimum requirements of procedural fairness in s3. Section 3(1) states that

when administrative action ‘materially and adversely affects the rights or legitimate expectations of any

person’ that administrative action must, in order to be valid, be procedurally fair. While the Act

acknowledges that what is fair depends on the circumstances of each case, s3(2)(b) provides that the

following are the minimum requirements of procedural fairness:

f) ‘adequate notice of the nature and purpose of the proposed administrative action;

g) a reasonable opportunity to make representations;

h) a clear statement of the administrative action;

i) adequate notice of any right of review or internal appeal, where applicable; and

j) adequate notice of the right to request reasons in terms of section 5.’

In certain instances the above minimum requirements may be insufficient: procedural fairness may

demand more. For this reason, administrators are granted a discretion to allow a person to be assisted or

legally represented, in serious or complex cases, to ‘present and dispute information and arguments’ and

to appear in person before the administrator concerned.54

Section 5 gives effect to the fundamental right to reasons for administrative action that adversely affects a

person. This section provides, in essence, for a procedure for requesting and obtaining reasons. For

present purposes s5(3) is of some importance. It states that if ‘an administrator fails to furnish adequate

reasons for an administrative action, it must, subject to sub-section (4) and in the absence of proof to the

contrary, be presumed in any proceedings for judicial review that the administrative action was taken

without good reason’.55

Section 6 of the Act codifies the grounds for judicially reviewing administrative action. Section 6(1)

provides that any person ‘may institute proceedings in a court … for the judicial review of an

administrative action’. Section 6(2) provides the grounds for doing so. They fall, essentially, into the three

broad categories mentioned by Lord Diplock in Council of Civil Service Unions v Minister for the Civil

Service56 namely ‘illegality’, ‘irrationality’ and ‘procedural impropriety’.

So, for instance, an administrative act may be set aside if the administrator in question was not

authorised by the empowering provision to perform that act,57 if he or she acted under an unauthorised

54 Section 3(3). Note that other requirements – essentially notice and comment procedures for public enquiries – are required for purposes of complying with the requirements of procedural fairness in respect of administrative action that affects the public generally. In other words, s4 applies when an administrator wants to make subordinate legislation. 55 For comment on what is meant by the word ‘reasons’, see Plasket and Khoza ‘The Fundamental Right to Reasons for Administrative Action’ (2001) 22 ILJ 52. See to Nomala v Permanent Secretary, Department of Welfare, Eastern Cape Provincial Government 2001 (8) BCLR 844 (E). 56 [1984] 3 All ER 935 (HL), 950h – i. 57 Section 6(2)(a)(i).


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delegation of power,58 if ‘a mandatory and material procedure or condition prescribed by an empowering

provision was not complied with’,59 if the administrative action was ‘materially influenced by an error of

law’,60 was taken for ‘a reason not authorised by the empowering provision’,61 for an ‘ulterior purpose or

motive’62 or ‘because of the unauthorised or unwarranted dictates of another person or body’.63

Administrative action may be set aside for want of procedural fairness if the administrator who took the

action concerned ‘was biased or reasonably suspected of bias’64 or if the action was procedurally unfair

(as defined in s3 and s4).65

Administrative action may be set aside for want of reasonableness if ‘irrelevant considerations were taken

into account or relevant considerations were not considered’,66 if the action was taken in bad faith67 or

was taken arbitrarily or capriciously,68 if the administrative action was not rationally connected to either

the purpose for which it was taken, the purpose of the empowering provision, the information before the

decision-maker or the reasons given for it by the decision-maker69 or if ‘the exercise of the power or the

performance of the function authorised by the empowering provision, in pursuance of which the

administrative action was purportedly taken, is so unreasonable that no reasonable person could have so

exercised the power or performed the function’.70

In addition to the above, s6(2)(f) makes the failure to take a decision (in circumstances where such a duty

exists) a ground for review and, finally, s6(2)(i) provides that administrative action that is ‘otherwise

unconstitutional or unlawful’ can be set aside on review.

The Act places two significant hurdles in the way of members of the public who wish to review

administrative action. In the first place, s7(1) provides that proceedings for judicial review must be

instituted within 180 days of the exhaustion of any internal remedies created by a particular statute, or

where no such internal remedies exist, of the date on which the affected person was either informed of

the adverse administrative action, became aware of it and of the reasons for it or ‘might reasonably have

been expected to have become aware of the action and the reasons’. This period of 180 days may, in

terms of s9(1)(b) and s9(2) be extended for a fixed period, either by agreement between the parties or by

a court on application by the party concerned and in which the court is satisfied that the interests of

justice require such an extension to be granted.

58 Section 6(2)(a)(ii). 59 Section 6(2)(b). 60 Section 6(2)(d). 61 Section 6(2)(e)(i). 62 Section 6(2)(e)(ii). 63 Section 6(2)(e)(iv). 64 Section 6(2)(a)(iii). 65 Section 6(2)(c). 66 Section 6(2)(e)(iii). 67 Section 6(2)(e)(v). 68 Section 6(2)(e)(vi). 69 Section 6(2)(f)(ii). 70 Section 6(2)(h).


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Section 7(2) places an obligation on a party wishing to judicially review administrative action to exhaust

any internal remedies at his or her disposal before approaching a court. A court may exempt such a

person from this duty if he or she brings an application in which he or she is able to establish exceptional

circumstances and that the interests of justice require such an exemption.

The above sections may well be unconstitutional infringements of the rights to just administrative action

and of access to court.

Section 8(1) of the Act provides that a court that has judicially reviewed administrative action ‘may grant

any order that is just and equitable’. Such an order may include a range of what may be termed the usual

remedies. These include orders to compel administrators to give reasons or to act in a particular manner,

to prohibit administrators from acting in a particular manner, the setting aside of administrative action, the

declaring of the rights of parties in relation to administrative action, the granting of temporary interdicts or

other interim relief and the making of orders of costs.

It is against the legal provisions that have been set out above that decisions of functionaries involved in

the regulation of the commercial fishing industry will be tested. By the same token, the regulatory

framework is to be tested against these principles with a view to determining whether it complies with

constitutional requirements and values.

4.4. THE MARINE LIVING RESOURCES ACT

The purpose of the Marine Living Resources Act is set out in the following terms in its long title:

‘To provide for the conservation of the marine ecosystem, the long term sustainable

utilisation of marine living resources and the orderly access to exploitation, utilisation and

protection of certain marine living resources; and for these purposes to provide for the

exercise of control over marine living resources in a fair and equitable manner to the

benefit of all the citizens of South Africa; and to provide for matters connected therewith.’

Along with this broad statement of its purpose, s2 contains a more detailed statement of objectives

and principles. It provides:

‘The Minister and any organ of State shall in exercising any power under this Act, have

regard to the following objectives and principles:

a) The need to achieve optimum utilisation and ecologically sustainable development of

marine living resources;

b) the need to conserve marine living resources for both present and future

generations;

c) the need to apply precautionary approaches in respect of the management and

development of marine living resources;


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d) the need to utilise marine living resources to achieve economic growth, human

resource development, capacity building within fisheries and mariculture branches,

employment creation and a sound ecological balance consistent with the

development objectives of the national government;

e) the need to protect the ecosystem as a whole, including species which are not

targeted for exploitation;

f) the need to preserve marine biodiversity;

g) the need to minimise marine pollution;

h) the need to achieve to the extent practicable a broad and accountable participation in

the decision-making processes provided for in this Act;

i) any relevant obligation of the national government or the Republic in terms of any

international agreement or applicable rule of international law; and

j) the need to restructure the fishing industry to address historical imbalances and to

achieve equity within all branches of the fishing industry.’

To this end, the Act creates a regulatory system in terms of which permits may be issued to authorise the

exploitation of marine living resources.71 Such permits have a limited lifespan, which may not exceed one

year, may be issued subject to conditions and may only be issued after a prescribed fee has been paid.72

The foundation for much of the regulatory system is to be found in a range of powers granted to the

Minister. Indeed, for all intents and purposes the Minister is the only functionary who exercises any

power to speak of in terms of the Act.73

In terms of s14, the Minister is empowered to determined the total allowable catch,74 the total applied

effort75 or a combination of the two76 and to allocate annually the total allowable catch, total applied effort

or a combination of both to ‘subsistence, recreational, local commercial and foreign fishing respectively’.77

In determining the total allowable catch, total applied effort or combination of both, the Minister may

decide that they apply ‘in a particular area, or in respect of particular species or a group of species of fish’

and ‘in respect of the use of particular gear, fishing methods or types of fishing vessels’.78 If the allowable

commercial catch79 is increased, the Minister is empowered to allocate that increase80 but the Minister is

also empowered to decide that the total allowable catch, a portion thereof or an allocation thereof shall be

nil.81

71 Section 13(1). Note that the Act creates a great deal of unnecessary confusion by using the term ‘permit’ at certain times,’right’ at others and ‘licence’ at yet others. The drafters of the Act evince some confusion, particularly in respect of their use of the term ‘permit’ and ‘right’. This will be dealt with below. 72 Section 13(2). 73 Note that the Minister is advised by the Consultative Advisory Forum for Marine Living Resources, established in terms of s5. 74 This term is defined in s1 to mean ‘the maximum quantity of fish of individual species or groups of species made available annually, or during such other period of time as may be prescribed, for combined recreational, subsistence, commercial and foreign fishing in terms of section 14’. 75 This term is defined in s1 as ‘the maximum number of fishing vessels, the type, size and engine power thereof or the fishing method applied thereby for which fishing vessel licences or permits to fish may be issued for individual species or groups of species, or the maximum number of persons on board a fishing vessel for which fishing licences or permits may be issued to fish individual species or groups of species’. 76 Section 14(1), 77 Section 14(2). 78 Section 14(3). 79 This term is defined in s1 to mean ‘that part of the total allowable catch available annually for commercial fishing rights in terms of section 14’. 80 Section 14(4). 81 Section 14(5).


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In terms of s15, the Minister may proclaim any area within the territorial waters of South Africa to be a

fisheries management area for the management of specified species of fish82 and may approve plans for

the conservation, management and development of such fisheries.83

In terms of s16, the Minister may suspend all or any fishing in a fishery or part thereof, restrict the number

of vessels that may fish in a particular fishery or restrict the mass of fish which may be taken from a

fishery if an emergency occurs that ‘endangers or may endanger stocks of fish or aquatic life, or any

species or class of fish or aquatic life in any fishery or part of a fishery’.

The Minister may, in terms of s17, declare priority fishing areas if he believes that ‘special measures are

necessary to ensure that authorised fishing within any area of South African waters is not impeded or

otherwise interfered with’.

In terms of s18(1), the Minister is empowered to grant people rights to undertake or engage in

commercial or subsistence fishing, mariculture or fish processing. Before considering the grant of such a

right, the Minister may require an applicant to submit an environmental impact assessment84 and he or

she must take into account the factors listed in s2 and have particular regard to ‘the need to permit new

entrants, particularly those from historically disadvantaged sectors of society’.85 In terms of s18(6) rights

granted in terms of the section ‘shall be valid for the period determined by the Minister, which period shall

not exceed fifteen years, whereafter it shall automatically terminate and revert back to the State to be

reallocated in terms of the provisions of this Act relating to the allocation of such rights’.86

Sections 21 to 28 deal specifically with the regulation of commercial fishing.87

Section 21(3) empowers the Minister to make regulations regarding such matters as ‘the formula by

which a commercial fishing right as a portion of the allowable commercial catch, the total applied effort, or

a combination thereof, shall be determined’88 and ‘the maximum or minimum portion of the allowable

commercial catch, the total applied effort, or a combination thereof, which may be allocated or transferred

to, or acquired or otherwise held by, any person’89 to name but two of the nine matters mentioned in the

section.

Section 21(1) provides that commercial fishing rights may be ‘leased, divided or otherwise transferred’,

subject to the provisions of the Act.

82 Section 15(1). 83 Section 15(2). 84 Section 18(3). 85 Section 18(5). 86 Note that there appears to be a conflict between this provision and s13. Section 13(1) provides that no right in terms of s18 may be exercised unless a permit has been issued to authorise the exercise of that right and s13(2) provides that a permit contemplated by s13(1) shall ‘be issued for a specific period not exceeding one year’. This issue is dealt with below. 87 On subsistence and recreational fishing see s19 and s20. On foreign fishing, see s38 and s39 and on high-seas fishing, see s40 to s42. All of these forms of fishing are beyond the scope of this paper and will not be discussed. 88 See section 21(3)(a). 89 Section 21(3)(c).


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Such a right may, in terms of s21(2), be transferred in whole or in part if the Minister, after considering an

application duly submitted to him approves the transfer in writing.

The Act also makes provision for the leasing of rights from the State. Section 22 allows for commercial

fishing rights to be allocated by the Minister, acting in terms of s31(1), to the Fisheries Transformation

Council90 which, in turn, is empowered by s31(2) to lease these rights ‘according to criteria determined by

the Minister, to persons from historically disadvantaged sectors of society and to small and medium sized

enterprises’.

Section 23(1) provides that no-one may use a vessel to exercise a right to fish commercially unless a

local fishing vessel licence has been issued to that person. An application for this licence must be made,

in terms of s23(2), to the Minister.

In terms of s27, fees may also be charged for the use of fishing harbours or portions of fishing harbours

designated as fishing harbours.

Section 28 empowers the Minister to cancel or suspend rights that have been granted in terms of the Act.

The bases upon which such action can be taken are: if the person concerned has furnished incomplete or

untrue information when applying for a commercial fishing right;91 has contravened or failed to comply

with a condition imposed on the exercise of the right;92 has contravened or failed to comply with a

provision of the Act;93 is convicted of an offence created by the Act;94 or fails to effectively use the right in

question.95

The procedure for the cancellation or suspension of a commercial fishing right is that:

a) the Director General gives written notice to the person concerned that he or she must show cause

in writing within 21 days why the rights in question should not be ‘revoked, suspended, cancelled,

altered or reduced’;96

b) thereafter, the Director General refers the matter, together with the person’s explanation, to the

Minister for a decision;97

c) the Minister may then either revoke, suspend for a period, cancel or alter the terms of the right or

decide not to do any of the above.98

90 This Council is established by s29 with the main object being to ‘facilitate the achievement of fair and equitable access to the rights referred to in section 18’. (Section 30.) The Minister may dissolve the Council, in terms of s37. I have been informed that he has, indeed, dissolved it. 91 Section 28(1)(a). 92 Section 28(1)(b). 93 Section 28(1)(c). 94 Section 28(1)(d). 95 Section 28(1)(e). 96 Section 28(1). 97 Section 28(2). 98 Section 28(3).


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Section 28(4) appears to contemplate a ‘no fault’ revocation, suspension, cancellation or reduction of a

right if the Minister is of the opinion that such action is ‘in the interests of the promotion, protection or

utilisation on a sustainable basis of a particular marine living resources’.

In terms of s79(1) the Minister may ‘upon the conditions that he or she deems fit, delegate any or all the

powers conferred upon him or her in terms of this Act, save a power to make regulations, to the Director

General or an officer of the Department nominated by the Director General’ or ‘delegate any power

conferred upon him or her in terms of this Act, excluding the power to make regulations, to an authority in

the local sphere of government’.

In terms of s79(2) the Director General may delegate ‘any power conferred upon him or her in terms of

this Act to an officer in the Department upon the conditions that he or she deems fit’.

In terms of s79(3) a delegation of power by either the Minister or the Director General does not rob either

of that power: they may still exercise that power even though they may have delegated it to another

person.

Section 80(1) creates an appeal to the Minister against ‘a decision taken by any person acting under a

power delegated in terms of this Act or section 238 of the Constitution’.99

4.5. COMMENTS ON THE ACT AND ITS REGULATORY SYSTEM

The comments on the Act and the system that it creates to regulate the commercial exploitation of fish is

discussed below under three headings. These comments will deal with problems associated with the

drafting of the Act and certain concepts that it uses; the powers of the Minister and the profile of the

administrative system that the Act creates; and the Act’s failure to give adequate substantive and

procedural guidance to decision-makers.

By way of introduction, and before addressing the above issues, it is necessary to consider briefly the

nature of the relationship between the State, when it regulates an activity, and the individual who

undertakes that activity or wishes to undertake that activity.

It is sometimes tempting for those who grant or refuse rights to undertake activities to believe that all that

they are doing is granting or withholding privileges to which no-one can lay claim as of right.

This approach to economic regulations has long been discredited. The changing nature of the functions of

the State – from the night-watchman of the Victorian era to the benefactor of the post-Second World War

era – brought with it a change in conception of rights: that entrepreneurs had rights, not privileges, to

economic incentives, that welfare recipients were not the objects of charity from the State, subject to

99 Section 238 of the Constitution provides that an executive organ of State in any of the three spheres of government may ‘delegate any power or function that is to be exercised or performed in terms of legislation to any other executive organ of State, provided the delegation is consistent with the legislation in terms of which the power is exercised or the function is performed’ or ‘exercise any power or perform any function for any other executive organ of State on an agency or delegation basis’.


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cancellation at will, but enjoyed rights to social assistance. In the modern State, what may once have

been regarded as privileges are now regarded, in the terminology of Professor Reich, as a new form of

property.100

It is not only Harvard Law professors who viewed regulation in this way. In South Africa, and indeed prior

to the publication of Reich’s seminal article, in a case involving the withholding of a road transportation

permit on racial grounds, the Appellate Division approached the issue in the same way: In Tayob v

Ermelo Local Road Transportation Board 101 Centlivres CJ held:

‘The Chairman went on to suggest that the granting of an exemption was not a right but

merely a privilege. It almost amounts to saying that the granting of an exemption is in the

gift of the Commission or a local board. This is a wrong approach to adopt by a statutory

board which is empowered by Parliament to grant permission to carry on a trade. It is not

an exceptional privilege or a monopoly which depends on the issuing of the permission.

Even the humblest citizen has the right to approach such a board and he is entitled to get

the permission he requires, unless there are sound reasons to the contrary.’

Much the same sentiment was expressed in the judgment of Beck J in Tabakain v District Commissioner,

Saulsbury102 in which the respondent had refused the applicant permission to operate a smelting pot for

smelting metals:

‘The further question is whether the District Commisioner’s decision is one which may

affect rights of the applicant or involve legal consequences to him. Subject to necessary

measures of control there is a general right to be allowed to trade. The complexities of

modern society have enormously multiplied the controls to which people are subjected in

the exercise of their general rights, and there is an increasingly insidious tendency to

regard permits of all kinds as a form of privilege. I would resist the notion of regarding a

permit under sec 4 of the Secondhand Goods Act as a sort of delectable crumb that might

or might not be dropped from the bureaucratic dinner table. To withhold such a permit is

to affect the citizen adversely in his rights by denying to him the opportunity of exercising

his trade in a manner that is normal for anyone of good character.’

4.5.1 Drafting and Concepts

The drafting of certain sections of the Act leaves much to be desired. In a constitutional State such as

ours, based as it is on the rule of law, this creates a problem because one of the requirements of the rule

of law is that the law must be certain.103

100 ‘The New Property’ (1964) Yale Law Journal 733. 101 1951 (4) SA 440 (A), 449A-C. 102 1974 (1) SA 604 ®, 606E-F. 103 Jowell ‘The Rule of Law Today’ in Jowell and Oliver (eds) The Changing Constitution (3ed), 57, 62-64; Dawood v Minister of Home Affairs, 2000 (8) BCLR 837 (CC), para 47; De Lange v Smuts NO 1998 (7) BCLR 779 (CC), para 46.


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The most obvious and serious difficulty is the conflict between s13 and s18. The difficulty is three fold.

First, there appears to be conceptual confusion about the nature of a right to fish commercially and the

permit that evidences the granting of that right. These two concepts are split into separate and potentially

contradictory elements of the right of access. This is at odds, most certainly, with the common

understanding of these terms in the law and in everyday parlance.

Secondly, the Act draws a distinction between the two that renders the provisions of the two sections

irrational. The Act appears to envisage that it is possible that a person may be granted a right to fish but

be denied a permit to fish. That, with respect, defeats the purpose of the Act – which is to allow certain

people to fish commercially by allocating to them what are termed rights of access – and is probably

unconstitutional: to grant a right with commercial value and then prevent its exercise would amount to an

arbitrary deprivation of property as envisaged by s25(1) of the Constitution. It would also render the right

vulnerable to cancellation in terms of s28(1)(e) because a person with a right but no permit cannot

effectively utilise that right.

There is a conflict between the period of validity of the right – a maximum of fifteen years – and the permit

– a period not exceeding one year. It is possible that a person may have a right for fifteen years but,

because his or her permit expires after one year, the right cannot be used. Again, this raises the spectre

of an arbitrary deprivation of property.

To complicate the issue still further, the Act also makes provision, in s23, for the licencing of fishing

vessels. This is, however, more rational than the distinction between a right of access and a permit

because, one presumes, such matters as suitability for the purpose, safety and seaworthiness, and ability

to monitor will be relevant to whether a vessel ought to be licenced or not.

Section 21(1) provides that a commercial fishing right may be ‘leased, divided or otherwise transferred’.

Section 21(2) provides that the Minister may, subject to the provisions of the Act, sanction the transfer of

such a commercial fishing right or part thereof.

One would have expected that s21(2) would have used language consistent with s21(1) but this is not the

case. In other words, s21(2) only deals with transfers of rights whereas s21(1) envisages the letting of

rights or their division.

The answer probably lies in the assertion that leasing, dividing and transferring all, ultimately, involve the

transfer of the right and, consequently, when a right is leased, the Minister’s approval must be sought,

just as it must be sought when it is transferred pursuant to a sale.

The difficulty with this interpretation is that the leasing of the right does not necessarily involve its transfer.

All that has been passed to the lessee is the use and enjoyment of the right. Ownership of the right still

vests, in its entirety, in the lessor.


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If the intention of the legislature is that the leasing of rights ought to be regulated – and this appears to fit

into the scheme of the Act – it may be wise to say so explicitly. It is not difficult to envisage the reasoning

of a court wishing to limit the Act’s impact on common law freedoms: it may reason that the Act must be

interpreted strictly because it limits the freedom of people to exploit the resources of the sea at will; if

Parliament had wished to regulate the leasing of rights of access to the sea’s largesse, it would have said

so, and its silence on the issue must be taken instead as an indication that it had no intention of

regulating the leasing of rights.

Such an interpretation would probably create problems for those charged with the enforcement of the Act.

The final issue in respect of the concepts used in the Act relates to the constitutionality of indemnities

contained in s59 and s66.

Section 59 contains two indemnities. Section 59(1) provides that the ‘State, the Minister, any person in

the employment of an organ of State or any person appointed to perform any function in terms of this Act

shall not be liable by virtue of anything done in good faith under a provision of this Act’.

Section 59(2) purports to create a more specific indemnity for the same category of actors. They are

indemnified in respect of all acts or omissions, except intentional ones, for loss or damage resulting from

‘any bodily injury, loss of life or loss of or damage to any property’ in circumstances in which a person,

acting outside of the provisions of the Act or any other law, suffers damage or loss while using a vessel,

vehicle or aircraft belonging to or under the control of the State, while present in any fishing harbour or

having left any vessel or any other property in a fishing harbour or while making use of facilities in a

fishing harbour.

Section 66 indemnifies the State for ‘any loss, damage to or deterioration in the condition of any vessel,

vehicle, aircraft or other thing while in the custody of the State in terms of this Act’.

All three of the above provisions infringe the fundamental right of access to court entrenched in s34 of the

Constitution.104

When people are killed or injured in the circumstances contemplated in s59, these provisions infringe, in

addition the fundamental rights to life, entrenched in s11105 and to freedom and security of the person

entrenched in s12(1).106

104 Section 34 provides: ‘Everyone has the right to have any dispute that can be resolved by the application of law decided in a fair public hearing before a court or, where appropriate, another independent and impartial tribunal or forum.’ See in this regard Beinash v Young 1999 (2) BCLR 125 (CC), para 16 in which Mokgoro J found that s2(1)(b) of the Vexatious Proceedings Act 3 of 1956 infringed this fundamental right. She held that the Act imposed ‘a procedural barrier to litigation on persons who are found to be vexatious litigants. This serves to restrict the access of such persons to court. That is its very purpose. In doing so, it is inconsistent with section 34 of the Constitution which protects the right of access for everyone and does not contain any internal limitation of the right. The barrier which may be imposed under section 2(1)(b) therefore does limit the right of access to court protected in section 34 of the Constitution’. She held, however, that in the circumstances, this limitation was a reasonable and justifiable one in terms of s36(1) of the Constitution. It is unlikely that the indemnities in the Act will meet the requirements of s36 and thus be saved from constitutional invalidity. 105 Section 11 of the Constitution provides simply: ‘Everyone has the right to life.’ 106 Section 12(1) provides that everyone ‘has the right to freedom and security of the person’ and it then lists five aspects of the right such as the right to ‘be free from all forms of violence from either public or private sources’ (s12(1)(d)) and the right ‘not to be treated or punished in a cruel, inhuman or degrading way’ (s12(1)(e)). The scope of the right is greater than the specific manifestations of it cited above.


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When property is affected, the provisions constitute an arbitrary deprivation of property, as envisaged by

s25(1) of the Constitution.

In effect, s59(2) purports to bar all claims based on negligence; s59(1) purports to bar claims in which the

defendant has even acted intentionally but in good faith; and s66 purports to bar all claims for loss,

damage or deterioration in respect of property, irrespective of how that loss, damage or deterioration was

caused.

It is extremely unlikely that these provisions, which purport to place officials above the law, would be held

to be reasonable and justifiable limitations of the fundamental rights mentioned above. A court would,

accordingly, strike them down.

4.5.2 The Powers of the Minister and the Administrative System

It has been noted above that the Minister is central to the regulatory system created by the Act.

The Minister is vested with virtually every power that the Act creates, including a power to consider

appeals from decisions taken by others. As those powers vested in others are rather limited, the Minister

presumably delegates a substantial proportion of his or her powers to those others, in terms of s79.

Those more familiar with the day to day administration of the Act will be able to assess how well this

system works but it appears, on paper, to have certain weaknesses because of the centralisation of

power in the hands of the Minister.

In the first place, the centralisation of power serves little purpose: it would be most surprising if the

Minister was able to exercise all of the powers and perform all of the functions vested in him or her,

having properly applied his or her mind in each case. If large slices of power are delegated by the

Minister this, in fact, makes the point.

Centralisation of power also seems to serve as an impediment to the development of a proper

administrative system and to proper processes and procedures. The Act creates and maintains a

rudimentary administrative system that may not operate as openly, accountably and responsively as it

should because the locus of decision-making is hidden from public view by a delegation of power and the

mode of decision-making is cocooned by legislative silence.

I have been informed, for instance, that the Minister’s powers of allocation are exercised by an allocation

committee that considers applications, decides on who should have rights allocated to them and refers

these decisions to the Minister for the final decision – some would say for his or her rubber stamp.


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The first problem that this arrangement generates is that no provision is made in the Act or regulations for

an allocations committee.107 The delegation provision, s79(1), does not appear to contemplate the

creation of, and empowerment through delegation of, tribunals and is designed to keep delegations of

power within the ranks of officials of the department: the section speaks of the Minister delegating power

to ‘the Director-General or an official of the department nominated by the Director-General’.108

Secondly, if the Minister has not delegated powers to the allocations committee, a problem of legality

exists in respect of its exercise of powers or performance of functions.109

A problem of legality is also created in respect of the Minister’s ‘rubber stamping’ of the decisions of the

allocations committee: if the allocations committee considers the applications and decides who should

have rights allocated to them and the Minister simply gives effect to those decisions, it may well be

argued that his or her decisions are all nullities because he or she did not apply his or her mind to the

matter or failed to exercise a discretion, or fettered his or her discretion and acted under dictation.110

The centralisation of power in the Act limits the efficacy of the internal appeal created by s80. As the

Minister is given the lion’s share of the powers and functions created by the Act, he or she may only

determine appeals when the Act vests a decision-making power in another official or, perhaps, when the

Minister has delegated a decision-making power to an official.

It must be questioned whether the centralisation of power in the hands of the Minister is an effective and

rational allocation of administrative functions. It may be preferable to design a more formal and

structured administrative system to deal with important decision-making functions, such as the allocation

of rights, their transfer and their cancellation or suspension, and to vest in the Minister the power of policy

formulation and to decide appeals.

4.5.3 Legislative Guidance and the Exercise of Discretion

The final issue dealt with in this paper focuses on what may best be termed the underdeveloped nature of

the regulatory system, from both a substantive and a procedural perspective.

107 I was informed that at one stage the Consultative Advisory Forum for Marine Living Resources fulfilled the function that is now fulfilled by the allocation committee. Even that must have been of doubtful legality as the provisions of s6 relate to policy generation rather than implementation. 108 See Opperman v Witvoerende Komitee van die Verteenwoordigende Owerheid van die Blankes 1991 (1) SA 372 (SWA) in which it was held that an identically worded delegation provision did not entitle the first respondent to delegate power to a board, as distinct from an official. 109 See s6(2)(a)(i) of the Promotion of Administrative Justice Act which provides that a court may judicially review administrative action if the administrator who took that action ‘was not authorised to do so by the empowering provision’. See too The Monastery Diamond Mining Corporation (Edms) Bpk v Schimper 1983 (3) SA 538 (O); Bacela v Member of the Executive Council for Welfare (Eastern Cape Provincial Government) [1998] 1 All SA 525 (E); Onshelf Trading Nine (Pty) Ltd v De Klerk NO [1997] 1 All SA 682 (W). Baxter Administrative Law, 384 sets out the principle thus: ‘Public authorities possess only so much power as is lawfully authorised and every administrative act must be justified by reference to some lawful authority for that act. Moreover, on account of the institutional nature of law, the public authority itself exists as an office or body created by law. A valid exercise of administrative power requires both a lawful authorisation for the act concerned and the exercise of that power by the proper or lawful authorities.’ 110 See s6(2)(g) of the Promotion of Administrative Justice Act which provides that administrative action may be set aside if ‘the action concerned consists of a failure to take a decision’ and s6(2)(e)(iv) states that a court may set aside administrative action if it was taken ‘because of the unauthorised or unwarranted dictates of another person or body’. See Northwest Townships Ltd v Administrator, Transvaal 1975 (4) SA 1 (T), 8D – G; Johannesburg Stock Exchange v Witwatersrand Nigel Ltd 1988 (3) SA 132 (A), 152A – E; Vokwana v National Transport Commission 1984 (2) SA 245 (Tk); Union Government (Minister of Mines and Industry) v Union Steel Corporation (South Africa) Ltd 1928 AD 220; Britten v Pope 1916 AD 150; Chotabhai v Union Government (Minister of Justice) 1911 AD 13; Hofmeyr v Minister of Justice 1992 (3) SA 108 (C).


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It is true that s2 sets out broad objectives and principles that are meant to be taken into account when

powers are exercised in terms of the Act. To that extent, the guidance on how decision-makers must

approach their decision-making powers is to be welcomed.

But, the objectives and principles that are set out in s2 are broad: how much practical guidance they

provide in canalising discretion to ensure a correct, or at least rational, result is probably anyone’s guess.

From what I have been informed, internal policy has been developed to give effect to the broad objectives

and principles of s2. I have also been informed that this policy has not been translated into legislative

form – for instance, as regulations – and is not made available to applicants for rights or to the public

more generally.111

In my view, it is of importance, not only from an administrative law perspective but also, in all probability,

from the perspective of quality control, to ensure that the policy is converted into a set of requirements for

the proper exercise of discretion in terms of the Act.

It is also important to do this because different powers and functions contemplated by the Act require

consideration of different facts and circumstances: the factors that go into the mix for the decision as to

whether a person should be allocated a right of access are self evidently different to the factors that must

be considered when the owner of a fishing vessel applies for a licence for that vessel. Section 2 gives

only the vaguest of guidance in respect of the first issue and far more nebulous guidance, at best, in

respect of the second issue and there is nothing to inform the lay person – especially applicants –

whether the decision-maker concerned has properly applied his or her mind.

The need for legislative guidance on how to exercise administrative discretion is recognised by the

Constitutional Court as an important part of the legislature’s duty to respect, protect, promote and fulfil the

rights in the Bill of Rights.112 In Dawood v Minister of Home Affairs113 O’Regan J held:

‘We must not lose sight of the fact that rights enshrined in the Bill of Rights must be

protected and may not be unjustifiably infringed. It is for the legislature to ensure that,

when necessary, guidance is provided as to when limitation of rights will be justifiable. It is

therefore not ordinarily sufficient for the legislature merely to say that discretionary powers

that may be exercised in a manner that could limit rights should be read in a manner

consistent with the Constitution in the light of the constitutional obligations placed on such

officials to respect the Constitution. Such an approach would often not promote the spirit,

purport and objects of the Bill of Rights. Guidance will often be required to ensure that the

Constitution takes root in the daily practice of governance. Where necessary, such

guidance must be given.

111 Note that the Promotion of Access to Information Act 2 of 2000 now gives everyone the right of access to information held by the State. 112 Constitution, s7(2). 113 2000 (8) BCLR 837 (CC), paragraph 54.


87

Guidance could be provided either in the legislation itself, or where appropriate by a

legislative requirement that delegated legislation be properly enacted by a competent

authority.’

From a procedural perspective, it is as important that guidance be given as to how decisions will be

made. It has been held in numerous cases that procedural fairness requires that the administration

discloses to those whose rights or legitimate expectations stand to be adversely affected what case they

have to meet.114 Feetham J gave the reason for this requirement of procedural fairness in Kadalie v

Hemsworth NO115 when he said that a ‘man who has to give evidence that he is of a respectable and

deserving character is merely beating the air if the tribunal before which he goes declines to give him any

indication of the points against him which have to be met’.

More recently, and in a case which is directly in point, it has been held in Tseleng v Unemployment

Insurance Board116 that it is procedurally unfair to keep a person in the dark about a policy that may be

applied in his or her case.

The final issue that bears mention is the scheme that the Act creates of rights with limited life-spans. It is

risky to assume that because a person’s right has expired, he or she has no legitimate claim for the

renewal of the right. This point is made in Mafuya v Mutare City Council117 a matter involving the renewal

of hawking licences.

The respondent decided to limit the number of licences to 300 and to allocate on a first come, first served

basis, an approach described as arbitrary by Dumbutshena JP because it was an approach which

allowed for the refusal of applications without even considering their merits.118 Dumbutshena JP

confirmed that the respondent, in allocating licences, was under a duty to act fairly. He held that it had

failed to act fairly for various reasons including because it had failed to distinguish between existing

licence holders – some of whom had been hawking for 25 years – and new applicants.119

In the light of the legitimate expectation doctrine that triggers the right to a hearing, an obligation would

rest on the department to give a hearing to a person who has exercised a right of access in the past, and

whose right has been renewed in the past as a matter of course, in the event of it contemplating not

renewing the right.120

114 Baxter Administrative Law, 546 – 548. 115 1928 TPD 495, 506. See too Lawson v Cape Town Municipality 1982 (4) SA 1 (C) in which the failure of the respondent to disclose the contents of a police report on the applicant’s activities vitiated its decision to refuse him a licence. 116 1995 (2) BCLR 138 (T), 150G – 151G. 117 1984 (2) SA 124 (ZHC). 118 At 128H. 119 At 131H. 120 Administrator, Transvaal v Traub 1989 (4) SA 731 (A).


88

4.6. CONCLUDING REMARKS

This paper was written by one who is an outsider in relation to the operation of the Act. Those who are

involved in the day-to-day administration of the Act may have answers to many of the problems that have

been raised above. Without wishing to be seen to be trumpeting the benefits of ignorance, this outsider’s

view of the Act may serve as a spur to address what appears to be an underdeveloped administrative

system that regulates an industry that generates billions of rands: if there are credible answers to some of

the criticisms, those answers can perhaps be made explicit for the benefit of lay persons (and their

lawyers) who will come into contact with the Act; if there are no credible answers to some of the

criticisms, law reform, whether through amending the Act or promulgating regulations, may resolve the

problems.

It is important to bear in mind that failures to meet the standards of the Constitution, either in the design of

the regulatory system or in its operation, may have frighteningly severe consequences. If, for instance,

the allocation committee is not empowered to do what it does and the Minister is held not to have properly

applied his or her mind to the allocations, the entire process could be set aside on review: that is a

consequence of adherence to the principle of legality and the rule of law.

It is important for another reason that the regulatory system is properly designed. It is not an end in itself.

It exists in order to generate rational and fair decisions that further the policy that the Act is created to

give effect to, and to do so in the public interest. To achieve these results, the regulatory system is

required to deliver decisions that, in broad and general terms, balance the economic imperatives, the

environmental concerns and the goals of transformation and, ideally, harmoniously maximise all of the

above.

It is only when these goals and how they are to be achieved are explicitly set out and fed into the

decision-making process that they can be achieved in the accountable, responsive and open way

contemplated by s1(d) of the Constitution and that applicants for rights and the public more generally will

be able to assess whether the regulatory system functions property or not. It is hoped that this paper is of

some use in this process.

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 5. Employment, Skills and Income

89

5. SURVEY RESULTS: EMPLOYMENT, SKILLS AND INCOME SUMMARY The results from the ESS survey with regard to employment, skills and income by fishery and by race are

presented in this part of the report. The following table gives a very broad representation of racial

distinctions between employment number, employment income and average yearly income per sector

and for the fishing industry as a whole.

The fishing industry is an important employer that pays relatively high yearly incomes. This is elaborated

on in Part 8: socio-economic base line and impact. The skills distribution by sector, however, shows a

steady decline in proportion of Black employment as skills levels increase. This is illustrated on the figure

below.

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

% Black employment

Professional/Managerial 52.2% 46.5% 49.6%

Skilled 64.8% 68.0% 65.1%

Middle services 62.6% 77.2% 71.9%

Semi-skilled 92.1% 98.5% 94.5%

Unskilled 96.0% 98.6% 98.0%


Fishing industry

Issues of the absorption of Black people into skilled work in the fishing industry are related to the specific

fishery concerned. Also, as skills take time to acquire, nondiscriminatory employment practice will not

necessarily immediately reflect a balanced skills profile between the Black and White people. These

issues are dealt with in Part 7: understanding and measuring transformation.

5.1. INTRODUCTION

The results from the ESS survey with regard to employment, skills and income are presented in this part

of the report. These results give an indication of the importance of human beings to the fishing industry

as well as the significance of the fishing industry to the people directly involved. The degree of

transformation in employment, or social transformation, in each fishery is presented in Part 7 of this

report. In Part 8, an attempt is made to quantify the socio-economic impact of the fishing industry in

terms of skills and employment.

The separation is between the primary sector (vessels only) and the primary sector (including on-shore

support activities). This is made, because as indicated previously, the market fails in the primary sector


90

particularly with regard to harvesting. Secondly, employment and income, by skills level, are discussed

with regard to the aggregated primary and primary sector support services. The secondary and tertiary

sector fishing industry activities (processing and marketing) are then examined from the usual

employment, skills and income perspective. Finally the total figures from the ESS survey with regard to

employment and income by skills level are presented.

5.2. EMPLOYMENT, SKILLS AND INCOME (primary sector vessels only)

The results of the ESS survey per fishery of vessel-based employment (primary sector) are presented in

tabular form. The percentage coverage of each fishery is indicated. Firstly, the survey results of total

employment numbers and employment income from vessels in the South African commercial fishery are

presented. Secondly, average yearly income is calculated for each group. In particular, this highlights

the differences in yearly income between race groups.

It is also important to get some indication of wage rates (the price of labour) in the primary sector of the

fishing industry. With this in mind, the average yearly incomes are adjusted for part-time employment into

daily wages by using the surveyed average number of days spent at sea in each fishery. The full-time

employed categories are similarly adjusted by assuming a working year of 220 days. Based on these

measures the difference in wage rates between part-time and full-time employees is highlighted.

5.2.1 Survey Results: Total Employment and Income

Tables 5.1 and 5.2 present the total employment and income by fishery, skills group, race and part-time

or full-time employment. The skills categories represented show that most ocean going crew are either

skilled fishers or semi-skilled fishers. The survey indicates that no unskilled or middle services people are

employed on fishing vessels.

The survey coverage for most fisheries is not 100%, an approximated coverage for the entire primary

sector of the fishing industry is 85%. Estimates of the total employment may be extrapolated without

affecting the distribution of the population. For income, it is better to use averages per person to

extrapolate the values.

From Table 5.1 about 29% of all those employed are skilled fishers. Of all the skilled fishers in the

survey, 56% are Black. Black fishers comprise a greater than proportionate majority (92%) of the semi-

skilled group. The survey also indicates that 95% of skilled fishers in the Inshore Hake fishery are Black.

In the region of 90% of skilled fishers in the Deep-sea Hake and South Coast Rock Lobster fisheries are

Black. These three fisheries show the highest absorption rates of Black skilled fishers in the fisheries

surveyed. At the bottom end of the scale, the survey results point out that the Hake Handline fishery has

the lowest absorption rate (24%) of skilled Black fishers in all the fisheries surveyed. This is, perhaps, due

to the small scale of the fishing firms. It might not be easy to absorb skilled fishers over and above the

rights holder. This, however, only holds if the majority of rights holders are White and they fish

themselves.


91

Table 5.2 shows the total income obtained from aggregated employment income survey data in their

respective categories from the vessels covered. The sample yields a total employment income to all 13

117 fishers of almost half a billion Rand per year. From table 5.1 and table 5.2, approximately 34% of all

income accrues to skilled fishers (a total of 29% of all fishers employed are skilled). Of total employment

income in the skilled fisher category, 51% goes to Black skilled fishers. As 56% of skilled fishers are

Black there is an indication that these people are still paid somewhat less than their White counterparts.

In this regard the fisheries that stand out as being ahead are:

¶ The Deep-sea Hake fishery where 84% of skilled employment income accrues to Black people (90%

of all skilled fishers are Black).

¶ The Inshore Hake fishery where 85% of skilled employment income accrues to Black fishers (95% of

all skilled fishers are Black).

¶ The South Coast Rock Lobster fishery shows that 81% of employment income goes to Black fishers

(90% Skilled Black fishers).


92

Table 5.1. Primary fishing sector (vessels only) employment numbers by race and skills group per fishery.

Figure 5.1. Skills distribution of vessel base employment.

Totals Professional/managerial Skilled Semi-skilled Full-time Part-time Full-time Part-time Full-time Part-time Fishery %

Cover Total Black White Black White Black White Black White Black White Black White Black White

SHARK LONGLINE 100% 160 143 17 0 0 11 0 3 8 8 2 0 0 121 7

HAKE LONGLINE 80% 683 598 85 0 0 0 0 30 41 23 26 147 10 398 8

PELAGIC 81% 558 332 226 1 0 0 0 18 8 78 110 43 0 192 108

TOOTHFISH 100% 149 80 69 1 1 0 0 13 20 0 0 66 24 0 24

HAKE HANDLINE 50% 950 743 207 0 0 0 0 17 68 12 25 232 52 482 62

ABALONE 100% 114 98 16 0 0 0 0 9 6 4 3 52 7 33 0

LINEFISH 66% 2993 2385 608 5 0 0 0 162 237 40 47 1091 136 1087 188

TUNA BAITBOAT 67% 1456 1259 197 0 0 0 0 22 45 70 104 129 5 1038 43

TUNA LONGLINE 100% 335 269 66 0 0 0 0 33 20 16 19 96 9 124 18

DEEP-SEA HAKE 84% 1739 1679 60 16 2 0 0 348 37 9 2 1245 18 61 1

INSHORE HAKE 97% 258 252 6 0 0 0 0 90 5 7 0 137 1 18 0

SQUID 85% 1900 1732 168 0 0 0 0 37 43 58 84 261 7 1376 34 WC ROCK LOBSTER 79% 1495 1331 164 20 0 11 0 139 49 66 49 568 12 527 54 SC ROCK LOBSTER 89% 218 214 4 5 0 2 0 26 1 10 3 102 0 69 0

PRAWN TRAWL 100% 109 103 6 0 0 0 0 29 6 0 0 74 0 0 0

TOTAL 13117 11218 1899 48 3 24 0 976 594 401 474 4243 281 5526 547 APPROXIMATE COVERAGE = 85%

BlackSkilled11% White

8%

BlackSemi-Skilled

75%

White6%


93

Table 5.2a. Primary fishing sector (vessels only) total employment income (in Rand) by race and skills group per fishery

Table 5.2b. Primary fishing sector (vessels only) total employment income (in Rand) by fishery

FISHERY TOTAL BLACK WHITE FISHERY TOTAL BLACK WHITE

SHARK LONGLINE 5,604,000 4,950,000 654,000 TUNA LONGLINE 16,278,000 10,356,000 5,922,000

HAKE LONGLINE 28,182,000 22,290,000 5,892,000 DEEP-SEA HAKE 111,456,000 102,528,000 8,928,000

PELAGIC 52,356,000 27,474,000 24,882,000 INSHORE HAKE 9,216,000 8,430,000 786,000

TOOTHFISH 8,814,000 4,716,000 4,098,000 SQUID 65,616,000 52,194,000 13,422,000

HAKE HANDLINE 28,500,000 19,548,000 8,952,000 WCRL 40,188,000 31,554,000 8,634,000

ABALONE 2,250,000 1,776,000 474,000 SCRL 9,654,000 8,934,000 720,000

LINEFISH 64,626,000 45,858,000 18,768,000 PRAWN TRAWL 5,058,000 4,068,000 990,000

TUNA BAITBOAT 38,790,000 30,906,000 7,884,000 TOTAL R 486,588,000 375,582,000 111,006,000

Professional/managerial Skilled Semi-skilled Full-time Part-time Full-time Part-time Full-time Part-time Fishery %

Coverage Black White Black White Black White Black White Black White Black White

SHARK LONGLINE 100% 0 0 198,000 0 72,000 420,000 336,000 126,000 0 0 4,344,000 108,000

HAKE LONGLINE 80% 0 0 0 0 1,986,000 3,336,000 810,000 1,944,000 4,626,000 414,000 14,868,000 198,000

PELAGIC 81% 36,000 0 0 0 1,248,000 942,000 6,708,000 14,388,000 1,818,000 0 17,664,000 9,552,000

TOOTHFISH 100% 90,000 210,000 0 0 1,530,000 1,902,000 0 0 3,096,000 1,122,000 0 864,000

HAKE HANDLINE 33% 0 0 0 0 1,032,000 3,792,000 540,000 2,250,000 5,652,000 1,512,000 12,324,000 1,398,000

ABALONE 100% 0 0 0 0 180,000 312,000 90,000 72,000 1,110,000 90,000 396,000 0

LINEFISH 66% 90,000 0 0 0 4,218,000 10,470,000 912,000 2,136,000 18,084,000 2,382,000 22,554,000 3,780,000

TUNA BAITBOAT 67% R 0 0 0 0 864,000 2,616,000 2,076,000 4,122,000 3,672,000 144,000 24,294,000 1,002,000

TUNA LONGLINE 100% R 0 0 0 0 2,436,000 2,388,000 1,110,000 2,730,000 2,502,000 432,000 4,308,000 372,000

DEEP-SEA HAKE 84% 1,314,000 360,000 0 0 37,320,000 6,708,000 828,000 576,000 60,960,000 1,134,000 2,106,000 150,000

INSHORE HAKE 97% 0 0 0 0 3,768,000 750,000 342,000 0 3,672,000 36,000 648,000 0

SQUID 85% 0 0 0 0 2,418,000 3,258,000 2,502,000 9,054,000 8,928,000 198,000 38,346,000 912,000

WC ROCK LOBSTER 79% 288,000 0 198,000 0 3,690,000 2,784,000 1,710,000 3,366,000 12,582,000 252,000 13,086,000 2,232,000

SC ROCK LOBSTER 89% 180,000 0 72,000 0 2,016,000 210,000 960,000 510,000 3,672,000 0 2,034,000 0

PRAWN TRAWL 100% 0 0 0 0 2,274,000 990,000 0 0 1,794,000 0 0 0

TOTAL 1,998,000 570,000 468,000 0 65,052,000 40,878,000 18,924,000 41,274,000 132,168,000 7,716,000 156,972,000 20,568,000 84% OF TOTAL VESSELS SURVEYED


94

At the bottom is the Hake Handline fishery with 21% of total skilled employment income going to the 21%

of Black skilled fishers.

The important considerations are:

¶ That the majority of people employed in the primary sector of the fishery are Black, namely 86%.

¶ Also, the main portion, 77%, of employment income accrues Black fishers.

¶ 56% of all skilled fishers are Black, but Black people earn only 51% of total income in this group.

¶ The Linefish fishery employs the highest number of fishers (2 993 fishers – 66% sample of the fleet),

followed by the Squid fishery (1 900 fishers – 85% sample of the fleet) and the Deep-sea Hake

fishery (1 739 fishers – 84% sample of the fleet).

Figure 5.2. Distribution of numbers employed in the primary fishing sector (vessels only) of the commercial fishing industry surveyed. (Note: sample sizes are not the same in each fishery).

Although employment numbers are not adjusted to represent the population size of each fleet in each

fishery, table 5.3 gives a broad indication of the importance of each fishery with regard to the number

employed, but not for total or individual incomes.

It should be clear that total employment income from tables 5.2a and 5.2b give some indication of income

from employment share and the racial distribution of employment among the different fisheries. They

cannot, however, provide much insight into the issue of transformation. Average incomes differ between

race groups in similar skills levels (see table 5.3). A simple interpretation of the ‘follow-the-buck’ social

transformation index gives an over-estimation of transformation. For example, about 77% of total

employment income from the survey data accrues to Black people. To ensure that the transformation

criteria holds, that is, a fully transformed society is one where one cannot distinguish racial characteristics

based on skills and yearly income, the figure must be adjusted by a demographic factor (1.25 if 80% of all

0500

100015002000250030003500

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Deeps

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ake

WC Roc

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Tuna B

aitbo

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Hake H

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Hake L

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Pelagic

Tuna l

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Insho

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SC Roc

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Shark

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Prawn T

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Num

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Total Black White


95

people in South Africa are Black). The level of social transformation under the simple ‘follow-the-buck’

system comes out at 96.5%. Clearly not a true result if one takes into consideration the income

differential, let alone the racial difference in skills levels by number. Issues around measuring this kind of

transformation are covered in Part 7: Understanding and measuring transformation.

The next step is to convert total employment income and employment to average incomes.

5.2.2 Survey Results: Average Yearly Income The average yearly income per fisher in a particular fishery is calculated from tables 5.1 and 5.2 that are

derived from the ESS survey and are displayed in table 5.3. Average income differentials can be used to

determine several measures of social transformation. For example, a broad based measure can be

obtained by calculating the percentage difference between the average incomes earned by Black people

to that of White people. As the racial distribution of skilled employment is important, a weighted index

based on the difference in average income per skills group is calculated in Part 7 of the ESS report.

Table 5.3. Primary fishing sector (vessels only) average yearly employment income (in Rand) by race and skills group.

Average incomes tell a different story to that of total income. For example, the 11 218 Black fishers

employed in the primary sector (vessels only) of the fishing industry earn on average R33 480 per year.

Whites on the other hand earn on average 76% more (R58 455 per year) than Black fishers. This

difference is largely a result of higher fishing industry primary sector average earnings by White fishers in

the full-time professional/managerial (356% more) and part-time skilled (85% more) categories.

Professional/managerial Skilled Semi-skilled

Full-time Part-time Full-time Part-time Full-time Part-time

TOTAL BLACK WHITE Black White Black White Black White Black White Black White Black White SHARK LONGLINE 35,025 34,615 38,471 18,000 24,000 52,500 42,000 63,000 35,901 15,429

HAKE LONGLINE 41,262 37,274 69,318 66,200 81,366 35,217 74,769 31,469 41,400 37,357 24,750

PELAGIC 93,828 82,753 110,097 36,000 69,333 117,750 86,000 130,800 42,279 92,000 88,444

TOOTHFISH 59,154 58,950 59,391 90,000 210,000 117,692 95,100 46,909 46,750 36,000

HAKE HANDLINE 30,000 26,310 43,246 60,706 55,765 45,000 90,000 24,362 29,077 25,568 22,548

ABALONE 19,737 18,122 29,625 20,000 52,000 22,500 24,000 21,346 12,857 12,000

LINEFISH 21,592 19,228 30,868 18,000 26,037 44,177 22,800 45,447 16,576 17,515 20,749 20,106

TUNA BAITBOAT 26,641 24,548 40,020 39,273 58,133 29,657 39,635 28,465 28,800 23,405 23,302

TUNA LONGLINE 48,591 38,498 89,727 73,818 119,400 69,375 143,684 26,063 48,000 34,742 20,667

DEEP-SEA HAKE 64,092 61,065 148,800 82,125 180,000 107,241 181,297 92,000 288,000 48,964 63,000 34,525 150,000

INSHORE HAKE 35,721 33,452 131,000 41,867 150,000 48,857 26,803 36,000 36,000

SQUID 34,535 30,135 79,893 65,351 75,767 43,138 107,786 34,207 28,286 27,868 26,824 WC ROCK LOBSTER 26,882 23,707 52,646 14,400 18,000 26,547 56,816 25,909 68,694 22,151 21,000 24,831 41,333 SC ROCK LOBSTER 44,284 41,748 180,000 36,000 36,000 77,538 210,000 96,000 170,000 36,000 29,478

PRAWN TRAWL 46,404 39,495 165,000 78,414 165,000 24,243 TOTAL (averages) 37,096 33,480 58,455 41,625 190,000 19,500 66,652 68,818 47,192 87,076 31,150 27,459 28,406 37,601


96

Total average income per year is lowest in the Abalone fishery (R19 737 per year) and highest in the

Pelagic fishery (R93 828). Black fishers also earn on average more in the Pelagic fishery than in any

other fishery. However, care should be taken when comparing average incomes in an industry that

employs fishers on a seasonal basis. A better measure would be to compare wage rates, preferably

hourly wages. The percentage difference in wage rates are calculated and presented per fishery in table

5.4.

The importance of average yearly income is that differences between Black fishers and White fishers in

particular skills categories can be calculated, thus giving rise to a better measure of the social

transformation in employment.

On average yearly income is lower for Black (R33 480 per year) than for White fishers (R58 455).

However, in the Toothfish and Hake Handline fisheries full-time Black skilled fishers earn more than their

white counterparts. For the Toothfish, Abalone and Squid fisheries in the full-time semi-skilled category

Black fishers earn more than their White counterparts. Equal employment practices among the races

take time to come into effect due to the slow pace of skills acquisition. Part-time incomes in all skills

categories are not at this stage comparable as the number of days or hours worked is not factored into

the average yearly incomes. The survey results indicate that all fishers – skilled and unskilled – earn

above the bread line.

5.2.3 An Indication of Daily Wages Total annual average income per fisher in their respective fisheries is useful, but it does not give an

indication of either the approximate length of time the fisher works for, or the daily wages the fisher will

receive. Table 5.5 over the page, provides a summary of part-time and full-time daily wages per fishery.

Table 5.4 below, displays the percentage differences in daily wages between full-time employment and

part-time employment by race and skills group.

With regard to table 5.4, a value of 100% means that there are no full-time fishers in that category and a

blank cell indicates that there are no part-time fishers. The table is arranged from the highest percentage

of part-time employers (Shark Longline 93%) to the lowest (Prawn Trawl - no part-timers).


97

Table 5.4. Percentage premium in daily wages paid to part-time fishers.


% Part-time

employees Black White Black White Black White

SHARK LONGLINE 93% 100.0% - 80.5% 71.6% 100.0% 100.0%

PELAGIC 87% - - 72.5% 69.3% 84.3% 100.0%

TUNA BAITBOAT 86% - - 54.9% 50.0% 58.5% 57.9%

SQUID 82% - - 48.4% 76.0% 58.2% 64.1%

HAKE LONGLINE 67% - - 35.9% 62.9% 71.3% 43.0%

HAKE HANDLINE 61% - - 54.0% 78.9% 67.5% 56.0%

TUNA LONGLINE 53% - - 63.7% 71.7% 74.4% 20.8%

WC ROCK LOBSTER 47% 72.7% - 65.1% 71.8% 69.6% 82.7%

LINEFISH 46% - - 61.1% 66.9% 72.8% 70.3%

SC ROCK LOBSTER 39% 65.9% - 72.5% 57.9% 58.4% -

ABALONE 35% - - 69.7% 26.1% 39.4% -

TOOTHFISH 16% - - - - - 55.7%

INSHORE HAKE 10% - - 70.8% - 74.6% -

DEEP-SEA HAKE 04% - - 60.3% 78.5% 51.7% 85.7%

PRAWN TRAWL 00% - - - - - - Table 5.4 indicates that in all categories where fishers are employed (indicated by a percentage) part-time

fishers earn more per day than their full-time counterparts. This ranges from a premium of 35.9% in the

hake longline Black skilled category to 100% premium in the shark longline semi-skilled group.

The following observations are important:

¶ The percentage of fishers employed on a part-time basis gives a good indication of the seasonal

nature of the fishery.

¶ All part-time fishers in all fisheries earn more per day than the full-time ones.

As indicated in Part 2: A simple economic system of the fishing microeconomy, for labour markets that

work, the wage rate is the price of labour and is determined by its demand and supply. If the labour

market is not competitive the price will not indicate a market clearing quantity of labour. Assuming a

competitive labour market, table 5.5 indicates:

1. The 1 481 Black full-time semi-skilled deep-sea hake fishers121 will be demanded and supplied at a

price (or wage rate) of R460.33 per day for the full year. By any standards this is a good wage.

2. At the other end of the scale the linefish fishery is not unionised and there are many different

employers. Here the market is expected to clear with 1 653 Black semi-skilled fishers122 earning a

daily wage of R79.61. Issues with this labour market will, however, arise as different wages are most

likely paid in different geographic locations.

121 The number is an extrapolation based on percentage coverage and the number of fishers displayed in table 5.1. 122 The number is an extrapolation based on percentage coverage and the number of fishers displayed in table 5.1.


98

At this stage the ESS cannot provide a labour market analysis for the fisheries. The survey result do,

however, give a good first picture of the labour requirements, average incomes and daily wages of the

fishers employed in the primary fishing sector (vessels only). The next section deals with the entire

primary fishing sector (including on-shore support).

Table 5.5. Primary fishing sector (vessels only) daily full-time and part-time wages (in Rand) per fishery.

5.3. EMPLOYMENT, SKILLS AND INCOME (primary sector including on-shore support)

Still using the ESS survey database123 ocean support employment is added to the primary sector

harvesting employment. Daily wages cannot be calculated, as there is no data to indicate the average

length of time part-time employees work in each fishery when including the ocean support activities.

Percentage coverage for primary sector ocean support services is unknown. The total employment

numbers are illustrated in table 5.6, total income in the various skills groups by race in table 5.7 and

average yearly income in table 5.8.

In essence, the presentation framework is identical to that of primary sector (vessel only) employment,

income and skills results from the ESS survey. The differences that stand out, however, are briefly

discussed below. By including primary sector support activities:

1. Total employment in the primary sector has increased from 13 117 people employed to 16 854, a

percentage increase of 28.5% or 3 737 people.

123 Updated data is again provided in the Deep-sea and Inshore Hake fisheries report in part 10 of the ESS report. To ensure consistency with the ESS database, the appropriate numbers that have not been updated are used.


Days worked

Full-time Part-time Full-time Part-time Full-time Part-time

Full-time Part-

time Black White Black White Black White Black White Black White Black White

SHARK LONGLINE 220 75 240.00 109.09 238.64 560.00 840.00 478.68 205.71

HAKE LONGLINE 220 103 300.91 369.84 469.57 996.92 143.04 188.18 498.09 330.00

PELAGIC 220 134 163.64 315.15 535.23 1,146.67 1,744.00 192.18

1,226.6

7 1,179.26

TOOTHFISH 220 409.09 954.55 534.97 432.27 213.22 212.50 480.00

HAKE HANDLINE 220 168 275.94 253.48 600.00 1,200.00 110.74 132.17 340.91 300.65

ABALONE 220 24 90.91 236.36 300.00 320.00 97.03 58.44 160.00

LINEFISH 220 150 81.82 118.35 200.81 304.00 605.96 75.34 79.61 276.65 268.09

TUNA BAITBOAT 220 127 178.51 264.24 395.43 528.46 129.39 130.91 312.06 310.70

TUNA LONGLINE 220 173 335.54 542.73 925.00 1,915.79 118.47 218.18 463.23 275.56

DEEP-SEA HAKE 220 186 373.30 818.18 487.46 824.08 1,226.67 3,840.00 222.56 286.36 460.33 2,000.00

INSHORE HAKE 220 178 190.30 681.82 651.43 121.83 163.64 480.00

SQUID 220 150* 297.05 344.40 575.17 1,437.14 155.49 128.57 371.57 357.65 WC ROCK LOBSTER 220 85 65.45 240.00 120.67 258.26 345.45 915.92 100.69 95.45 331.08 551.11

SC ROCK LOBSTER 220 205 163.64 480.00 352.45 954.55 1,280.00 2,266.67 163.64 393.04

PRAWN TRAWL 220 356.43 750.00 110.20


99

2. The percentage of Black people employed remains more or less the same (86% in ocean based

employment and 85% in the total primary sector employment).

3. Wage income has increased by 32.2% from about R487 million to R644 million per year.

4. The higher percentage increase in employment income than the increase in employment shows that

on average, ocean based support individuals earn more than their seagoing counterparts.

5. In the primary sector fishing activities (including on-shore support), on average, Black people earn

R33 812 per year with Whites earning about 88% more at R63 491 per year. A large portion of the

discrepancy, however, could be attributed to the very high relative salaries of White

professional/managerial people in the toothfish and deep-sea hake fisheries – see table 5.9.

Table 5.6. Primary fishing sector (including on-shore support) employment numbers by race and skills group per fishery.

P/M Skilled Middle services Semi-skilled Unskilled

Total Black White

Black White Black White Black White Black White Black White

SHARK LONGLINE 172 149 23 12 2 11 10 1 0 121 11 4 0

HAKE LONGLINE 977 878 99 11 7 138 68 13 3 695 20 21 1

PELAGIC 800 544 256 17 9 156 119 5 6 309 122 57 0

TOOTHFISH 152 81 71 2 3 13 20 0 0 66 48 0 0

HAKE HANDLINE 982 764 218 7 8 32 94 0 2 714 114 11 0

ABALONE 1384 1252 132 33 36 392 36 36 13 682 47 109 0

LINEFISH 3133 2475 658 13 21 207 298 8 9 2194 324 53 6

TUNA BAITBOAT 1600 1355 245 7 9 108 158 10 10 1211 65 19 3

TUNA LONGLINE 367 293 74 1 6 50 39 5 2 223 27 14 0

DEEP-SEA HAKE 2774 2511 263 61 54 658 133 65 23 1478 49 249 4

INSHORE HAKE 573 535 38 7 7 121 12 2 6 368 2 37 11

SQUID 2004 1797 207 5 24 100 136 4 6 1640 41 48 0

WC ROCK LOBSTER 1575 1367 208 34 10 208 102 3 11 1122 84 0 1

SC ROCK LOBSTER 218 214 4 7 0 36 4 0 0 171 0 0 0

PRAWN TRAWL 142 129 13 1 4 49 8 3 1 74 0 2 0

TOTAL 16854 14344 2509 218 200 2279 1237 155 92 11068 954 624 26


100

Table 5.7. Primary fishing sector (including on-shore support) total employment income (in Rand) by race and skills group per fishery. P/M Skilled Middle services Semi-skilled Unskilled

Total Black White


SHARK LONGLINE 6,234,000 5,184,000 1,050,000 348,000 300,000 408,000 546,000 36,000 0 4,344,000 204,000 48,000 0

HAKE LONGLINE 37,578,000 30,882,000 6,696,000 936,000 468,000 5,436,000 5,316,000 402,000 216,000 23,472,000 684,000 636,000 12,000

PELAGIC 67,170,000 38,400,000 28,770,000 2,124,000 2,520,000 11,040,000 15,420,000 414,000 342,000 23,124,000 10,488,000 1,698,000 0

TOOTHFISH 9,240,000 4,806,000 4,434,000 180,000 546,000 1,530,000 1,902,000 0 0 3,096,000 1,986,000 0 0

HAKE HANDLINE 30,150,000 20,436,000 9,714,000 516,000 600,000 1,644,000 6,078,000 0 126,000 17,976,000 2,910,000 300,000 0

ABALONE 37,290,000 30,324,000 6,966,000 1,620,000 4,440,000 13,512,000 1,152,000 1,008,000 420,000 11,484,000 954,000 2,700,000 0

LINEFISH 69,522,000 48,450,000 21,072,000 396,000 1,350,000 5,436,000 13,242,000 372,000 246,000 41,106,000 6,162,000 1,140,000 72,000

TUNA BAITBOAT 43,524,000 33,606,000 9,918,000 282,000 912,000 3,678,000 7,062,000 396,000 414,000 28,830,000 1,494,000 420,000 36,000

TUNA LONGLINE 17,844,000 11,100,000 6,744,000 90,000 642,000 3,582,000 5,118,000 186,000 180,000 6,918,000 804,000 324,000 0

DEEP-SEA HAKE 173,586,000 143,652,000 29,934,000 9,684,000 10,074,000 53,262,000 15,252,000 3,528,000 1,530,000 69,696,000 2,952,000 7,482,000 126,000

INSHORE HAKE 19,518,000 16,494,000 3,024,000 630,000 810,000 5,280,000 1,170,000 72,000 198,000 8,946,000 72,000 1,566,000 774,000

SQUID 71,358,000 54,684,000 16,674,000 540,000 2,298,000 5,316,000 12,888,000 120,000 378,000 47,382,000 1,110,000 1,326,000 R 0 WC ROCK LOBSTER 43,638,000 32,154,000 11,484,000 546,000 702,000 5,490,000 6,348,000 102,000 1,290,000 26,016,000 3,132,000 0 12,000

SC ROCK LOBSTER 9,654,000 8,934,000 720,000 252,000 0 2,976,000 720,000 0 0 5,706,000 0 0 R 0

PRAWN TRAWL 7,998,000 5,898,000 2,100,000 210,000 840,000 3,588,000 1,170,000 270,000 90,000 1,794,000 0 36,000 R 0

TOTAL 644,304,000 485,004,000 159,300,000 18,354,000 26,502,000 122,178,000 93,384,000 6,906,000 5,430,000 319,890,000 2,952,000 17,676,000 1,032,000


101

Figure 5.3. Total employment income, primary sector (including on-shore support) in R 000s Table 5.8. Primary fishing sector (including on-shore support) average employment income (in Rand) by race and skills group per fishery.

P/M

Skilled Middle services Semi-skilled Unskilled

Total Black White


SHARK LONGLINE 36,244 34,792 45,652 29,000 150,000 37,091 54,600 36,000 35,901 18,545 12,000

HAKE LONGLINE 38,463 35,173 67,636 85,091 66,857 39,391 78,176 30,923 R 72,000 33,773 34,200 30,286 12,000

PELAGIC 83,963 70,588 112,383 124,941 280,000 70,769 129,580 82,800 R 57,000 74,835 85,967 29,789

TOOTHFISH 60,789 59,333 62,451 90,000 182,000 117,692 95,100 46,909 41,375

HAKE HANDLINE 30,703 26,749 44,560 73,714 75,000 51,375 64,660 R 63,000 25,176 25,526 27,273

ABALONE 26,944 24,220 52,773 49,091 123,333 34,469 32,000 28,000 R 32,308 16,839 20,298 24,771

LINEFISH 22,190 19,576 32,024 30,462 64,286 26,261 44,436 46,500 R 27,333 18,736 19,019 21,509 12,000

TUNA BAITBOAT 27,203 24,801 40,482 40,286 101,333 34,056 44,696 39,600 R 41,400 23,807 22,985 22,105 12,000

TUNA LONGLINE 48,621 37,884 91,135 90,000 107,000 71,640 131,231 37,200 R 90,000 31,022 29,778 23,143

DEEP-SEA HAKE 62,576 57,209 113,817 158,754 186,556 80,945 114,677 54,277 R 66,522 47,156 60,245 30,048 31,500

INSHORE HAKE 34,063 30,830 79,579 90,000 115,714 43,636 97,500 36,000 R 33,000 24,310 36,000 42,324 70,364

SQUID 35,608 30,431 80,551 108,000 95,750 53,160 94,765 30,000 R 63,000 28,891 27,073 27,625

WC ROCK LOBSTER 27,707 23,522 55,212 16,059 70,200 26,394 62,235 34,000 R 117,273 23,187 37,286 12,000

SC ROCK LOBSTER 44,284 41,748 180,000 36,000 82,667 180,000 33,368

PRAWN TRAWL 56,324 45,721 161,538 210,000 210,000 73,224 146,250 90,000 R 90,000 24,243 18,000

TOTAL 38,229 33,812 63,491 84,193 132,510 53,610 75,492 44,555 R 59,022 28,902 34,541 28,327 39,692

6234799892409654

1784419518

301503729037578

4352443638

671706952271358

173586

SHARK LONGLINEPRAWN TRAWL

TOOTHFISHSC ROCK LOBSTER

TUNA LONGLINEINSHORE HAKE

HAKE HANDLINEABALONE

HAKE LONGLINETUNA BAITBOAT

WC ROCK LOBSTERPELAGICLINEFISH

SQUIDDEEPSEA HAKE


102

SLL HLL

PL

TF

HHLAB

LFTBB

TLL

DSH

ISH SQWCRL

SCRL

PT

Average

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

Ave

rage

yea

rly in

com

e

Figure 5.4. Average yearly employment income, primary sector (including on-shore support). (Key in descending order : PL- Pelagic, DSH-Deep-sea Hake, TF-Toothfish, PT-Prawn Trawl, TLL-Tuna Longline, SCRL-South Coast Rock Lobster, HLL-Hake Longline, SLL-Shark Longline, SQ-Squid, ISH-Inshore Hake, HHL-Hake Handline, WCRL-West Coast Rock Lobster, TBB-Tuna Baitboat, AB-Abalone, LF-Linefish) Table 5.9. Secondary and tertiary fishing sector employment numbers by race and skills group per fishery

P/M Skilled Middle services Semi-skilled Unskilled SECONDARY Factory Total Black White


SHARK 5 120 105 15 3 9 1 3 4 3 22 0 75 0

HAKE 22 4798 4582 216 111 94 114 30 173 26 3861 61 323 5

PELAGIC 11 3684 3576 108 20 34 47 31 70 22 2129 17 1310 4

ABALONE 3 60 54 6 2 4 1 0 6 2 20 0 25 0

LINEFISH 20 718 641 77 12 24 13 3 19 4 474 23 123 23

SQUID 5 114 104 10 7 2 1 1 1 0 67 7 28 0

ROCK LOBSTER 11 1326 1262 64 5 13 15 22 50 21 747 6 445 2

PRAWNS 1 56 31 25 0 4 1 1 5 19 11 1 14 0

TOTAL 78 10876 10355 521 160 184 193 91 328 97 7331 115 2343 34


103

Table 5.10. Secondary and tertiary fishing sector total employment income (in Rand) by race and skills group per fishery

P/M Skilled Middle services Semi-skilled Unskilled SECONDARY Factory Total

(Rand) Black (Rand)

White (Rand) Black White Black White Black White Black White Black White

SHARK 5 2,808,000 2,106,000 702,000 54,000 486,000 36,000 108,000 144,000 108,000 414,000 0 1,458,000 0

HAKE 22 204,096,000 180,930,000 23,166,000 15,210,000 14,586,000 5,886,000 2,238,000 13,104,000 1,368,000 138,780,000 4,794,000 7,950,000 180,000

PELAGIC 11 84,864,000 76,638,000 8,226,000 2,454,000 4,212,000 3,744,000 2,196,000 4,590,000 1,260,000 43,146,000 486,000 22,704,000 72,000

ABALONE 3 2,496,000 1,884,000 612,000 300,000 540,000 36,000 0 324,000 72,000 666,000 0 558,000 0

LINEFISH 20 10,008,000 6,276,000 3,732,000 234,000 2,670,000 252,000 90,000 180,000 126,000 3,348,000 432,000 2,262,000 414,000

SQUID 5 2,064,000 1,758,000 306,000 180,000 126,000 36,000 36,000 18,000 0 1,080,000 144,000 444,000 0

ROCK LOBSTER 11 38,244,000 32,736,000 5,508,000 516,000 1,890,000 1,242,000 1,926,000 1,908,000 1,458,000 21,036,000 198,000 8,034,000 36,000

PRAWNS 1 3,516,000 1,134,000 2,382,000 R 0 600,000 36,000 36,000 450,000 1,710,000 396,000 36,000 252,000 0

TOTAL 78 348,096,000 303,462,000 44,634,000 18,948,000 25,110,000 11,268,000 6,630,000 20,718,000 6,102,000 208,866,000 6,090,000 43,662,000 702,000 Table 5.11. Secondary and tertiary fishing sector average yearly employment income (in Rand) by race and skills group per fishery


Total Black White


SHARK 23,400 20,057 46,800 18,000 54,000 36,000 36,000 36,000 36,000 18,818 19,440

HAKE 42,538 39,487 107,250 137,027 155,170 51,632 74,600 75,746 52,615 35,944 78,590 24,613 36,000

PELAGIC 23,036 21,431 76,167 122,700 123,882 79,660 70,839 65,571 57,273 20,266 28,588 17,331 18,000

ABALONE 41,600 34,889 102,000 150,000 135,000 36,000 54,000 36,000 33,300 22,320

LINEFISH 13,939 9,791 48,468 19,500 111,250 19,385 30,000 9,474 31,500 7,063 18,783 18,390 18,000

SQUID 18,105 16,904 30,600 25,714 63,000 36,000 36,000 18,000 16,119 20,571 15,857

ROCK LOBSTER 28,842 25,940 86,063 103,200 145,385 82,800 87,545 38,160 69,429 28,161 33,000 18,054 18,000

PRAWNS 62,786 36,581 95,280 150,000 36,000 36,000 90,000 90,000 36,000 36,000 18,000

TOTAL 32,006 29,306 85,670 118,425 136,467 58,383 72,857 63,165 62,907 28,491 52,957 18,635 20,647


104

P

H AB

AverageRL

SH PLSQ

LF

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Figure 5.5. Average yearly income for secondary and tertiary sectors (Key: P-prawn trawl, H-Hake, AB-Abalone, RL-Rock Lobster, SH-Shark, PL-Pelagic, SQ-Squid, LF-Linefish) 5.4. EMPLOYMENT, SKILLS AND INCOME (secondary and tertiary sector)

The data collection phase that makes up the ESS database124 captured 78 factories representing 9

fisheries (89% estimated coverage) involved in secondary and tertiary activities (Table 5.10).

1. The factories employ 10 876 people, 95% are Black and earn 87% of the total income of R348

million (table 5.9).

2. Table 5.11 indicates that the average income earned across-the-board is R32 006 per year; with

Whites earning on average almost 200% higher incomes (R85 670) than Blacks (R29 306). Whites

also have higher incomes in all skills categories than Blacks. The highest yearly income discrepancy

is in the managerial and professional category where whites earn 57% more than Blacks.

These figures again corroborate the very false picture that emerges by applying a simple ‘follow-the-buck’

indicator, or an absolute percentage employment of Blacks, to represent transformation.

5.5. EMPLOYMENT, SKILLS AND INCOME (Totals for the fishing industry)

For the entire fishing industry, the important aspects of the ESS survey are listed below.

1. A total of 27 730 people employed in the fishing industry made up of 16 854 in the primary sector

(vessels and on-shore support) and 10 876 in the secondary and tertiary sectors.

2. Similarly, total wage income is R992.4 million with R644.3 million accruing to people working in the

primary sector (vessels and on-shore support) and the remaining R348.1 million to the secondary

and tertiary sectors.

124 Updated data is again provided for the Deep-sea and Inshore Hake fisheries in part 10 of the ESS report. To ensure consistency with the ESS database, the appropriate numbers that have not been updated are used.


105

3. Primary sector (vessels and on-shore support) employees earn on average R38 222 per year and

secondary and tertiary sector employees earning slightly less at R32 005. The average yearly

income for the fishing industry is R35 788.

4. In the region of 89.1% of all employees in the fishing industry are Black, they capture 79.5% of the

total yearly income. For the entire fishing industry and on average, Whites earn 110.8% more than

Blacks. These numbers are illustrated in table 5.13 along with a breakdown per skills group and

race.

Figure 5.6 below shows the percentage of total employment in the fishing industry between skills group

and race. The majority of employment in the fishing industry is in the skilled and semi-skilled category.

0%5%

10%15%20%25%30%35%40%45%



% T

otal

em

ploy

men

t

primary secondary and tertiary

Figure 5.6. Percentage of total employment per race group and skills category.

The average yearly income in primary sector skilled category is R53 610 for 2 279 Black people and R75

492 for the 1 237 Whites. In the semi-skilled group in the primary sector (vessels and on-shore support),

the 11 068 Black fishers earn on average R28 902 per year as compared to the 954 White fishers who

earn on average R35 541 per year. The important professional/managerial (P/M) skills group shows a

50/50 racial balance (racial equity would be an 80:20 black/white ratio based on South Africa national

demographic makeup), but Whites earn 57% more per year than their Black counterparts (the aim is to

have equivalence). This phenomenon is not unique to the fishing industry. In particular, it takes time to

create equivalence in income and racial demographic balance in skills levels particularly at the

professional/managerial level. It would be safe to state that the fishing industry is well advanced in this

respect.

Figure 5.7 below shows an expected pattern where the shares of income are skewed toward the more

skilled groupings. For example, comparing figure 5.6 with figure 5.7, the percentage number of people

employed in the semi-skilled group is larger than the percentage income that accrues to this group. In the

more skilled occupations more income than is proportional to employment numbers is accounted for –


106

simply put, people with higher-level skills earn more. This skewing effect manifests itself in an apparently

high difference in total average incomes between the race groups, that is, the average income for White

people in the fishing industry (R67 304 per year) is, as has already been stated, 110.8% higher than

Black people (R31 923 per year). White people still dominate the more skilled jobs in the fishing industry.

0.0%5.0%

10.0%15.0%20.0%25.0%30.0%35.0%


P/M Skilled Middleservices

Semi-skilled Unskilled

% T

otal

inco

me


Figure 5.7. Percentage of total income per race group in skills category. Table 5.12 below highlights the fact that Black labour absorption into the more skilled categories of the

fishing industry lags behind that of the lower level skills. For example, going from unskilled to the

professional/managerial group, the percentage of Black employment in the fishing industry falls steadily

from 98.0% to 49.6%. This trend is consistent when disaggregated into the primary sector (vessels and

on-shore support) and secondary and tertiary sectors of the fishing industry.

Table 5.12. Percentage Black employment by primary fishing sector (vessels and on-shore support), and

secondary and tertiary sectors of the fishing industry.

Total P/M Skilled M S Semi-skilled Unskilled

Primary 85.1% 52.2% 64.8% 62.8% 92.1% 96.0%

Secondary &Tertiary 95.2% 46.5% 68.0% 77.2% 98.5% 98.6%

Fishing industry 89.1% 49.6% 65.1% 71.9% 94.5% 98.0%


107

Table 5.13. Employment numbers, total employment income (in Rand) and average employment incomes (in Rand) in the primary, secondary and tertiary sectors, and the fishing industry as a whole by race and skills group.


Total Black White Black White Black White Black White Black White Black White

E 16854 14344 2509 218 200 2279 1237 155 92 11068 954 624 26

Y 644,304,000 485,004,000 159,300,000 18,354,000 26,502,000 122,178,000 93,384,000 6,906,000 5,430,000 319,890,000 32,952,000 17,676,000 1,032,000 Primary

Ya 38,229 33,812 63,491 84,193 132,510 53,610 75,492 44,555 59,022 28,902 34,541 28,327 39,692

E 10876 10355 521 160 184 193 91 328 97 7331 115 2343 34

Y 348,096,000 303,462,000 44,634,000 18,948,000 25,110,000 11,268,000 6,630,000 20,718,000 6,102,000 208,866,000 6,090,000 43,662,000 702,000 Secondary Tertiary

Ya 32,006 29,306 85,670 118,425 136,467 58,383 72,857 63,165 62,907 28,491 52,957 18,635 20,647

E 27730 24699 3030 378 384 2472 1328 483 189 18399 1069 2967 60

Y 992,400,000 788,466,000 203,934,000 37,302,000 51,612,000 133,446,000 100,014,000 27,624,000 11,532,000 528,756,000 39,042,000 61,338,000 1,734,000 Total

Ya 35,788 31,923 67,305 98,683 134,406 53,983 75,312 57,193 61,016 28,738 36,522 20,673 28,900

(Key: E: Employment numbers. Y: Employment income (in Rand). Ya: Average employment income (in Rand))


108

5.6 CONCLUSION

The employment, skills and income characteristics of the fishing industry differ when viewed from the

primary sector (vessels only and ocean support)125 and the secondary and tertiary fishing sector. Figure

5.8 illustrates the results of the ESS survey in terms of the percentage of Black people employed in the

various skills groups in the fishing industry.

0%

20%

40%

60%

80%

100%

% Black employment

Primary 52.2% 64.8% 62.8% 92.1% 96.0%

Secondary and tertiary 46.5% 68.0% 77.2% 98.5% 98.6%

Fishing industry 49.6% 65.1% 71.9% 94.5% 98.0%

Prof/Man Skilled Middle Services

Semi-skilled

Unskilled

Figure 5.8. Percentage of skilled Black employment.

The ESS survey results show that 89% of all people employed in the fishing industry are Black – 85.1% of

all people employed in the primary sector (vessels and on-shore support) are Black and 95.2% in the

secondary and tertiary sectors.

14344

2509

10355

5210

2000400060008000

10000120001400016000

Black White Black White

Primary Secondary&Tertiary

Tota

l num

ber e

mpl

oyed

Figure 5.9. The total number of people employed by race and by primary sector (vessels and on-shore support) and secondary and tertiary sectors in the fishing industry.

The total income distribution between Black and White people employed in the fishing industry is

presented in figure 5.10 below.

125 The employment skills and income characteristics also differ when disaggregated into primary sector (vessels only) and primary sector (vessels and on-shore support).


109

485.0

159.3

303.1

44.6

0.0

100.0

200.0

300.0

400.0

500.0

600.0

Black White Black White

Primary Secondary&TertiaryTota

l em

ploy

men

t inc

ome

R m

illio

n

Figure 5.10. The total employment income by race and by primary sector (vessels and on-shore support) and secondary and tertiary sectors in the fishing industry.

Table 5.14 below provides a summary of the ESS survey in terms of employment numbers, employment

income and average income per sector and for the fishing industry as a whole.

Table 5.14. Total employment, total employment income and average income by race in the primary sector and the secondary and tertiary sectors of the fishing industry.

Total Black White

E 16854 14344 2509 Y 644,304,000 485,004,000 159,300,000 Primary

Ya 38,229 33,812 63,491 E 10876 10355 521 Y 348,096,000 303,462,000 44,634,000 Secondary

Tertiary Ya 32,006 29,306 85,670 E 27730 24699 3030 Y 992,400,000 788,466,000 203,934,000 Total

Ya 35,788 31,923 67,305 (Key: E: Employment numbers, Y: Employment income (in Rand), Ya: Average employment income (in Rand).

The fishing industry is an important employer that generates a substantial total income. On the whole,

the fishing industry provides high quality employment (see Volume 2: Fishery Profiles, for clarification at

the fishery level) with high annual incomes (see Part 7: Understanding and measuring transformation,

and Part 8: Socio-economic baseline and impact). Black participation, although relatively low in high

skills groups, most likely compares very favourably with the employment characteristics in other sectors

of the economy particularly the primary agricultural sector.

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 6: Classification

110


111

6. SURVEY RESULTS: CLASSIFICATION (size and shape) SUMMARY The aim of this part of the ESS is to provide a classification system of the primary fishing sector (vessels

only). This summary gives the broad findings based on the ESS survey data. The table below is an

enterprise classification system in terms of capital value and vessel size.

Enterprise classification system based on the capital value of vessels

Class group or

Enterprise size

Size-groups of

vessel

Number of

vessels surveyed

Size group: Average


(R million)



(R million)

Estimated replacement

value (R million)

(factor 3.33) 3m - 5m 445 0.07 0.23

> 5m - 8m 299 0.17 0.57 > 8m - 12m 108 0.31 1.03

SME

>12m - 14m 60 1.01

0.19

3.36 >14m - 18m 173 1.22 4.06 >18m - 20m 86 1.53 5.09 Medium >20m - 25m 79 1.74

1.42 5.79

>25m - 30m 30 3.60 12.0 >30m - 35m 18 5.28 17.58 Large >35m - 40m 13 8.28

5.09 27.57

>40m - 50m 37 11.67 38.86 >50m - 60m 7 10.64 35.43 >60m - 70m 9 14.00 46.62

Very Large

>70m 7 20.30

12.91

67.60 With regard to the table above, the following might apply when defining the enterprise in terms of a Micro

or Small enterprise (SME) or Medium enterprise for individuals who own vessels, who fish for a living and

whose businesses are separate and distinct entities:




market value less than R1 million (or a replacement value of less than R3.4 million), the enterprise

is a SME. The national Small Business Act (1996) allows a value of total gross assets of less than

R4.5 million in this category.

¶ Medium Scale Enterprise: If the vessel is the only one owned by a single proprietor, or group, and

it has a market value of less that R1.74 million (or a replacement value of R5.8) the operation is a

Medium scale enterprise. Similarly, if the single proprietor, or group of proprietors, own vessels with

a market value less than R1.74 million (or a replacement value of less than R5.8 million), the

enterprise is a Medium scale one.

Caution, however, should be exercised when applying this definition across fisheries.


112

The size structure of the South African fishing fleet surveyed by the ESS and represented in figure 6.1:

Classification (size and shape): ESS survey is summarised in the figure below.

050

100150200250300350400450500

3m -

5m >5m

- 8m

>8m -

>12m -

>14m -

>18m -

>20m -

>25m -

>30m -

>35m -

>40m -

>50m -

>60m -

>70m

Micro Small Medium Large Very large

Num

ber o

f ves

sels

Size and scale distribution of the South African commercial fishing fleet based on the ESS survey data Other classification systems and fishery rankings are provided, namely, by the capital intensity of vessels,

by the contribution per fishery to total employment, by the contribution of the fishery to total employment

income and by the contribution of the fishery to total capital value.

The vessel based classification system provides some insight into the issue of structural transformation

(the restructuring of the scale of enterprise distribution usually from big to small). The broad conclusions

are as follows:

1. On balance, structural adjustment cannot be justified only on the grounds that small fishing

companies provide more job opportunities.

2. Black Economic Empowerment can be implemented at all levels and in all fisheries in the South

African fishing industry.

3. As a general rule, if an abuse of market power were suspected it would be better to use the

appropriate instruments created for that purpose by the Competitions Act.

4. In all cases from big corporations to micro sized enterprises, the concept and applicability of a

minimum viable quota is important if a ‘paper’ transformation is to be avoided.

Structural transformation should, however, be viewed in light of the broader transformation issues

presented in Part 7: understanding and measuring transformation.


113

INSERT FIG 6.1 – DINTY’S SECOND DIAGRAM (Size & Shape)


114


115

6.1. INTRODUCTION

The classification, or size and shape, of the primary sector fishing (vessels only) activity in the fishing

industry economic system is highlighted in Part 2: a simple economic system of the fishing industry in

Figure 2.1: A simple economic system for South African fishing: infrastructure and resource flows.

Because the major market failure in the fishing industry occurs in harvesting activities, its classification

along with certain specific economic and social indicators is of primary importance. It is, therefore, crucial

to know:

¶ What vessels are engaged in harvesting what resource/s?

¶ What is their harvesting capacity?

¶ How can the vessels be arranged into relevant groupings?

¶ What are the labour requirements for economic activity to occur?

A large portion of this information was not available prior to the ESS survey. The classification systems

outlined below provide a logical framework from which to structure the transformation of the fishing

industry. The classification system is presented in the accompanying Figure 6.1: Classification (size and

shape): ESS survey.

6.2. CLASSIFICATION (criteria)

There are a number of criteria that can be used to classify primary sector fishing activities. Briefly, these

are: a classification based on the size of fishing vessels, a classification in terms of a single species

fishery or a classification in terms of a number of different species, a multi-species fishery. The

classification based on the size of vessels is consistent with a classification in terms of either a single

species fishery or a multi-species fishery. All fishing vessels are thus classified into a size group.

Several size groups are then lumped into either a micro, small, medium, large or very large class group.

The characteristics of each group and class group are represented in table 6.1 below.


116

Table 6.1. Classification scheme by size group and class group.

Size group of vessels Class group Standard characteristics

3m – 5m > 5m – 8m Micro

> 8m – 12m > 12m – 14m Small

> 14m – 18m > 18m – 20m > 20m – 25m

Medium

> 25m – 30m > 30m – 35m > 35m – 40m

Large

> 40m – 50m > 50m – 60m > 60m – 70m > 70m

Very Large

Within each size group for each single species fishery the following characteristics are presented:

1. The number of vessels 2. The average age of vessels 3. The average number of days spent at sea per vessel 4. The average catch performance per vessel 5. The average market value of a vessel in the group 6. The average replacement value of a vessel in the group

The above characteristics should give an indication of the age structure, harvesting capacity, capitalisation, and utilisation of a particular fleet of vessels that harvest a specific species. When this classification is applied across species, the logic of a multi-species fishery becomes more apparent.

Aggregated and average indicators per fishery that pertain to all vessels in all size groups and class

groups are also presented. From an economic point of view, these summary statistics give a first picture

of some of the more important indicators particular to a given fishery and in a few instances to a probably

multi-species fishery. Table 6.2 below gives a schematic of the information that will be presented.

Table 6.2. Scheme for economic indicators per single species fishery

Indicator Comment

% Coverage by the ESS Total figures on figure 6.1 represent the % coverage by the ESS and are not extrapolated.

Number of vessels in the fishery This gives an indication of the size of the fleet

1. Total employment in the fishery 2. % of Black people employed in the fishery 3. Average yearly income of Black fishers 4. Average yearly income of White fishers 5. % of fishers employed part-time 6. Social transformation index

Using these criteria, an indication of the social demographics of the fleet is presented for each fishery. Employment, skills and income results of the ESS survey are explained in part 5 of the ESS report. The social transformation index is a demographically adjusted and skills weighted measure. The calculations and explanations are elaborated on in part 7 (Understanding and measuring transformation).

TAC/TAE Total Allowable Catch (TAC) or Total Allowable Effort (TAE)

1. Total catch performance of all vessels 2. Average catch performance per vessel 3. Average number of days at sea per vessel

Total catch performance and days at sea statistics when compared with the TAC may give a fair estimation of fleet capacity. This requires that the sample sizes be adjusted according to the % coverage by the ESS.

1. Total market value of all vessels 2. Total replacement value of all vessels

The market value of all vessels will give an indication of capitalisation in relation to capacity and expected return on capital.

1. Ratio of total catch performance to total market value of the vessels 2. Ratio of total catch performance to the total number of fishers employed in the fishery 3. Ratio of market value of the vessel to the total number of fishers employed in the fishery

The three ratios can provide relatively specific economic indicators per single-species fishery. 1. The average catch per R million gives a first measure of the expected return to capital in living marine resource terms. 2. The average catch per fisher provides a first indication of the expected return to the labour input. 3. The capital/labour ratio is an indicator of capital intensiveness.

The data from the ESS survey database is used to place the various characteristics of each fishery and

each vessel size group within that fishery.


117

6.3. CLASSIFICATION (size and shape)

The ESS survey data according to the schemes outlined in tables 6.1 and 6.2 and presented in figure 6.1

gives arranges the South African Commercial fishing fleet into size-groups and class-groups per fishery.

Figure 6.1 shows that certain fisheries naturally fall into a few specific size and class groups as well as

the very probable likelihood of an existing multi-species fisheries. It also provides an easily

understandable framework when considering microeconomic structural adjustment programmes

(artificially biasing small fishing enterprise in favour of large ones, or visa versa) through rights

redistribution.

6.4. CLASSIFICATION (Micro, Small, Medium, Large and Very Large)

In this section, the South African commercial fishing fleet is arranged according to size and average

capital value. It gives an indication of a potential classification based on the scale of enterprise.

Unfortunately the ESS vessel ownership data is unreliable and subject to change. It is thus impossible to

arrange the primary sector fishing activities (vessels only) into enterprise categories based on vessel

class groups. However, this section gives an indication of how these enterprises may be classified in the

future. The vessel numbers per class group and average capital value are shown on Figure 6.2.

0

100

200

300

400

500

600

700

800

Num

ber o

f ves

sels

0

2

4

6

8

10

12

14

16

Aver

age

capi

tal v

alue

(r m

illion

)

Vessels 744 168 338 61 60

AvgKm 0.11 0.56 1.42 5.09 12.91

Micro Small Med Large V Large

Figure 6.2. Class groups arranged according to the number of vessels and average capital value The trend lines (MS Excel) on figure 6.1 indicate that average capital value increases exponentially with

the size of the vessel. Also, that as the capital value increases the number of vessels decrease

exponentially. As larger companies, for example in the abalone and west coast rock lobster fisheries, may

own many small vessels, this does not give an indication of enterprise size.

A first step in enterprise classification, a grouping in terms of capital value and vessel size is presented.

Table 6.3 indicates an enterprise grouping system based on vessel size and market, or estimated

replacement, value.


118

Table 6.3. Enterprise classification system based on the capital value and vessel size of individually owned vessels.

Class group or

Enterprise size Size-groups of

vessel Number

of vessels surveyed

Size group: Average


(R million)



(R million)

Estimated replacement value

(R million) (factor 3.33)

3m - 5m 445 0.07 0.23

> 5m - 8m 299 0.17 0.57

> 8m - 12m 108 0.31 1.03 SME

>12m - 14m 60 1.01

0.19

3.36

>14m - 18m 173 1.22 4.06

>18m - 20m 86 1.53 5.09 Medium

>20m - 25m 79 1.74 1.42

5.79

>25m - 30m 30 3.60 12.0

>30m - 35m 18 5.28 17.58 Large

>35m - 40m 13 8.28 5.09

27.57

>40m - 50m 37 11.67 38.86

>50m - 60m 7 10.64 35.43

>60m - 70m 9 14.00 46.62 Very Large

>70m 7 20.30

12.91

67.60 Based on table 6.3, the following might apply when defining the enterprise in terms of a Micro or Small

enterprise (SME) or Medium enterprise for individuals who own vessels, who fish for a living and whose

businesses are separate and distinct entities:




market value less than R1 million (or a replacement value of less than R3.4 million), the enterprise

is a SME. The national Small Business Act (1996) allows a value of total gross assets of less than

R4.5 million in this category.

¶ Medium Scale Enterprise: If the vessel is the only one owned by a single proprietor, or group, and

it has a market value of less that R5.80 million (or a replacement value of R27.6) the operation is a

Medium scale enterprise. Similarly, if the single proprietor, or group of proprietors, own vessels with

a market value less than R5.8 million (or a replacement value of less than R27.6 million), the

enterprise is a Medium scale one.

Caution, however, should be exercised when applying this definition across fisheries. Some living marine

resources have a high value and low cost of capture, for example the abalone fishery, while others who

use a similar class of vessels have lower values and higher costs of capture, for example the Linefish

fishery. The capital intensity and catch-to-labour ratio per fishery would give a more complete picture in

this regard. It is important to briefly view the classification scheme, based on vessel size, fishery by

fishery.


119

6.5. CLASSIFICATION (by fishery)

A detailed description and analysis of each fishery will not be presented in this section – Volume 2:

Fishery Profiles, provides descriptions of the peculiarities inherent to each fishery. Five out of the 15

fisheries surveyed by the ESS reported 100% coverage of vessels that make up their respective fleets.

Table 6.4 below is a list of the actual surveyed values and extrapolated values of the relevant

characteristics of South Africa’s commercial fishing fleets.

Table 6.4. Surveyed and extrapolated total values for the South African fishing fleet by vessel numbers, total employment, market value and total catch performance.

% Coverage

(ESS) Conversion

Factor V Ve L Le Km Kme P Pe

Abalone 100 1.000 118 118 114 114 24.9 24.9 419 .0 419.0

WCRL 79 1.266 292 370 1495 1892 85.0 107.6 2003.0 2535.4

Linefish 66 1.515 458 694 2993 4535 81.7 123.8 11191.0 16956.1

Hake Handline 50 2.000 90 180 950 1900 34.8 69.7 3184.0 6368.0

Hake Longline 80 1.250 45 56 683 854 88.7 110.9 4293.0 5366.3

Shark Longline 100 1.000 12 12 160 160 18.2 18.2 155.7 155.7

Tuna Longline 100 1.000 19 19 335 335 91.8 91.8 874.8 874.8

Tuna Baitboat 67 1.493 82 122 1456 2173 108.8 162.4 4856.0 7247.8

Squid Jig 85 1.176 95 112 1900 2235 129.7 152.6 5191.0 6107.1

SCRL 89 1.124 9 10 218 245 48.5 54.5 287.7 323.3

Pelagic 81 1.235 54 67 558 689 134.6 166.2 97120.0 119901.2

Inshore Trawl 97 1.031 28 29 258 266 44.4 45.8 14957.0 15419.6

Deep-sea Trawl 84 1.190 56 67 1739 2070 734.2 874.0 118159.0 140665.5

Toothfish 100 1.000 5 5 149 149 77.5 77.5 1530.0 1530.0

Prawn Trawl 100 1.000 8 8 109 109 35.6 35.6 421.0 421.0

Totals 1371 1868 13117 17726 1738.4 2115.3 (Key: V: Number of vessels, Ve Extrapolated number of vessels, L: number of people employed, Le: extrapolated number of people employed, Km: market value of vessels (R million), Kme: extrapolated value of vessels (R million), P: total catch performance (tons), Pe; extrapolated total catch performance (tons)) The extrapolations are based on the assumption that the sample is representative of the population group

and that a simple proportional change will not influence the population distribution. This is not altogether

valid as it is a known fact that it is more difficult to capture the smaller vessel in a survey than the larger

ones – small vessels are more widely and heterogeneously distributed. For this reason the size groups

have not been adjusted and it is also recognised that there is a probable bias in distribution toward the

larger vessel sizes. However, to get a broad picture of the fishery characteristics the data is sufficient.

Vessels that are not captured by the ESS survey may not necessarily be active in the fleet. To avoid

unnecessary bias, the catch performance to capital and labour usage as well as the capital to labour

usage is determined using the un-adjusted ESS survey results.

With reference to figure 6.2 and the ESS survey data, the following information will be provided for each

fishery (vessels only primary sector):

¶ Total market value of the vessels that make up the fleet – this is, for convenience sake, called the

capital value of the fishery.


120

¶ Total employment for the fishery and total employment income.

¶ The average age of the fleet.

¶ A figure of the size distribution of the fleet.

¶ Indicators and a brief discussion on catch performance to capital usage, catch performance to labour

usage and capital to labour usage ratios.

¶ Comment on capacity.

From table 6.4, the important estimated total values in the vessel based primary sector commercial

fishing activities are; a total employment of 17 726 fishers, 1 868 vessels with a capital value of just over

R2 billion (R2 115.3 million). Employment income extrapolations are made in a similar way to the above.

This is illustrated on table 6.5 below. Total estimated employment income on fishing vessels is R631.8

million per year.

Table 6.5. Surveyed and extrapolated total employment income values for the South African fishing fleet.

Employment income

(ESS survey)

Employment income

(estimate) Abalone 2.25 2.25 WCRL 40.19 50.87 Linefish 64.63 97.92 Hake Handline 28.50 57.00 Hake Longline 28.18 35.23 Shark Longline 5.60 5.60 Tuna Longline 16.28 16.28 Tuna baitboat 38.79 57.90 Squid Jig 65.62 77.20 SCRL 9.65 10.84 Pelagic 52.36 64.64 Inshore Trawl 9.22 9.51 Deep-sea Trawl 111.46 132.69 Toothfish 8.81 8.81 Prawn Trawl 5.06 5.06 Totals 486.60 631.80

6.5.1 The Abalone Fishery

The ESS survey covered 100% of vessels (118 vessels) in this fishery. The vessels have a surveyed

capital value of R24.9 million (1.2% of the estimated total capital value of the fisheries surveyed) and an

average capital value of R211 000. The fishing vessels employ 114 people (0.6% of total employment)

who together earn an annual income of R2.25 million (0.4% of total employment income).


121

The average age of the fleet is 15 years with a range between 23 and nine years (see figure 6.3). The

Abalone fishery falls into the micro and small vessel categories. The size structure of the abalone fishing

fleet is shown on Figure 6.3 below.

0

10

20

30

40

50

60

70

80

90

3m - 5m >5m - 8m >8m - 12m >12m - 14m >14m - 18m

Micro Small Medium

Num

ber o

f ves

sels

Figure 6.3. The size structure of the abalone fishing fleet.

The ESS survey indicates 118 vessels involved at some time or other in harvesting, but only 114 fishers

employed in this fishery. This could probably be a result of the short period needed to harvest the

abalone, each vessel on average spending 24 days at sea and collecting on average 3.6 tons each.

Another explanation might be that not all vessels are at sea during the same time and that most vessels

use the same crew. The average yearly income per fisher (R18 122 for Black fishers and R26 625 for

White fishers) seems not to corroborate this, particularly if the wage rate, which is the lowest for all

fisheries in the South African commercial fishing primary sector, is taken into account. For example,

reported Black skilled abalone fishers earn on average R90.91 per day compared to R534.97 for Skilled

Black fishers in the pelagic fishery (table 5.5, part 5). In addition, only 35.1% of the fishers are reported

as part-time. Perhaps, the most likely reason is the fact that many abalone fishers are also abalone

factory workers, and are accounted for under the employment structure of the secondary and tertiary

sectors of this part of the fishing industry.

The above data problem obviously means that the capital/labour ratio is over-estimated – the capital

requirements to harvest abalone is calculated at R0.22 million per person as compared to R0.06 for the

west coast rock lobster fishery. The west coast rock lobster fishery has in some respects a similar

structure to the abalone fishery.


122

The surveyed total performance (100% sample) is 419.0 tons of abalone, about 13% above the TAC for

2000. Although the surveyed amounts do not show an over-capacity (or over-capitalisation) problem, the

short fishing periods, low total annual incomes for fishers and the fact that the ESS survey reported more

vessels than fishers, points out that the simple performance to TAC measure is not a suitable indicator to

capture an over-capacity problem.

6.5.2 The West Coast Rock Lobster Fishery

The ESS survey covered 79% of the vessels (292 of an estimated 370 vessels) in this fishery. All the

vessels have an estimated capital value of R107.6 million (5.1% of the estimated total capital value of the

fisheries surveyed). The fishery employs an estimated 1 892 fishers with a total income of R50.87 million,

10.7% of total employment in the primary sector (vessels only) and 8.1% of total income.

The west coast rock lobster fishery has vessels ranging from micro sized all the way to medium sized.

The average age of the fleet is 26 years. Most of the older vessels are in the medium size class group

(similar in ages to shark longline, tuna baitboat and squid vessels of the same sizes). The age structure

of the micro and small vessels mirror that of the linefish vessels in the same size groups. Figure 6.4

displays the size structure of the west coast rock lobster fishing fleet.

0

20

40

60

80

100

120

140

160

3m - 5m >5m - 8m >8m - 12m >12m -14m

>14m -18m

>18m -20m

>20m -25m

Micro Small Medium

Num

ber o

f ves

sels

Figure 6.4. The size structure of the west coast rock lobster fishing fleet.

The tri-modal distribution of vessels reflects that of the quota distribution between small rights holders,

medium rights holders and large rights holders described in the South African West Coast Rock Lobster

Fishery report in Volume 2: Fishery Profiles of the ESS.

The average time spent at sea is 84 days with an average yearly harvest of 6.9 tons per vessel. Only

47.3% of all fishers surveyed are in part-time employment. In comparison, 86.2% of all fishers in the tuna

baitboat fishery, which spends an average of 108 days at sea a year, are part-time employees.


123

This fishery has a very low capital intensity – a capital to labour usage of R60 000 per fisher (11% of that

for the toothfish fishery) is recorded. On the other hand, the catch performance to the capital value is

comparatively high, that is, on average 23.6 tons of west coast rock lobster is caught for every R1 million

of capital. Usually, the higher this value, the more has to be caught to cover the cost of capital. The

fishery also has a very low average performance per fisher (1.3 tons per fisher). Apart from the shark

longline fishery, this result is identical to that of the south coast rock lobster fishery and the lowest of the

fisheries surveyed.

The surveyed total catch performance is 2 003 tons. The estimated total catch performance is 2 535.4

tons, which is 57% more than the TAC. This coupled with the usage ratios, low part-time employment,

and comparatively low average yearly incomes, indicate that this fishery has an over-capacity problem.

However, it is likely that the ESS surveyed the most active vessels in the fleet (66% coverage). This may

indicate that the over-capacity problem might not be as severe as is indicated above.

6.5.3 The Linefish Fishery

The ESS survey covered 66% (458 of an estimated 694 vessels) of all vessels in the Linefish fishery fleet.

The fleet has a total estimated capital value of R123.8 million (5.9% of the total estimated value of all

fisheries surveyed). It employs an estimated 25.6% of all fishers (4 535 fishers) and captures only 15.5%

of total estimated employment income.

Figure 6.5 indicates that the linefish fishery is dominated by micro sized fishing vessels. The average age

of the vessels is 17 years. On average linefish vessels spend 150 days at sea catching an average of

24.4 tons of fish per year.

0

50

100

150

200

250

3m - 5m >5m - 8m >8m - 12m >12m - 14m >14m - 18m

Micro Small Medium

Num

ber o

f ves

sels

Figure 6.5. The size structure of the linefish fishing fleet.


124

The survey results show that 137 tons of linefish are caught for every R1 million of capital and each fisher

catching on average 3.7 tons. The vessel market value of R30 000 per fisher indicates a very low capital

intensity of this fishery (the lowest in all fisheries surveyed).

The results indicate that this is an important fishery from a human livelihood point of view; a low paid

labour intensive fishery employing 26.5% of all fishers in the South African commercial fishery surveyed.

Being effort controlled, resource management criteria will determine whether or not the fishery has an

over-capacity problem. The sheer importance of this fishery in human terms places a massive burden on

the resource manager. There are an estimated four and a half thousand households who are affected by

resource decisions.

6.5.4 The Hake Handline Fishery

The ESS survey covered 50% (90 of the estimated 180 vessels) of the vessels in this fishery. The

estimated capital value of this fishery is R69.7 million or 3.3% of the total capital of the fisheries surveyed.

It employs an estimated 1 900 fishers (10.7% of employment) who earn in total R57 million (9.0% of total

employment income).

Vessels in this fishery fall into micro, small and medium size groups. The average age of the vessels is

17 years. The hake handline fishery has a similar size and age distribution in the micro range to that of

the linefish fishery (see figure 6.5). In the small and medium range the size and age structure is similar to

that of west coast rock lobster fishery (see figure 6.4).

05

1015202530354045

3m - 5m >5m - 8m >8m - 12m >12m -14m

>14m -18m

>18m -20m

>20m -25m

Micro Small Medium

Num

ber o

f ves

sels

Figure 6.6: The size structure of the hake handline fishing fleet.

On average these vessels spend 167 days at sea and catch 38.4 tons each. Catch performance to

labour usage (3.4 tons per fisher) and capital intensity (R40 000 per fisher) is similar to the linefish fishery

indicators. Yearly catch per R million of capital is 91.4 tons, lower than in the linefish fishery.


125

Yearly catch performance is 155% higher than the TAC indicating a probable over-capacity problem. A

possible explanation is that if, for example, the linefish fishery and the hake handline fishery are in fact a

multi-species fishery, financial logic dictates that a declining linefish catch means an increasing hake

handline catch126.

6.5.5 The Hake Longline Fishery The ESS survey covered 80% (45 of an estimated 56 vessels) of the vessels in this fishery. The

estimated capital value of this fishery is R110.9 million or 5.2% of the total capital of the fisheries

surveyed. It employs an estimated 858 fishers (4.8% of employment) who earn in total R35.23 million

(5.6% of total employment income). The fleet is almost exclusively medium sized, the median vessel

between 25 metres and 30 metres long. The fleet has an average age of 30 years.

02468

1012141618

>14m - 18m >18m - 20m >20m - 25m >25m - 30m >30m - 35m >35m - 40m

Medium Large

Num

ber o

f ves

sels

Figure 6.7: The size structure of the hake longline fishing fleet.

The vessels spend an average of 68 days at sea catching hake with an average yearly surveyed catch

performance of 95.4 tons.

An average of 48.8 tons of hake are harvested per year for every R million of capital, with each fisher

catching 6.3 tons. The capital value per fisher is R130 000. As is expected, The Hake Longline fishery is

more capital intensive than the West Coast Rock Lobster, the Linefish and Hake Handline fisheries.

6.5.6 The Shark Longline Fishery The ESS survey covered 100% (12) of the vessels in this fishery. The capital value of this fishery is

R18.2 million or 0.9% of the total capital of the fisheries surveyed. It employs 160 fishers (0.9% of

employment) who earn in total R5.60 million (0.9% of total employment income). The figures show that

shark longline fishers earn an average yearly income similar to the average for the entire primary fishing

sector (vessels only).

126 It is well known that the many new entrants on B-permits are causing an increase in effort.


126

The fleet consists only of medium sized fishing vessels with the oldest average age (40 years) in the

South African commercial fishing fleet.

0

1

2

3

4

5

6

>14m - 18m >18m - 20m >20m - 25m

Medium

Num

ber f

ves

sels

Figure 6.8. The size structure of the shark longline fishing fleet.

The shark longline vessels spend on average 61 days a year catching shark. Each vessel harvests an

average of 13.0 tons of shark a year.

The capital to fisher indicator, a value R110 000 of capital per fisher, is similar to that of the hake longline

fishery. The catch per fisher (0.98 tons per year) and the catch per million Rand of capital (8.6 tons per R

million) are lower than that of the hake longline fishery. The low number of days at sea and a similar

capital fisher ratio probably indicates that this is part of the hake longline fishery (a multi-species fishery).

6.5.7 The Tuna Longline Fishery

The ESS survey covered 100% (19) of the vessels in this fishery. The capital value of this fishery is

R91.8 million or 4.3% of the total capital of all the fisheries surveyed. It employs 335 fishers (1.9% of

employment) who earn in total R16.28 million (2.6% of total employment income).

The tuna longline fleet falls in size between the medium and large vessel categories. The median size

vessel is between 25 and 30 metres. The fleet has an outlier of 2 vessels in the Very Large category.

The average age of the fleet is 24 years.


127

0123456789

10

>14m -18m

>18m -20m

>20m -25m

>25m -30m

>30m -35m

>35m -40m

>40m -50m

>50m -60m

Medium Large Very Large

Num

ber o

f ves

sels

Figure 6.9. The size structure of the tuna longline fishing fleet.

The ESS survey indicates that the tuna longline vessels spend on average 173 days at sea with an

average performance of 45.6 tons of tuna caught per year.

Tuna longlining is, according to the data from the ESS survey, the most capital intensive of the longlining

fisheries. It requires a capital of R270 000 per fisher with a low reported average catch of 2.6 tons per

fisher. The catch performance per R million is also low. These indicators only tell a consistent story if the

price received for longlined tuna is substantially higher than that for baitboat tuna, or if the performance

data is under-recorded.

6.5.8 The Tuna Baitboat Fishery The ESS survey covered 67% (82 of an estimated 122 vessels) of the vessels in this fishery. The


surveyed. It is the third largest primary fishing sector (vessels only) employer with an estimated 2173

fishers (12.3% of employment) who earn in total R57.9 million (9.2% of total employment income). The

fleet is in the medium to large sized range with the majority of vessels in the medium sized category. It is

the second oldest fleet after the shark longline fleet, with an average age of 37 years.


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Figure 6.10. The size structure of the tuna baitboat fishing fleet

The tuna baitboats fish on average for 108 days per year with an average performance of 59.2 tons of

tuna. This is a better result than the ESS survey shows for the tuna longline vessels who spend on

average 173 days at sea catching 45.6 tons of tuna per year.

This fishery has a low capital to fisher ratio, R70 000 of capital per fisher, which is perhaps due to the

average age of the fleet (37 years). The catch performance per R million is similar to that of the hake

longline fishery (44.6 tons per fisher as opposed to 48.8 tons per fisher in the hake longline fishery), but

the catch per fisher is almost half (3.3 tons per fisher per year). Again this is an effort-based fishery and

capacity measures are dependent on the calculations of the resource manager and stock assessment

scientist.

6.5.9 The Squid Jig Fishery The ESS survey covered 85% (95 of an estimated 112 vessels) of the vessels in this fishery. The


surveyed. It is the second largest primary fishing sector (vessels only) employer with an estimated 2 235

fishers (12.6% of employment) who earn in total R77.2 million (12.2% of total employment income). Like

the shark longline fishers, the squid fishers earn an average yearly income similar to the average income

for all fishers in all fisheries surveyed.

The commercial squid fishing vessels range from micro size (ski-boats) into the small to medium vessel

range. The average vessel is 13 years old, most of the small to medium vessels are, however, relatively

young, being 9 to 11 years old. Overall this fleet is the youngest fleet surveyed during the ESS study.

This reflects the relative youth of the fishery and the trend over time for fishing entrepreneurs to move

from ski-boat based fishing to larger freezer vessels (see Volume 2: Fishery Profiles).


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0

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Figure 6.11. The size structure of the squid jig fishing fleet.

The vessels in the 3 to 5 metre range are all ski-boats. The small to medium vessels dominate the

commercial Squid Jig fishery, the median size falling between 14 metres and 18 metres.

The capital to fisher ratio (R70 000 per fisher), the catch performance per million Rand (40.0 tons per

million Rand of capital) and the average catch performance per fisher (2.7 tons per fisher) are very similar

to that of the Tuna Baitboat fishery. These fisheries are unlikely to be part of a multi-species fishery; one

has the youngest fleet and the other the oldest, as well as using very different gear types and harvesting

methods.

6.5.10 The South Coast Rock Lobster Fishery

The ESS survey covered 89% (9 of an estimated 10 vessels) of the vessels in this fishery. The estimated

capital value of this fishery is R54.5 million or 2.6% of the total capital of the fisheries surveyed. The

vessels in the fishery employ an estimated 245 fishers (1.4% of employment) who earn in total R10.84

million (1.7% of total employment income). This is the first fishery discussed where fishers earn an above

average income per year, that is, it employs 1.4% of all fishers but pays 1.7% of total employment

income.

The south coast rock lobster fishing vessels fall either into the large or very large size groups. The

average age of the vessels is 33 years, the smaller vessels being generally younger. The vessels are at

sea for an average of 205 days of the year with each vessel harvesting 32.0 tons of south coast rock

lobster per year.


130

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Figure 6.12. The size structure of the south coast rock lobster fishing fleet.

The south coast rock lobster fishery is capital intensive using R220 000 of capital per fisher. Catch per

million Rand of capital is low, 5.9 tons, indicating a very high value product. This is supported by the low

catch performance per fisher, namely, 1.3 ton per fisher. The catch performance per fisher in the south

coast rock lobster fishery is the same as in the west coast rock lobster fishery (1.3 tons per fisher).

However, the 267% higher capital to fisher ratio and the 75% lower performance per million Rand,

indicates that the south coast rock lobster fishery requires a significantly higher start-up capital and higher

average quota allocations than the west coast rock lobster fishery.

6.5.11 The Pelagic Fishery

The ESS survey covered 81% (54 of an estimated 67 vessels) of the fleet in this fishery. The estimated

capital value of this fishery is R166.2 million or 7.9% of the total capital of the fisheries surveyed. It

employs an estimated 689 fishers (3.9% of employment) who earn in total R64.64 million (10.2% of total

employment income). The 7.9% of total employment to 10.2% of total employment income indicates that

fishers in the pelagic fishery earn very high annual incomes. Indeed, the part-time daily wages (part 5,

table 5.5) for Black and White skilled and semi-skilled fishers in the pelagic fishery are generally very

high. Black part-time skilled fishers in the pelagic fishery earn the third highest daily wages (R1 146.67

per day) after their counterparts in the south coast rock lobster fishery (R12 800.00 per day) and the

deep-sea hake fishery (R1 226 per day). Black part-time semi-skilled pelagic fishers earn almost 150%

more in daily wages than in any other fishery surveyed. The full-time pelagic fishers, however, rank only

10th highest in terms of daily wages out of the 15 fisheries surveyed. The south coast rock lobster fishery

pays the highest daily wages for full-time skilled White fishers and the toothfish fishery pays the highest

full-time daily wages for Black fishers.


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The majority of vessels in the pelagic fishing fleet are medium sized vessels. The average age of the

vessels is 25 years. The smallest three vessels in this fleet (in the 14 to 12 metre category) are the

youngest in the entire sample of vessels surveyed by the ESS. They have an average age of three

years.

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Figure 6.13. The size structure of the pelagic fishing fleet.

Figure 6.13 indicates that the majority of the vessels are in the 20 to 25 metre size group. As with all the

fisheries, this illustrates that, in terms of return to capital, this size of vessel yields on average the best

result. The very young age of the smallest group may however be an indicator that the changing social

climate in the South African fisheries, and the pelagic fishery in particular, brings with it a new dimension

to the return on capital.

The vessels in this fishery spend on average 135 days a year at sea, each harvesting on average 798.5

tons of fish per vessel per year.

The pelagic fishery is relatively capital intensive having a capital to fisher ratio of R240 000 per fisher.

The low value of the product is reflected in the fact that average catch performance per R million (721.5

tons per R million) and per fisher (174.1 tons per fisher) is very high.

The total surveyed catch performance (97 120 tons) and estimated catch performance (119 901.2 tons) is

95% of TAC. Catch performance is lower than TAC. The highly variable catch in this fishery and different

vessels within similar classes producing different products (The Pelagic Fleet in Volume 2) makes it

difficult to make a meaningful statement with regard to capacity at this stage.


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6.5.12 The Inshore Trawl Fishery The ESS survey covered 97% (28 of an estimated 29 vessels) of the fleet in this fishery. Updated data for

this fishery is presented in the respective fishery report in Volume 2: Fishery Profiles. These figures are

not used for the simple reason that it is important to be consistent with the ESS database. The estimated

capital value of this fishery is R45.8 million or 2.2% of the total capital of the fisheries surveyed. It

employs an estimated 266 fishers (1.5% of employment) who earn in total R9.51 million (1.5% of total

employment income). The fishers in this fishery earn an average yearly income of R35 721, however,

very few are employed part-time (9.7%). This points directly to a higher quality of employment than in the

other fisheries discussed so far (see Figure 6.1: Classification Size and Shape: ESS Survey for part-time

employment percentages per fishery).

The vessels in the inshore trawl fishery fall into the medium and large size groups, with a median vessel

in the 20 to 25 metre category. The average age of the vessels is 24 years, again the smallest vessels

being the youngest. The smallest nine vessels (between 14 and 18 metres) have an average age of 7.4

years.

0

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Figure 6.14. The size structure of the inshore trawl fishery.

The vessels in this fishery spend on average 91 days a year at sea, each harvesting on average 534.8

tons of fish per year.

The inshore trawl fishery has the lowest capital intensity of the trawl fisheries, namely, R170 000 per

fisher. This is reflected in a lower average catch performance per fisher (58.05 tons per fisher) and a

higher average catch performance per million Rand of capital (336.9 tons per R million), than for example,

in the deep-sea trawl.


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6.5.13 The Deep-sea Hake Trawl Fishery

The ESS survey covered 84% (56 of an estimated 67 vessels) of the fleet in this fishery. Updated data

for this fishery is presented in the relevant fishery report in Volume 2: Fishery Profiles. These figures are

not used, as it is important to be consistent with the ESS database. The estimated capital value of this

fishery is R166.2 million or 41.3% of the total capital of the fisheries surveyed. It is the 4th highest

employer after the linefish, tuna baitboat and squid jig fisheries. The deep-sea hake trawl fishery employs

an estimated 2 070 fishers (11.7% of employment) in its ocean going activities. Estimated total

employment income is R132.69 million (21.0% of total employment income). Most of the fishers in the

deep-sea hake trawl fishery are full-time employees. A total of 95.8% of fishers employed in this fishery

are full-time employees and who earn 73% per year more than the average South African commercial

fisher. In addition, the high percentage of full-time employment highlights the fact that most of these

fishers have a fair degree of job security. The deepsea fishers are unionised and salaries and

commission, which are based on rank and years of experience, are negotiated annually on the basis of

employment equity plans.

The deep-sea hake trawl fishing fleet is mainly comprised of very large vessels with an average age for

all vessels of 24 years.

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Figure 6.15. The size structure of the deep-sea hake trawl fishing fleet.

The deep-sea hake trawl fishery has a bi-modal size structure. Most vessels are between 40 and 50

metres, with a second mode at between 60 and 70m.

The vessels in this fishery spend on average 186 days a year at sea, each harvesting on average 2 110

tons of hake per year.


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After the toothfish fishery, the deep-sea hake trawl fishery has the highest capital intensity (R420 000 of

capital per fisher). This stands to reason given the very large size of the vessels used to harvest. Fishing

operations are also efficient with regard to capital and labour. This is borne out by the fact of a high

average catch performance per million Rand and a high average catch performance per fisher, namely

160.9 tons per R million and 67.9 tons per fisher respectively.

The estimated total catch performance of this fishery is 140 665.5 tons per year. This is 8.6% higher than

the deep-sea hake TAC, probably reflecting the by-catch of this fishery. The results from the survey

indicate that there is not an over-capacity problem in the deep-sea hake trawl fishery.

6.5.14 The Toothfish Fishery

The ESS survey covered 100% (5 vessels) of the fleet in this fishery. The capital value of this fishery is

R77.5 million or 3.7% of the total capital of the fisheries surveyed. It employs 149 fishers (0.8% of

employment) who earn in total R8.81 million (1.4% of total employment income). Yearly incomes are 3rd

highest in this fishery, but wage rates for part-time skilled Black fishers are the highest surveyed.

The five vessels in the fishery all fall within the 40 to 50 metre size group, with an average age of 25

years. The vessels spend an average of 68 days a year at sea and catch an average of 306 tons p.a.

The fishery is very capital intensive, R520 000 of capital per fisher. The low catch performance figures

(19.7 tons per R million and 10.3 tons per fisher) and capital-intensive operations corroborate the fact that

the species commands a very high value.

6.5.15 The Prawn Trawl Fishery

The ESS survey covered 100% (8 vessels) of the vessels in this fishery. The capital value of this fishery

is R35.6 million or 1.7% of the total capital of the fisheries surveyed. It employs 109 fishers (0.6% of

employment) who earn in total R5.06 million (0.8% of total employment income). The fishers earn above

average yearly incomes and are all full-time employees.

The 8 vessels all fall into the large vessel group with an average age of 28 years, the largest two vessels

are the oldest. The vessels spend an average of 151 days at sea with an average vessel catch

performance of 56.2 tons.

This fishery is the 3rd most capital intensive, after the toothfish and deep-sea hake trawl fisheries, of the

fisheries surveyed. It has a capital to labour ratio of R330 000 per fisher.


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Figure 6.16. The size structure of the prawn trawl fishing fleet.

Catch performance measures are low (11.8 tons per R million and 3.9 tons per fisher) indicating high

value species and/or a marginal fishery.

6.5.16 The South African Commercial Fishing Fleet Figure 6.17 shows the size structure of the South African commercial fishing vessels surveyed by the

ESS. This structure seems to have three distributions, namely, a micro-small distribution, a medium-large

distribution and a very large distribution.

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Figure 6.17. The size structure of the South African commercial fishing fleet.


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The micro-small distribution consists exclusively of the:

¶ Abalone fishery

¶ West coast rock lobster fishery

¶ Linefish fishery

¶ Hake handline fishery

Apart from some spill over from micro-small fisheries, the medium-large distribution may be considered to

consist of two groups, namely, medium size line fisheries and inshore trawl fisheries. The inshore trawl

fishery vessels are on average larger than the medium sized linefish vessels.

The medium size line fisheries comprise the following:

¶ The hake longline fishery

¶ The shark longline fishery

¶ The tuna longline fishery

¶ The tuna baitboat fisher

¶ The squid jig fishery

The inshore trawl fisheries consist of:

¶ The inshore hake and sole trawl fishery

¶ The pelagic fishery

¶ The prawn trawl fishery

Finally, the very large distribution of vessels consists of three fisheries all using different gear types:

¶ The south coast rock lobster fishery

¶ The deep-sea hake trawl fishery

¶ The toothfish longline fishery

The possibility of multi-species fisheries is inherent in this arrangement. There is, however, an important

distinction between a multi-species fishery and a single-species fishery that is subsidised by another

single-species fishery.

¶ A multi-species fishery is one where a number of different species are targeted by the same

vessel on a sustainable basis and should be managed accordingly.

¶ A single-species fishery subsidises another if the other fishery is over-fished and needs to

redirect its catch to the subsidising fishery.

The ESS did not specifically deal with capturing the characteristics of a multi-species fishery, thus

evidence cannot be provided to back up the following observations. They should therefore be treated as

conjecture.


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6.6. CLASSIFICATION (other schemes)

The fisheries, vessels only, can be classified according to many different criteria. Part 7: Understanding

and Measuring Transformation provides a ranking by various measures of social transformation. In this

section the fisheries are ranked according to the level of capital intensity, the percentage of total

employment, the percentage of total employment income and the percentage of total capital.

6.6.1 Fisheries Ranked by Capital Intensity

The fisheries are ranked according to their respective average capital to labour ratios. Table 6.6 ranks

the fisheries from the highest average capital (market value of vessels) per fisher employed.

Table 6.6. Fisheries ranked by capital intensity.

Capital value (R 000)

per fisher % of total

employment %of total

employment income % of total

capital value

Toothfish 520 0.84 1.39 3.66

Deep-sea Trawl 420 11.68 21.00 41.32

Prawn Trawl 330 0.61 0.80 1.68

Tuna Longline 270 1.89 2.58 4.34

Pelagic 240 3.89 10.23 7.86

Abalone 220 0.64 0.36 1.18

SCRL 220 1.38 1.72 2.58

Inshore Trawl 170 1.50 1.50 2.16

Hake Longline 130 4.82 5.58 5.24

Shark Longline 110 0.90 0.89 0.86

Tuna baitboat 70 12.26 9.16 7.68

Squid Jig 70 12.61 12.22 7.21

WCRL 60 10.68 8.05 5.09

Hake Handline 40 10.72 9.02 3.29

Linefish 30 25.58 15.50 5.85

A labour-intensive ranking of the fisheries would work similarly as in table 6.6, but from the bottom up.


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6.6.2 Ranking by Contribution to Total Employment

Ranking the fisheries by contribution to total employment gives an indication of the importance by fishery

to human livelihoods and jobs.

This ranking does not provide any information regarding the quality of employment or annual income to

the respective fishers. The linefish fishery is the most labour intensive (lowest ranking on a capital

intensity classification) and provides the highest number of jobs.

Table 6.7. Ranking by contribution to total employment.

Capital value (R 000) per fisher

% of total employment

%of total employment income

% of total capital value

Linefish 30 25.58 15.50 5.85

Squid Jig 70 12.61 12.22 7.21

Tuna baitboat 70 12.26 9.16 7.68

Deep-sea Trawl 420 11.68 21.00 41.32

Hake Handline 40 10.72 9.02 3.29

WCRL 60 10.68 8.05 5.09

Hake Longline 130 4.82 5.58 5.24

Pelagic 240 3.89 10.23 7.86

Tuna Longline 270 1.89 2.58 4.34

Inshore Trawl 170 1.50 1.50 2.16

SCRL 220 1.38 1.72 2.58

Shark Longline 110 0.90 0.89 0.86

Toothfish 520 0.84 1.39 3.66

Abalone 220 0.64 0.36 1.18

Prawn Trawl 330 0.61 0.80 1.68


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6.6.3 Ranking by Contribution to Total Employment Income

The importance of the fishery in terms of employment income is depicted on table 6.8 below.

Table 6.8. Ranking by contribution to total employment income (vessels only).



employment % of total


capital value Deep-sea Trawl 420 11.68 21.00 41.32

Linefish 30 25.58 15.50 5.85

Squid Jig 70 12.61 12.22 7.21

Pelagic 240 3.89 10.23 7.86

Tuna baitboat 70 12.26 9.16 7.68

Hake Handline 40 10.72 9.02 3.29

WCRL 60 10.68 8.05 5.09

Hake Longline 130 4.82 5.58 5.24

Tuna Longline 270 1.89 2.58 4.34

SCRL 220 1.38 1.72 2.58

Inshore Trawl 170 1.50 1.50 2.16

Toothfish 520 0.84 1.39 3.66

Shark Longline 110 0.90 0.89 0.86

Prawn Trawl 330 0.61 0.80 1.68

Abalone 220 0.64 0.36 1.18

In this ranking system the deep-sea fishery ranks highest, but only 4th in total employment. This simply

indicates that the fishers in this fishery earn well above the primary fishing sector average annual income.

This has been explained in the previous section and in the deep-sea hake report in Part 10 of the ESS

report.

6.6.4 Ranking by Contribution to Total Capital Value This ranking system should be able to classify the primary fishing sector (vessels only) according to its

contribution to the total capital value of the fisheries surveyed. This ranking system reflects not only the

capital intensity but also the number of vessels.


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Table 6.9. Ranking by contribution to total capital value.



employment %of total


capital value Deep-sea Trawl 420 11.68 21.00 41.32 Pelagic 240 3.89 10.23 7.86 Tuna baitboat 70 12.26 9.16 7.68 Squid Jig 70 12.61 12.22 7.21 Linefish 30 25.58 15.50 5.85 Hake Longline 130 4.82 5.58 5.24 WCRL 60 10.68 8.05 5.09 Tuna Longline 270 1.89 2.58 4.34 Toothfish 520 0.84 1.39 3.66 Hake Handline 40 10.72 9.02 3.29 SCRL 220 1.38 1.72 2.58 Inshore Trawl 170 1.50 1.50 2.16 Prawn Trawl 330 0.61 0.80 1.68 Abalone 220 0.64 0.36 1.18 Shark Longline 110 0.90 0.89 0.86

6.7. STRUCTURAL TRANSFORMATION

Structural transformation refers to the re-structuring of the scale distribution of enterprises usually from

big business to small business. This section briefly reviews this notion with regard to the South African

commercial fishing industry. The logical place to start is the Competitions Act 1998, which implicitly

articulates the position of central Government with regard the issue of big business versus small

business.

The South African Competitions Act 1998 aims at preventing restrictive practices that arise from the

abuse of a dominant position, but it does not condemn market concentration. The objective of the Act is

to eradicate and avoid abusive behaviour, promote competition where feasible, encourage efficiency and

international competitiveness, provide easier access to medium, small and micro sized enterprises,

diversify ownership in favour of historically repressed people and to create new job opportunities in the

economy.

The significant questions regarding structural transformation in the South African fishery are thus:

1. Do small fishing companies provide greater employment and economic benefit than the larger

companies? Bearing in mind that it is a well-established fact in the international literature that greater

competition in a fishery will enhance the incentive by all firms to over-exploit the resource.

Parts 5 and 6 of the ESS report and Part 7, section 7 clearly indicate that fisheries that use micro-small

sized vessels are more labour intensive and on the whole employ more people than their larger counter

parts. However, as is expected, all the micro-small vessel fisheries are over-subscribed.


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On the other hand, the fisheries that use bigger vessels (thus necessitating larger companies and greater

capital outlays) pay their fishers more and provide stable employment. In addition, where large and very

large vessels are concerned, the logic to vertically integrate (part 2, section 3.2) enables value adding

and an export orientation to an otherwise stagnant industry.

On balance, structural adjustment cannot be justified only on the grounds that small fishing companies

provide more jobs opportunities.

2. Are smaller fishing companies better suited to Black Economic Empowerment initiatives than the

larger fishing companies in the South African fishing industry?

Black Economic Empowerment in the fishing industry explicitly implies both economic control (ownership

of capital and fishing rights) and participation (operational and/or managerial).

It is reasonable to assume that the Black Economic Empowerment of large fishing companies (mainly

those that utilise large to very large vessels) can only realistically happen in the capital market (for

example, mergers with Black Economic Empowerment corporations). However, in conjunction with the

Employment Equity Act, MCM can also encourage internal transformation. Part 7: Understanding and

Measuring Transformation focuses on providing indicators for internal transformation.

If transformation is expected to occur outside of the capital market, it is likely only to take place (initially at

least) in the fisheries that use micro, small and medium sized fishing vessels. Encouraging Black

entrepreneurs into micro, small and medium scale fishing business thus requires an external

transformation objective, namely, a reallocation of rights127. However, not only is the reallocation rights

difficult from an administrative justice point of view (see Part 4: The regulation of Commercial Fishing in

South Africa) it is also problematic when dealing with single boat owners operating in the medium, small

and micro vessel class fisheries128.

Black Economic Empowerment can be implemented at all levels and in all fisheries in the South African

fishing industry.

3. If it is suspected that abuse of market power does exist, the important issue becomes: does MCM

have the mandate and the ability to successfully break up ‘Big Business’ in fisheries by using its

allocations policy? Alternatively, is it better done by the various institutions and policy instruments

created by Competitions Act 1998?

127 External transformation of this kind would often necessitate external development initiatives, for example, assistance with access to finance. 128 This is strengthened by the fact that when dealing with single vessel owners (from the medium vessel range downwards) internal transformation is difficult to enforce and would usually not fall under the Employment Equity Act.


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As is clearly illustrated in Figure 6.1 and throughout the discussion in this part of the ESS, in each fishery

the vessels tend to concentrate into sizes that are optimal from an economic perspective. An attempt to

use rights reallocations to change that size distribution could result in sub-optimal and inefficient

outcomes. As previously stated, rights reallocations are fraught with administrative justice issues (see

Part 4: The regulation of Commercial Fishing in South Africa). As a general rule, if an abuse of market

power were suspected it would be better to use the appropriate instruments created for that purpose by

the Competitions Act.

4. Does the size of the allocation matter and does the size of the allocation affect the structure of the

fishing industry?

This final issue in structural transformation involves the economics of allocations (Part 3) and the concept

of Minimum Viable Quotas. As is argued in Part 3: The Economics of Allocations, the major determinants

are the degree of transferability of the right and the attachment criteria. If rights are unattached and

tradable, the structure of the primary fishing sector cannot be substantially altered in the short run.

However, the structure of the industry could influence the size of allocations. Alternatively, with smaller

bundles of rights being awarded, the size of allocations could influence the structure of the fishing

industry in the long run. The occurrence of younger and smaller vessels in the pelagic fishery may

provide clues to unraveling this argument – more research is needed to establish this as a fact.

The important issue in South African commercial fishing, however, is to enable Black Economic

Empowerment. With regard to the fisheries that require substantial capital investment, viable quotas are

important to ensure the continued profitability and thus also prospects of Black Empowerment capital

entering the industry. In the fisheries that need less capital and can be operated by micro, small or

medium Black entrepreneurs, suitable size quanta are necessary to ensure participation and backing by

financial institutions. In all cases, the concept and applicability of a minimum viable quota is important if a

‘paper’ transformation is to be avoided.

6.8. CONCLUSION

Vessels in the primary fishing sector have been classified according to size class groups, viz. micro,

small, medium, large and very large and size sub-groups. This is directly translatable into enterprise

grouping under certain ownership criteria. Each single-species fishery fits roughly into a class group.

These grouping provide indicators of potential multi-species fisheries.

Each fishery is classified according to the size and value of vessels it uses, total employment, total capital

value, and the following ratios: catch performance to total market value ratio, total catch performance to

fisher ratio and the capital to fisher ratio.

In addition, each fishery is ranked according to capital intensity, contribution to total employment,

contribution to total employment income and total capital value.


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Finally, the vessel classification system provides a backdrop to better understand the issue of structural

transformation and Minimum Viable Quotas. The general conclusions on structural adjustment are:

1. On balance, structural adjustment cannot be justified only on the grounds that small fishing

companies provide more job opportunities.

2. Black Economic Empowerment can be implemented at all levels and in all fisheries in the South

African fishing industry.

3. As a general rule, if an abuse of market power were suspected it would be better to use the

appropriate instruments created for that purpose by the Competitions Act.

4. In all cases, from big corporations to micro sized enterprises, the concept and applicability of a

minimum viable quota is important if a ‘paper’ transformation is to be avoided.

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 7: Transformation

144


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7. UNDERSTANDING AND MEASURING TRANSFORMATION

SUMMARY

The final goal of managed transformation is a normal society where it should not be possible to

distinguish between race and gender based on economic and socio-economic characteristics. The

Marine Living Resources Act 1998 is used to distinguish:

1. Social transformation, that is, transformation in skills, employment, income, ownership and

control, and access rights.

2. Economic transformation deals with increases in productivity and other welfare enhancing

processes.

3. Structural transformation usually deals with the managed change from big to smaller business.

Social transformation in employment, skills and income is the focus of this part of ESS. Economic

transformation is not covered and structural transformation is dealt with in Part 6: Survey Results:

Classification.

Four indicators of social transformation are derived from the ESS survey data, namely:

1. The percent Black employment.

2. ‘Follow the buck’ is the proportion of total income accruing to Black people.

3. The ratio of the average yearly income of Black people to that of White people.

4. A weighted employment transformation indicator uses the proportion of Black people employed

per weighted skills group and the difference between average yearly incomes per weighted skills

group.

These transformation indicators are derived for 1) primary fishing sector (vessels only), 2) the primary

fishing sector (including on-shore support) and 3) the secondary and tertiary fishing industry sectors. The

weighted employment transformation indicator is the most comprehensive.

The results of the weighted transformation indicators are presented in figures (a) and (b) for the primary

sector (vessels only) and secondary and tertiary sectors of the fishing industry. Assuming an 80:20 rule,

an 80% score means that it would not be possible to distinguish the employment characteristics of the

fishing industry on the basis of race.


146

itTf

pt scrldsh pl ab Ti sq

tf wcrl sllhll tbb tll

lf

hhl

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

Wei

ghte

d em

ploy

men

t ind

icat

or

Weighted employment transformation indicators for fisheries, primary sector (vessels only). (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support) Tf: primary fishing sector (vessels only)).

The fisheries weighted employment transformation indicators (Figure below) mirror to some extent the

scale distribution of the fisheries outlined in Part 6 of the ESS report. There is broad parallel between the

capital intensity of the fishery (the size of vessel and type of gear used) and its ability to absorb skilled

Black fishers.

Abalone

Hake

Pelagic TspSquid

Rock lobster

Shark

Prawns Linefish20%

25%

30%

35%

40%

45%

50%

55%

60%

Wei

ghte

d em

ploy

men

t ind

icat

or

Weighted employment transformation indicators for the secondary and tertiary sectors of the fishing industry. (Tsp: secondary and tertiary reference point).


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Using the weighted employment transformation indicator and according to the ESS survey results:

1. The primary fishing sector (vessels only) is 55.1% transformed and with a demographic

conversion factor is 68.8% transformed.

2. The primary fishing sector (including on-shore support) is 45.3% transformed – 56.6% with a

demographic conversion.

3. The combined secondary and tertiary sectors are 49.1% transformed or 61.4% (demographically

adjusted).



employed as relative measures within fisheries, or secondary and tertiary sector groups. Care should

also be exercised when comparing the transformation indicator of a fishery, or a secondary and tertiary

sector group, with that of the transformation indicators for the primary sector (vessels only) and/or the

primary sector (including on-shore support) and/or the secondary and tertiary sector.

7.1. INTRODUCTION

It is well understood that MCM needs a comprehensive transformation management plan that is

transparent, measurable and sets realistic transformation goals. The ESS does not presume to present

such a plan; however, it provides a broad framework that such a plan can be conceived. The objectives

of this section are to 1) provide a working definition of transformation, 2) place transformation into some

context, and 3) provide a measure of transformation based on the findings of the ESS survey.

7.2. THE NEED FOR MANAGED TRANSFORMATION

It is a fact that apartheid policies effectively created a comprehensive and insidious non-market, or non-

price, discrimination based on racial origin. This resulted in human, political and economic repression of

Black people in South Africa. Political normalisation alone is a necessary, but not sufficient, condition to

correct for non-market economic prejudice (economic apartheid). In other words, non-market

interventions are necessary to correct for non-market phenomena, thus the need for managed

transformation129.

129 With this in mind, the labeling of Black people as previously disadvantaged individuals (PDI) is misleading. In a sense, economic apartheid still exists and it is widely acknowledged that social transformation is still an ongoing process. In other words, most Black people are still disadvantaged from a socio-economic, economic and skills point of view. A better term, if one must be used, is historically repressed people/community/individual.

The final goal of managed transformation is obviously a normal society where it should not be possible to distinguish between race and gender based on economic and socio-economic characteristics.


148

This means that rights, skills, employment, income and ownership criteria should reflect the racial and

gender demographics of South Africa.

7.3. DEFINITION

The purpose of carefully defining transformation is to focus its:

1. Goals and milestones over time.

2. Measurement.

3. Implementation.

In light of the final goal of transformation, the definition should contain elements of racial (and gender)

equality in rights distribution, skills and employment, income earnings and ownership of capital. To avoid

confusion and to enable careful transformation management strategies, a distinction should be made

between social transformation, economic transformation and structural transformation. The Marine Living

Resources Act 1998 is used to focus these definitions.

The Marine Living Resources Act 1998 (s2, j) states “the need to restructure the fishing industry to

address historical imbalances and to achieve equity within all branches of the fishing industry”. This

imperative deals with a broad based social transformation. It incorporates the fundamental elements of

transformation, namely an equitable distribution of access rights and ownership, equity in employment

and income earnings and the development of human capital (skills).

The Marine Living Resources Act 1998 (s2, d) outlines the need to utilise marine living resources to

achieve: economic growth, capacity building within fisheries and mariculture, employment creation, sound

ecological balance and to be consistent with the development objectives of the government. This implies

an economic transformation by increasing economic welfare gains – favourable changes in individual

choice and income. It does not necessarily deal with racial and gender issues. Without doubt, however,

social transformation produces economic welfare gains and visa versa. Thus by achieving the one the

other is also achieved. However, managing economic transformation is often difficult. To realise what

are essentially growth objectives, value adding and export orientation strategies need to be considered.

This is recognised in the green and white papers preceding the Marine Living Resources Act of 1998.

Structural transformation is a sub-set of economic transformation and in terms of the Marine Living

Resources Act (1998) it deals with encouraging micro, small and medium enterprise. Structural

transformation is dealt with in the Part 6 of the ESS report.


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7.4. TRANSFORMATION IN THE SOUTH AFRICA FISHING INDUSTRY (context)

An attempt to place transformation management issues into context is provided in this section.

Bearing in mind the existing system of fishing rights in South Africa (see Part 3 of the ESS report) the

dominant transformation questions in the allocation of rights are:

Initially, opening up the application process to the general public created an exponential growth in new

applications and, given the low application fee, was for many applicants analogous to a lottery. This

inevitably put the administrative resources of MCM under strain. In the 2001 rights application process,

MCM responded by using higher application fees to discourage opportunistic applications130.

The verification of bona fide new applications, or those who had an interest in actively participating in

fishing activities, as opposed to opportunistic cases131 where the individual concerned were simply trying

to capture economic rent, is difficult to apply correctly. MCM responded by tightening up the application

procedure and establishing an outsourced verification unit.

New allocations are often made in sub-economic quanta. This is a logical result from a political point of

view; namely, the simple solution to increasing legitimate access right demands is to decrease the

quantum of rights allocated per application while staying within the TAC or TAE.

Assuming that individuals think strategically132, the expected response from rational new entrants, even if

there is an initial intention to participate, to a sub-economic quantum, is to lease out the rights. On the

other hand, the expected rational response of an established fishing company with a reduced quantum is

to lease in the rights. If rights are unattached and tradable, sub-economic allocations are expected to

create a market for rights (or ‘paper permits’). The market forces do work to promote minimum viable

units (vessels with a sufficient quanta of rights to cover at least variable costs including a return to the

vessel owner). However, with economic agents that behave strategically the market would not be

expected to work in favour of transformation, that is, the rights will be traded back to the original rights

holder/vessel owner. Anecdotal evidence suggests that the market has predominantly behaved in this

way. In a few instances, however, real transformation seems to have occurred.

MCM responded by investigating the issue of Minimum Viable Quotas (MVQs) and internal

transformation. The concept of MVQs met with extreme resistance that is perhaps due to:

130 Unfortunately, the fee structure is regressive and non-refundable if the application is unsuccessful. This could lead to a variety of issues particularly in the case of unsuccessful applicants who apply for small quanta of rights. 131 This rent seeking behaviour – capturing access rights for the sole purpose of leasing them back to the individuals or organisations from whom they where taken away from in the first place – is in fact rational. It makes sense to have a risk free stream of income by leasing rights than by taking, in many cases, extreme risk by engaging in fishing activities. Thus the exponential growth in new applications is rationally explained, and the access rights policy as implemented in effect resulted in an enhanced market for rights.

Who gets the reallocated rights and in what quantum are these allocations made?


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1. The abstract nature of the concept. The logic of the existing system of fishing rights is not consistent

with MVQs.

2. The inclusiveness of a catch capacity measure when applied to resource management issues (see

part 3, part 6 and part 9).

3. The awarding of sub-economic quanta during previous allocations makes it difficult in terms of the

‘new property’ argument, presented in Part 4, to take rights away from certain individuals and

consolidate them into fewer economically viable units.

Generally, MCM shifted from viewing rights allocations as the primary instrument of transformation to

‘internal transformation’ backed by allocations criteria. The aim is to encourage, and provide incentives

where possible, better employment practice in terms of the racial and gender equality, increased Black

(equity) ownership of fishing companies and greater participation at higher levels of management by

Black people. The Employment Equity Act, particularly for the bigger fishing enterprises, enhances this

process, but it becomes progressively more difficult to achieve these goals as the size of the fishing

company decreases (see section 7, Part 6 on structural transformation). Without a transparent and non-

arbitrary system of administration (see Part 4), withholding or removing rights as an incentive is difficult to

implement. In addition, the ‘new property’ argument, presented in Part 4, places an added burden on

MCM to efficiently manage transformation in the absence of a transparent and well-articulated

transformation management plan.

Regardless of the legal issues surrounding reallocating rights, the fishing industry seems committed to

transformation. The fishery rulebooks, produced by industry at the request of MCM, highlight the

transformation issues that concern fishing industry. They are:

1. An accepted and firm definition of transformation

2. A fair and equitable measure of transformation.

3. The transformation goals they can work towards.

These are reasonable and easily definable issue. To restate, “the final goal of managed transformation is

a normal society where it should not be possible to distinguish between race and gender based on

economic and socio-economic characteristics”. That is; rights, skills, employment, income and ownership

criteria should reflect the racial and gender demographics of South Africa. This, however, is not that

helpful, as it will take a long time for it to be achieved. What are important are the fundamentals of

transformation management plan, namely, clearly defined, measurable and comparable short term

transformation goals. As previously stated, the ESS does not set out to provide these; it can however,

provide a baseline and a starting point.

132 This school of thought uses hyper rationality and strategic positioning in game theoretic analyses to model, and predict, the behaviour of economic agents.


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7.5. MEASURING TRANSFORMATION

Transformation is by its nature a change situation and should be measured against some baseline. In the

context of a social and economic system, it has to be seen as a process rather than an event. This

means that transformation indicators must be tracked and evaluated in terms of changes – similar in

effect to measuring economic growth. As primary sector fishing activities are already managed as a

microeconomic system (due to the common property nature of the resource) its transformation should be

more easily accomplished than with a market driven microeconomic system. Obviously, the

measurement criteria will depend on what type of transformation is being measured. Social

transformation, economic transformation and structural transformation should be treated separately but

not exclusively.

7.5.1 Measuring social transformation For purposes of measurement, social transformation is divided into:

1. Human skills, employment and income

2. Access rights

3. Ownership

The ESS survey only enables a measurement of racial transformation in human skills, employment and

income. The ability to unravel the ownership structures of fishing companies and right holding fishing

companies with any degree of confidence was not possible.

7.5.1.1 Human skills, employment and income

A measurement of transformation in human skills, employment and income should be able to provide a

balanced picture of the racial (and gender) distribution of these criteria in an industry. Abstracting from

gender issues, there are at least four measures that can be used, namely:

1. Measuring the absolute percentage of Black people employed. Although this is an important

criterion, it does not capture the proportion of income accruing to Black people.

2. Measuring the absolute percentage of total yearly income to Black people (Tfb). This form of the

‘follow the buck’ system133 does not, however, provide a distinction in terms of the proportional

difference in annual incomes and skills levels between Black and White people.

133 The ‘follow the buck system’ is in effect an accounting based one. For this to work correctly, transformation items that are included in accounting statements need to be carefully analysed. For example, if the firm’s technology is labour intensive, meaning that a large proportion of costs are labour items, the firm might score very highly using a ‘follow the buck system’ even if there are discriminatory employment practices in the more skilled occupations.


152

3. Measuring the difference between average annual income between Black people and White people

(Tb). This measure highlights the absolute difference between income earned by White people and

Black people in aggregate. It does not, however, bring out discrepancies that may exist in the racial

distribution of skills and average income per skills group.

4. A more comprehensive measure weights skills groups and captures income differentials by skills

group (Ta) and can be weighted by a demographic factor (Tad). This measure captures the racial

characteristics of income differentials of those employed in different skills categories in the fishing

industry. It also provides a weighting in favour of the more highly skilled occupations.

The income differentials provide an indication of the gaps in the income earning capacity of Black people

and White people. An income differential does not necessarily indicate racial discrimination in

employment and compensation; rather it may show a lag in skills acquisition particularly in the higher

skills categories. Skills weightings capture, to some extent, the importance of corrective action in

employment and human skills development in an industry.

The scoring system is most easily understood using a percentage indicator. A 100% score should reflect

the racial and gender demographics of South Africa. In other words the measures should, where

applicable, be adjusted by a demographic factor, for example, multiplied by 1.25 to reflect the 80:20

split134 between Black and White people in South Africa’s commercial harbour towns (see Part 8 for the

demographics). In other words, an unadjusted score of 80% means that the goal of transformation in

employment has been achieved as is measured by a particular indicator.

7.5.1.2 Access Rights

It is possible to get a measure of the racial distribution of access rights by recording as accurately as

possible the racial characteristics of rights holders. In the majority of cases companies hold access

rights, therefore the ownership structures should be carefully evaluated, preferably using audited

accounting statements135. The effective quantity of access rights held by Black people can then be

apportioned correctly and adjusted by a demographic factor, that is,

Tar = %TAC (or TAE)*1.25

Where: Tar: Transformation index in access rights.

As already stated, the ESS cannot provide an overall measure for transformation in the allocation of

access rights.

134 The demographic factor is calculated as: 1.00/ 0.80 = 1.25. 135 The racial characteristics of ownership structure are difficult to measure for a listed company. The Black Economic Commission recommends that the racial distribution of functional board members and the racial composition of management are important indicators of transformation.


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7.5.1.3 Ownership of Capital

Fishing industry companies may have many different combinations of ownership and control (cross-

ownership) that are subject to change without notice and whose real racial characteristics are difficult to

determine. Getting the shape of the racial ownership structures would require following the generally

accepted accounting practice (GAAP) to account for control and consolidation136 and audited company

statements to verify ownership. Survey data is not a reliable source of ownership characteristics and

should be treated accordingly. However, if reliable ownership data is available, a simple proportionate

measure multiplied by a demographic factor (to be consistent with the aim of transformation) will provide

a reliable indicator of transformation in ownership of capital (Tk).

7.5.1.4 A Composite Measure of Social Transformation

A composite measure of social transformation in fisheries should include the racial distribution of access

rights, the racial distribution of the employment, skills and income and a racial distribution of ownership.

For example,

Tcomp = Tar(Ŭ) + Tad(ɓ) + Tk(ɔ) Where Ŭ + ɓ + ɔ= 1.

The factors, Ŭ, ɓ, and ɔ are weighting indices that can be used to assign the relative importance of

ownership, access rights and employment, skills and income. These factors must equal one to ensure

that the comprehensive transformation index is a percentage value. Finally the transformation values

should be plotted over time to track the changes that occur so that they can be evaluated within the

domestic economic, social and political environment.

7.5.2 Measuring Economic Transformation The goal of economic transformation in South Africa is to create jobs and to expand, or improve on,

output. Economic transformation is measured in terms of relative efficiency. The system of national

accounts deals with this to some extent, and compares the results to growth criteria and statistics in all

sectors of the economy.

7.5.3 Measuring Structural Transformation Measuring structural transformation involves tracking the changes in ownership patterns in the industry,

particularly the change in distribution of Black micro and small enterprises catching and processing fish.

It also means measuring the socio-economic and economic effects that structural adjustment will have on

the fishing industry. In its most basic form, it means measuring the effects of social and economic

engineering. It is a well-known fact that most structural adjustment programmes are viewed with

trepidation, particularly in the developing world. Measuring structural transformation necessitates

knowledge of the ownership structures of the fishing industry.

136 For example, AC 132 (subsidiaries) and AC110 (associates).


154

It may be better to identify existing Black micro and small enterprise participation in the fishery and

provide these entrepreneurs with support structures and minimum viable quotas. Government

departments can cooperate with regard to the support structures; for example, the Centre for Small

Business Promotion (CSBP) was established within the DTI tasked with an overall responsibility for small

business matters. A good working relationship between the CSBD and MCM to encourage and establish

Black small and micro fishing enterprises is a potential alternative to a full-scale microeconomic structural

adjustment programme.

7.6. CALCULATING EMPLOYMENT TRANSFORMATION INDICATORS

The toothfish fishery is used to illustrate how the various measures of transformation are calculated.

There is no significance in choosing the toothfish fishery as an example. In section 7, these measures

are compared for the surveyed fisheries, across secondary and tertiary fishing related activities and for

the fishing industry as a whole.

The box below illustrates the ‘follow the buck’ system (Tfb), that is the total proportion of income to Black

people, and the demographically adjusted ‘follow the buck system’ (Tfbd).

The average income transformation (Tb) is not adjusted for the demographic distribution as equivalence

gives 1 or 100%.

Tfb and Tfbd for the Toothfish fishery: Total Black income = (R 4 716 000) Total income (R 8 814 000) 1. Tfb = 53.5% Multiplying 1. by the demographic factor 1.25 2. Tfbd = 66.9%

Average income transformation, Tb Black average income (R 58 950) White average income (R 59 391)

3. Tb = 0.99 or 99%


155

The weighted skills and income differential employment transformation index (Ta) is calculated as follows:

Ta = {[(%Black fishers in skilled full-time employment)*(1 – differential between Black and

White fisher incomes in full-time skilled employment)*0.50] + [(%Black fishers in skilled

part-time employment)*(1 – differential between Black and White fisher incomes in part-

time skilled employment)*0.30] + [(%Black fishers in semi-skilled full-time employment)*(1

– differential between Black and White fisher incomes in full-time semi-skilled

employment)*0.15] + [(%Black fishers in semi-skilled part-time employment)*(1 –

differential between Black and White fisher incomes in part-time semi-skilled

employment)*0.05]}.

The weightings for the skills levels are indicated in bold and negative income differences are adjusted to

unity. In the primary fishing sector (vessels only) part-time and full-time skilled and semi-skilled are used.

The weightings are selected on the basis that the more skilled the occupation, the greater the effect

apartheid had on its distribution and thus a greater bias for correction is needed. In terms of employment,

a demographically adjusted score of 100% means that racial distinctions cannot be made on economic

grounds. This measure also gives a fairly good indicator of the socio-economic status of those employed

in the fishing industry. Thus a demographically adjusted score of 100% also fulfills the socio-economic

criterion included in the final goal of transformation articulated in section 2. The score, however, cannot

be used to distinguish issues of gender balance137.

The five measures of transformation will be ranked according to fishery and fishery-related secondary and

tertiary activities in the following section.

7.7. TRANSFORMATION INDICATORS BASED ON THE ESS SURVEY

The five transformation indices described in the above section are presented and ranked by fishery and

separated into primary fishing sector (vessels only) activities, primary fishing sector (including shore

based support activities) and combined secondary and tertiary fishing sector activities.

137 Gender balance in the fishing industry is a particularly difficult transformation goal. Fishing, like mining, is an established male dominated activity. For example, only recently have professional women been allowed to work underground in the South African mining industry and no women are used as labour.

Ta and Tad for the Toothfish fishery Weighted employment transformation, Ta 4. Ta = 42.56% Weighted demographically adjusted Ta 5. Tad = 53.2%


156

7.7.1 Primary Fishing Sector (vessels only) The set of transformation indicators, namely, the percentage Black employment, an employment ‘follow

the buck’ system, an average income transformation indicator and a weighted employment transformation

indicator are presented in tabular and graphic form. In each sub-section, the fisheries are ranked, in

bold, according to the transformation index under discussion.

7.7.1.1 Ranking by Percentage of Black Fishers

Table 7.1 and figure 7.1 rank the fisheries by the percentage of Black fishers. The ESS survey data

shows that 85.5% of all people employed in the primary sector (vessels only) are Black.

Table 7.1. Fisheries ranked by the percentage of Black fishers. (Tb)Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd)Demograhically adjusted ‘follow the buck’ system, (Tad) Demograhically adjusted weighted skills and income differential employment transformation index. %Black Tfb Tb Ta Tfbd Tad 1. SC ROCK LOBSTER 98.2% 92.5% 23.2% 50.8% 115.7% 63.5% 2. INSHORE TRAWL 97.7% 91.5% 25.5% 59.3% 114.3% 74.1% 3. DEEP-SEA HAKE 96.5% 92.0% 41.0% 47.2% 115.0% 59.0% 4. PRAWN TRAWL 94.5% 80.4% 23.9% 51.5% 100.5% 64.4% 5. SQUID 91.2% 79.5% 37.7% 44.3% 99.4% 55.4% 6. SHARK LONGLINE 89.4% 88.3% 90.0% 41.1% 110.4% 51.4% 7. WC ROCK LOBSTER 89.0% 78.5% 45.0% 41.2% 98.1% 51.5% 8. HAKE LONGLINE 87.6% 79.1% 53.8% 39.4% 98.9% 49.2% 9. TUNA BAITBOAT 86.5% 79.7% 61.3% 39.2% 99.6% 49.0% 10. ABALONE 86.0% 78.9% 61.2% 45.8% 98.7% 57.3% 11. TUNA LONGLINE 80.3% 63.6% 42.9% 37.7% 79.5% 47.1% 12. LINEFISH 79.7% 71.0% 62.3% 35.8% 88.7% 44.7% 13. HAKE HANDLINE 78.2% 68.6% 60.8% 29.6% 85.7% 37.0% 14. PELAGIC 59.5% 52.5% 75.2% 46.8% 65.6% 58.5% 15.TOOTHFISH 53.7% 53.5% 99.3% 42.6% 66.9% 53.2% The following observations can be made with regard to table 7.1.

1. There are six fisheries that have a higher percentage of Black fishers than the primary fishing sector

(vessels only) average, namely the South Coast Rock Lobster, Inshore Hake, Deep-sea Hake Trawl,

Prawn Trawl and the Shark Longline fisheries.

2. The fisheries that are ranked 7th, 8th, 9th and 10th in table 7.1 have lower percentage of Black fishers

than the primary sector (including ocean support), but higher than the average for the primary fishing

sector (vessels only).

3. Those fisheries ranked 11th, 12th, 13th, 14th and 15th have lower than average percentage Black

fishers.

These observations are illustrated graphically on figure 7.1 below.


157

scrl it dshpt

sqsll Ti wcrl hll tbb ab Tf

tll lf hhl

pl

tf50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

% B

lack

em

ploy

men

t

Figure 7.1. Scatter plot of fisheries ranked by the percentage of Black fishers. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector reference point. Tf: primary fishing sector (vessels only)).

7.7.1.2 Ranking by ‘Follow the Buck’ in Employment (Tfb, Tfbd)

The ‘follow the buck’ employment indicator provides a percentage of total income that accrues to Black

employees.

Table 7.2. Fisheries ranked by ‘follow the buck’ in employment. (Tb)Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd)Demograhically adjusted ‘follow the buck’ system, (Tad)Demograhically adjusted weighted skills and income differential employment transformation index %Black Tfb Tb Ta Tfbd Tad 1. SC ROCK LOBSTER 1. 98.2% 92.5% 23.2% 50.8% 115.7% 63.5% 2. DEEP-SEA HAKE 3. 96.5% 92.0% 41.0% 47.2% 115.0% 59.0% 3. INSHORE TRAWL 2. 97.7% 91.5% 25.5% 59.3% 114.3% 74.1% 4. SHARK LONGLINE 6. 89.4% 88.3% 90.0% 41.1% 110.4% 51.4% 5. PRAWN TRAWL 4. 94.5% 80.4% 23.9% 51.5% 100.5% 64.4% 6. TUNA BAITBOAT 9. 86.5% 79.7% 61.3% 39.2% 99.6% 49.0% 7. SQUID 5. 91.2% 79.5% 37.7% 44.3% 99.4% 55.4% 8. HAKE LONGLINE 8. 87.6% 79.1% 53.8% 39.4% 98.9% 49.2% 9. ABALONE 10. 86.0% 78.9% 61.2% 45.8% 98.7% 57.3% 10. WC ROCK LOBSTER 7. 89.0% 78.5% 45.0% 41.2% 98.1% 51.5% 11. LINEFISH 12. 79.7% 71.0% 62.3% 35.8% 88.7% 44.7% 12. HAKE HANDLINE 13. 78.2% 68.6% 60.8% 29.6% 85.7% 37.0% 13. TUNA LONGLINE 11. 80.3% 63.6% 42.9% 37.7% 79.5% 47.1% 14. TOOTHFISH 15. 53.7% 53.5% 99.3% 42.6% 66.9% 53.2% 15. PELAGIC 14. 59.5% 52.5% 75.2% 46.8% 65.6% 58.5%


158

In all fisheries the ‘follow the buck’ indicator has a lower percentage score than in the percentage Black

employment indicator. This is a result of the fact that most of the Black employees are in the lower paid

semi-skilled category than White fishers. The survey data indicates that 77.2% of all employment income

from the primary sector (vessels only) accrues to Black fishers.

Fisheries that move up the list pay relatively higher annual incomes than the fisheries that move down the

list.

1. The Deep-sea Hake Trawl, Shark Longline, Tuna Baitboat, Abalone, Linefish, Hake Handline and

Toothfish fisheries moved up the list.

2. The Hake Longline fishery kept its ranking at 8th place.

3. The rest of the fisheries moved down the ranking system.

Figure 7.2 plots the fisheries by the system of ‘follow the buck’ in employment.

scrl dsh itsll

pt tbb sq Ti hll ab wcrl Tf

lfhhl

tll

tf pl50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

'Fol

low

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buck

' ind

icat

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Figure 7.2. Scatter plot of fisheries ranked by ‘follow the buck’ in employment. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support), Tf: primary fishing sector (vessels only)).

The two reference points Ti and Tf show the proportion of employment income that accrues to fishers in

the primary sector (vessels only) and in the primary sector (including on-shore support). The important

characteristic of figure 7.2 is that the fisheries fall roughly into three groups, namely,

1. A small group with indicators above the reference points. These are the South Coast Rock Lobster,

Deep-sea Hake Trawl, Inshore Trawl and Shark Longline fisheries.

2. A larger group that all fall within one percentage point of the primary fishing sector (including shore

based activities) reference point and above the primary fishing sector (vessel only) reference point.


159

3. A group of five fisheries that fall below the primary fishing sector reference point.

The definition of a transformed society, section 2, necessitates conversion of the ‘follow the buck’

indicator by a demographic factor, that is, a 80:20 racial split requires that the proportion is multiplied by a

factor of 1.25. This calculation yields a 100% or greater than 100% result for the top five fisheries.

However, in every fishery the percentage measure of the ‘follow the buck’ indicator is lower than the

percentage measure of Black fishers in the fishery. For whatever reason, this clearly indicates that Black

people earn less than White people in the primary fishing sector (vessels only). The next indicator

measures the difference in average yearly income between Black and White fishers.

7.7.1.3 Average Income Transformation Indicators (Tb)

The ratio of average yearly income for all Black fishers to the average yearly income for all White fishers

should equal one (or 100% in percentage terms) in a fully transformed society. No demographic

conversion is needed for this measure. Table 7.3 below, ranks the fisheries by an average income

transformation indicator.

Table 7.3. Fisheries ranked by average income of Black fishers as a % of average income of White fishers. (Tb)Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd)Demograhically adjusted ‘follow the buck’ system, (Tad) Demograhically adjusted weighted skills and income differential employment transformation index

%Black Tfb Tb Ta Tfbd Tad 1. TOOTHFISH 15. 53.7% 14. 53.5% 99.3% 42.6% 66.9% 53.2% 2. SHARK LONGLINE 6. 89.4% 4. 88.3% 90.0% 41.1% 110.4% 51.4% 3. PELAGIC 14. 59.5% 15. 52.5% 75.2% 46.8% 65.6% 58.5% 4. LINEFISH 12. 79.7% 11. 71.0% 62.3% 35.8% 88.7% 44.7% 5. TUNA BAITBOAT 9. 86.5% 6. 79.7% 61.3% 39.2% 99.6% 49.0% 6. ABALONE 10. 86.0% 9. 78.9% 61.2% 45.8% 98.7% 57.3% 7. HAKE HANDLINE 13. 78.2% 12. 68.6% 60.8% 29.6% 85.7% 37.0% 8. HAKE LONGLINE 8. 87.6% 8. 79.1% 53.8% 39.4% 98.9% 49.2% 9. WC ROCK LOBSTER 7. 89.0% 10. 78.5% 45.0% 41.2% 98.1% 51.5% 10. TUNA LONGLINE 11. 80.3% 13. 63.6% 42.9% 37.7% 79.5% 47.1% 11. DEEP-SEA HAKE 3. 96.5% 2. 92.0% 41.0% 47.2% 115.0% 59.0% 12. SQUID 5. 91.2% 7. 79.5% 37.7% 44.3% 99.4% 55.4% 13. INSHORE TRAWL 2. 97.7% 3. 91.5% 25.5% 59.3% 114.3% 74.1% 14. PRAWN TRAWL 4. 94.5% 5. 80.4% 23.9% 51.5% 100.5% 64.4% 15. SC ROCK LOBSTER 1. 98.2% 1. 92.5% 23.2% 50.8% 115.7% 63.5% These indicators are fishery averages and do not reflect the characteristics of individual fishing

companies. The results per fishery can be used as points of reference for individual fishing companies

operating within that fishery.


160

In the primary fishing sector (vessels only) as a whole:

1. Black fishers get on average 55.1% of the amount White fishers earn.

2. The ranking structure reflects the divergence between the previous two measures. A bigger

difference between the percentage of Black fishers and the percentage of total income accruing to

Black fishers gives, in effect, a lower score.

3. In light of the above, the ranking structure has, very broadly speaking, reversed. For example, the

toothfish and pelagic fisheries ranked either 14th or 15th in the previous two indicators, but now rank

1st and 2nd respectively. This means, that although these fisheries do not employ as large a

percentage of Black fishers as the others, their fishers earn more equal incomes.

4. The inshore trawl and south coast rock lobster fisheries have moved from the top two positions to the

bottom of the ranking structure. In other words, these fisheries employ a large proportion of Black

fishers but do not do as well in terms of racial equity in annual income. For example, Black fishers

earn on average 23.2% of the yearly average income of White fishers in South Coast Rock Lobster

fishery.

The ranking structure is illustrated in figure 7.3.

tf

sll

pl

lf tbb ab hhlTf

hllTi wcrl tll dsh

sq

it pt scrl20%

30%

40%

50%

60%

70%

80%

90%

100%

Aver

age

inco

me

indi

cato

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tf

Figure 7.3. Scatter plot of fisheries ranked by average income indicators. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support), Tf: primary fishing sector (vessels only).

With regard to figure 7.3:

1. The toothfish, shark longline and pelagic fisheries all score well above the two reference points, that

is, they are higher than the averages for both the primary sector (vessels only) and primary sector

(including on-shore support).


161

2. The linefish, tuna baitboat, abalone, hake handline and hake longline fisheries cluster around the

average for the primary fishing sector (vessels only).

3. The west coast rock lobster, deep-sea hake trawl and squid fisheries fall just below the primary

fishing sector (including on-shore support) average and within about 7% points of each other.

4. Black fishers working in the bottom end cluster of fisheries, namely, the inshore trawl, prawn trawl

and south coast rock lobster fisheries, earn about 24% of the annual yearly incomes of White fishers.

This gross measure of racial differences in yearly average income does not fully capture the racial

discrepancy in terms of skills and the racial income differentials in particular skills groups. In light of the

definition of transformation, racial differences in skills and income earnings in those skills groups should

not exist. In addition, because of previous racial discrimination in terms of jobs and skills, transformation

criteria should place a heavier weighting on tasks that require higher-level skills. The next section

provides a more comprehensive indicator of transformation.

7.7.1.4 Weighted Employment Transformation (Ta, Tad)

This indicator is in many respects a translation of the average income indicator. The difference is that it

examines income differences at the skills group level, rather than for the entire fishery, as well as bringing

to bear a bias in favour of the higher skilled groups though a weighting system. This is justifiable as the

process of transformation is, as is previously stated, managing and correcting for previous non-market

discrimination. Only skilled and semi-skilled groups are used to calculate this indicator for vessels only

primary fishing sector activities.

The weighted employment transformation indicator for the primary sector (vessels only) is 55.1%, and

45.3% for the primary sector (including on-shore support) – these are illustrated as reference points on

figure 7.4. Only one fishery scores above the primary fishing sector (vessels only) indicator. Bearing in

mind that positive yearly income differentials must be adjusted to unity for the transformation indicator to

show a true result, certain fisheries with Black yearly incomes in the full-time skilled group that are higher

than their White counterparts will move the indicator for the whole fishery upwards. Figure 5.3 in Part 5:

Survey results: Employment, Skills and Income illustrates the above point.


162

Table 7.4. Fisheries ranked by weighted employment transformation indicators. (Tb)Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd)Demograhically adjusted ‘follow the buck’ system, (Tad)Demograhically adjusted weighted skills and income differential employment transformation index %Black Tfb Tb Ta Tfbd Tad 1. INSHORE TRAWL 2. 97.7% 3. 91.5% 12. 25.5% 59.3% 114.3% 74.1% 2. PRAWN TRAWL 4. 94.5% 5. 80.4% 13. 23.9% 51.5% 100.5% 64.4% 3. SC ROCK LOBSTER 1. 98.2% 1. 92.5% 14. 23.2% 50.8% 115.7% 63.5% 4. DEEP-SEA HAKE 3. 96.5% 2. 92.0% 10. 41.0% 47.2% 115.0% 59.0% 5. PELAGIC 14, 59.5% 15. 52.5% 3. 75.2% 46.8% 65.6% 58.5% 6. ABALONE 10. 86.0% 9. 78.9% 6. 61.2% 45.8% 98.7% 57.3% 7. SQUID 5. 91.2% 7. 79.5% 11. 37.7% 44.3% 99.4% 55.4% 8. TOOTHFISH 15. 53.7% 14. 53.5% 1. 99.3% 42.6% 66.9% 53.2% 9. WC ROCK LOBSTER 7. 89.0% 10. 78.5% 8. 45.0% 41.2% 98.1% 51.5% 10. SHARK LONGLINE 6. 89.4% 4. 88.3% 2. 90.0% 41.1% 110.4% 51.4% 11. HAKE LONGLINE 8. 87.6% 8. 79.1% 8. 53.8% 39.4% 98.9% 49.2% 12. TUNA BAITBOAT 9. 86.5% 6. 79.7% 5. 61.3% 39.2% 99.6% 49.0% 13. TUNA LONGLINE 11. 80.3% 13. 63.6% 9. 42.9% 37.7% 79.5% 47.1% 14. LINEFISH 12. 79.7% 11. 71.0% 4. 62.3% 35.8% 88.7% 44.7% 15. HAKE HANDLINE 13. 78.2% 12. 68.6% 7. 60.8% 29.6% 85.7% 37.0% The ranking structure per fishery is illustrated on figure 7.4 below. As implied above, the reference point

(Tf) for the primary sector (vessels only) is only a good indicator for the entire South African commercial

fishing fleet. Individual company transformation can only be judged using the weighted employment

indicator particular to a fishery.

itTf

pt scrldsh pl ab Ti sq tf wcrl sll hll tbb tll

lf

hhl

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

Wei

ghte

d em

ploy

men

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Figure 7.4. Scatter plot of fisheries ranked by weighted employment transformation indicators. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support). Tf: primary fishing sector (vessels only)).

A target of 80% is consistent with a fully transformed labour force in a particular fishery and for the

primary sector (vessels only).

The fisheries weighted employment transformation indicators mirror to some extent the scale distribution

of the fisheries outlined in Part 6: Survey Results: Classification (Size and Shape).


163

1. The low value fisheries using micro and small vessels, namely linefish and hake handline, have a

very limited ability to absorb skilled Black fishers into their structures particularly if most fishing firms

are single owner enterprise. As is expected these fisheries have the lowest employment

transformation indicators.

2. High value fisheries using micro and small vessels (abalone and west coast rock lobster) display

large returns to capital. The ability to employ and absorb skilled Black labour is far greater than for

their low value counterparts, thus the recorded higher weighted employment transformation

indicators. The abalone fishery scores better than the west coast rock lobster fishery because it has

an overall lower racial income discrepancy138.

3. The weighted employment transformation indicators for medium vessel fisheries using line gear

(shark longline, hake longline, tuna baitboat, squid and tuna longline) group into 44.3% to 37.7%

range. Small and medium fishing entrepreneurs operate a large proportion of these vessels. They

have a limited ability, but greater than the low value micro and small scale vessel fisheries, to absorb

skilled Black fishers.

4. The medium to large trawl vessels used by the pelagic, inshore trawl and prawn trawl fisheries,

display still higher weighted employment transformation indicators. These fisheries are more capital

intensive (see part 6 of the ESS report), fishers earn higher yearly incomes and they are generally

more vertically integrated than the former three groups discussed above. A higher level of skilled

Black fisher absorption is thus possible. The pelagic fishery scores the worst out of the three in this

group simply because they employ more part-time fishers. The fact that pelagic fishers are on the

whole better off in terms of daily wages and yearly incomes – see Part 5 – points to the care with

which any indicator should be used and interpreted.

5. At the upper end of the scale in terms of weighted employment transformation indicators, capital

intensity and size of vessel are the deep-sea hake trawl, south coast rock lobster and toothfish

fisheries.

There is broad parallel between the capital intensity of the fishery (the size of vessel and type of gear

used) and its ability to absorb skilled Black fishers.



used as relative measures within fisheries. Care should also be exercised when comparing the

transformation indicator of a fishery with that of the transformation indicator for the primary sector

(vessels only) as a whole.

138 The Abalone fishing industry is probably more concentrated (or vertically integrated).


164

7.7.2 Primary Fishing Sector (including on-shore support)

Transformation indicators are recalculated in this section by adding the survey data on employment, skills

and income of shore-based support fishing activities to the primary sector (vessels only). The reason for

doing this is that on-shore support activities are often inextricably linked to the secondary and tertiary

sectors. Because over-capacity and overcapitalisation issues are directly linked to vessels, it is important

to be able to separate primary activities from the secondary and tertiary activities in the fishing industry.

The four transformation measures are calculated in an identical manner as in the previous section.

Rankings from the previous measures are added in sub-script.


The percentage of Black employees in the primary fishing sector are ranked and tabulated in table 7.5

and illustrated in figure 7.5. As displayed below, the ranking of the fisheries by the percentage of Black

employees is not substantially different when adding the on-shore support activities. The Abalone fishery,

however, has moved from 10th place to 5th place indicating a greater proportion of Black people work on-

shore than as fishers.

Table 7.5. Fisheries ranked percentage Black employment. (Tb) Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd)Demograhically adjusted ‘follow the buck’ system, (Tad) Demograhically adjusted weighted skills and income differential employment transformation index %Black Tfb Tb Ta Tfbd Tad 1. SC ROCK LOBSTER 98.2%1 92.5% 23.2% 16.3% 115.7% 20.3% 2. INSHORE HAKE 93.4%2 84.5% 38.7% 40.7% 105.6% 50.9% 3. PRAWN TRAWL 90.8%4 73.7% 28.3% 38.6% 92.2% 48.3% 4. DEEP-SEA HAKE 90.5%3 82.8% 50.3% 53.5% 103.4% 66.8% 5. ABALONE 90.5%10 81.3% 45.9% 45.0% 101.6% 56.3% 6. HAKE LONGLINE 89.9%8 82.2% 52.0% 57.7% 102.7% 72.1% 7. SQUID 89.7%5 76.6% 37.8% 28.4% 95.8% 35.5% 8. WC ROCK LOBSTER 86.8%7 73.7% 42.6% 21.4% 92.1% 26.8% 9. SHARK LONGLINE 86.6%6 83.2% 76.2% 36.4% 103.9% 45.5% 10. TUNA BAITBOAT 84.7%9 77.2% 61.3% 31.5% 96.5% 39.4% 11. TUNA LONGLINE 79.8%11 62.2% 41.6% 26.8% 77.8% 33.6% 12. LINEFISH 79.0%12 69.7% 61.1% 29.9% 87.1% 37.4% 13. HAKE HANDLINE 77.8%13 67.8% 60.0% 42.4% 84.7% 52.9% 14. PELAGIC 68.0%14 57.2% 62.8% 36.3% 71.5% 45.4% 15. TOOTHFISH 53.3%15 52.0% 95.0% 24.4% 65.0% 30.5%


165

scrl

itpt dsh ab hll sq Ti

wcrl sll Tf tbb

tll lf hhl

pl

tf50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

% B

lack

em

ploy

men

t

Figure 7.5. Scatter plot of fisheries ranked by the percentage of Black employees. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support)).


Table 7.6. Fisheries ranked by ‘follow the buck’ in employment. (Tb) Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd)Demograhically adjusted ‘follow the buck’ system, (Tad) Demograhically adjusted weighted skills and income differential employment transformation index %Black Tfb Tb Ta Tfbd Tad 1. SC ROCK LOBSTER 1. 98.2% 92.5%1 23.2% 16.3% 115.7% 20.3% 2. INSHORE HAKE 2. 93.4% 84.5%3 38.7% 40.7% 105.6% 50.9% 3. SHARK LONGLINE 9. 86.6% 83.2%4 76.2% 36.4% 103.9% 45.5% 4. DEEP-SEA HAKE 4. 90.5% 82.8%2 50.3% 53.5% 103.4% 66.8% 5. HAKE LONGLINE 6. 89.9% 82.2%8 52.0% 57.7% 102.7% 72.1% 6. ABALONE 5. 90.5% 81.3%9 45.9% 45.0% 101.6% 56.3% 7. TUNA BAITBOAT 10. 84.7% 77.2%6 61.3% 31.5% 96.5% 39.4% 8. SQUID 7. 89.7% 76.6%7 37.8% 28.4% 95.8% 35.5% 9. PRAWN TRAWL 3. 90.8% 73.7%5 28.3% 38.6% 92.2% 48.3% 10. WC ROCK LOBSTER 6. 86.8% 73.7%10 42.6% 21.4% 92.1% 26.8% 11. LINEFISH 12. 79.0% 69.7%11 61.1% 29.9% 87.1% 37.4% 12. HAKE HANDLINE 13. 77.8% 67.8%12 60.0% 42.4% 84.7% 52.9% 13. TUNA LONGLINE 11. 79.8% 62.2%13 41.6% 26.8% 77.8% 33.6% 14. PELAGIC 14. 68.0% 57.2%15 62.8% 36.3% 71.5% 45.4% 15. TOOTHFISH 15. 53.3% 52.0%14 95.0% 24.4% 65.0% 30.5%

The ranking of the fisheries when including shore-based support employment is not substantially different

to the primary sector (vessels only) ranking.


166

scrl

it sll dsh hll abTi

tbb sq Tfpt wcrl

lfhhl

tll

pl

tf50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

'Fol

low

the

buck

' ind

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Figure 7.6. Scatter plot for fisheries ranked by ‘follow the buck’ in employment. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support)).


Table 7.7, below ranks the ratio of average income to Black employees to the average income to White

employees. As previously stated, no demographic factor is needed to adjust this measure.

Table 7.7. Fisheries ranked by average income of Black fishers as a % of average income of White fishers. (Tb) Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd)Demograhically adjusted ‘follow the buck’ system, (Tad) Demograhically adjusted weighted skills and income differential employment transformation index %Black Tfb Tb Ta Tfbd Tad 1. TOOTHFISH 15. 53.3% 15. 52.0% 95.0%1 24.4% 65.0% 30.5% 2. SHARK LONGLINE 9. 86.6% 3. 83.2% 76.2%2 36.4% 103.9% 45.5% 3. PELAGIC 14. 68.0% 14. 57.2% 62.8%3 36.3% 71.5% 45.4% 4. TUNA BAITBOAT 10. 84.7% 7. 77.2% 61.3%5 31.5% 96.5% 39.4% 5. LINEFISH 12. 79.0% 11. 69.7% 61.1%4 29.9% 87.1% 37.4% 6. HAKE HANDLINE 13. 77.8% 12. 67.8% 60.0%7 42.4% 84.7% 52.9% 7. HAKE LONGLINE 6. 89.9% 5. 82.2% 52.0%8 57.7% 102.7% 72.1% 8. DEEP-SEA HAKE 4. 90.5% 4. 82.8% 50.3%11 53.5% 103.4% 66.8% 9. ABALONE 5. 90.5% 6. 81.3% 45.9%6 45.0% 101.6% 56.3% 10. WC ROCK LOBSTER 8. 86.8% 10. 73.7% 42.6%9 21.4% 92.1% 26.8% 11. TUNA LONGLINE 11. 79.8% 13. 62.2% 41.6%10 26.8% 77.8% 33.6% 12. INSHORE HAKE 2. 93.4% 2. 84.5% 38.7%13 40.7% 105.6% 50.9% 13. SQUID 7. 89.7% 8. 76.6% 37.8%12 28.4% 95.8% 35.5% 14. PRAWN TRAWL 3. 90.8% 9. 73.7% 28.3%14 38.6% 92.2% 48.3% 15. SC ROCK LOBSTER 1. 98.2% 1. 92.5% 23.2%15 16.3% 115.7% 20.3%


167

tf

sll

pl tbb lf hhl

Tf hll dshTi ab

wcrl tllit sq

ptscrl

20%

30%

40%

50%

60%

70%

80%

90%

100%

Aver

age

inco

me

indi

cato

r

Figure 7.7. Scatter plot of fisheries ranked by average income indicators. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support)).

As in vessels only primary fishing sector average income indicators, the ranking system has reversed.

Those that ranked highest in the percentage of Black employment, tend to rank lower in equality in

income.


Table 7.8. Fisheries ranked by weighted employment transformation indicators. (Tb)Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd)Demograhically adjusted ‘follow the buck’ system, (Tad)Demograhically adjusted weighted skills and income differential employment transformation index

%Black Tfb Tb Ta Tfbd Tad 1. HAKE LONGLINE 6. 89.9% 5. 82.2% 7. 52.0% 57.7%11 102.7% 72.1% 2. DEEP-SEA HAKE 4. 90.5% 4. 82.8% 8. 50.3% 53.5%4 103.4% 66.8% 3. ABALONE 5. 90.5% 6. 81.3% 9. 45.9% 45.0%6 101.6% 56.3% 4. HAKE HANDLINE 13. 77.8% 12. 67.8% 6. 60.0% 42.4%15 84.7% 52.9% 5. INSHORE HAKE 2. 93.4% 2. 84.5% 12. 38.7% 40.7%1 105.6% 50.9% 6. PRAWN TRAWL 3. 90.8% 9. 73.7% 14. 28.3% 38.6%2 92.2% 48.3% 7. SHARK LONGLINE 9. 86.6% 3. 83.2% 2. 76.2% 36.4%10 103.9% 45.5% 8. PELAGIC 14. 68.0% 14. 57.2% 3. 62.8% 36.3%5 71.5% 45.4% 9. TUNA BAITBOAT 10. 84.7% 7. 77.2% 4. 61.3% 31.5%12 96.5% 39.4% 10. LINEFISH 12. 79.0% 11. 69.7% 5. 61.1% 29.9%14 87.1% 37.4% 11. SQUID 7. 89.7% 8. 76.6% 13. 37.8% 28.4%7 95.8% 35.5% 12. TUNA LONGLINE 11. 79.8% 13. 62.2% 11. 41.6% 26.8%13 77.8% 33.6% 13. TOOTHFISH 15. 53.3% 15. 52.0% 1. 95.0% 24.4%8 65.0% 30.5% 14. WC ROCK LOBSTER 8. 86.8% 10. 73.7% 10. 42.6% 21.4%9 92.1% 26.8% 15. SC ROCK LOBSTER 1. 98.2% 1. 92.5% 15. 23.2% 16.3%3 115.7% 20.3%


168

hll

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sqtll

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icat

or

Figure 7.8. Scatter plot of fisheries ranked by weighted employment transformation indicators. (Where: scrl: South Coast Rock Lobster, it: Inshore Trawl, dsh: Deep-sea Hake Trawl, pt: Prawn Trawl, sq: Squid, sll: Shark Longline, wcrl: West Coast Rock Lobster, hll: Hake Longline, tbb: Tuna Baitboat, ab: Abalone, tll: Tuna Longline, hhl: Hake Handline, pl: Pelagic, tf: Toothfish. Ti: primary fishing sector (including on-shore support)).

The weighted employment transformation indicator is the most comprehensive of the four indicators

presented. The fisheries that rank highest in this indicator do best in transformation.

The results of the ranking system when including the shore-based primary fishing sector activities to the

vessels only primary fishing sector are counter-intuitive. The expectation is that they should mirror each

other, perhaps with some small discrepancy. This, however, is not the case; it may be due to survey

problems or a real effect. The data is insufficient to test the validity of the discrepancy. The better

indicator, however, would be the vessels only one – it captures a similar pattern to the classification (size

and shape) part of the ESS report – and is more intuitively pleasing. The selection of the better and more

reflective ranking system is left to the discretion of the reader.

7.7.3 Secondary and Tertiary Fishing Sector Activities Secondary and tertiary sector activities that use marine living resources as the main input into production

do not necessarily display market failure; they employ people with different skills, have more opportunity

for returns to scale and generally specialise in processing and marketing (as opposed to fishing)

activities. The incidence of vertical integration by joining the primary, secondary and tertiary activities into

a single company, discussed in Part 2, does not detract from the above facts. They are thus treated

separately.


169


Table 7.9. Secondary and tertiary groups ranked by the percentage of Black employees. (Tb) Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd) Demograhically adjusted ‘follow the buck’ system, (Tad)Demograhically adjusted weighted skills and income differential employment transformation index. %Black Tfb Tb Ta Tfbd Tad 1. PELAGIC 97.1% 90.3% 28.1% 49.6% 112.9% 62.0% 2. HAKE 95.5% 88.6% 36.8% 52.9% 110.8% 66.1% 3. ROCK LOBSTER 95.2% 85.6% 30.1% 33.4% 107.0% 41.7% 4. SQUID 91.2% 85.2% 55.2% 46.7% 106.5% 58.4% 5. ABALONE 90.0% 75.5% 34.2% 58.0% 94.4% 72.5% 6. LINEFISH 89.3% 62.7% 20.2% 22.2% 78.4% 27.8% 7. SHARK 87.5% 75.0% 42.9% 26.6% 93.8% 33.2% 8. PRAWNS 55.4% 32.3% 38.4% 23.0% 40.3% 28.8% The percentage of Black employees in the primary pelagic fishery (68.0%) is lower than for the

corresponding secondary and tertiary group using pelagic fish as a major input (97.1%). The secondary

and tertiary hake related group employs more Black people (95.5%) than the primary hake fisheries,

namely, deep-sea hake (93.4%), inshore trawl (90.5%), hake longline (89.9%) and hake handline

(77.8%). Similarly, the rock lobster, squid, linefish, shark and prawn groups employ a higher percentage

of Black people than in their primary sector counterparts. The abalone fishery alone employs a greater

proportion of Black people in their primary sector activities than the secondary and tertiary sector.

PelagicHake Tps Rock lobster

Squid AbaloneShark

Linefish

Prawns

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

% B

lack

em

ploy

men

t

Figure 7.9. Scatter plot of the secondary and tertiary groups ranked by the percentage of Black employees. (Tps: secondary and tertiary reference point).

The ranking of the fisheries with regard to the percentage Black employment is illustrated on figure 7.9.

With the exception of the Prawn group, all the groups fall within 10 percentage points of each other.


170


Table 7.10. Secondary and tertiary groups ranked by the ‘follow the buck’ in employment. (Tb) Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd)Demograhically adjusted ‘follow the buck’ system, (Tad) Demograhically adjusted weighted skills and income differential employment transformation index. %Black Tfb Tb Ta Tfbd Tad 1. PELAGIC 1. 97.1% 90.3% 28.1% 49.6% 112.9% 62.0% 2. HAKE 2. 95.5% 88.6% 36.8% 52.9% 110.8% 66.1% 3. ROCK LOBSTER 3. 95.2% 85.6% 30.1% 33.4% 107.0% 41.7% 4. SQUID 4. 91.2% 85.2% 55.2% 46.7% 106.5% 58.4% 5. ABALONE 5. 90.0% 75.5% 34.2% 58.0% 94.4% 72.5% 6. SHARK 7. 87.5% 75.0% 42.9% 26.6% 93.8% 33.2% 7. LINEFISH 6. 89.3% 62.7% 20.2% 22.2% 78.4% 27.8% 8. PRAWNS 8. 55.4% 32.3% 38.4% 23.0% 40.3% 28.8%

Apart from the linefish group, the ‘follow the buck’ in employment group rankings are almost exactly the

same as the ranking for the percentage of Black employment per group.

Pelagic Hake Tps Lobster Squid

Abalone Shark

Linefish

Prawns30%

40%

50%

60%

70%

80%

90%

100%

'Fol

low

the

buck

' ind

icat

or

Figure 7.10. Scatter plot of the secondary and tertiary groups ranked by ‘follow the buck’ in employment. (Tps: secondary and tertiary reference point).


171


The difference in annual incomes between Black and White employees across skills levels is ranked

according to secondary and tertiary groups described above.

Table 7.11. Secondary and tertiary groups ranked by average income of Black fishers as a % of average income of White fishers. (Tb) Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd) Demograhically adjusted ‘follow the buck’ system, (Tad)Demograhically adjusted weighted skills and income differential employment transformation index.

%Black Tfb Tb Ta Tfbd Tad 1. SQUID 4. 91.2% 4. 85.2% 55.2% 46.7% 106.5% 58.4% 2. SHARK 7. 87.5% 6. 75.0% 42.9% 26.6% 93.8% 33.2% 3. PRAWNS 8. 55.4% 8. 32.3% 38.4% 23.0% 40.3% 28.8% 4. HAKE 2. 95.5% 2. 88.6% 36.8% 52.9% 110.8% 66.1% 5. ABALONE 5. 90.0% 5. 75.5% 34.2% 58.0% 94.4% 72.5% 6. ROCK LOBSTER 3. 95.2% 3. 85.6% 30.1% 33.4% 107.0% 41.7% 7. PELAGIC 1. 97.1% 1. 90.3% 28.1% 49.6% 112.9% 62.0% 8. LINEFISH 6. 89.3% 7. 62.7% 20.2% 22.2% 78.4% 27.8% The primary fishing sector (vessels only), the primary fishing sector (including on-shore support) and the

secondary and tertiary sector groups all display a general trend; as the percentage of Black people

employed increases, the gap between Black and White annual incomes becomes larger. In other words,

the Black to White average yearly income ratio (or percentage as illustrated in this report) falls the more

labour intensive an operation becomes.

Squid

Tps

Shark

praw nsHake

Abalone

Rock LobsterPelagic

Linefish20%

25%

30%

35%

40%

45%

50%

55%

60%

Aver

age

inco

me

indi

cato

r

Figure 7.11. Scatter plot of the secondary and tertiary groups ranked average income indicators. (Tps: secondary and tertiary reference point).

The spread in terms of actual values of the average income transformation indicators is more pronounced

than for the percentage Black and ‘follow the buck’ in employment indicators. This is illustrated on Figure

7.11.


172


Table 7.12. Secondary and tertiary groups ranked by weighted employment transformation indicators. (Tb) Average income transformation, (Tfb) ‘follow the buck’ system, (Ta) weighted skills and income differential employment transformation index, (Tfbd) Demograhically adjusted ‘follow the buck’ system, (Tad) Demograhically adjusted weighted skills and income differential employment transformation index. %Black Tfb Tb Ta Tfbd Tad 1. ABALONE 5. 90.0% 5. 75.5% 5. 34.2% 58.0% 94.4% 72.5% 2. HAKE 2. 95.5% 2. 88.6% 4. 36.8% 52.9% 110.8% 66.1% 3. PELAGIC 1. 97.1% 1. 90.3% 7. 28.1% 49.6% 112.9% 62.0% 4. SQUID 4. 91.2% 4. 85.2% 1. 55.2% 46.7% 106.5% 58.4% 5. ROCK LOBSTER 3. 95.2% 3. 85.6% 6. 30.1% 33.4% 107.0% 41.7% 6. SHARK 7. 87.5% 6. 75.0% 2. 42.9% 26.6% 93.8% 33.2% 7. PRAWNS 8. 55.4% 8. 32.3% 3. 38.4% 23.0% 40.3% 28.8% 8. LINEFISH 6. 89.3% 7. 62.7% 8. 20.2% 22.2% 78.4% 27.8%

The weighted employment transformation indices place the Abalone group first even though it scores 5th

in the percentage of Black employees (90.0%), ‘follow the buck’ (75.5%) and the average income

indicator (34.5%) which means that the skills distribution is more even. The Pelagic group falls below the

Hake group simply because it does worse in the average income indicator.

Abalone

Hake

Pelagic TspSquid

Rock lobster

Shark

Prawns Linefish20%

25%

30%

35%

40%

45%

50%

55%

60%

Wei

ghte

d em

ploy

men

t ind

icat

or

Figure 7.12. Scatter plot of the secondary and tertiary groups ranked by weighted employment transformation indicators. (Tsp: secondary and tertiary reference point).

Figure 7.12 shows that:

1. The abalone, hake and pelagic secondary and tertiary groups have weighted employment

transformation index above that for the combined secondary and tertiary sectors.

2. The squid group falls by 2.4% below the combined secondary and tertiary sectors transformation

reference point (49.1%).


173

Again discretion should be used when interpreting and using the transformation indices for the secondary

and tertiary fishing industry sectors.

7.8. CONCLUSION

Various types of transformation are implicit in the Marine Living Resources Act (1998); however, social

transformation is the focus area in the ESS report. Social transformation is defined in terms of socio-

economic and economic characteristics, that is, in a normal society where it should not be possible to

distinguish between race and gender based on economic and socio-economic characteristics. Four

measures of transformation in employment are calculated from the ESS survey results and presented in

tabular and graphical form.

Using the weighted employment transformation indicator and according to the ESS survey results:

1. The primary fishing sector (vessels only) is 55.1% transformed and with a demographic

conversion factor, 68.8% transformed.

2. The primary fishing sector (including on-shore support) is 45.3% transformed – 56.6% with a

demographic conversion.

3. The combined secondary and tertiary sectors are 49.1% transformed or 61.4% (demographically

adjusted).



employed as relative measures within fisheries, or secondary and tertiary sector groups. Care should

also be exercised when comparing the transformation indicator of a fishery, or a secondary and tertiary

sector group, with that of the transformation indicators for the primary sector (vessels only) and/or the

primary sector (including on-shore support) and/or the secondary and tertiary sector.

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 8. Socio-economic

174


175

8. SOCIO-ECONOMIC BASELINE AND IMPACT SUMMARY

The importance of this part is to place the demographic information of the South African fishing industry,

from the ESS survey, into a broader social picture.

Socio-economic baseline tables are constructed for the commercial fishing harbour towns and

aggregated into provincial national level. The text in the main body provides a guide to interpretation of

the tables.

The ESS employment, skills and income survey results are arranged according to province. A summary

outlining the percentage distribution of employment in the fishing industry and employment income per

province is presented in the table below. Average provincial yearly incomes are also provided.

The average income per province and the distribution of total fishing industry income and employment per province.

% Income % Employment Average income Northern Cape 0.5% 0.8% R 21,517 Western Cape 71.8% 71.0% R 35,473 North West Coast 10.8% 17.2% R 22,053 West Coast 52.5% 42.9% R 42,861 South West Coast 3.3% 4.5% R 25,715 South East Coast 5.2% 6.4% R 28,895 Eastern Cape 11.0% 11.6% R 33,095 KwaZulu-Natal 1.9% 1.9% R 35,762 Unspecified 14.8% 14.7% R 35,193 TOTAL FISHING INDUSTRY 100.0% 100.0% R 35,227

Due to data compatibility problems, total employment and average incomes are used to determine a

socio-economic impact of the commercial fishing industry on the relevant communities. The figure below

shows the contribution to employment in the relevant communities by the fishing industry.

The contribution to total employment is estimated at 4.8% of the total number of Black people and 0.9%

of all White people working in commercial harbour towns in South Africa. In the Northern Cape Province

13.0% of Black people are employed in the fishing industry, 17.8% in the Western Cape Province, 1.2%

in the Eastern Cape Province and 0.2% in KwaZulu-Natal Province.


176

0

50000

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450000

Num

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empl

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Black White Unspec

The number of people employed in the fishing industry and in other occupations in the South African commercial harbour towns.

The fishing industry pays comparatively high wages to Black people. The figure below indicates the

income group distribution of Black fishing industry workers compared to Black people working in other

industries. The four income groups are in R 000s.

0

20000

40000

60000

80000

100000

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Num

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eopl

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Other 45558 187006 92979 51263


< 6 > 6 - 18 > 18 - 42 > 42 - 360 Income R'000

Annual income by category comparing the number of Black people employed in the fishing industry and in other activities in South African commercial harbour towns.

As a proportion of income earners among Black people in the South African harbour towns, the fishing

industry contributes 1.2% to the low income group (between R6 000 and R18 000 per year), 14.5% to the

middle income group (R18 000 to R42 000 per year) and 3.8% to the high income group (above R42

000). The fishing industry has an insignificant impact on White employment and White income groupings.


177

8.1. INTRODUCTION

The importance of this part is to place the demographic information of the South African fishing industry,

from the ESS survey, into a broader social picture. Census 1996 data is used to construct a series of

tables per harbour town, per province and aggregated into a South African commercial fishing socio-

economic baseline. Because of the large amount of data applicable to the baseline study, the baseline

(including a guide to understanding and interpreting the tables) is placed in appendix 8.1. A brief

provincial overview in light of the ESS survey is provided followed by a socio-economic impact of the

fishing industry in terms of employment and income groups.

8.2. ESS SURVEY: EMPLOYMENT, SKILLS AND INCOME BY PROVINCE

The important characteristics from the ESS survey on employment, skills and income by province are

briefly presented in this section. Detailed ESS survey results are presented in tables 8.3, 8.4 and 8.5.

Table 8.1 below shows the percentage distribution of total employment and employment income, as well

as the average income across skills groups per province. Discrepancies between the percentage

employment and the percentage of employment income are reflected in lower average yearly incomes.

Table 8.1. Provincial income and employment distribution and average income

% Income % Employment Average income Northern Cape 0.5% 0.8% R 21,517 Western Cape 71.8% 71.0% R 35,473 North West Coast 10.8% 17.2% R 22,053 West Coast 52.5% 42.9% R 42,861 South West Coast 3.3% 4.5% R 25,715 South East Coast 5.2% 6.4% R 28,895 Eastern Cape 11.0% 11.6% R 33,095 Kwazulu-Natal 1.9% 1.9% R 35,762 Unspecified 14.8% 14.7% R 35,193 TOTAL FISHING INDUSTRY 100.0% 100.0% R 35,227

The survey indicates that fishing industry employees earn least in the Northern Cape. This is indicative of

the structure of employment; most fishing industry employees in the Northern Cape are involved in ocean

going activities, thus there are few high paying managerial/professional level jobs to raise the average

incomes. This is further illustrated on figures 8.4, 8.5 and 8.6. Total income distribution by province is

illustrated on figure 8.1 below and total employment distribution by province on figure 8.2.


178

NC0.5%

WC71.8%

KZN1.9%

EC11.0%

Unspecified14.8%

Figure 8.1. Total fishing industry income distribution between provinces.

NC0.8%

WC71.0%

EC11.6%

KZN1.9%

Unspecified14.7%

Figure 8.2. Total fishing industry employment distribution between provinces. The ESS survey results on skills distribution per province is presented by total number in table 8.2 and as

figure 8.3. The fact that most people employed in the fishing industry are in a semi-skilled category is

highlighted. Linking the skills classification outlined in the baseline (appendix 8.1) shows a discrepancy

between skills levels. The original intention was to lump the skills categories into five groups. Agricultural

and fishery related workers are recorded by StatsSA in the unskilled category. It is unanimously agreed

in the fishing industry that these workers should be classified under the semi-skilled category.


179

Table 8.2. Fishing industry skills distribution by province

P/M Skilled MiddleServices Semi-skilled Un-skilled Total

Northern Cape 1 12 4 157 0 174 Western Cape 298 1625 338 11565 2077 15903 Eastern Cape 28 270 36 2202 66 2602 KwaZulu-Natal 4 86 24 300 14 428 Unspecified 67 495 19 2512 208 3301 TOTAL INDUSTRY 398 2488 421 16736 2365 22408

P/M: professional/managerial

The Western Cape is the major fishing province in South Africa; it employs 71% of all fishing industry

employees and pays out 71.8% of total income accruing to workers in the fishing industry.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

NorthernCape

WesternCape

EasternCape

Kw aZulu-Natal

Unspecif ied Industry


Figure 8.3. Histogram of fishing industry skills distribution by province.

Of particular interest are the discrepancies in average yearly income in each province. Figure 8.4

illustrates that KwaZulu-Natal and the Western Cape provinces pay more on average than the Eastern

Cape and Northern Cape provinces.


180

KZN WC Us Industry

EC

NC20,000

22,000

24,000

26,000

28,000

30,000

32,000

34,000

36,000

38,000

Aver

age

inco

me

Figure 8.4. Scatter plot of yearly average income by province. (KZN: KwaZulu-Natal, WC: Western Cape, Us: unspecified, EC: Eastern Cape, NC: Northern Cape).

Figure 8.5 displays the differences in yearly average incomes between provinces of Black employees in

the fishing industry.

Us WC Industry

KZN EC

NC20,000

22,000

24,000

26,000

28,000

30,000

32,000

34,000

36,000

38,000

Aver

age

inco

me

(Bla

ck)

Figure 8.5. Scatter plot of Black yearly fishing industry income per province. (KZN: KwaZulu-Natal, WC: Western Cape, Us: unspecified, EC: Eastern Cape, NC: Northern Cape).


181

Table 8.3. The number of fishing industry employees by skills group, part-time or full-time employment and race by province.

Total Professional Skilled Middle services Semi-skilled Unskilled

All Black White Full-time Part-time Full-time Part-time Full-time Part-time Full-time Part-time Full-time Part-time

Black White Black White Black White Black White Black White Black White Black White Black White Black White Black White

Northern Cape 174 173 1 1 3 9 4 51 105 1

Western Cape 15903 14414 1489 143 138 15 2 759 401 216 249 271 63 4 6351 294 4584 336 989 2 1082 4

North west coast 3864 3768 96 20 25 3 2 123 30 17 93 16 3 721 11 1967 10 446 1 375 1

West coast 9612 8656 956 111 92 12 492 234 185 240 157 31 1 4561 132 1991 223 449 1 697 3

South west coast 1004 852 152 6 11 56 42 11 3 6 13 357 71 325 12 91

South east coast 1423 1138 285 6 10 88 95 3 6 15 3 712 80 301 91 3 10

Eastern Cape 2602 2327 275 15 11 2 63 70 49 88 24 12 677 23 1431 71 3 63

KwaZulu-Natal 428 334 94 4 36 41 2 7 0 0 5 19 185 12 92 11 14

Unspecified 3301 2821 480 38 25 3 1 256 115 70 54 16 3 1390 64 868 190 180 28

TOTAL 38311 34483 3828 340 312 35 9 1876 1028 562 647 582 141 17 19 15005 687 11664 945 1981 4 2421 36

Table 8.4. Total income of fishing industry employees by skills group, part-time or full-time employment and race by province.


All Black White Full-time Part-time Full-time Part-time Full-time Part-time Full-time Part-time Full-time Part-time


Northern Cape 3,744 3,732 12 36 84 108 144 2,100 1,260 12

Western Cape 564,132 465,564 98,568 17,130 20,388 252 45,972 28,206 11,382 20,556 17,766 3,870 36 215,448 10,740 120,306 14,700 20,076 36 17,196 72

North west coast 85,212 78,240 6,972 2,316 3,120 18 4,284 2,280 192 5,508 954 15,324 354 36,378 228 9,822 18 4,398 18

West coast 411,984 336,474 75,510 13,884 15,216 234 36,804 18,870 10,854 20,196 11,448 1,962 36 176,112 7,290 66,192 11,904 8,292 18 12,618 54

South west coast 25,818 20,922 4,896 390 1,134 1,242 1,458 198 108 270 846 5,898 1,026 11,016 324 1,908

South east coast 41,118 29,928 11,190 540 918 3,642 5,598 138 252 540 108 18,114 2,070 6,720 2,244 54 180

Eastern Cape 86,112 68,340 17,772 1,542 1,068 180 4,722 4,242 2,100 9,762 1,602 414 19,320 738 37,902 1,548 54 918

KwaZulu-Natal 15,306 9,972 5,334 600 2,886 2,412 24 234 450 1,710 4,914 192 1,446 186 252

Unspecified 116,172 91,158 25,014 1,968 3,210 36 150 20,178 8,184 3,318 4,872 576 108 38,724 1,752 21,408 6,144 4,950 594

TOTAL 1,349,598 1,104,330 245,268 37,806 45,054 720 750 119,814 71,250 28,314 55,980 37,710 8,262 666 1,710 495,954 24,162 302,628 37,290 40,206 72 40,512 738


182

Table 8.5. Average income of fishing industry employees by skills group, part-time or full-time employment and race by province.


All Black White Full time Part-time Full time Part-time Full time Part-time Full time Part-time Full time Part-time


Northern Cape 21,517 21,572 12,000 36,000 28,000 12,000 36,000 41,176 12,000 12,000

Western Cape 35,473 32,299 66,197 119,790 147,739 16,800 60,569 70,339 52,694 82,554 65,557 61,429 9,000 #DIV/0! 33,923 36,531 26,245 43,750 20,299 18,000 15,893 18,000

Eastern Cape 33,095 29,368 64,625 102,800 97,091 90,000 74,952 60,600 42,857 110,932 66,750 34,500 28,538 32,087 26,486 21,803 18,000 14,571

KwaZulu-Natal 35,762 29,856 56,745 150,000 80,167 58,829 12,000 33,429 90,000 90,000 26,562 16,000 15,717 16,909 18,000

Unspecified 35,193 32,314 52,113 51,789 128,400 12,000 150,000 78,820 71,165 47,400 90,222 36,000 36,000 27,859 27,375 24,664 32,337 27,500 21,214

TOTAL 35,227 32,025 64,072 111,194 144,404 20,571 83,333 63,867 69,309 50,381 86,522 64,794 58,596 39,176 90,000 33,053 35,170 25,945 39,460 20,296 18,000 16,734 20,500


183

As has been illustrated in Part 7: Understanding and Measuring Transformation, Black yearly incomes are

below that of their White counterparts. This is further illustrated on figure 8.6 below (note that there is

only one White fishing industry employee in the Northern Cape).

WC Us

NC

KZN Industry ECWC Us

NC

KZN IndustryEC

WC

Us

NC

KZN

Industry EC

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000Av

erag

e in

com

e

All Black White

Figure 8.6. Scatter plot of Black, White and aggregated averaged yearly fishing industry incomes per province. Figure 8.6 clearly illustrates that in all provinces Black people in the fishing industry are paid less per year

than White people. The Eastern Cape and the Western Cape have the largest yearly income differentials.

The income differences are not necessarily a result of discriminatory pay or employment practice, but

may be the outcome from a racially skewed skills distribution (see Part 7: Understanding and Measuring

Transformation).

8.3. SOCIO-ECONOMIC IMPACT

The socio-economic impact study is confined to dealing with the effect that employment and income from

the fishing industry has on the local coastal harbour towns in aggregate and by province. Skills

compositions are not included as the skills grouping in the census data is not consistent with the skills

groupings that were adapted at the time of the ESS survey.

Table 8.6 provides a summary breakdown of the 1996 census data applicable to each province as

employment indicators. As stated in appendix 8.1, the extended labour force (ELF) is greater than the

conventional labour force (CLF) because it includes people who are able to work but are not actively

seeking jobs. The employed and unemployed in table 8.6 make up the ELF. The number of income

earners is larger than the number of employed people; some people who are not employed earn an

income, for example pensioners.


184

Table 8.6. Important socio-economic indicators for the employment impact of the fishing industry.

Population CLF ELF Employed Unemployed Income earnersBlack 4783 2070 3082 1336 1746 1843 White 429 191 284 167 117 147

Unspecified 41 12 21 9 12 18 Northern

Cape Total 5253 2273 3387 1512 1875 2008 Black 225651 101253 145471 81185 64286 94858 White 159110 70762 104386 67325 37061 91297

Unspecified 15273 4928 7207 4391 2816 4577 Western

Cape Total 400034 176943 257064 152901 104163 190732 Black 846928 328349 547497 186537 360960 253537 White 204294 89964 134950 85757 49193 111536

Unspecified 8198 2393 4138 1807 2331 2121 Eastern Cape

Total 1059420 420706 686585 274101 412484 367194 Black 428568 203517 297016 150053 146963 172871 White 225539 104023 149395 98277 51118 132191

Unspecified 9060 3058 4724 2432 2292 2739 KwaZulu

Natal Total 663167 310598 451135 250762 200373 307801 Black 1505930 635189 993066 419111 573955 523109 White 589372 264940 389015 251526 137489 335171

Unspecified 32572 10391 16090 8639 7451 9455

Coastal Harbour Towns

Total 2127874 910520 1398171 679276 718895 867735 Source: Census 1996

CLF: Conventional labour force, ELF: extended labour force

The socio-economic impact of fishing industry employment is discussed firstly at provincial level then

aggregated to include all commercial fishing harbour towns.

8.3.1 The Northern Cape Province

The total population of Port Nolloth and Hondeklip Bay is 5 253 people with 64.5% making up the ELF. In

terms of the total population 28.8% have work. Employment by race group is 27.9% Black, 38.9% White

and 22.0% unspecified. Black people make up 91.1% of the total population. Figure 8.7 displays this

information by number.


185

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Num

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f peo

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Unemployed 1746 117 12

Employed 1336 167 9

Not w orking 1701 145 20

Black White Unspec

Figure 8.7. The number of people employed, unemployed and not working, in the Northern Cape Province commercial harbour towns.

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Black White Unspec

Figure 8.8. The number of people employed in the fishing industry and other employment in Northern Cape Province commercial harbour towns.

Figure 8.8 indicates that about 13% of Black people with employment work in the fishing industry in the

applicable Northern Cape commercial fishing towns. The one White person captured by the ESS survey

indicates a negligible White involvement in the Northern Cape fishing industry.

The number of people earning income in the Northern Cape commercial harbour towns is illustrated on

figure 8.9.


186

0

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Num

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f peo

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earn

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inco

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< 6 000 776 24 5

> 6 000 - 18 000 698 23 10

> 18 000 - 42 000 294 47 3

> 42 000 - 360 000 75 53

Black White Unspecif iedIncome R'000

Figure 8.9. The number of people earning income by income group in the Northern Cape Province commercial harbour towns.

Figure 8.9 indicates that on average Black people earn lower incomes than White people. This trend

occurs throughout the relevant coastal commercial fishing towns in South Africa.

Although all income earners are not all employed, it is still important to determine the number of people

who rely on the fishing industry for livelihoods, that is, for earning an income. The income groups of the

people working in the fishing industry give an indicator of their socio-economic well-being and prospects.

Obviously, the higher the income group the better off those people are in terms of socio-economic criteria.

Figure 8.10 shows the number of people per income group who work in the fishing industry and those

who earn income elsewhere. As there is one observation of a White person working in the fishing

industry, only Black people are considered in this province.

There are no fishing industry workers in the lowest yearly income group. About 1.7% of all income

earners in the R6 000 to R18 000 Black income group are fishing industry people. The majority of Black

middle-income earners (greater than R18 000 per year to R42 000 per year) work in the fishing industry

(54.8%).


187

0

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Num

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eopl

e ea

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Other 776 686 133 75

Fishing industry 12 161

< 6 > 6 - 18 > 18 - 42 > 42 - 360 Income R'000

Figure 8.10. Annual income by category comparing the number of Black people employed in the fishing industry and in other activities in the Northern Cape Province commercial harbour towns.

The fishing industry is an important contributor to the socio-economic well-being of people in the relevant

coastal communities in the Northern Cape Province. The province is, however, very small in comparison

with the entire South African coastal harbour socio-economy – it makes up 0.25% of the relevant

population, equivalent to Elandsbaai or about 30% of Port Alfred, which is considered a small Eastern

Cape town.

8.3.2 The Western Cape Province

The total population of the Western Cape Province harbour towns is 400 034 people – 64.5% making up

the ELF. In terms of the total population 38.2% are employed. By race group 36.0% of the Black

population have work, 42.3% White and 28.8% unspecified are employed. Black people make up 56.4%

of the total population and White people 39.8%, the unspecified group comprise the balance. The

population distribution by race for all the harbour towns is 70.8% Black, 27.7% White and 1.5%

unspecified. The population structure of the Western Cape Povince is not typical of either the harbour

town group or South Africa as a whole.

The Western Cape Province is the largest fishing industry contributor to employment and income (71.0%

of total fishing industry employment and 71.8% of employment income). Its population comprises 18.8%

of the relevant commercial fishing coastal town in South Africa. Figure 8.11 provides this information in

numbers.


188

0

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100000

150000

200000

250000

Num

ber o

f peo

ple

Unemployed 64286 37061 2816

Employed 81185 67325 4391

Not w orking 80180 54724 8066

Black White Unspec

Figure 8.11. The number of people employed, unemployed and not working, in the Western Cape Province commercial harbour towns.

As is stated previously, the first point of focus is on the contribution of employment in the province by the

fishing industry. For the Western Cape Province, this is illustrated on Figure 8.12 below.

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

Num

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empl

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Black White Unspec

Figure 8.12. The number of people employed in the fishing industry and other employment in Western Cape Province commercial harbour towns.

The fishing industry, according to 1996 census data139 and the ESS survey, employs 17.8% of all Black

people in the relevant commercial fishing coastal towns in the Western Cape Province.

139 Comparing employment figures from the 1996 census and the 2000 ESS data will most likely result in understating the employment contribution by the fishing industry resulting from falling employment numbers in South Africa. This is, of course, assuming that the fishing industry has not shed labour at the same rate as the rest of the economy.


189

0

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Num

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< 6 000 24576 8492 698

> 6 000 - 18 000 43404 15420 1220

> 18 000 - 42 000 19111 28217 1408

> 42 000 - 360 000 7767 39168 1251


Figure 8.13. The number of people earning income (in yearly income groups) in the Western Cape Province commercial harbour towns.

0

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Num

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eopl

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Other 24576 41333 8176 6359


< 6 > 6 - 18 > 18 - 42 > 42 - 360 Income R'000

Figure 8.14. The number of Black income earners by income group in the fishing industry and other activities in the Western Cape Province commercial harbour towns.

Figure 8.13 indicates a similar trend to the rest of the country, and the commercial fishing towns, that on

average Black people earn lower incomes than White people. Figure 8.14 provides good evidence that

Black fishing industry employees and entrepreneurs earn more than their counterparts involved in other

activities. That is the median yearly income of fishing industry workers falls into the R18 000 to R42 00

group but those not working in the fishing industry fall into the R6 000 to R18 000 group.


190

0

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10000

15000

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25000

30000

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Num

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te p

eopl

e ea

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g in

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e

Other 8492 15414 27587 38315


< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.15. The number of White income earners (R 000s) in the fishing industry and other activities in the Western Cape Province commercial harbour towns.

0

10000

20000

30000

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50000

60000

70000

Num

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f peo

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earn

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Other 33766 57967 37171 45925


< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.16: The number of all income earners (R 000s) in the fishing industry and other activities in the Western Cape Province commercial harbour towns.

The percentage of fishing industry workers to the Black group, the White group and all income earners is

presented in table 8.7 below.

Table 8.7. Percentage contribution to yearly income earning groups by the fishing industry in the Western Cape Province.

>R6 000 – R18 000 >R18 000 – R42 000 >R42 000 – R360 000 Black 4.7% 57.2% 18.1% White 2.2% 2.2% All 3.5% 23.7% 4.7%

Table 8.7 provides a clear picture that the fishing industry is an important socio-economic contributor in

the Western Cape Provincial commercial fishing towns.


191

8.3.3 The Eastern Cape Province

The total population of the Eastern Cape Province harbour towns is 1 059 420 people – 64.6% making up

the ELF. In terms of the total population, 25.9% are employed. By race group 22.0% of the Black

population have work, 42.0% White and 22.0% unspecified are employed. Black people make up 79.9%

of the total population and White people 19.3%, the unspecified group comprise the balance.

The Eastern Cape, and KwaZulu-Natal in section 3.4, will be illustrated graphically in terms of a

population breakdown and the contribution of the fishing industry to employment and income earning

groups. However, compared to the Western Cape and Northern Cape, neither province is important from

a fishing industry point of view. The discussion is thus kept to a minimum.

0

200000

400000

600000

800000

1000000

Num

ber o

f peo

ple

Unemployed 360960 49193 2331

Employed 186537 85757 1807

Not w orking 299431 69344 4060

Black White Unspec

Figure 8.17. The number of people employed, unemployed and not working, in the Eastern Cape Province commercial harbour towns.

Figure 8.18 shows, in number terms, that the fishing industry contributes 1.25% of Black employment and

0.3% to White employment in the Eastern Cape Province’s commercial fishing towns.


192

0

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140000

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180000

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Black White Unspec

Figure 8.18. The number of people employed in the fishing industry and other employment in Eastern Cape Province commercial harbour towns.

The income earnings distribution in the Eastern Cape Province is similar to the other provinces.

However, the median income group in the Northern Cape Province and the Eastern Cape Province falls

into the lowest income earning bracket (less than R6 000 per year). This indicates high levels of poverty

in both of these provinces.

0

20000

40000

60000

80000

100000

120000

Num

ber o

f peo

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earn

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< 6 000 100882 11013 455

> 6 000 - 18 000 87757 20662 598

> 18 000 - 42 000 46489 36085 547

> 42 000 - 360 000 18409 43776 521


Figure 8.19. The number of people earning income (divided into yearly income group) in the Eastern Cape Province commercial harbour towns.


193

0

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80000

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120000

Num

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Other 100882 87757 46489 18409


< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.20. The number of Black income earners (R 000s) in the fishing industry and other activities in the Eastern Cape Province commercial harbour towns.

Figure 8.20 indicates that 4.3% of Black people earning between R18 000 and R42 000 per year in the

commercial harbour towns in the Eastern Cape Province work in the fishing industry. White fishing

industry workers make up 0.3% of the higher income group (greater than R42 000 per year).

0

5000

10000

15000

20000

25000

30000

35000

40000

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50000

Num

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Other 11013 20662 36085 43776


< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.21. The number of White income earners (R 000s) in the fishing industry and other activities in the Eastern Cape Province commercial harbour towns.


194

0

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100000

120000

Num

ber o

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earn

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inco

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Other 112350 108951 80907 62384


< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.22. The number of all income earners (R 000s) in the fishing industry and other activities in the Eastern Cape Province commercial harbour towns.

In the Eastern Cape Province’s commercial fishing harbour towns the fishing industry contributes 1.25%

to Black employment and 4.3% to the Black middle income group. From a White socio-economic point of

view, the fishing industry accounts for 0.3% of White employment and 0.3% of the upper income bracket.

8.3.4 KwaZulu-Natal Province

The KwaZulu-Natal Province has a total commercial harbour town population of 663 167 people, 64.6%

of whom are Black and 68.0% are part of the ELF.

0

100000

200000

300000

400000

500000

Num

ber o

f peo

ple

Unemployed 146963 51118 2292

Employed 150053 98277 2432

Not w orking 131552 76144 4336

Black White Unspec

Figure 8.23. The number of people employed, unemployed and not working, in the KwaZulu-Natal Province commercial harbour towns.


195

A total of 31.9% of the population are employed (27.8% of the Black population have employment and

42.7% of the White population). The fishing industry contributes 0.2% to Black employment and 0.1% to

White employment in the commercial fishing harbour towns of KwaZulu-Natal (figure 8.24).

0

20000

40000

60000

80000

100000

120000

140000

160000N

umbe

r of p

eopl

e em

ploy

ed



Black White Unspec

Figure 8.24. The number of people employed in the fishing industry and other employment in KwaZulu-Natal Province commercial harbour towns.

The median income for Black income earners falls into the R6 000 to R18 000 group. Poverty in the

KwaZulu-Natal harbour towns is not as serious as in the Northern Cape Province and the Eastern Cape

Province.

0

10000

20000

30000

40000

50000

60000

70000

Num

ber o

f peo

ple

earn

ing

inco

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< 6 000 45558 11884 514

> 6 000 - 18 000 57478 21985 730

> 18 000 - 42 000 42819 40951 720

> 42 000 - 360 000 27016 57371 775


Figure 8.25. The number of people earning income (in yearly income groups) in the KwaZulu-Natal Province commercial harbour towns.


196

0

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30000

40000

50000

60000

70000

Num

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f Bla

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eopl

e ea

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com

e

Other 45558 57464 42542 26973


< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.26. The number of Black income earners (R 000s) in the fishing industry and other activities in the KwaZulu-Natal Province commercial harbour towns.

As with the commercial harbour towns in the other three provinces, the fishing industry contributes (0.6%)

more than proportionately to the middle income group of Black income earners (figure 8.26). Contribution

to the White income earners falls into two distinct groups (figure 8.27), lower income (0.1%) and upper

income (0.1%).

0

10000

20000

30000

40000

50000

60000

70000

Num

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Other 11884 21962 40951 57300


< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.27. The number of White income earners (R 000s) in the fishing industry and other activities in the KwaZulu-Natal Province commercial harbour towns.


197

0

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90000

Num

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Other 57956 80156 84213 85048

Fishing industry 0 37 277 114

< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.28. The number of all income earners (R 000s) in the fishing industry and other activities in the KwaZulu-Natal Province commercial harbour towns.

The fishing industry contributes 0.2% to Black employment and 0.6% to the Black middle income group in

the KwaZula-Natal Province’s commercial fishing harbour towns. The fishing industry accounts for 0.1%

of White employment and 0.1% of the upper income bracket.

8.3.5 South African Commercial Harbour Towns

The commercial fishing harbour towns have a total population of 2 127 874 people, about 10% of South

Africa’s total population, of whom 70.8% are Black.

0

500000

1000000

1500000

2000000

Num

ber o

f peo

ple

Unemployed 573955 137489 7451

Employed 419111 251526 8639

Not w orking 512864 200357 16482

Black White Unspec

Figure 8.29. The number of people employed, unemployed and not working, in South Africa’s commercial harbour towns.


198

From the total population, 31.9% are employed. Using a conventional labour force (CLF) measure,

25.4% are unemployed and 51.4% if taken from an extended labour force (ELF) measure. Using CLF,

34.0% of Black people are unemployed (5.1% of White people) in these regions and 57.8% (35.3% of

White people) using an ELF measure. The discrepancy in measures for White people is probably

because most unemployed Whites are not actively seeking work.

0

50000

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150000

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250000

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400000

450000

Num

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Black White Unspec

Figure 8.30. The number of people employed in the fishing industry and other employment in South Africa’s commercial harbour towns.

Figure 8.30 focuses on those employed and is divided between people working in the fishing industry and

those involved in other activities. The fishing industry is more important to overall Black employment in

South Africa’s harbour towns. It employs 4.8% of all Black people and 0.9% of all White people working

in these regions.

There are 867 735 people, or 40.8% of the population, who earn income of some kind or the other (not

necessarily employment income). By race, 34.7% of Black people earn an income compared with 56.9%

of White people.

Table 8.8. Percentages of Black and White people in different income groups.

Income group % of Black people earning an income % of White people earning an income

< R6 000 32.8% 9.4% > R6 000 – R18 000 36.2% 17.3% > R18 000 – R42 000 20.8% 31.4% > R42 000 10.2% 41.9% 100% 100%

As is evident in each province, the structure of income earners between Black people and White people is

substantially different. This is illustrated on table 8.8 and figure 8.31. The majority of Black income

earners (36.2%) fall into the low income group (R6 000 to R18 000 per year) where most White income

earners (41.9%) have an income above R42 000 per year.


199

0

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150000

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Num

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f peo

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earn

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inco

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< 6 000 171792 31413 1672

> 6 000 - 18 000 189337 58090 2558

> 18 000 - 42 000 108713 105300 2678

> 42 000 - 360 000 53267 140368 2547


Figure 8.31. The number of people earning income (in yearly income groups) in South Africa’s commercial harbour towns.

As a proportion of income earners among Black people in the South African harbour towns, the fishing

industry contributes 1.2% to the low income earning group, 14.5% to the middle income earning group

(R18 000 to R42 000 per year) and 3.8% to the high income earning group.

0

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Other 45558 187006 92979 51263


< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.32. The number of Black income earners (R 000s) in the fishing industry and other activities in South Africa’s commercial harbour towns.

In terms of income earning by White people in South Africa’s commercial harbour towns, the fishing

industry is less important. The fishing industry contributes 1.0% to the White middle income earning

group and 0.9% to the White high income earning group. This is illustrated on figure 8.33.


200

0

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Num

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Other 31413 58090 104264 139065


< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.33. The number of White income earners (R 000s) in the fishing industry and other activities in South Africa’s commercial harbour towns.

The overall contribution to income earning by the fishing industry is 0.9% to the low income group, 7.7%

to the middle income group and 1.7% to the high income group.

0

50000

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Num

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Other 204877 247654 199921 192875

Fishing industry 0 2331 16770 3307

< 6 > 6 - 18 > 18 - 42 > 42 - 360

Figure 8.34. The number of all income earners (R 000s) in the fishing industry and other activities in South Africa’s commercial harbour towns.

No individuals captured by the ESS survey earned below the breadline (less than R6 000 per year).


201

The ESS survey captured 22 408 people working in the fishing industry (an estimated sample size of 60%

of primary sector and 83% of secondary and tertiary sector). Based on the sample statistic and census

1996 data for the South African harbour towns, the fishing industry employs 4.8% of all Black people

working in the regions. It also contributes 1.2% of Black incomes to the Black low income group, 14.5%

to the Black middle income group and 3.8% to the Black high income group. Similarly, it employs 0.9% of

all White people working in commercial harbour towns and contributes 1.0% to the White middle income

group and 0.9% to the White high income group.

8.4. CONCLUSION

The socio-economic contribution in terms of employment and income by the fishing industry is important

to Black people in the South African commercial harbour towns. This is particularly relevant in the

Western Cape Province and the Northern Cape province. The fishing industry is not that important to the

contribution to White employment and income earnings.


202


203

APPENDIX 8.1: SOCIO-ECONOMIC BASELINE OF SOUTH AFRICA’S COMMERCIAL HARBOURS: 1996 CENSUS

A8.1 INTRODUCTION The baseline study attempts to provide a broad snapshot picture of the socio-economic status140 at the

time of the 1996 population census. The baseline study deals with:

1. The aggregated South African coastal communities where living marine resources are commercially

landed.

2. Coastal communities by province (in the case of the Western Cape Province into four coastal

regions) where living marine resources are commercially landed.

3. The coastal communities by harbour where living marine resources are commercially landed.

Marine and Coastal Management provided the following list of commercial fishing harbour towns.

Northern Cape Province Port Nolloth Hondeklip Bay

Western Cape Province north west coast Doringbaai

Lamberts Bay

Elandsbaai

Velddrif

St Helenabaai

Paternoster

west coast Saldana Bay

Yzerfontein

Cape Town (MD)

Hout Bay Harbour

Kommetjie

Simons Town

south west coast Kalk Bay

Strand

Gordons bay

Kleinmond

Hermanus

Gansbaai

140 The socio-economic status deals with the economic well-being of people in their society.


204

south east coast Struisbaai

Arniston

Mossel Bay

Knysna

Plettenberg Bay

Eastern Cape Province Jeffreys Bay/St Francis

Port Elizabeth

Port Alfred

East London

KwaZulu-Natal

Port Shepstone

Durban

Richards Bay

Mthunzini

St Lucia

A8.2 THE 1996 CENSUS DATA The 1996 population census data is used to capture the socio-economic status necessary for this study.

The purpose of selecting this database is that it provides a relatively comprehensive, comparable and

easily accessible set of data that can be disaggregated down to the enumerator level. Disaggregation, as

pointed out above, is taken to harbour level where fish are commercially landed. There are, however,

many problems with the data set that should be taken into consideration when interpreting and using this

base line study. The most important ones are:

1. Comparative data hve not been incorporated, either from other parts of South Africa or other data

sources. The results should therefore be considered as a hypothesis that may be confirmed or

refuted by application of further data. In particular, levels of in- and out-migration to a geographical

area cannot be measured.

2. The survey represents a snapshot picture on the day of the census during 1996. It is reasonable,

and is necessary, to assume that very little socio-economic or economic structural change has

occurred in the relevant communities since 1996. However, bearing in mind the recent building of

the St Francis Bay harbour, demographic information is used from Jeffreys Bay in an attempt to

capture the expected socio-economic status of that region.

3. The specific questions asked during the census bring to bear much interpretation and validity issues

which will not be dealt with here. In addition, the literacy levels as well as the willingness and ability

of individuals to complete honestly the questions asked during the census also present problems

with the data.


205

4. Where specific harbour towns are small, thus a small statistical sample, results drawn are specific to

that group and cannot be generalised. To compound this problem the government statistician, for

purposes of privacy, has to randomise observations of less than six.

Limitations aside, a significant amount of useful information can be provided by this data. In addition, it is

the only relatively complete data set that is available. A8.3 INTERPRETING THE DATA To avoid unnecessary duplication, an explanation of the construction of the socio-economic tables from

the census data is presented. The baseline study is presented in four parts. An aggregate picture of the

population structure is established in the first section, followed by non-labour market measures of socio-

economic welfare. The third and fourth sections deal with access to the labour market and a

classification of skills, respectively. The HRG group represents the Historically Repressed Group

(consisting of African/Black, Coloured and Indian/Asian).

A8.3.1 Population structure This section provides a basic human demography.

¶ Tables A8.2.1-9 (section A8.4 below) show the racial and gender demographics in whole numbers

(values less than six are randomised).

589372

1505930

32572

White HRG Unspecif ied

Figure A8.1. The population structure, of the SA coast by racial group (Source data: Census 1996).


206

283454

305918

726472

779458

32572

White male White female HRG male HRG female Unspecified

Figure A8.2. The population structure, of the SA coast by gender and racial group (Source data: Census 1996). Figures A8.1, A8.2 and A8.3 display the racial and gender demographics of the 2.1 million people

surveyed in South Africa’s harbour towns during the 1996 census. This comprises approximately 10% of

the country’s population.

¶ Tables A8.3.1-9 (section A8.4 below) represent the racial and gender demographics in percentage

terms for the SA Coast and provinces.

0

200000

400000

600000

800000

1000000

1200000

Unspecified 9060 8198 41 15273

HRG 428568 846928 4783 225651

White 225539 204294 429 159110

KwaZulu-Natal Eastern Cape Northern Cape Western Cape

Figure A8.3: The population structure by racial group and province (Source data: Census 1996).


207

27.7%

70.8%

1.5%

White HRG Unspecif ied

Figure A8.4. The population structure (%) of the SA coast by racial group (Source data: Census 1996).

¶ Tables A8.4.1-9 (section A8.4 below) show the degree to which males outnumber females with

regard to the working age population. The values are formulated by taking prime working age females

as a ratio of the prime working age population (26-55).141 If, for example, the ratio of working age

females is approximately half of the working age population, then the area might not be affected by

in- or out-migration of the type that is sex specific.

¶ Tables A8.5.1-9 (section A8.4 below) give the age structure of the population by taking the ratio of

working age population to children. This provides further indication of population mobility. A ratio

below one is expected in a stable geographically static population. If this ratio is above one it

suggests age specific migration. A ratio above one, suggests that for every ten children there are

more than ten prime age working adults. There are two basic reasons for this. Firstly, it could

indicate that some of the children have moved to another location. For example, it may be that the

children are scholars attending boarding school outside the area. Secondly, it may be that there is

migration of prime working age people into the area.

A8.3.2 Non labour market indicators of wealth and deprivation This section discusses indicators of wealth and deprivation using non labour market factors. The tables

as follows:

¶ Tables A8.6.1-9 (section A8.4 below) give a measure of personal income by race and gender.

Although in essence this is labour market determined, there is only an indirect relationship.

141 The prime working age, 26-55, excludes scholars, students and early-retired people.


208

¶ Tables A8.7.1-9 (section A8.4 below) look at the percentage of individuals, by race and gender, below

the minimum income range (R116 per week). In most cases it is apparent that the White category is

the wealthiest group. It is also evident that there are differences between genders within specific

racial groups. In particularly when looking at the African population, women have dramatically lower

access to income opportunities.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

White 16.3% 9.9% 9.0% 9.3% 9.4%

HRG 42.1% 39.8% 26.4% 25.9% 32.8%

Unspecified 27.8% 21.5% 18.8% 15.3% 17.7%

Northern Cape

Eastern Cape

KwaZulu-Natal

Western Cape SA Coast

Figure A8.5. The percentage of individuals by race and gender that earn below the minimum income range (Data source: Census 1996).

¶ Tables A8.8.1-7 (section A8.4 below) look at the percentage differences in housing types by racial

group. This provides one indicator of socio-economic status. Generally, the tables indicate that the

majority of Whites live in formal housing and Blacks in informal housing. The previous income

distribution tables reflect this.

A8.3.3 Patterns of labour market access

The conventional labour force (CLF) is defined as people who are working plus those who are actively

searching for work. Only the group actively searching for work, but who have no employment are

classified as unemployed – conventional unemployment (CU). The unemployed are included in the

labour force because they are regarded as being available for work. The labour force participation rate

(LFPRC) is a measure of the labour force as a percentage of working age population (ages 16 to 60).

This basically indicates the percentage of the working age population who actively involved in some form

of work or are actively seeking work. The extended labour force (ELF) includes the conventionally

measured labour force plus those who are not actively searching for work.


209

¶ Tables A8.9.1-9 (section A8.4 below) indicate conventional unemployment142 rates and the labour

force participation rates.

0%5%

10%

15%20%25%30%35%

40%45%50%

White 5.5% 4.7% 12.6% 4.9% 5.2%

HRG 26.3% 43.2% 35.5% 19.8% 34.4%

Unspecif ied 20.5% 24.5% 25.0% 10.9% 17.0%

Kw aZulu-Natal Eastern Cape Northern Cape Western Cape SA Coast

Figure A8.6. Conventional unemployment rates (%) by racial group (Source data: Census 1996).

0%

10%

20%

30%

40%

50%

60%

70%

80%

White 69.6% 66.7% 67.3% 67.8% 68.2%

HRG 68.5% 60.0% 67.2% 69.6% 64.1%

Unspecified 64.7% 57.8% 57.1% 68.4% 64.7%

KwaZulu-Natal Eastern Cape Northern Cape Western Cape SA Coast

Figure A8.7. Labour force participation rates (LFPRC) by racial group (%) (Source data: Census 1996).

¶ Tables A8.10.1-9 (section A8.4 below) provide a measure of the conventional labour force in

whole numbers, as mentioned above (people who work and the unemployed).

142 Conventional unemployment is merely the unemployed, who are looking for work but cannot find a job.


210

0

50000

100000

150000

200000

250000

300000

350000

White 191 89964 104023 70762

HRG 2070 328349 203517 101253

Unspecified 12 2393 3058 4928

Northern Cape

Eastern Cape

KwaZulu-Natal

Western Cape

Figure A8.8. The conventional labour force by racial group and province (Source data: Census 1996).

¶ Tables A8.11.1-9 (section A8.4 below) give the number of people in the extended labour force.

¶ Tables A8.12.1-9 (section A8.4 below) give the values, in percentage terms, for the conventional

labour force (CLF) and the extended labour force (ELF) in terms of participation rates. Generally,

labour force participation is highest among men, corroborating the observation that women have

lower access to personal income.

The difference between conventional unemployment (CU) as a percentage of the conventional labour

force (CLF) and extended unemployment (EU) as a percentage of the extended labour force (ELF) gives

the “Discouraged worker effect”. People who are discouraged are likely to disassociate themselves from

the labour market and are therefore classified as not searching for work. Possible reasons could be the

low level of job opportunities, skilled people only being able to get unskilled jobs and extended education

choices for skilled individuals.

¶ Tables A8.13.1-9 (section A8.4 below) compare the two different measures (conventional and

extended) of unemployment as a percentage of the extended labour force to give an indication of the

discouraged worker effect.

From a fishing industry point of view it is useful to know the levels of part-time employment and self-

employment.

¶ Tables A8.14.1-9 (section A8.4) give the amount of people that are unemployed in whole numbers.


211

0%

10%

20%

30%

40%

50%

White 6.9% 5.8% 14.4% 6.1% 6.3%

HRG 28.4% 46.4% 37.1% 21.8% 18.8%

Unspecif ied 22.2% 27.9% 25.0% 12.2% 27.9%

Kw aZulu Natal Eastern Cape Northern Cape Western Cape SA Coast

Figure A8.9. The extended labour force (% ELF) by racial group and province (Source data: Census 1996).

0

20000

40000

60000

80000

100000

120000

140000

160000

White 24 4207 5746 3437HRG 734 141812 53464 20068Unspecified 3 586 626 537

Northern Cape Eastern Cape KwaZulu-Natal Western Cape

Figure A8.10. The unemployed by racial group and province (Source data: Census 1996).

A8.3.4 Availability and Classification of Skills The quality of the labour supply is assessed using a skills structure. Discrepancies are identified between

race and gender. The usefulness of a skills structure based on the Census 1996 data is limited for the

following reasons; the skills classification provided in the census data is aggregated and the census

categories are neither occupational nor sectoral, but a hybrid of both. An attempt, however, has been

made to create a skills structure in the fishing industry similar to the scheme used by the ESS report.

This is necessary because the impact on employment and availability of skills is an important criterion to

understand the effect of the fishing industry on the relevant employment structures. The skills structure

represented by the census 1996 data is provided in table A8.1 below. The various categories are divided

into specific occupational structures. However, there is not always a perfect representation.


212

Table A8.1. Skills classification.

1- Labourers 2- Operators 3- Middle service 4- Artisans 5- Professionals

Agricultural fishery and related labourers

Corporate managers Drivers and mobile plant operators

Customer service clerks

General managers Legislators and senior officials Office clerks

Life science and health

Labourers in mining, construction, manufacturing and transport

Metal, machinery and related trades workers

Market-oriented skilled agricultural and fishery workers

Other professionals Sales and services elementary occupations

Physical, mathematical and engineering science

Subsistence agricultural and fishery workers

Stationary-plant and related operators Teaching professionals

Machine operators and assemblers

Models, salespersons and demonstrators Life science and health

associate professionals

Teaching associate professionals

Extraction and building trades workers

Other craft and related trades workers

Personal and protective services workers

Precision, handicraft, printing and related trades workers

Other associate professionals

The next step is to classify skill by education level. The quantity and quality of labour supply according to

functional category is then derived, which is then matched to the various levels of education. The

assumptions to derive probable educational levels per functional category are presented below.

SKILL LEVEL FUNCTIONAL CATEGORY EDUCATIONAL LEVEL Level 1 probably illiterate 50% of Std 1-4, 50% less than

Std 1

Level 2 unskilled 50% of 1-4, 50% Std 5-7

Level 3 semi-skilled Std 8-9

Level 4 skilled Std 10

Level 5 higher some tertiary education

¶ Tables A8.15.1-9 (section A8.4 below) give the whole numbers for the various skills levels.

¶ Tables A8.16.1-9 (section A8.4 below) give the whole numbers for the various educational levels.


213

0%

10%

20%

30%

40%

50%

60%

White 4.7% 7.5% 34.9% 2.3% 50.6%HRG 14.6% 13.3% 48.8% 4.1% 19.3%Unspecified 8.7% 11.4% 39.4% 4.2% 36.3%

1 2 3 4 5

Figure A8.11. The skills levels for the SA coast by racial group (%) (Source data: Census 1996).

A8.4. COMMERCIAL HARBOUR AND PROVINCIAL SOCIO-ECONOMIC TABLES: Census 1996

The lists of tables that follow are arranged according to the scheme established in section A8.3. Each

group of tables contains the indicators particular to that set of tables for each harbour town and

aggregated into provincial and national level.

For example, the group of tables 1.x will contain the racial and gender demographics per harbour town,

and aggregated into provincial and national levels.


214

Racial and gender demographics in whole numbers:

Table A8.2.1. Racial and gender demographics: SA Coastal harbour towns, by province

African/Black Coloured Indian/Asian White HRG Unspecified Total

M F T M F T M F T M F T M F T M F T M F Total

Natal 116172 114600 230772 23459 26390 49849 71536 76411 147947 108409 117130 225539 211167 217401 428568 4741 4319 9060 324317 338850 663167

Eastern Cape 290551 325587 616138 103699 113229 216928 6896 6966 13862 98761 105533 204294 401146 445782 846928 4112 4086 8198 504019 555401 1059420

Northern Cape 379 432 811 1980 1979 3959 7 6 13 222 207 429 2366 2417 4783 25 16 41 2613 2640 5253

Western Cape 35347 29815 65162 73900 81631 155531 2546 2412 4958 76062 83048 159110 111793 113858 225651 8605 6668 15273 196460 203574 400034

SA Coast 442449 470434 912883 203038 223229 426267 80985 85795 166780 283454 305918 589372 726472 779458 1505930 17483 15089 32572 1027409 1100465 2127874

Table A8.2.2. Racial and gender demographics: Northern Cape harbour towns

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T

Port Nolloth 344 412 756 1812 1818 3630 7 6 13 209 202 411 2163 2236 4399 25 16 41 2397 2454 4851

Hondeklip Bay 35 20 55 168 161 329 0 0 0 13 5 18 203 181 384 0 0 0 216 186 402

Northern Cape 379 432 811 1980 1979 3959 7 6 13 222 207 429 2366 2417 4783 25 16 41 2613 2640 5253

Table A8.2.3. Racial and gender demographics: Western Cape harbour towns

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total

NW Coast 891 627 1518 6666 6989 13655 25 7 32 1706 1822 3528 7582 7623 15205 102 111 213 9390 9556 18946

West Coast 10551 8659 19210 34846 40179 75025 2185 2064 4249 50297 54092 104389 47582 50902 98484 7008 5002 12010 104887 109996 214883

SW Coast 10192 8335 18527 13382 14712 28094 225 254 479 14014 16226 30240 23799 23301 47100 634 734 1368 38447 40261 78708

SE Coast 13713 12194 25907 19006 19751 38757 111 87 198 10045 10908 20953 32830 32032 64862 861 821 1682 43736 43761 87497

Western Cape 35347 29815 65162 73900 81631 155531 2546 2412 4958 76062 83048 159110 111793 113858 225651 8605 6668 15273 196460 203574 400034


215

Table A8.2.4. Racial and gender demographics: North West Coast harbour towns

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total Doringbaai 5 2 7 405 418 823 0 0 0 0 0 0 410 420 830 5 5 10 415 425 840 Lambert's Bay 94 50 144 1597 1629 3226 6 0 6 383 467 850 1697 1679 3376 34 43 77 2114 2189 4303 Elandsbaai 256 206 462 2057 2109 4166 8 3 11 369 359 728 2321 2318 4639 20 18 38 2710 2695 5405 Velddrif 9 8 17 41 39 80 0 0 0 569 628 1197 50 47 97 0 0 0 619 675 1294 St Helenabaai 500 332 832 2112 2322 4434 11 4 15 354 338 692 2623 2658 5281 15 13 28 2992 3009 6001 Paternoster 27 29 56 454 472 926 0 0 0 31 30 61 481 501 982 28 32 60 540 563 1103 NW Coast 891 627 1518 6666 6989 13655 25 7 32 1706 1822 3528 7582 7623 15205 102 111 213 9390 9556 18946

Table A8.2.5. Racial and gender demographics: West Coast harbour towns


M F T M F T M F T M F T M F T M F T M F Total Saldanha 1245 774 2019 5323 5495 10818 73 57 130 1747 1615 3362 6641 6326 12967 248 239 487 8636 8180 16816

Yzerfontein 0 4 4 0 7 7 0 4 4 192 201 393 0 15 15 0 0 0 192 216 408

Cape Town- (MD) 8781 7762 16543 26922 32039 58961 1987 1940 3927 45150 49174 94324 37690 41741 79431 6489 4460 10949 89329 95375 184704 Hout Bay Harbour 170 51 221 2356 2514 4870 27 18 45 223 221 444 2553 2583 5136 204 238 442 2980 3042 6022

Kommetjie 10 19 29 41 30 71 0 0 0 931 934 1865 51 49 100 31 30 61 1013 1013 2026

Simons Town 345 49 394 204 94 298 98 45 143 2054 1947 4001 647 188 835 36 35 71 2737 2170 4907

West Coast 10551 8659 19210 34846 40179 75025 2185 2064 4249 50297 54092 104389 47582 50902 98484 7008 5002 12010 104887 109996 214883

Table A8.2.6. Racial and gender demographics: SW Coast harbour towns

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total Kalk Bay 45 34 79 223 237 460 5 0 5 400 377 777 273 271 544 19 17 36 692 665 1357 Strand 6221 5479 11700 9049 10143 19192 171 214 385 7394 8939 16333 15441 15836 31277 488 575 1063 23323 25350 48673 Gordon's Bay 77 57 134 1169 1213 2382 24 24 48 2255 2224 4479 1270 1294 2564 17 25 42 3542 3543 7085 Kleinmond 377 205 582 693 685 1378 0 0 0 840 985 1825 1070 890 1960 45 52 97 1955 1927 3882 Hermanus 3037 2250 5287 1404 1572 2976 21 11 32 2534 3089 5623 4462 3833 8295 48 52 100 7044 6974 14018 Gansbaai 435 310 745 844 862 1706 4 5 9 591 612 1203 1283 1177 2460 17 13 30 1891 1802 3693 SW Coast 10192 8335 18527 13382 14712 28094 225 254 479 14014 16226 30240 23799 23301 47100 634 734 1368 38447 40261 78708


216

Table A8.2.7. Racial and gender demographics: SE Coast harbour towns


M F T M F T M F T M F T M F T M F T M F T Struisbaai 25 18 43 582 583 1165 0 5 5 329 345 674 607 606 1213 23 12 35 959 963 1922

Arniston 20 9 29 427 421 848 0 0 0 42 39 81 447 430 877 12 15 27 501 484 985

Mossel Bay 5889 5401 11290 9724 10091 19815 44 39 83 4868 5218 10086 15657 15531 31188 229 235 464 20754 20984 41738

Knysna 5488 4812 10300 6554 6846 13400 33 25 58 3482 3832 7314 12075 11683 23758 513 493 1006 16070 16008 32078

Plettenberg Bay 2291 1954 4245 1719 1810 3529 34 18 52 1324 1474 2798 4044 3782 7826 84 66 150 5452 5322 10774

SE Coast 13713 12194 25907 19006 19751 38757 111 87 198 10045 10908 20953 32830 32032 64862 861 821 1682 43736 43761 87497

Table A8.2.8. Racial and gender demographics: Eastern Cape harbour towns


M F T M F T M F T M F T M F T M F T M F T Jeffreys Bay 1410 1067 2477 1653 1836 3489 1 1 2 2475 2828 5303 3064 2904 5968 41 48 89 5580 5780 11360

Port Elizabeth 200241 224019 424260 85505 93954 179459 4918 5063 9981 64611 68774 133385 290664 323036 613700 2978 3089 6067 358253 394899 753152

Port Alfred 6178 7092 13270 583 592 1175 11 9 20 1397 1609 3006 6772 7693 14465 85 82 167 8254 9384 17638

East London 82722 93409 176131 15958 16847 32805 1966 1893 3859 30278 32322 62600 100646 112149 212795 1008 867 1875 131932 145338 277270

Eastern Cape 290551 325587 616138 103699 113229 216928 6896 6966 13862 98761 105533 204294 401146 445782 846928 4112 4086 8198 504019 555401 1059420

Table A8.2.9. Racial and gender demographics: KwaZulu-Natal harbour towns

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total Port Shepstone 11354 14230 25584 948 1101 2049 5895 6269 12164 11299 12604 23903 18197 21600 39797 218 272 490 29714 34476 64190 Durban 102251 97466 199717 22026 24775 46801 62042 66583 128625 87603 95617 183220 186319 188824 375143 4102 3654 7756 278024 288095 566119 Richards Bay 1148 1260 2408 479 501 980 3592 3551 7143 8489 7917 16406 5219 5312 10531 348 170 518 14056 13399 27455 Mthunzini 1349 1566 2915 6 9 15 3 6 9 644 682 1326 1358 1581 2939 14 152 166 2016 2415 4431 St Lucia 70 78 148 0 4 4 4 2 6 374 310 684 74 84 158 59 71 130 507 465 972 Natal 116172 114600 230772 23459 26390 49849 71536 76411 147947 108409 117130 225539 211167 217401 428568 4741 4319 9060 324317 338850 663167


217

Racial and gender demographics (%)

Table A8.3.1. Racial and gender demographics (%): SA Coast harbour towns African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Natal 18.0 17.2 35.2 3.9 4.4 8.3 10.9 11.7 22.7 15.5 16.9 32.4 31.8 32.8 64.6 0.7 0.7 1.4 49.1 50.9 Eastern Cape 27.4 30.7 58.2 9.8 10.7 20.5 0.7 0.7 1.3 9.3 10.0 19.3 37.9 42.1 79.9 0.4 0.4 0.8 47.6 52.4 Northern Cape 7.2 8.2 15.4 37.7 37.7 75.4 0.1 0.1 0.2 4.2 3.9 8.2 45.0 46.0 91.1 0.5 0.3 0.8 49.7 50.3 Western Cape 8.8 7.5 16.3 18.5 20.4 38.9 0.6 0.6 1.2 19.0 20.8 39.8 2.2 1.7 3.8 2.2 1.7 3.8 49.1 50.9 SA Coast 20.8 22.1 42.9 9.5 10.5 20.0 3.8 4.0 7.8 13.3 14.4 27.7 34.1 36.6 70.8 0.8 0.7 1.5 48.3 51.7

Table A8.3.2. Racial and gender demographics (%): Northern Cape harbour towns

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F

Port Nolloth 7.1 8.5 15.6 37.4 37.5 74.8 0.1 0.1 0.3 4.3 4.2 8.5 44.6 46.1 90.7 0.5 0.3 0.8 49.4 50.6 Hondeklip Bay 8.7 5.0 13.7 41.8 40.0 81.8 0.0 0.0 0.0 3.2 1.2 4.5 50.5 45.0 95.5 0.0 0.0 0.0 53.7 46.3 Northern Cape 7.2 8.2 15.4 37.7 37.7 75.4 0.1 0.1 0.2 4.2 3.9 8.2 45.0 46.0 91.1 0.5 0.3 0.8 49.7 50.3

Table A8.3.3. Racial and gender demographics (%): Western Cape harbour towns


NW Coast 4.7 3.3 8.0 35.2 36.9 72.1 0.1 0.0 0.2 9.0 9.6 18.6 40.0 40.2 80.3 0.5 0.6 1.1 49.6 50.4 West Coast 4.9 4.0 8.9 16.2 18.7 34.9 1.0 1.0 2.0 23.4 25.2 48.6 22.1 23.7 45.8 3.3 2.3 5.6 48.8 51.2 SW Coast 12.9 10.6 23.5 17.0 18.7 35.7 0.3 0.3 0.6 17.8 20.6 38.4 30.2 29.6 59.8 0.8 0.9 1.7 48.8 51.2 SE Coast 15.7 13.9 29.6 21.7 22.6 44.3 0.1 0.1 0.2 11.5 12.5 23.9 37.5 36.6 74.1 1.0 0.9 1.9 50.0 50.0 Western Cape 8.8 7.5 16.3 18.5 20.4 38.9 0.6 0.6 1.2 19.0 20.8 39.8 27.9 28.5 56.4 2.2 1.7 3.8 49.1 50.9


218

Table A8.3.4. Racial and gender demographics (%): NW Coast harbour towns


Doringbaai 0.6 0.2 0.8 48.2 49.8 98.0 0.0 0.0 0.0 0.0 0.0 0.0 48.8 50.0 98.8 0.6 0.6 1.2 49.4 50.6 Lambert's Bay 2.2 1.2 3.3 37.1 37.9 75.0 0.1 0.0 0.1 8.9 10.9 19.8 39.4 39.0 78.5 0.8 1.0 1.8 49.1 50.9 Elandsbaai 4.7 3.8 8.5 38.1 39.0 77.1 0.1 0.1 0.2 6.8 6.6 13.5 42.9 42.9 85.8 0.4 0.3 0.7 50.1 49.9 Velddrif 0.7 0.6 1.3 3.2 3.0 6.2 0.0 0.0 0.0 44.0 48.5 92.5 3.9 3.6 7.5 0.0 0.0 0.0 47.8 52.2 St Helenabaai 8.3 5.5 13.9 35.2 38.7 73.9 0.2 0.1 0.2 5.9 5.6 11.5 43.7 44.3 88.0 0.2 0.2 0.5 49.9 50.1 Paternoster 2.4 2.6 5.1 41.2 42.8 84.0 0.0 0.0 0.0 2.8 2.7 5.5 43.6 45.4 89.0 2.5 2.9 5.4 49.0 51.0 NW Coast 4.7 3.3 8.0 35.2 36.9 72.1 0.1 0.0 0.2 9.0 9.6 18.6 40.0 40.2 80.3 0.5 0.6 1.1 49.6 50.4

Table A8.3.5. Racial and gender demographics (%): W Coast harbour towns


Saldanha 7.4 4.6 12.0 31.7 32.7 64.3 0.4 0.3 0.8 10.4 9.6 20.0 39.5 37.6 77.1 1.5 1.4 2.9 51.4 48.6 Yzerfontein 0.0 1.0 1.0 0.0 1.7 1.7 0.0 1.0 1.0 47.1 49.3 96.3 0.0 3.7 3.7 0.0 0.0 0.0 47.1 52.9 Cape Town-(MD) 4.8 4.2 9.0 14.6 17.3 31.9 1.1 1.1 2.1 24.4 26.6 51.1 20.4 22.6 43.0 3.5 2.4 5.9 48.4 51.6 Hout Bay Harbour 2.8 0.8 3.7 39.1 41.7 80.9 0.4 0.3 0.7 3.7 3.7 7.4 42.4 42.9 85.3 3.4 4.0 7.3 49.5 50.5 Kommetjie 0.5 0.9 1.4 2.0 1.5 3.5 0.0 0.0 0.0 46.0 46.1 92.1 2.5 2.4 4.9 1.5 1.5 3.0 50.0 50.0 Simons Town 7.0 1.0 8.0 4.2 1.9 6.1 2.0 0.9 2.9 41.9 39.7 81.5 13.2 3.8 17.0 0.7 0.7 1.4 55.8 44.2 West Coast 4.9 4.0 8.9 16.2 18.7 34.9 1.0 1.0 2.0 23.4 25.2 48.6 22.1 23.7 45.8 3.3 2.3 5.6 48.8 51.2

Table A8.3.6. Racial and gender demographics (%): SW Coast harbour towns


Kalk Bay 3.3 2.5 5.8 16.4 17.5 33.9 0.4 0.0 0.4 29.5 27.8 57.3 20.1 20.0 40.1 1.4 1.3 2.7 51.0 49.0 Strand 12.8 11.3 24.0 18.6 20.8 39.4 0.4 0.4 0.8 15.2 18.4 33.6 31.7 32.5 64.3 1.0 1.2 2.2 47.9 52.1 Gordon's Bay 1.1 0.8 1.9 16.5 17.1 33.6 0.3 0.3 0.7 31.8 31.4 63.2 17.9 18.3 36.2 0.2 0.4 0.6 50.0 50.0 Kleinmond 9.7 5.3 15.0 17.9 17.6 35.5 0.0 0.0 0.0 21.6 25.4 47.0 27.6 22.9 50.5 1.2 1.3 2.5 50.4 49.6 Hermanus 21.7 16.1 37.7 10.0 11.2 21.2 0.1 0.1 0.2 18.1 22.0 40.1 31.8 27.3 59.2 0.3 0.4 0.7 50.2 49.8 Gansbaai 11.8 8.4 20.2 22.9 23.3 46.2 0.1 0.1 0.2 16.0 16.6 32.6 34.7 31.9 66.6 0.5 0.4 0.8 51.2 48.8 SW Coast 12.9 10.6 23.5 17.0 18.7 35.7 0.3 0.3 0.6 17.8 20.6 38.4 30.2 29.6 59.8 0.8 0.9 1.7 48.8 51.2


219

Table A8.3.7. Racial and gender demographics (%): SE Coast harbour towns

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Struisbaai 1.3 0.9 2.2 30.3 30.3 60.6 0.0 0.3 0.3 17.1 18.0 35.1 31.6 31.5 63.1 1.2 0.6 1.8 49.9 50.1 Arniston 2.0 0.9 2.9 43.4 42.7 86.1 0.0 0.0 0.0 4.3 4.0 8.2 45.4 43.7 89.0 1.2 1.5 2.7 50.9 49.1 Mossel Bay 14.1 12.9 27.0 23.3 24.2 47.5 0.1 0.1 0.2 11.7 12.5 24.2 37.5 37.2 74.7 0.5 0.6 1.1 49.7 50.3 Knysna 17.1 15.0 32.1 20.4 21.3 41.8 0.1 0.1 0.2 10.9 11.9 22.8 37.6 36.4 74.1 1.6 1.5 3.1 50.1 49.9 Plettenberg Bay 21.3 18.1 39.4 16.0 16.8 32.8 0.3 0.2 0.5 12.3 13.7 26.0 37.5 35.1 72.6 0.8 0.6 1.4 50.6 49.4 SE Coast 15.7 13.9 29.6 21.7 22.6 44.3 0.1 0.1 0.2 11.5 12.5 23.9 37.5 36.6 74.1 1.0 0.9 1.9 50.0 50.0

Table A8.3.8. Racial and gender demographics (%): Eastern Cape harbour towns

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Jeffreys Bay 12.4 9.4 21.8 14.6 16.2 30.7 0.0 0.0 0.0 21.8 24.9 46.7 27.0 25.6 52.5 0.4 0.4 0.8 49.1 50.9 Port Elizabeth 26.6 29.7 56.3 11.4 12.5 23.8 0.7 0.7 1.3 8.6 9.1 17.7 38.6 42.9 81.5 0.4 0.4 0.8 47.6 52.4 Port Alfred 35.0 40.2 75.2 3.3 3.4 6.7 0.1 0.1 0.1 7.9 9.1 17.0 38.4 43.6 82.0 0.5 0.5 0.9 46.8 53.2 East London 29.8 33.7 63.5 5.8 6.1 11.8 0.7 0.7 1.4 10.9 11.7 22.6 36.3 40.4 76.7 0.4 0.3 0.7 47.6 52.4 Eastern Cape 27.4 30.7 58.2 9.8 10.7 20.5 0.7 0.7 1.3 9.3 10.0 19.3 37.9 42.1 79.9 0.4 0.4 0.8 47.6 52.4

Table A8.3.9. Racial and gender demographics (%): KwaZulu-Natal harbour towns


Port Shepstone 17.7 22.2 39.9 1.5 1.7 3.2 9.2 9.8 18.9 17.6 19.6 37.2 28.3 33.7 62.0 0.3 0.4 0.8 46.3 53.7 Durban 18.1 17.2 35.3 3.9 4.4 8.3 11.0 11.8 22.7 15.5 16.9 32.4 32.9 33.4 66.3 0.7 0.6 1.4 49.1 50.9 Mthunzini 30.4 35.3 65.8 0.1 0.2 0.3 0.1 0.1 0.2 14.5 15.4 29.9 30.6 35.7 66.3 1.3 0.6 1.9 45.5 54.5 Richards Bay 4.2 4.6 8.8 1.7 1.8 3.6 13.1 12.9 26.0 30.9 28.8 59.8 19.0 19.3 38.4 0.3 3.4 3.7 51.2 48.8 St Lucia 7.2 8.0 15.2 0.0 0.4 0.4 0.4 0.2 0.6 38.5 31.9 70.4 7.6 8.6 16.3 6.1 7.3 13.4 52.2 47.8 Natal 18.0 17.2 35.2 3.9 4.4 8.3 10.9 11.7 22.7 15.5 16.9 32.4 31.8 32.8 64.6 0.7 0.7 1.4 49.1 50.9


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Gender imbalances

Table A8.4.1. Prime working age females as a ratio of prime working age population (26-55), SA Coastal harbour towns.

African Coloured Indian/Asian White Unspecified Total Natal 0.18 0.04 0.12 0.17 0.01 0.50 Eastern Cape 0.31 0.10 0.01 0.10 0.00 0.53 Northern Cape 0.10 0.36 0.00 0.05 0.00 0.51 Western Cape 0.08 0.23 0.01 0.24 0.02 0.69 SA Coast 0.23 0.10 0.04 0.15 0.01 0.55

Table A8.4.2. Prime working age females as a ratio of prime working age population (26-55), Northern Cape harbour towns.

African Coloured Indian/Asian White Unspecified Total Port Nolloth 0.10 0.36 0.00 0.05 0.00 0.51 Hondeklip Bay 0.07 0.42 0.00 0.02 0.00 0.51 Northern Cape 0.10 0.36 0.00 0.05 0.00 0.51

Table A8.4.3. Prime working age females as a ratio of prime working age population (26-55), Western Cape harbour towns

African Coloured Indian/AsianWhite Unspecified Total NW Coast 0.03 0.37 0.00 0.10 0.01 0.51 West Coast 0.04 0.18 0.01 0.26 0.02 0.52 SW Coast 0.10 0.19 0.00 0.19 0.01 0.50 SE Coast 0.14 0.22 0.00 0.12 0.01 0.49 Western Cape 0.07 0.20 0.01 0.21 0.02 0.51

Table A8.4.4. Prime working age females as a ratio of prime working age population (26-55), NW Coast harbour towns

African/Black Coloured Indian/Asian White Unspecified Total Doringbaai 0.00 0.51 0.00 0.00 0.01 0.53 Lambert's Bay 0.02 0.38 0.00 0.10 0.01 0.50 Elandsbaai 0.03 0.41 0.00 0.07 0.00 0.51 Velddrif 0.01 0.04 0.00 0.47 0.00 0.53 St Helenabaai 0.05 0.38 0.00 0.06 0.00 0.50 Paternoster 0.03 0.44 0.00 0.03 0.03 0.52 NW Coast 0.03 0.37 0.00 0.10 0.01 0.51

Table A8.4.5. Prime working age females as a ratio of prime working age population (26-55), West Coast harbour towns

African/Black Coloured Indian/Asian White Unspecified Total Saldanha 0.05 0.33 0.00 0.10 0.01 0.49 Yzerfontein 0.03 0.02 0.03 0.53 0.00 0.60 Cape Town-(MD) 0.04 0.17 0.01 0.27 0.02 0.52 Hout Bay Harbour 0.01 0.41 0.00 0.05 0.04 0.51 Kommetjie 0.01 0.02 0.00 0.48 0.01 0.52 Simons Town 0.01 0.02 0.01 0.43 0.01 0.48 West Coast 0.04 0.18 0.01 0.26 0.02 0.52


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Table A8.4.6. Prime working age females as a ratio of prime working age population (26-55), SW Coast harbour towns

African/Black Coloured Indian/Asian White Unspecified Total Kalk Bay 0.16 0.13 0.00 0.19 0.00 0.48 Strand 0.02 0.17 0.00 0.28 0.01 0.47 Gordon's Bay 0.01 0.17 0.00 0.32 0.00 0.50 Kleinmond 0.11 0.21 0.00 0.17 0.01 0.51 Hermanus 0.05 0.20 0.00 0.22 0.01 0.48 Gansbaai 0.07 0.24 0.00 0.17 0.00 0.48 SW Coast 0.10 0.19 0.00 0.19 0.01 0.50

Table A8.4.7. Prime working age females as a ratio of prime working age population (26-55), SE Coast harbour towns

African/Blac

k Coloured Indian/Asian White Unspecified Total Struisbaai 0.02 0.30 0.00 0.18 0.00 0.51 Arniston 0.01 0.44 0.00 0.01 0.00 0.49 Mossel Bay 0.13 0.23 0.00 0.13 0.00 0.50 Knysna 0.14 0.21 0.00 0.12 0.01 0.49 Plettenberg Bay 0.18 0.17 0.00 0.13 0.00 0.48 SE Coast 0.14 0.22 0.00 0.12 0.01 0.49

Table A8.4.8. Prime working age females as a ratio of prime working age population (26-55), Eastern Cape harbour towns

African Coloured Indian/Asian White Unspecified Total Jeffreys Bay 0.10 0.16 0.00 0.24 0.00 0.50 Port Elizabeth 0.31 0.12 0.01 0.09 0.00 0.53 Port Alfred 0.42 0.04 0.00 0.08 0.00 0.54 East London 0.33 0.06 0.01 0.12 0.00 0.52 Eastern Cape 0.31 0.10 0.01 0.10 0.00 0.53

Table A8.4.9. Prime working age females as a ratio of prime working age population (26-55), KwaZulu-Natal harbour towns

African Coloured Indian/Asia

n White Unspecified Total Port Shepstone 0.24 0.02 0.11 0.19 0.00 0.55 Durban 0.18 0.04 0.12 0.16 0.01 0.50 Mthunzini 0.35 0.00 0.00 0.19 0.01 0.55 Richards Bay 0.05 0.01 0.12 0.30 0.01 0.49 St Lucia 0.19 0.00 0.00 0.33 0.03 0.54 Natal 0.18 0.04 0.12 0.17 0.01 0.50

Age structure: ratio of working age to children

Table A8.5.1. Age structure: the ratio of working age (26-50) category to children (0-15), South African coastal harbour towns


Natal 0.48 0.57 0.52 0.98 0.84 0.91 0.8 0.72 0.76 0.54 0.5 0.52 0.63 0.65 0.64 0.75 1 0.86 0.6 0.61

Eastern Cape 0.89 0.80 0.84 1.12 0.96 1.03 0.80 0.81 0.80 0.61 0.57 0.59 0.94 0.84 0.89 1.35 1.14 1.24 0.88 0.79

Northern Cape 0.61 0.68 0.65 1.06 0.79 0.92 0 0.2 0.09 0.53 0.52 0.53 0.97 0.76 0.86 0.63 1.25 0.83 0.91 0.74

Western Cape 0.59 0.77 0.67 0.96 0.82 0.88 0.64 0.66 0.65 0.45 0.43 0.44 0.44 0.82 0.80 0.92 0.80 0.86 0.68 0.65

SA Coast 0.75 0.74 0.75 1.04 0.89 0.96 0.80 0.72 0.76 0.54 0.50 0.52 0.83 0.78 0.81 0.96 0.94 0.95 0.75 0.71


222

Table A8.5.2. Age structure: the ratio of working age (26-50) category to children (0-15), Northern Cape coastal harbour towns


M F T M F T M F T M F T M F T M F T M F

Port Nolloth 0.64 0.68 0.66 1.05 0.78 0.91 0.00 0.20 0.09 0.55 0.53 0.54 0.96 0.76 0.85 0.63 1.25 0.83 0.91 0.74

Hondeklip Bay 0.35 0.60 0.44 1.26 0.80 1.01 0.00 0.00 0.00 1.04 0.77 0.90 0.99 0.75

Northern Cape 0.61 0.68 0.65 1.06 0.79 0.92 0.00 0.20 0.09 0.53 0.52 0.53 0.97 0.76 0.86 0.63 1.25 0.83 0.91 0.74

Table A8.5.3. Age structure: the ratio of working age (26-50) category to children (0-15), Western Cape coastal harbour towns



NW Coast 0.55 0.84 0.66 1.11 0.96 1.03 0.35 0.00 0.25 0.60 0.60 0.60 0.79 0.69 0.73 1.00 0.71 0.86 0.96 0.88

West Coast 0.47 0.56 0.51 0.90 0.71 0.80 0.65 0.65 0.65 0.39 0.39 0.39 1.03 0.94 0.99 0.84 0.71 0.77 0.58 0.54

SW Coast 0.63 0.90 0.74 1.01 0.92 0.96 0.57 0.93 0.75 0.59 0.50 0.54 0.82 0.91 0.87 1.03 1.10 1.07 0.75 0.77

SE Coast 0.63 0.78 0.70 0.90 0.85 0.88 0.67 0.41 0.55 0.53 0.47 0.50 0.83 0.89 0.86 1.19 1.15 1.17 0.73 0.74

Western Cape 0.59 0.77 0.67 0.96 0.82 0.88 0.64 0.66 0.65 0.45 0.43 0.44 0.82 0.80 0.81 0.92 0.80 0.86 0.68 0.65

Table A8.5.4. Age structure: the ratio of working age (26-50) category to children (0-15), NW coast coastal harbour towns



Doringbaai 1.26 1.15 1.20 1.28 1.15 1.21 0.25 1.50 1.32 1.12

Lambert's Bay 0.16 0.46 0.27 1.05 0.99 1.02 0.00 0.00 0.56 0.53 0.54 0.97 0.97 0.97 1.11 0.83 0.95 0.90 0.89

Elandsbaai 0.55 1.02 0.73 1.08 0.86 0.96 1.00 0.00 0.57 0.65 0.58 0.61 1.01 0.87 0.94 0.80 1.75 1.07 0.96 0.84

Velddrif 0.50 0.00 0.21 0.17 0.25 0.20 0.60 0.57 0.58 0.22 0.19 0.21 0.53 0.51

St Helenabaai 0.62 0.88 0.72 1.18 1.02 1.09 0.22 0.00 0.15 0.61 0.71 0.66 1.06 0.99 1.03 0.80 0.25 0.46 1.00 0.96

Paternoster 1.00 0.82 0.90 1.21 0.94 1.07 0.50 0.89 0.68 1.20 0.93 1.06 0.79 0.70 0.75 1.13 0.92

NW Coast 0.55 0.84 0.66 1.11 0.96 1.03 0.35 0.00 0.25 0.60 0.60 0.60 1.03 0.94 0.99 1.00 0.71 0.86 0.96 0.88

Table A8.5.5. Age structure: the ratio of working age (26-50) category to children (0-15), west coast coastal harbour towns



Saldanha 0.35 0.82 0.50 1.10 0.94 1.01 0.91 1.60 1.18 0.73 0.73 0.73 0.91 0.93 0.92 1.30 0.86 1.06 0.89 0.89

Yzerfontein 0.00 0.00 0.67 0.67 0.43 0.13 0.26 0.29 0.29 0.43 0.15

Cape Town-(MD) 0.51 0.54 0.52 0.85 0.66 0.74 0.66 0.63 0.64 0.37 0.37 0.37 0.75 0.64 0.69 0.80 0.68 0.73 0.55 0.50

Hout Bay Harbour 0.13 0.83 0.27 1.16 0.96 1.05 0.60 1.00 0.71 0.39 0.55 0.47 1.07 0.95 1.01 1.10 1.07 1.09 1.00 0.93

Kommetjie 0.80 0.56 0.64 0.53 0.36 0.45 0.65 0.48 0.56 0.58 0.43 0.51 1.75 0.75 1.08 0.66 0.49

Simons Town 0.10 0.39 0.16 0.49 0.60 0.53 0.38 0.50 0.42 0.48 0.45 0.46 0.26 0.51 0.33 0.85 0.80 0.82 0.43 0.46

West Coast 0.47 0.56 0.51 0.90 0.71 0.80 0.65 0.65 0.65 0.39 0.39 0.39 0.79 0.69 0.73 0.84 0.71 0.77 0.58 0.54


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Table A8.5.6. Age structure: the ratio of working age (26-50) category to children (0-15), south west coastal harbour towns



Kalk Bay 0.38 0.55 0.44 0.51 0.47 0.49 0.00 0.00 0.27 0.23 0.25 0.47 0.48 0.47 0.00 1.40 0.47 0.34 0.35

Strand 0.64 0.86 0.74 1.07 0.96 1.01 0.63 1.13 0.88 0.60 0.51 0.55 0.87 0.92 0.90 1.07 1.08 1.07 0.80 0.80

Gordon's Bay 0.20 0.63 0.37 1.03 0.82 0.92 1.14 0.23 0.55 0.54 0.53 0.54 0.98 0.80 0.89 0.56 1.33 0.87 0.70 0.64

Kleinmond 0.26 0.73 0.38 0.85 0.89 0.87 0.62 0.48 0.54 0.60 0.86 0.71 1.00 0.88 0.92 0.61 0.71

Hermanus 0.65 1.01 0.79 0.88 0.82 0.85 0.09 0.43 0.22 0.65 0.49 0.56 0.72 0.92 0.81 2.17 1.75 1.93 0.70 0.77

Gansbaai 0.66 1.11 0.83 0.88 0.95 0.91 0.00 0.67 0.50 0.65 0.56 0.60 0.81 0.99 0.89 1.14 1.20 1.17 0.77 0.85

SW Coast 0.63 0.90 0.74 1.01 0.92 0.96 0.57 0.93 0.75 0.59 0.50 0.54 0.82 0.91 0.87 1.03 1.10 1.07 0.75 0.77

Table A8.5.7. Age structure: the ratio of working age (26-50) category to children (0-15), south east coastal harbour towns



Struisbaai 0.58 0.08 0.32 1.06 1.05 1.05 0.00 0.00 0.54 0.41 0.47 1.03 0.97 1.00 2.75 4.50 3.33 0.93 0.83

Arniston 1.33 0.80 1.09 0.94 0.97 0.96 0.22 0.17 0.19 0.96 0.97 0.96 0.94 0.93

Mossel Bay 0.72 0.80 0.75 0.92 0.91 0.91 0.75 0.80 0.77 0.66 0.61 0.64 0.83 0.87 0.85 1.50 1.31 1.40 0.80 0.81

Knysna 0.65 0.90 0.75 1.07 0.95 1.01 0.67 0.33 0.50 0.59 0.50 0.54 0.85 0.93 0.89 1.17 1.07 1.12 0.81 0.83

Plettenberg Bay 0.55 0.78 0.64 0.97 0.87 0.92 0.82 0.36 0.59 0.49 0.49 0.49 0.71 0.82 0.76 1.20 2.14 1.54 0.67 0.75

SE Coast 0.66 0.83 0.73 0.98 0.93 0.95 0.74 0.44 0.60 0.61 0.55 0.58 0.83 0.89 0.86 1.30 1.24 1.27 0.79 0.81

Table A8.5.8. Age structure: the ratio of working age (26-50) category to children (0-15), Eastern Cape coastal harbour towns



Jeffreys Bay 0.40 0.60 0.48 1.00 0.99 0.99 0.00 0.00 0.00 0.63 0.56 0.59 0.67 0.83 0.75 2.43 1.46 1.80 0.66 0.72

Port Elizabeth 0.85 0.77 0.81 1.12 0.96 1.04 0.83 0.87 0.85 0.62 0.56 0.59 0.92 0.82 0.87 1.34 1.12 1.22 0.87 0.78

Port Alfred 1.06 0.88 0.96 1.04 0.91 0.97 0.80 0.00 0.40 0.56 0.51 0.54 1.06 0.88 0.96 1.44 2.31 1.81 0.99 0.84

East London 0.99 0.89 0.94 1.10 0.98 1.04 0.72 0.67 0.70 0.60 0.57 0.58 1.00 0.90 0.95 1.34 1.13 1.25 0.91 0.82

Eastern Cape 0.89 0.80 0.84 1.12 0.96 1.03 0.80 0.81 0.80 0.61 0.57 0.59 0.94 0.84 0.89 1.35 1.14 1.24 0.88 0.79

Table A8.5.9. Age structure: the ratio of working age (26-50) category to children (0-15), KwaZulu-Natal coastal harbour towns



Port Shepstone 1.12 0.83 0.95 1.17 1.00 1.08 0.86 0.79 0.82 0.58 0.51 0.54 1.03 0.83 0.92 1.84 1.57 1.69 0.88 0.73

Durban 0.45 0.53 0.49 0.97 0.82 0.89 0.78 0.69 0.73 0.51 0.48 0.49 0.60 0.62 0.61 0.73 0.94 0.82 0.58 0.58

Mthunzini 1.37 0.99 1.15 0.50 0.20 0.29 0.00 1.00 0.50 0.67 0.68 0.68 1.36 0.98 1.14 4.00 8.22 7.45 1.09 0.96

Richards Bay 0.67 0.66 0.67 1.31 1.24 1.28 0.87 0.92 0.90 0.65 0.67 0.66 0.85 0.88 0.87 1.27 1.08 1.16 0.72 0.75

St Lucia 0.18 0.08 0.12 0.40 0.39 0.40 0.18 0.08 0.12 0.00 0.00 0.00 0.33 0.25

KwaZulu-Natal 0.48 0.57 0.52 0.98 0.84 0.91 0.80 0.72 0.76 0.54 0.50 0.52 0.63 0.65 0.64 0.75 1.00 0.86 0.60 0.61


224

Non labour market indicators of wealth and deprivation

Personal income by race and gender:

Table A8.6.1. Personal income by race and gender: SA Coast African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 42762 76432 119194 16269 25069 41338 4513 6747 11260 10635 20778 31413 63544 108248 171792 606 1066 1672 74785 130092 204877 R116-R346 67494 39540 107034 29850 32936 62786 9861 9656 19517 23187 34903 58090 107205 82132 189337 1208 1350 2558 131600 118385 249985 R347-R808 26838 13892 40730 26368 18349 44717 14764 8502 23266 46757 58543 105300 67970 40743 108713 1362 1316 2678 116089 100602 216691 R809 – R6 929 or more 10033 7503 17536 12188 5791 17979 13277 4475 17752 97731 42637 140368 35498 17769 53267 1666 881 2547 134895 61287 196182 Total 147127 137367 284494 84675 82145 166820 42415 29380 71795 178310 156861 335171 274217 248892 523109 4842 4613 9455 457369 410366 867735

Table A8.6.2. Personal income by race and gender: Northern Cape African/Black Coloured Indian/Asian White HRG Unspecified Total

M F T M F T M F T M F T M F T M F T M F Total R1-R115 48 65 113 264 397 661 2 0 2 9 15 24 314 462 776 4 1 5 327 478 805 R116-R346 67 34 101 407 187 594 3 0 3 14 9 23 477 221 698 6 4 10 497 234 731 R347-R808 17 4 21 233 40 273 0 0 0 31 16 47 250 44 294 3 0 3 284 60 344 R809 – R6 929 or more 5 3 8 42 21 63 4 0 4 45 8 53 51 24 75 0 0 0 96 32 128 Total 137 106 243 946 645 1591 9 0 9 99 48 147 1092 751 1843 13 5 18 1204 804 2008

Table A8.6.2.1. Personal income by race and gender: Port Nolloth African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 48 62 110 240 371 611 2 0 2 9 15 24 290 433 723 4 1 5 303 449 752 R116-R346 56 32 88 390 181 571 3 0 3 11 9 20 449 213 662 6 4 10 466 226 692 R347-R808 14 4 18 208 36 244 0 0 0 31 16 47 222 40 262 3 0 3 256 56 312 R809 – R6 929 or more 5 3 8 40 19 59 4 0 4 45 8 53 49 22 71 0 0 0 94 30 124 Total 123 101 224 878 607 1485 9 0 9 96 48 144 1010 708 1718 13 5 18 1119 761 1880


225

Table A8.6.2.2. Personal income by race and gender: Hondeklip Bay African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 0 3 3 24 26 50 0 0 0 0 0 0 24 29 53 0 0 0 24 29 53 R116-R346 11 2 13 17 6 23 0 0 0 3 0 3 28 8 36 0 0 0 31 8 39 R347-R808 3 0 3 25 4 29 0 0 0 0 0 0 28 4 32 0 0 0 28 4 32 R809 – R6 929 or more 0 0 0 2 2 4 0 0 0 0 0 0 2 2 4 0 0 0 2 2 4 Total 14 5 19 68 38 106 0 0 0 3 0 3 82 43 125 0 0 0 85 43 128

Table A8.6.3. Personal income by race and gender: Western Cape African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 3741 3905 7646 6263 10390 16653 115 162 277 2861 5631 8492 10119 14457 24576 264 434 698 13244 20522 33766 R116-R346 9918 3749 13667 14249 15029 29278 242 217 459 6123 9297 15420 24409 18995 43404 582 638 1220 31114 28930 60044 R347-R808 2057 939 2996 9596 5872 15468 364 283 647 12737 15480 28217 12017 7094 19111 721 687 1408 25475 23261 48736 R809 – R6 929 or more 883 498 1381 3890 1955 5845 395 146 541 26420 12748 39168 5168 2599 7767 817 434 1251 32405 15781 48186 Total 16599 9091 25690 33998 33246 67244 1116 808 1924 48141 43156 91297 51713 43145 94858 2384 2193 4577 102238 88494 190732

Table A8.6.4. Personal income by race and gender: NW Coast African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 175 100 275 945 1204 2149 1 2 3 127 195 322 1121 1306 2427 7 12 19 1255 1513 2768 R116-R346 289 130 419 1542 1521 3063 2 4 6 229 255 484 1833 1655 3488 19 9 28 2081 1919 4000 R347-R808 107 6 113 519 172 691 2 0 2 347 206 553 628 178 806 9 5 14 984 389 1373 R809 – R6 929 or more 6 3 9 128 64 192 2 0 2 479 77 556 136 67 203 4 0 4 619 144 763 Total 577 239 816 3134 2961 6095 7 6 13 1182 733 1915 3718 3206 6924 39 26 65 4939 3965 8904

Table A8.6.4.1. Personal income by race and gender: Doringbaai African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 3 0 3 99 88 187 0 0 0 0 0 0 102 88 190 0 0 0 102 88 190 R116-R346 1 0 1 28 32 60 0 0 0 0 0 0 29 32 61 0 0 0 29 32 61 R347-R808 0 0 0 33 6 39 0 0 0 0 0 0 33 6 39 0 0 0 33 6 39 R809 – R6 929 or more 0 0 0 7 2 9 0 0 0 0 0 0 7 2 9 0 0 0 7 2 9 Total 4 0 4 167 128 295 0 0 0 0 0 0 171 128 299 0 0 0 171 128 299


226

Table A8.6.4.2. Personal income by race and gender: Lambert's Bay African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 14 8 22 239 311 550 0 0 0 30 45 75 253 319 572 2 4 6 285 368 653 R116-R346 32 5 37 305 200 505 2 0 2 35 74 109 339 205 544 10 1 11 384 280 664 R347-R808 13 0 13 69 23 92 0 0 0 67 57 124 82 23 105 3 0 3 152 80 232 R809 – R6 929 or more 3 0 3 23 12 35 0 0 0 125 26 151 26 12 38 3 0 3 154 38 192 Total 62 13 75 636 546 1182 2 0 2 257 202 459 700 559 1259 18 5 23 975 766 1741

Table A8.6.4.3. Personal income by race and gender: Elandsbaai African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 133 67 200 346 518 864 0 0 0 30 44 74 479 585 1064 4 5 9 513 634 1147 R116-R346 50 25 75 539 436 975 0 4 4 74 49 123 589 465 1054 0 4 4 663 518 1181 R347-R808 2 3 5 153 49 202 2 0 2 65 35 100 157 52 209 1 4 5 223 91 314 R809 – R6 929 or more 1 0 1 31 20 51 0 0 0 89 8 97 32 20 52 0 0 0 121 28 149 Total 186 95 281 1069 1023 2092 2 4 6 258 136 394 1257 1122 2379 5 13 18 1520 1271 2791

Table A8.6.4.4. Personal income by race and gender: Velddrif African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 3 0 3 3 10 13 0 0 0 52 85 137 6 10 16 0 0 0 58 95 153 R116-R346 4 4 8 10 2 12 0 0 0 90 85 175 14 6 20 0 0 0 104 91 195 R347-R808 0 0 0 21 3 24 0 0 0 149 69 218 21 3 24 0 0 0 170 72 242 R809 – R6 929 or more 0 0 0 2 0 2 0 0 0 134 26 160 2 0 2 0 0 0 136 26 162 Total 7 4 11 36 15 51 0 0 0 425 265 690 43 19 62 0 0 0 468 284 752

Table A8.6.4.5. Personal income by race and gender: St Helenabaai African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 21 22 43 219 216 435 1 2 3 13 21 34 241 240 481 1 0 1 255 261 516 R116-R346 197 96 293 516 753 1269 0 0 0 28 38 66 713 849 1562 0 4 4 741 891 1632 R347-R808 91 3 94 218 78 296 0 0 0 61 41 102 309 81 390 2 1 3 372 123 495 R809 – R6 929 or more 2 3 5 62 28 90 2 0 2 124 17 141 66 31 97 1 0 1 191 48 239 Total 311 124 435 1015 1075 2090 3 2 5 226 117 343 1329 1201 2530 4 5 9 1559 1323 2882


227

Table A8.6.4.6. Personal income by race and gender: Paternoster African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 1 3 4 39 61 100 0 0 0 2 0 2 40 64 104 0 3 3 42 67 109 R116-R346 5 0 5 144 98 242 0 0 0 2 9 11 149 98 247 9 0 9 160 107 267 R347-R808 1 0 1 25 13 38 0 0 0 5 4 9 26 13 39 3 0 3 34 17 51 R809 – R6 929 or more 0 0 0 3 2 5 0 0 0 7 0 7 3 2 5 0 0 0 10 2 12 Total 7 3 10 211 174 385 0 0 0 16 13 29 218 177 395 12 3 15 246 193 439

Table A8.6.5. Personal income by race and gender: West Coast African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 669 875 1544 2540 4774 7314 88 136 224 1856 3650 5506 3297 5785 9082 199 335 534 5352 9770 15122 R116-R346 2681 1474 4155 5533 7627 13160 198 186 384 3875 5745 9620 8412 9287 17699 400 469 869 12687 15501 28188 R347-R808 857 572 1429 5719 4247 9966 323 257 580 7952 10567 18519 6899 5076 11975 582 584 1166 15433 16227 31660 R809 – R6 929 or more 614 384 998 2604 1380 3984 353 136 489 18648 10291 28939 3571 1900 5471 676 366 1042 22895 12557 35452 Total 4821 3305 8126 16396 18028 34424 962 715 1677 32331 30253 62584 22179 22048 44227 1857 1754 3611 56367 54055 110422

Table A8.6.5.1. Personal income by race and gender: Saldana African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 71 52 123 203 375 578 1 3 4 32 75 107 275 430 705 11 16 27 318 521 839 R116-R346 541 132 673 1233 1515 2748 6 9 15 106 187 293 1780 1656 3436 40 51 91 1926 1894 3820 R347-R808 185 25 210 811 245 1056 23 10 33 347 235 582 1019 280 1299 29 16 45 1395 531 1926 R809 – R6 929 or more 25 1 26 198 66 264 9 4 13 578 120 698 232 71 303 27 3 30 837 194 1031 Total 822 210 1032 2445 2201 4646 39 26 65 1063 617 1680 3306 2437 5743 107 86 193 4476 3140 7616

Table A8.6.5.2. Personal income by race and gender: Yzerfontein African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 0 0 0 0 3 3 0 0 0 4 15 19 0 3 3 0 0 0 4 18 22 R116-R346 0 3 3 0 0 0 0 0 0 23 38 61 0 3 3 0 0 0 23 41 64 R347-R808 0 0 0 0 0 0 0 4 4 54 41 95 0 4 4 0 0 0 54 45 99 R809 – R6 929 or more 0 0 0 0 0 0 0 0 0 66 12 78 0 0 0 0 0 0 66 12 78 Total 0 3 3 0 3 3 0 4 4 147 106 253 0 10 10 0 0 0 147 116 263


228

Table A8.6.5.3. Personal income by race and gender: Cape Town-(MD) African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 593 804 1397 2115 4088 6203 81 130 211 1735 3363 5098 2789 5022 7811 174 295 469 4698 8680 13378 R116-R346 2068 1313 3381 3794 5541 9335 188 166 354 3542 5178 8720 6050 7020 13070 333 385 718 9925 12583 22508 R347-R808 638 539 1177 4674 3923 8597 290 238 528 7082 9705 16787 5602 4700 10302 523 543 1066 13207 14948 28155 R809 – R6 929 or more 576 377 953 2334 1286 3620 325 131 456 16742 9698 26440 3235 1794 5029 613 347 960 20590 11839 32429 Total 3875 3033 6908 12917 14838 27755 884 665 1549 29101 27944 57045 17676 18536 36212 1643 1570 3213 48420 48050 96470

Table A8.6.5.4. Personal income by race and gender: Hout Bay Harbour African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 3 1 4 215 291 506 3 0 3 5 11 16 221 292 513 14 23 37 240 326 566 R116-R346 51 3 54 493 555 1048 0 4 4 9 15 24 544 562 1106 25 30 55 578 607 1185 R347-R808 25 4 29 213 71 284 2 0 2 24 34 58 240 75 315 19 23 42 283 132 415 R809 – R6 929 or more 5 2 7 49 20 69 3 1 4 105 40 145 57 23 80 23 14 37 185 77 262 Total 84 10 94 970 937 1907 8 5 13 143 100 243 1062 952 2014 81 90 171 1286 1142 2428

Table A8.6.5.5. Personal income by race and gender: Kommetjie African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 0 4 4 0 3 3 0 0 0 23 60 83 0 7 7 0 1 1 23 68 91 R116-R346 0 8 8 7 1 8 0 0 0 58 97 155 7 9 16 0 0 0 65 106 171 R347-R808 3 0 3 9 4 13 0 0 0 116 180 296 12 4 16 2 1 3 130 185 315 R809 – R6 929 or more 1 4 5 5 3 8 0 0 0 347 122 469 6 7 13 4 0 4 357 129 486 Total 4 16 20 21 11 32 0 0 0 544 459 1003 25 27 52 6 2 8 575 488 1063

Table A8.6.5.6. Personal income by race and gender: Simons Town African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 2 14 16 7 14 21 3 3 6 57 126 183 12 31 43 0 0 0 69 157 226 R116-R346 21 15 36 6 15 21 4 7 11 137 230 367 31 37 68 2 3 5 170 270 440 R347-R808 6 4 10 12 4 16 8 5 13 329 372 701 26 13 39 9 1 10 364 386 750 R809 – R6 929 or more 7 0 7 18 5 23 16 0 16 810 299 1109 41 5 46 9 2 11 860 306 1166 Total 36 33 69 43 38 81 31 15 46 1333 1027 2360 110 86 196 20 6 26 1463 1119 2582


229

Table A8.6.6. Personal income by race and gender: SW Coast African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 1162 1158 2320 895 1854 2749 17 14 31 538 1179 1717 2074 3026 5100 32 46 78 2644 4251 6895 R116-R346 3658 1001 4659 3293 2880 6173 35 17 52 1297 2126 3423 6986 3898 10884 95 96 191 8378 6120 14498 R347-R808 561 143 704 1856 662 2518 36 23 59 2816 3101 5917 2453 828 3281 68 44 112 5337 3973 9310 R809 – R6 929 or more 121 40 161 509 237 746 26 8 34 4571 1608 6179 656 285 941 42 14 56 5269 1907 7176 Total 5502 2342 7844 6553 5633 12186 114 62 176 9222 8014 17236 12169 8037 20206 237 200 437 21628 16251 37879

Table A8.6.6.1. Personal income by race and gender: Kalk Bay African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 2 3 5 39 43 82 0 0 0 1 19 20 41 46 87 0 2 2 42 67 109 R116-R346 9 12 21 44 44 88 1 0 1 30 30 60 54 56 110 2 4 6 86 90 176 R347-R808 1 1 2 20 18 38 1 0 1 51 58 109 22 19 41 0 0 0 73 77 150 R809 – R6 929 or more 0 0 0 7 3 10 0 0 0 95 58 153 7 3 10 0 0 0 102 61 163 Total 12 16 28 110 108 218 2 0 2 177 165 342 124 124 248 2 6 8 303 295 598

Table A8.6.6.2. Personal income by race and gender: Strand African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 562 650 1212 544 1055 1599 9 13 22 317 677 994 1115 1718 2833 18 34 52 1450 2429 3879 R116-R346 2134 668 2802 2089 1942 4031 24 17 41 679 1190 1869 4247 2627 6874 78 79 157 5004 3896 8900 R347-R808 383 103 486 1287 491 1778 30 19 49 1437 1832 3269 1700 613 2313 60 39 99 3197 2484 5681 R809 – R6 929 or more 87 26 113 384 194 578 19 7 26 2510 918 3428 490 227 717 36 12 48 3036 1157 4193 Total 3166 1447 4613 4304 3682 7986 82 56 138 4943 4617 9560 7552 5185 12737 192 164 356 12687 9966 22653

Table A8.6.6.3. Personal income by race and gender: Gordons Bay African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 4 8 12 51 140 191 3 1 4 31 81 112 58 149 207 2 2 4 91 232 323 R116-R346 14 15 29 226 254 480 1 0 1 133 235 368 241 269 510 2 2 4 376 506 882 R347-R808 12 3 15 229 106 335 4 2 6 502 510 1012 245 111 356 4 1 5 751 622 1373 R809 – R6 929 or more 5 3 8 74 22 96 5 1 6 833 260 1093 84 26 110 1 0 1 918 286 1204 Total 35 29 64 580 522 1102 13 4 17 1499 1086 2585 628 555 1183 9 5 14 2136 1646 3782


230

Table A8.6.6.4. Personal income by race and gender: Kleinmond African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 27 31 58 35 101 136 0 0 0 23 50 73 62 132 194 4 5 9 89 187 276 R116-R346 180 55 235 202 137 339 0 0 0 62 107 169 382 192 574 9 9 18 453 308 761 R347-R808 3 3 6 98 3 101 0 0 0 148 108 256 101 6 107 0 3 3 249 117 366 R809 – R6 929 or more 1 0 1 5 3 8 0 0 0 188 37 225 6 3 9 3 2 5 197 42 239 Total 211 89 300 340 244 584 0 0 0 421 302 723 551 333 884 16 19 35 988 654 1642

Table A8.6.6.5. Personal income by race and gender: Hermanus African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 471 426 897 114 308 422 2 0 2 126 272 398 587 734 1321 1 2 3 714 1008 1722 R116-R346 1154 240 1394 416 354 770 6 0 6 271 488 759 1576 594 2170 2 2 4 1849 1084 2933 R347-R808 149 32 181 175 34 209 1 2 3 519 540 1059 325 68 393 4 1 5 848 609 1457 R809 – R6 929 or more 26 9 35 29 13 42 2 0 2 854 311 1165 57 22 79 2 0 2 913 333 1246 Total 1800 707 2507 734 709 1443 11 2 13 1770 1611 3381 2545 1418 3963 9 5 14 4324 3034 7358

Table A8.6.6.6. Personal income by race and gender: Gansbaai African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 96 40 136 112 207 319 3 0 3 40 80 120 211 247 458 7 1 8 258 328 586 R116-R346 167 11 178 316 149 465 3 0 3 122 76 198 486 160 646 2 0 2 610 236 846 R347-R808 13 1 14 47 10 57 0 0 0 159 53 212 60 11 71 0 0 0 219 64 283 R809 – R6 929 or more 2 2 4 10 2 12 0 0 0 91 24 115 12 4 16 0 0 0 103 28 131 Total 278 54 332 485 368 853 6 0 6 412 233 645 769 422 1191 9 1 10 1190 656 1846

Table A8.6.7. Personal income by race and gender: SE Coast African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 1735 1772 3507 1883 2558 4441 9 10 19 340 607 947 3627 4340 7967 26 41 67 3993 4988 8981 R116-R346 3290 1144 4434 3881 3001 6882 7 10 17 722 1171 1893 7178 4155 11333 68 64 132 7968 5390 13358 R347-R808 532 218 750 1502 791 2293 3 3 6 1622 1606 3228 2037 1012 3049 62 54 116 3721 2672 6393 R809 - R6 929 or more 142 71 213 649 274 923 14 2 16 2722 772 3494 805 347 1152 95 54 149 3622 1173 4795 Total 5699 3205 8904 7915 6624 14539 33 25 58 5406 4156 9562 13647 9854 23501 251 213 464 19304 14223 33527


231

Table A8.6.7.1. Personal income by race and gender: Struisbaai African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 2 1 3 168 150 318 0 3 3 19 23 42 170 154 324 0 0 0 189 177 366 R116-R346 1 4 5 130 62 192 0 0 0 33 31 64 131 66 197 3 3 6 167 100 267 R347-R808 0 0 0 17 4 21 0 0 0 69 44 113 17 4 21 0 0 0 86 48 134 R809 – R6 929 or more 0 0 0 1 0 1 0 0 0 93 16 109 1 0 1 1 0 1 95 16 111 Total 3 5 8 316 216 532 0 3 3 214 114 328 319 224 543 4 3 7 537 341 878

Table A8.6.7.2. Personal income by race and gender: Arniston African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 4 0 4 164 92 256 0 0 0 1 3 4 168 92 260 0 0 0 169 95 264 R116-R346 0 1 1 53 57 110 0 0 0 6 3 9 53 58 111 0 0 0 59 61 120 R347-R808 0 0 0 7 2 9 0 0 0 5 1 6 7 2 9 0 0 0 12 3 15 R809 – R6 929 or more 0 0 0 2 2 4 0 0 0 18 8 26 2 2 4 0 0 0 20 10 30 Total 4 1 5 226 153 379 0 0 0 30 15 45 230 154 384 0 0 0 260 169 429

Table A8.6.7.3. Personal income by race and gender: Mossel Bay African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 807 862 1669 725 1132 1857 4 2 6 177 280 457 1536 1996 3532 10 15 25 1723 2291 4014 R116-R346 831 280 1111 1750 1329 3079 3 6 9 352 533 885 2584 1615 4199 14 15 29 2950 2163 5113 R347-R808 185 59 244 917 478 1395 1 2 3 761 662 1423 1103 539 1642 18 14 32 1882 1215 3097 R809 – R6 929 or more 76 43 119 486 183 669 10 2 12 1273 308 1581 572 228 800 27 10 37 1872 546 2418 Total 1899 1244 3143 3878 3122 7000 18 12 30 2563 1783 4346 5795 4378 10173 69 54 123 8427 6215 14642

Table A8.6.7.4. Personal income by race and gender: Knysna African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 543 575 1118 605 897 1502 3 2 5 102 224 326 1151 1474 2625 12 16 28 1265 1714 2979 R116-R346 1727 671 2398 1565 1285 2850 3 4 7 242 438 680 3295 1960 5255 42 45 87 3579 2443 6022 R347-R808 242 121 363 419 226 645 0 0 0 572 637 1209 661 347 1008 43 39 82 1276 1023 2299 R809 – R6 929 or more 51 21 72 107 66 173 0 0 0 925 328 1253 158 87 245 66 40 106 1149 455 1604 Total 2563 1388 3951 2696 2474 5170 6 6 12 1841 1627 3468 5265 3868 9133 163 140 303 7269 5635 12904


232

Table A8.6.7.5. Personal income by race and gender: Plettenberg Bay African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 379 334 713 221 287 508 2 3 5 41 77 118 602 624 1226 4 10 14 647 711 1358 R116-R346 731 188 919 383 268 651 1 0 1 89 166 255 1115 456 1571 9 1 10 1213 623 1836 R347-R808 105 38 143 142 81 223 2 1 3 215 262 477 249 120 369 1 1 2 465 383 848 R809 – R6 929 or more 15 7 22 53 23 76 4 0 4 413 112 525 72 30 102 1 4 5 486 146 632 Total 1230 567 1797 799 659 1458 9 4 13 758 617 1375 2038 1230 3268 15 16 31 2811 1863 4674

Table A8.6.8. Personal income by race and gender: Eastern Cape African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 28740 51249 79989 8249 11783 20032 362 499 861 3778 7235 11013 37351 63531 100882 180 275 455 41309 71041 112350 R116-R346 37022 22310 59332 12928 14191 27119 619 687 1306 8390 12272 20662 50569 37188 87757 307 291 598 59266 49751 109017 R347-R808 14537 8057 22594 13036 8975 22011 1066 818 1884 16180 19905 36085 28639 17850 46489 263 284 547 45082 38039 83121 R809 – R6 929 or more 4940 4065 9005 5371 2502 7873 1160 371 1531 31969 11807 43776 11471 6938 18409 330 191 521 43770 18936 62706 Total 85239 85681 170920 39584 37451 77035 3207 2375 5582 60317 51219 111536 128030 125507 253537 1080 1041 2121 189427 177767 367194

Table A8.6.8.1. Personal income by race and gender: Jeffreys Bay African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 181 246 427 240 364 604 0 0 0 103 192 295 421 610 1031 3 3 6 527 805 1332 R116-R346 527 163 690 432 270 702 3 3 6 252 400 652 962 436 1398 6 3 9 1220 839 2059 R347-R808 125 31 156 164 39 203 0 0 0 631 480 1111 289 70 359 1 2 3 921 552 1473 R809 – R6 929 or more 12 3 15 31 20 51 0 0 0 821 188 1009 43 23 66 6 3 9 870 214 1084 Total 845 443 1288 867 693 1560 3 3 6 1807 1260 3067 1715 1139 2854 16 11 27 3538 2410 5948

Table A8.6.8.2. Personal income by race and gender: Port Elizabeth African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 18394 32208 50602 6460 9386 15846 270 369 639 2601 4717 7318 25124 41963 67087 132 196 328 27857 46876 74733 R116-R346 25244 15756 41000 10608 11746 22354 420 479 899 5377 7535 12912 36272 27981 64253 237 232 469 41886 35748 77634 R347-R808 11121 5869 16990 10717 7112 17829 702 504 1206 10166 12606 22772 22540 13485 36025 218 229 447 32924 26320 59244 R809 – R6 929 or more 3081 2392 5473 4427 2017 6444 730 193 923 20787 7946 28733 8238 4602 12840 262 150 412 29287 12698 41985 Total 57840 56225 114065 32212 30261 62473 2122 1545 3667 38931 32804 71735 92174 88031 180205 849 807 1656 131954 121642 253596


233

Table A8.6.8.3. Personal income by race and gender: Port Alfred African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 1054 1574 2628 93 107 200 3 2 5 53 149 202 1150 1683 2833 6 2 8 1209 1834 3043 R116-R346 580 296 876 73 47 120 3 0 3 146 248 394 656 343 999 2 4 6 804 595 1399 R347-R808 97 83 180 35 15 50 0 0 0 257 280 537 132 98 230 2 4 6 391 382 773 R809 – R6 929 or more 56 53 109 14 9 23 0 2 2 391 127 518 70 64 134 2 3 5 463 194 657 Total 1787 2006 3793 215 178 393 6 4 10 847 804 1651 2008 2188 4196 12 13 25 2867 3005 5872

Table A8.6.8.4. Personal income by race and gender: East London African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 9111 17221 26332 1456 1926 3382 89 128 217 1021 2177 3198 10656 19275 29931 39 74 113 11716 21526 33242 R116-R346 10671 6095 16766 1815 2128 3943 193 205 398 2615 4089 6704 12679 8428 21107 62 52 114 15356 12569 27925 R347-R808 3194 2074 5268 2120 1809 3929 364 314 678 5126 6539 11665 5678 4197 9875 42 49 91 10846 10785 21631 R809 – R6 929 or more 1791 1617 3408 899 456 1355 430 176 606 9970 3546 13516 3120 2249 5369 60 35 95 13150 5830 18980 Total 24767 27007 51774 6290 6319 12609 1076 823 1899 18732 16351 35083 32133 34149 66282 203 210 413 51068 50710 101778

Table A8.6.9. Personal income by race and gender: Kwa-Zulu Natal African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 10233 21213 31446 1493 2499 3992 4034 6086 10120 3987 7897 11884 15760 29798 45558 158 356 514 19905 38051 57956 R116-R346 20487 13447 33934 2266 3529 5795 8997 8752 17749 8660 13325 21985 31750 25728 57478 313 417 730 40723 39470 80193 R347-R808 10227 4892 15119 3503 3462 6965 13334 7401 20735 17809 23142 40951 27064 15755 42819 375 345 720 45248 39242 84490 R809 – R6 929 or more 4205 2937 7142 2885 1313 4198 11718 3958 15676 39297 18074 57371 18808 8208 27016 519 256 775 58624 26538 85162 Total 45152 42489 87641 10147 10803 20950 38083 26197 64280 69753 62438 132191 93382 79489 172871 1365 1374 2739 164500 143301 307801

Table A8.6.9.1. Personal income by race and gender: Durban-Port Shepstone African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 1372 3221 4593 107 188 295 406 650 1056 517 1056 1573 1885 4059 5944 9 27 36 2411 5142 7553 R116-R346 1666 1414 3080 145 151 296 1025 915 1940 1420 1963 3383 2836 2480 5316 23 30 53 4279 4473 8752 R347-R808 794 769 1563 145 101 246 1132 455 1587 2411 2195 4606 2071 1325 3396 21 11 32 4503 3531 8034 R809 – R6 929 or more 416 349 765 50 19 69 774 169 943 3179 1186 4365 1240 537 1777 21 8 29 4440 1731 6171 Total 4248 5753 10001 447 459 906 3337 2189 5526 7527 6400 13927 8032 8401 16433 74 76 150 15633 14877 30510


234

Table A8.6.9.2. Personal income by race and gender: Durban African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 8728 17597 26325 1369 2281 3650 3511 5249 8760 3281 6506 9787 13608 25127 38735 148 321 469 17037 31954 48991 R116-R346 18638 11815 30453 2093 3322 5415 7658 7446 15104 6885 10710 17595 28389 22583 50972 283 367 650 35557 33660 69217 R347-R808 9187 3988 13175 3296 3305 6601 11428 6526 17954 14524 19630 34154 23911 13819 37730 343 324 667 38778 33773 72551 R809 – R6 929 or more 3465 2510 5975 2735 1265 4000 10165 3641 13806 32591 15973 48564 16365 7416 23781 467 240 707 49423 23629 73052 Total 40018 35910 75928 9493 10173 19666 32762 22862 55624 57281 52819 110100 82273 68945 151218 1241 1252 2493 140795 123016 263811

Table A8.6.9.3. Personal income by race and gender: Richards Bay African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 46 226 272 13 28 41 117 187 304 166 290 456 176 441 617 1 8 9 343 739 1082 R116-R346 74 138 212 28 53 81 314 391 705 312 560 872 416 582 998 7 14 21 735 1156 1891 R347-R808 187 110 297 62 53 115 774 420 1194 792 1210 2002 1023 583 1606 10 8 18 1825 1801 3626 R809 - R6 929 or more 306 70 376 100 28 128 777 148 925 3228 794 4022 1183 246 1429 30 8 38 4441 1048 5489 Total 613 544 1157 203 162 365 1982 1146 3128 4498 2854 7352 2798 1852 4650 48 38 86 7344 4744 12088

Table A8.6.9.4. Personal income by race and gender: Mthunzini African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 62 114 176 4 2 6 0 0 0 12 29 41 66 116 182 0 0 0 78 145 223 R116-R346 93 68 161 0 3 3 0 0 0 19 61 80 93 71 164 0 3 3 112 135 247 R347-R808 48 22 70 0 0 0 0 0 0 47 74 121 48 22 70 0 1 1 95 97 192 R809 - R6 929 or more 14 8 22 0 1 1 2 0 2 233 98 331 16 9 25 1 0 1 250 107 357 Total 217 212 429 4 6 10 2 0 2 311 262 573 223 218 441 1 4 5 535 484 1019

Table A8.6.9.5. Personal income by race and gender: St Lucia African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total R1-R115 25 55 80 0 0 0 0 0 0 11 16 27 25 55 80 0 0 0 36 71 107 R116-R346 16 12 28 0 0 0 0 0 0 24 31 55 16 12 28 0 3 3 40 46 86 R347-R808 11 3 14 0 3 3 0 0 0 35 33 68 11 6 17 1 1 2 47 40 87 R809 - R6 929 or more 4 0 4 0 0 0 0 0 0 66 23 89 4 0 4 0 0 0 70 23 93 Total 56 70 126 0 3 3 0 0 0 136 103 239 56 73 129 1 4 5 193 180 373


235

Minimum income range % (less than R116 per week)

Table A8.7.1. Minimum income range % (less than R116 per week), South African coastal towns African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F Total Northern Cape 35.0 61.3 46.5 27.9 61.6 41.5 22.2 0.0 22.2 9.1 31.3 16.3 28.8 61.5 42.1 30.8 20.0 27.8 27.2 59.5 40.1 Eastern Cape 33.7 59.8 46.8 20.8 31.5 26.0 11.3 21.0 15.4 6.3 14.1 9.9 29.2 50.6 39.8 16.7 26.4 21.5 21.8 40.0 30.6 KwaZulu-Natal 22.7 49.9 35.9 14.7 23.1 19.1 10.6 23.2 15.7 5.7 12.6 9.0 16.9 37.5 26.4 11.6 25.9 18.8 12.1 26.6 18.8 Western Cape 22.5 43.0 29.8 18.4 31.3 24.8 10.3 20.0 14.4 5.9 13.0 9.3 19.6 33.5 25.9 11.1 19.8 15.3 11.1 19.8 15.3 SA Coast 29.1 55.6 41.9 19.2 30.5 24.8 10.6 23.0 15.7 6.0 13.2 9.4 23.2 43.5 32.8 12.5 23.1 17.7 16.4 31.7 23.6

Table A8.7.2. Minimum income range % (less than R116 per week), Northern Cape coastal towns African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Port Nolloth 39.0 61.4 49.1 27.3 61.1 41.1 22.2 0.0 22.2 9.4 31.3 16.7 28.7 61.2 42.1 30.8 20.0 27.8 27.1 59.0 40.0 Hondeklip Bay 0.0 60.0 15.8 35.3 68.4 47.2 0.0 0.0 29.3 67.4 42.4 28.2 67.4 41.4 Northern Cape 35.0 61.3 46.5 27.9 61.6 41.5 22.2 0.0 22.2 9.1 31.3 16.3 28.8 61.5 42.1 30.8 20.0 27.8 27.2 59.5 40.1

Table A8.7.3. Minimum income range % (less than R116 per week), Western Cape coastal towns African/Black Coloured Indian/Asian White HRG Unspecified Total

M F T M F T M F T M F T M F T M F T M F T NW Coast 30.3 41.8 33.7 30.2 40.7 35.3 14.3 33.3 23.1 10.7 26.6 16.8 30.2 40.7 35.1 17.9 46.2 29.2 25.4 38.2 31.1 West Coast 13.9 26.5 19.0 15.5 26.5 21.2 9.1 19.0 13.4 5.7 12.1 8.8 14.9 26.2 20.5 10.7 19.1 14.8 9.5 18.1 13.7 SW Coast 21.1 49.4 29.6 13.7 32.9 22.6 14.9 22.6 17.6 5.8 14.7 10.0 17.0 37.7 25.2 13.5 23.0 17.8 12.2 26.2 18.2 SE Coast 30.4 55.3 39.4 23.8 38.6 30.5 27.3 40.0 32.8 6.3 14.6 9.9 26.6 44.0 33.9 10.4 19.2 14.4 20.7 35.1 26.8 Western Cape 22.5 43.0 29.8 18.4 31.3 24.8 10.3 20.0 14.4 5.9 13.0 9.3 19.6 33.5 25.9 11.1 19.8 15.3 11.1 19.8 15.3


236

Table A8.7.4. Minimum income range % (less than R116 per week), NW coast coastal towns African/Black Coloured Indian/Asian White HRG Unspecified Total

M F T M F T M F T M F T M F T M F T M F T Doringbaai 75.0 75.0 59.3 68.8 63.4 59.6 68.8 63.5 59.6 68.8 63.5 Lambert's Bay 22.6 61.5 29.3 37.6 57.0 46.5 0.0 0.0 11.7 22.3 16.3 36.1 57.1 45.4 11.1 80.0 26.1 29.2 48.0 37.5 Elandsbaai 71.5 70.5 71.2 32.4 50.6 41.3 0.0 0.0 0.0 11.6 32.4 18.8 38.1 52.1 44.7 80.0 38.5 50.0 33.8 49.9 41.1 Velddrif 42.9 0.0 27.3 8.3 66.7 25.5 12.2 32.1 19.9 14.0 52.6 25.8 12.4 33.5 20.3 St Helenabaai 6.8 17.7 9.9 21.6 20.1 20.8 33.3 100.0 60.0 5.8 17.9 9.9 18.1 20.0 19.0 25.0 0.0 11.1 16.4 19.7 17.9 Paternoster 14.3 100.0 40.0 18.5 35.1 26.0 12.5 0.0 6.9 18.3 36.2 26.3 0.0 100.0 20.0 17.1 34.7 24.8 NW Coast 30.3 41.8 33.7 30.2 40.7 35.3 14.3 33.3 23.1 10.7 26.6 16.8 30.2 40.7 35.1 17.9 46.2 29.2 25.4 38.2 31.1

Table A8.7.5. Minimum income range % (less than R116 per week), west coast coastal towns African/Black Coloured Indian/Asian White HRG Unspecified Total

M F T M F T M F T M F T M F T M F T M F T

Saldanha 8.6 24.8 11.9 8.3 17.0 12.4 2.6 11.5 6.2 3.0 12.2 6.4 8.3 17.6 12.3 10.3 18.6 14.0 7.1 16.6 11.0 Yzerfontein 0.0 0.0 100.0 100.0 0.0 0.0 2.7 14.2 7.5 0.0 30.0 30.0 2.7 15.5 8.4 Cape Town-(MD) 15.3 26.5 20.2 16.4 27.6 22.3 9.2 19.5 13.6 6.0 12.0 8.9 15.8 27.1 21.6 10.6 18.8 14.6 9.7 18.1 13.9

Hout Bay Harbour 3.6 10.0 4.3 22.2 31.1 26.5 37.5 0.0 23.1 3.5 11.0 6.6 20.8 30.7 25.5 17.3 25.6 21.6 18.7 28.5 23.3

Kommetjie 0.0 25.0 20.0 0.0 27.3 9.4 4.2 13.1 8.3 0.0 25.9 13.5 0.0 50.0 12.5 4.0 13.9 8.6 Simons Town 5.6 42.4 23.2 16.3 36.8 25.9 9.7 20.0 13.0 4.3 12.3 7.8 10.9 36.0 21.9 0.0 0.0 0.0 4.7 14.0 8.8 West Coast 13.9 26.5 19.0 15.5 26.5 21.2 9.1 19.0 13.4 5.7 12.1 8.8 14.9 26.2 20.5 10.7 19.1 14.8 9.5 18.1 13.7

Table A8.7.6. Minimum income range % (less than R116 per week), SW coast coastal towns African/Black Coloured Indian/Asian White HRG Unspecified Total

M F T M F T M F T M F T M F T M F T M F T Kalk Bay 16.7 18.8 17.9 35.5 39.8 37.6 0.0 0.0 0.6 11.5 5.8 33.1 37.1 35.1 0.0 33.3 25.0 13.9 22.7 18.2 Strand 17.8 44.9 26.3 12.6 28.7 20.0 11.0 23.2 15.9 6.4 14.7 10.4 14.8 33.1 22.2 9.4 20.7 14.6 11.4 24.4 17.1 Gordons Bay 11.4 27.6 18.8 8.8 26.8 17.3 23.1 25.0 23.5 2.1 7.5 4.3 9.2 26.8 17.5 22.2 40.0 28.6 4.3 14.1 8.5 Kleinmond 12.8 34.8 19.3 10.3 41.4 23.3 5.5 16.6 10.1 11.3 39.6 21.9 25.0 26.3 25.7 9.0 28.6 16.8 Hermanus 26.2 60.3 35.8 15.5 43.4 29.2 18.2 0.0 15.4 7.1 16.9 11.8 23.1 51.8 33.3 11.1 40.0 21.4 16.5 33.2 23.4 Gansbaai 34.5 74.1 41.0 23.1 56.3 37.4 50.0 50.0 9.7 34.3 18.6 27.4 58.5 38.5 77.8 100.0 80.0 21.7 50.0 31.7 SW Coast 21.1 49.4 29.6 13.7 32.9 22.6 14.9 22.6 17.6 5.8 14.7 10.0 17.0 37.7 25.2 13.5 23.0 17.8 12.2 26.2 18.2


237

Table A8.7.7. Minimum income range % (less than R116 per week), SE coast coastal towns African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Struisbaai 66.7 20.0 37.5 53.2 69.4 59.8 100.0 100.0 8.9 20.2 12.8 53.3 68.8 59.7 0.0 0.0 0.0 35.2 51.9 41.7 Arniston 100.0 0.0 80.0 72.6 60.1 67.5 3.3 20.0 8.9 73.0 59.7 67.7 65.0 56.2 61.5 Mossel Bay 42.5 69.3 53.1 18.7 36.3 26.5 22.2 16.7 20.0 6.9 15.7 10.5 26.5 45.6 34.7 14.5 27.8 20.3 20.4 36.9 27.4 Knysna 21.2 41.4 28.3 22.4 36.3 29.1 50.0 33.3 41.7 5.5 13.8 9.4 21.9 38.1 28.7 7.4 11.4 9.2 17.4 30.4 23.1 Plettenberg Bay 30.8 58.9 39.7 27.7 43.6 34.8 22.2 75.0 38.5 5.4 12.5 8.6 29.5 50.7 37.5 26.7 62.5 45.2 23.0 38.2 29.1 SE Coast 30.4 55.3 39.4 23.8 38.6 30.5 27.3 40.0 32.8 6.3 14.6 9.9 26.6 44.0 33.9 10.4 19.2 14.4 20.7 35.1 26.8

Table A8.7.8. Minimum income range % (less than R116 per week), Eastern Cape coastal towns African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Jeffreys Bay 21.4 55.5 33.2 27.7 52.5 38.7 0.0 0.0 0.0 5.7 15.2 9.6 24.5 53.6 36.1 18.8 27.3 22.2 14.9 33.4 22.4 Port Elizabeth 31.8 57.3 44.4 20.1 31.0 25.4 12.7 23.9 17.4 6.7 14.4 10.2 27.3 47.7 37.2 15.5 24.3 19.8 21.1 38.5 29.5 Port Alfred 59.0 78.5 69.3 43.3 60.1 50.9 50.0 50.0 50.0 6.3 18.5 12.2 57.3 76.9 67.5 50.0 15.4 32.0 42.2 61.0 51.8 East London 36.8 63.8 50.9 23.1 30.5 26.8 8.3 15.6 11.4 5.5 13.3 9.1 33.2 56.4 45.2 19.2 35.2 27.4 22.9 42.4 32.7 Eastern Cape 33.7 59.8 46.8 20.8 31.5 26.0 11.3 21.0 15.4 6.3 14.1 9.9 29.2 50.6 39.8 16.7 26.4 21.5 21.8 40.0 30.6

Table A8.7.9. Minimum income range % (less than R116 per week), KwaZulu-Natal coastal towns African/Black Coloured Indian/Asian White HRG Unspecified Total

M F T M F T M F T M F T M F T M F T M F T Port Shepstone 32.3 56.0 45.9 23.9 41.0 32.6 12.2 29.7 19.1 6.9 16.5 11.3 23.5 48.3 36.2 12.2 35.5 24.0 15.4 34.6 24.8 Durban 21.8 49.0 34.7 14.4 22.4 18.6 10.7 23.0 15.7 5.7 12.3 8.9 16.5 36.4 25.6 11.9 25.6 18.8 12.1 26.0 18.6 Mthunzini 28.6 53.8 41.0 100.0 33.3 60.0 0.0 0.0 3.9 11.1 7.2 29.6 53.2 41.3 0.0 0.0 0.0 14.6 30.0 21.9 Richards Bay 7.5 41.5 23.5 6.4 17.3 11.2 5.9 16.3 9.7 3.7 10.2 6.2 6.3 23.8 13.3 2.1 21.1 10.5 4.7 15.6 9.0 St Lucia 44.6 78.6 63.5 0.0 0.0 8.1 15.5 11.3 44.6 75.3 62.0 0.0 0.0 0.0 18.7 39.4 28.7 KwaZulu-Natal 22.7 49.9 35.9 14.7 23.1 19.1 10.6 23.2 15.7 5.7 12.6 9.0 16.9 37.5 26.4 11.6 25.9 18.8 12.1 26.6 18.8

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 8. Socio-Economic

238

Housing by race group

Table A8.8.1. Type of dwelling by population group of head of household: Northern Cape coastal towns

% African Coloured

Indian/Asian White Unspecified Total Formal Port Nolloth 8.51 84.62 57.14 93.96 57.14 66.10 Hondeklip Bay 36.84 97.06 100.00 0.00 84.44 Informal Port Nolloth 90.27 11.41 42.86 1.34 0.00 30.35 Hondeklip Bay 47.37 2.94 0.00 0.00 12.22 Traditional Port Nolloth 0.00 0.25 0.00 2.01 0.00 0.39 Hondeklip Bay 10.53 0.00 0.00 0.00 2.22

Table A8.8.2. Type of dwelling by population group of head of household: NW Coast coastal towns

% African Coloured Indian/Asian White Unspecified Total

Formal Doringbaai 100.00 97.46 97.50 Lambert's Bay 64.29 90.67 100.00 97.66 100.00 92.37 Elandsbaai 44.86 88.31 100.00 97.36 50.00 83.95 Velddrif 100.00 82.35 98.45 97.89 St Helenabaai 25.40 85.31 100.00 95.80 100.00 76.33 Paternoster 58.33 87.62 84.21 44.44 84.40 Informal Doringbaai 0.00 2.03 2.00 Lambert's Bay 3.57 4.15 0.00 0.58 0.00 2.92 Elandsbaai 55.14 8.76 0.00 0.00 0.00 13.31 Velddrif 0.00 17.65 0.67 1.27 St Helenabaai 16.9 3.4 0.0 0.4 0.0 5.3 Paternoster 16.67 7.14 0.00 11.11 7.20 Traditional Doringbaai 0.00 0.00 0.00 Lambert's Bay 0.00 0.00 0.00 0.00 0.00 0.00 Elandsbaai 0.00 0.22 0.00 0.75 0.00 0.30 Velddrif 0.00 0.00 0.00 0.00 St Helenabaai 2.82 6.38 0.00 0.84 0.00 4.75 Paternoster 0.00 1.43 0.00 0.00 1.20


239

Table A8.8.3. Type of dwelling by population group of head of household: west coast coastal towns

% African Coloured

Indian/Asian White

Unspecified Total Formal Saldanha 21.34 87.69 100.00 95.96 84.21 78.74 Yzerfontein 100.00 100.00 100.00 97.22 97.37 Cape Town-(MD) 56.86 90.70 97.29 98.04 95.38 93.00 Hout Bay Harbour 60.87 78.84 54.55 91.72 89.41 80.60 Kommetjie 100.00 87.50 96.92 100.00 96.87 Simons Town 44.44 80.43 100.00 97.86 82.61 96.01 Informal Saldanha 56.19 10.48 0.00 0.00 11.58 15.38 Yzerfontein 0.00 0.00 0.00 0.00 0.00 Cape Town-(MD) 34.65 3.89 0.31 0.03 0.73 3.66 Hout Bay Harbour 39.13 18.56 18.18 0.00 10.59 16.10 Kommetjie 0.0 0.0 0.0 0.0 0.0 Simons Town 33.33 8.70 0.00 0.00 0.00 0.98 Traditional Saldanha 0.16 0.05 0.00 1.38 0.00 0.41 Yzerfontein 0.00 0.00 0.00 0.00 0.00 Cape Town-(MD) 0.32 0.21 0.21 0.22 0.34 0.23 Hout Bay Harbour 0.00 0.50 0.00 0.00 0.00 0.39 Kommetjie 0.00 0.00 0.44 0.00 0.43 Simons Town 0.00 0.00 0.00 0.27 0.00 0.25

Table A8.8.4. Type of dwelling by population group of head of household: SW Coast coastal towns % African Coloured Indian/Asian White Unspecified Total

Formal Kalk Bay 69.57 88.12 0.00 93.80 100.00 90.75 Strand 17.45 84.76 91.11 96.33 94.34 73.74 Gordon's Bay 87.50 91.51 100.00 95.83 100.00 94.73 Kleinmond 5.41 92.13 96.73 63.64 82.54 Hermanus 30.13 83.39 100.00 98.68 75.00 74.03 Gansbaai 38.62 62.29 33.33 97.53 25.00 70.83 Informal Kalk Bay 0.00 0.00 0.00 0.00 0.00 0.00 Strand 79.01 12.77 2.22 0.09 3.77 23.01 Gordon's Bay 4.17 7.53 0.00 0.00 0.00 1.85 Kleinmond 89.73 5.99 0.23 0.00 13.84 Hermanus 50.10 12.01 0.00 0.08 25.00 18.19 Gansbaai 23.98 29.59 66.67 0.45 75.00 17.13 Traditional Kalk Bay 0.00 0.00 0.00 0.00 0.00 0.00 Strand 0.58 0.78 1.11 1.38 1.42 1.02 Gordon's Bay 0.00 0.00 0.00 1.12 0.00 0.83 Kleinmond 0.00 1.12 0.35 0.00 0.45 Hermanus 14.34 0.33 0.00 0.51 0.00 5.03 Gansbaai 0.00 0.95 0.00 0.00 0.00 0.36


240

Table A8.8.5. Type of dwelling by population group of head of household: SE Coast coastal towns % African Coloured Indian/Asian White Unspecified Total

Formal Struisbaai 7.14 64.37 100.00 100.00 20.00 81.51 Arniston 100.00 98.92 60.98 92.21 Mossel Bay 43.35 83.64 100.00 97.64 91.03 76.37 Knysna 12.40 74.92 50.00 93.21 87.79 58.97 Plettenberg Bay 44.33 78.63 77.78 97.45 73.08 69.56 Informal Struisbaai 92.86 30.77 0.00 0.00 0.00 15.67 Arniston 0.00 0.54 0.00 0.43 Mossel Bay 52.32 12.84 0.00 0.03 3.85 20.25 Knysna 81.30 15.12 16.67 0.11 4.65 33.44 Plettenberg Bay 51.22 14.93 22.22 0.29 11.54 26.16 Traditional Struisbaai 0.00 0.81 0.00 0.00 0.00 0.35 Arniston 0.00 0.54 7.32 1.73 Mossel Bay 1.07 0.43 0.00 0.50 0.00 0.64 Knysna 3.37 6.18 16.67 2.33 1.74 3.88 Plettenberg Bay 2.80 0.82 0.00 0.39 0.00 1.54

Table A8.8.6. Type of dwelling by population group of head of household: Eastern Cape Coast coastal towns

% African Coloured Indian/Asian White Unspecified Total Formal Jeffreys Bay 19.34 75.85 96.48 66.67 77.60 Port Elizabeth 52.77 84.23 98.26 98.52 83.26 70.99 Port Alfred 21.15 76.60 100.00 92.00 63.16 43.94 East London 28.87 82.69 96.85 96.91 67.70 54.30 Informal Jeffreys Bay 70.76 20.30 0.14 22.22 17.65 Port Elizabeth 45.03 12.16 0.35 0.06 11.67 26.74 Port Alfred 15.74 4.91 0.00 0.31 0.00 10.82 East London 54.00 15.03 0.10 0.10 19.47 34.16 Traditional Jeffreys Bay 2.10 0.74 1.08 0.00 1.20 Port Elizabeth 0.34 0.99 0.22 0.28 0.00 0.44 Port Alfred 59.96 15.09 0.00 0.08 21.05 40.80 East London 14.46 0.34 0.61 0.27 3.98 8.90


241

Table A8.8.7. Type of dwelling by population group of head of household: KwaZulu-Natal Coast coastal towns

% African Coloured Indian/Asian White Unspecified Total Formal Port Shepstone 72.79 94.58 96.28 94.62 87.50 87.20 Durban 50.55 96.21 96.88 98.12 92.53 83.27 Mthunzini 63.92 100.00 100.00 94.81 60.00 76.80 Richards Bay 94.16 95.22 98.48 97.59 92.59 97.40 St Lucia 89.25 93.33 100.00 91.80 Informal Port Shepstone 8.59 1.81 0.27 0.08 0.00 3.12 Durban 44.90 1.12 0.60 0.07 4.40 13.89 Mthunzini 11.56 0.00 0.00 0.00 0.00 6.69 Richards Bay 0.97 1.91 0.12 0.06 0.00 0.20 St Lucia 3.23 0.00 0.00 1.23 Traditional Port Shepstone 3.40 0.45 0.34 0.41 0.00 1.45 Durban 0.85 0.20 0.28 0.23 0.28 0.43 Mthunzini 17.86 0.00 0.00 0.99 0.00 10.74 Richards Bay 0.49 0.48 0.00 0.74 0.00 0.53 St Lucia 0.00 2.00 0.00 1.23


242

Patterns of labour market access

Conventional unemployment (UC) and labour force participation rates (LFPRC)

Table A8.9.1. Conventional unemployment (UC) and labour force participation rates (LFPRC): SA Coastal harbours

% African/Black Coloured Indian/Asian White HRG Unspecified Total


KwaZulu-Natal

UC 36.0 33.9 35.0 23.7 17.9 20.9 9.7 11.5 10.4 5.3 5.8 5.5 26.1 26.4 26.3 22.5 18.1 20.5 19.2 19.3 19.3

LFPRC 79.5 69.7 74.6 75.3 59.2 66.7 77.7 42.4 59.2 78.6 61.1 69.6 78.5 58.9 68.5 73.5 56.7 64.7 78.5 59.6 68.8

Eastern Cape

UC 44.3 53.4 48.9 27.1 30.8 28.8 12.4 11.8 12.2 4.5 4.9 4.7 39.1 47.4 43.2 23.0 26.1 24.5 31.0 39.0 34.8

LFPRC 63.9 55.5 59.4 69.5 54.5 61.6 76.3 50.3 63.3 76.1 57.7 66.7 65.5 55.2 60.0 63.1 53.0 57.8 67.7 55.6 61.3

Northern Cape

UC 42.3 64.3 53.6 23.0 41.9 30.9 0.0 100.0 80.0 5.7 25.0 12.6 26.2 47.2 35.5 0.0 75.0 25.0 24.1 45.8 33.5

LFPRC 80.8 73.7 77.0 79.1 52.5 65.2 100.0 50.0 55.6 86.6 47.9 67.3 79.4 56.2 67.2 66.7 44.4 57.1 79.9 55.4 67.1

Western Cape

UC 22.4 40.1 29.5 14.1 16.9 15.5 9.2 11.4 10.1 4.7 5.0 4.9 16.9 23.3 19.8 10.8 11.0 10.9 11.8 15.7 13.6

LFPRC 79.6 64.1 72.6 76.9 61.3 68.5 73.6 50.4 62.2 76.9 59.3 67.8 77.8 61.8 69.6 77.9 59.7 68.4 77.4 60.7 68.8

SA Coast

UC 39.6 46.8 43.1 22.8 25.1 23.9 9.9 11.6 10.6 5.0 5.4 5.2 31.3 37.8 34.4 17.2 16.8 17.0 23.3 28.5 25.7

LFPRC 69.6 59.7 64.5 73.6 58.0 65.3 77.4 43.2 59.6 77.3 59.4 68.2 71.6 57.4 64.1 72.9 57.2 64.7 73.2 57.9 65.2

Table A8.9.2. Conventional unemployment (UC) and labour force participation rates (LFPRC): Northern Cape coastal harbours



Port Nolloth

UC 44.8 64.1 54.9 23.6 39.5 30.3 0.0 100.0 80.0 5.7 26.2 12.8 27.2 45.6 35.3 0.0 75.0 25.0 24.8 44.3 33.2

LFPRC 80.8 73.9 77.0 78.6 52.2 64.8 100.0 50.0 55.6 86.5 49.2 68.5 79.0 56.1 66.9 66.7 44.4 57.1 79.6 55.4 67.0

Hondeklip Bay

UC 7.7 66.7 31.8 16.7 67.8 38.7 0.0 0.0 0.0 15.4 67.6 37.7 15.2 64.8 36.8

LFPRC 81.3 69.2 75.9 84.8 56.2 69.5 100.0 30.0 36.4 84.3 57.6 70.4 84.4 55.5 68.8

Northern Cape

UC 42.3 64.3 53.6 23.0 41.9 30.9 0.0 100.0 80.0 5.7 25.0 12.6 26.2 47.2 35.5 0.0 75.0 25.0 24.1 45.8 33.5

LFPRC 80.8 73.7 77.0 79.1 52.5 65.2 100.0 50.0 55.6 86.6 47.9 67.3 79.4 56.2 67.2 66.7 44.4 57.1 79.9 55.4 67.1


243

Table A8.9.3. Conventional unemployment (UC) and labour force participation rates (LFPRC): Western Cape coastal harbours.



NW Coast

UC 13.6 16.7 14.5 16.0 15.3 15.7 22.2 0.0 15.4 3.5 6.7 4.7 15.7 15.4 15.6 10.0 36.0 21.8 13.6 14.5 14.0

LFPRC 90.9 63.7 80.5 79.3 63.9 71.2 100.0 100.0 100.0 69.0 38.2 53.2 81.0 63.9 72.3 75.0 51.0 61.8 78.6 58.8 68.5

West Coast

UC 20.2 26.5 22.9 13.4 12.6 13.0 7.4 11.1 8.9 4.4 4.7 4.6 14.8 15.2 15.0 10.2 9.0 9.7 9.2 9.7 9.4

LFPRC 80.0 68.9 74.8 76.5 62.4 68.8 73.8 51.1 62.5 80.1 65.1 72.4 77.2 63.1 69.7 80.1 61.9 70.6 78.8 64.0 71.1

SW Coast

UC 18.2 42.7 27.0 10.0 16.1 12.8 19.3 14.5 17.7 5.7 5.4 5.6 14.0 26.1 19.0 8.8 17.1 12.6 11.3 18.9 14.5

LFPRC 84.7 62.9 75.3 80.2 61.0 70.0 75.3 42.2 59.3 70.0 48.7 58.7 82.2 61.5 72.1 75.1 50.0 61.4 77.8 56.3 66.9

SE Coast

UC 28.8 50.1 37.8 18.2 28.0 22.8 20.5 12.5 17.5 5.2 6.5 5.8 23.0 37.2 29.4 17.5 17.3 17.4 19.1 30.7 24.3

LFPRC 74.6 61.7 68.5 74.3 58.2 65.8 60.9 51.1 56.8 70.1 47.6 58.3 74.4 59.6 66.9 67.3 55.5 61.4 73.3 56.7 64.8

Western Cape

UC 22.4 40.1 29.5 14.1 16.9 15.5 9.2 11.4 10.1 4.7 5.0 4.9 16.9 23.3 19.8 10.8 11.0 10.9 11.8 15.7 13.6

LFPRC 79.6 64.1 72.6 76.9 61.3 68.5 73.6 50.4 62.2 76.9 59.3 67.8 77.8 61.8 69.6 77.9 59.7 68.4 77.4 60.7 68.8

Table A8.9.4. Conventional unemployment (UC) and labour force participation rates (LFPRC): NW coast coastal harbours.



Doringbaai

UC 0.0 100.0 20.0 39.2 47.6 42.9 38.4 47.9 42.6 100.0 100.0 38.4 48.6 42.9

LFPRC 100.0 100.0 100.0 77.4 55.6 66.0 77.7 55.8 66.3 100.0 100.0 77.7 56.2 66.5

Lambert's Bay

UC 23.2 43.8 27.1 32.5 30.9 31.8 40.0 40.0 4.1 3.3 3.8 31.8 31.3 31.5 9.1 46.2 29.2 26.8 26.8 26.8

LFPRC 89.6 47.1 76.6 75.9 55.8 65.6 100.0 100.0 71.9 43.3 56.4 77.0 55.5 66.3 61.1 54.2 57.1 75.8 53.0 64.2

Elandsbaai

UC 6.0 6.8 6.3 8.6 9.7 9.1 0.0 0.0 0.0 3.9 3.8 3.9 8.2 9.4 8.8 66.7 0.0 25.0 7.8 9.0 8.3

LFPRC 90.3 66.2 80.3 78.6 66.0 72.0 100.0 100.0 100.0 78.8 33.6 55.7 80.1 66.0 72.9 75.0 71.4 72.7 79.9 61.7 70.6

Velddrif

UC 0.0 0.0 0.0 0.0 0.0 0.0 4.2 7.4 5.4 0.0 0.0 0.0 3.6 6.5 4.7

LFPRC 100.0 40.0 62.5 90.0 81.8 87.1 60.3 35.5 47.5 90.7 74.1 84.3 63.2 37.8 50.5

St Helenabaai

UC 15.7 19.1 16.7 9.5 5.9 7.7 0.0 0.0 0.0 1.7 12.9 5.5 11.1 7.6 9.5 0.0 0.0 0.0 10.0 8.0 9.1

LFPRC 92.5 67.3 83.2 82.5 69.6 75.5 100.0 100.0 100.0 72.1 41.2 57.3 84.9 69.3 77.0 71.4 50.0 61.5 83.2 65.9 74.5

Paternoster

UC 11.1 25.0 15.4 8.5 24.2 15.6 0.0 0.0 0.0 8.6 24.2 15.6 0.0 50.0 7.7 7.8 23.3 14.6

LFPRC 60.0 28.6 44.8 79.8 59.3 69.0 61.1 42.9 51.3 78.7 58.0 67.8 100.0 20.0 61.9 78.5 55.9 66.6

NW Coast

UC 13.6 16.7 14.5 16.0 15.3 15.7 22.2 0.0 15.4 3.5 6.7 4.7 15.7 15.4 15.6 10.0 36.0 21.8 13.6 14.5 14.0

LFPRC 90.9 63.7 80.5 79.3 63.9 71.2 100.0 100.0 100.0 69.0 38.2 53.2 81.0 63.9 72.3 75.0 51.0 61.8 78.6 58.8 68.5


244

Table A8.9.5. Conventional unemployment (UC) and labour force participation rates (LFPRC): west coast coastal harbours.


M F T M F T M F T M F T M F T M F T M F T Saldanha UC 26.7 50.3 33.3 12.4 13.0 12.7 10.0 0.0 6.7 1.5 8.6 4.0 16.2 18.1 17.0 16.7 15.2 16.0 13.3 16.6 14.6 LFPRC 93.4 72.0 86.2 78.5 63.6 70.8 83.3 54.1 70.6 82.6 48.9 66.5 82.0 64.6 73.6 81.6 57.1 68.8 82.1 61.3 72.1 Yzerfontein

UC 0.0 0.0 0.0 0.0 0.0 0.0 11.1 5.6 9.1 0.0 0.0 11.1 4.4 8.3

LFPRC 100.0 100.0 66.7 66.7 100.0100.0 50.8 26.3 37.9 90.0 90.0 50.8 30.6 39.9 Cape Town-(MD) UC 17.9 24.4 20.9 13.5 12.2 12.8 7.4 0.0 9.0 4.5 4.6 4.6 14.3 14.6 14.4 9.7 9.1 9.4 8.7 9.1 8.9 LFPRC 77.0 68.6 72.9 76.5 62.4 68.6 74.1 51.4 62.8 80.5 66.3 73.2 76.5 63.1 69.3 80.4 62.4 70.9 78.8 64.7 71.4 Hout Bay Harbour UC 49.0 21.4 46.6 15.2 18.4 16.8 28.6 0.0 18.2 3.7 8.3 5.7 19.5 18.4 19.0 11.9 1.2 6.5 17.5 16.3 17.0 LFPRC 93.1 38.9 83.2 72.7 60.3 66.1 35.0 40.0 36.7 75.7 61.5 69.1 74.3 59.7 66.9 71.8 60.7 65.8 74.3 59.9 67.0 Kommetjie UC 0.0 0.0 0.0 9.1 0.0 6.5 3.7 3.9 3.8 6.9 0.0 4.1 40.0 0.0 22.2 4.2 3.6 4.0 LFPRC 100.0 100.0 100.0 88.0 56.3 75.6 78.0 61.7 69.6 90.6 74.1 83.1 62.5 66.7 64.3 78.4 62.3 70.2 Simons Town UC 0.0 0.0 0.0 2.3 13.2 7.4 0.0 7.3 5.3 5.0 5.2 1.0 9.4 4.9 0.0 18.2 7.4 4.8 5.6 5.2 LFPRC 84.6 76.9 80.8 79.6 56.7 66.9 72.7 39.5 53.9 73.8 54.9 64.1 79.4 57.0 67.3 94.1 52.4 71.1 74.6 55.1 64.5 West Coast UC 20.2 26.5 22.9 13.4 12.6 13.0 7.4 11.1 8.9 4.4 4.7 4.6 14.8 15.2 15.0 10.2 9.0 9.7 9.2 9.7 9.4 LFPRC 80.0 68.9 74.8 76.5 62.4 68.8 73.8 51.1 62.5 80.1 65.1 72.4 77.2 63.1 69.7 80.1 61.9 70.6 78.8 64.0 71.1

Table A8.9.6. Conventional unemployment (UC) and labour force participation rates (LFPRC): SW coast coastal harbours


M F T M F T M F T M F T M F T M F T M F T Kalk Bay UC 44.4 0.0 16.0 5.9 8.6 7.1 0.0 0.0 5.6 2.8 4.4 8.9 7.2 8.1 0.0 0.0 0.0 6.8 4.5 5.8 LFPRC 39.1 80.0 58.1 80.2 55.9 67.2 100.0 100.0 81.7 69.4 75.7 74.2 58.8 66.1 66.7 26.7 38.1 78.4 63.2 70.7 Strand UC 20.5 42.2 28.8 11.4 17.3 14.1 21.1 16.1 19.2 5.2 5.5 5.3 15.7 26.8 20.4 10.4 17.6 13.5 12.6 20.1 15.8 LFPRC 84.2 63.2 74.7 77.6 57.4 66.7 72.6 43.4 57.7 70.8 51.1 60.3 80.4 59.3 69.8 76.0 49.6 61.7 77.3 56.3 66.5 Gordons Bay UC 11.1 10.3 10.8 8.7 12.2 10.4 30.8 0.0 25.0 7.8 6.6 7.3 9.3 12.1 10.6 0.0 0.0 0.0 8.3 8.8 8.5 LFPRC 76.6 65.9 71.4 83.0 64.7 73.3 92.9 42.9 76.2 79.1 52.9 65.8 82.8 64.6 73.2 63.6 50.0 56.0 80.2 57.0 68.3 Kleinmond UC 32.6 38.3 34.3 8.4 14.9 11.2 6.7 5.5 6.1 18.8 21.8 20.0 0.0 16.7 9.4 15.9 17.1 16.4 LFPRC 97.8 88.1 94.7 91.6 70.9 81.3 42.6 29.1 35.1 94.2 75.2 85.9 63.6 56.3 59.3 73.9 51.8 63.0 Hermanus UC 12.3 45.5 23.2 6.0 9.2 7.6 0.0 0.0 0.0 5.0 4.0 4.5 10.4 29.5 17.7 0.0 0.0 0.0 8.9 20.5 13.7 LFPRC 83.4 60.8 74.3 81.2 67.3 73.8 85.7 33.3 65.2 65.1 46.1 54.5 82.8 63.4 74.1 75.0 57.1 66.7 77.0 56.0 66.7

Gansbaai UC 9.1 52.1 20.5 7.2 24.1 15.0 0.0 0.0 4.5 8.5 5.8 7.8 29.3 16.6 0.0 42.9 21.4 6.9 25.3 14.1 LFPRC 92.0 51.6 76.2 92.9 82.3 87.7 50.0 0.0 33.3 70.9 32.7 51.2 92.4 73.9 83.9 100.0 77.8 87.5 85.7 59.1 73.0 SW Coast UC 18.2 42.7 27.0 10.0 16.1 12.8 19.3 14.5 17.7 5.7 5.4 5.6 14.0 26.1 19.0 8.8 17.1 12.6 11.3 18.9 14.5 LFPRC 84.7 62.9 75.3 80.2 61.0 70.0 75.3 42.2 59.3 70.0 48.7 58.7 82.2 61.5 72.1 75.1 50.0 61.4 77.8 56.3 66.9


245

Table A8.9.7. Conventional unemployment (UC) and labour force participation rates (LFPRC): SE coast coastal harbours.


M F T M F T M F T M F T M F T M F T M F T Struisbaai UC 0.0 0.0 0.0 4.7 14.2 8.6 0.0 0.0 3.6 1.6 2.7 4.6 13.7 8.3 0.0 0.0 4.3 10.9 7.1 LFPRC 53.3 26.7 40.0 82.7 55.1 68.7 66.7 66.7 39.7 26.3 32.5 81.6 54.2 67.6 75.0 0.0 27.3 66.6 43.1 54.4 Arniston

UC 0.0 50.0 40.0 7.2 13.4 9.6 17.6 20.0 18.5 7.1 15.5 10.5 7.9 15.8 11.1 LFPRC 66.7 100.0 90.9 75.5 51.5 63.9 65.4 34.5 49.1 75.4 53.0 64.4 74.5 51.2 63.0 Knysna UC 18.3 42.6 28.5 16.5 24.8 20.6 30.8 20.0 3.9 5.3 4.5 17.5 32.5 24.3 14.1 14.6 14.3 14.7 26.5 20.1 LFPRC 75.2 62.1 69.1 73.5 61.8 67.3 48.1 70.0 54.1 70.8 54.4 62.1 74.3 62.0 68.1 69.4 60.1 64.8 73.5 60.2 66.7 Mossel Bay UC 44.5 59.7 51.2 21.6 32.0 26.4 5.6 11.1 7.4 6.5 8.2 7.2 30.6 42.6 36.0 22.8 20.0 21.6 24.7 35.3 29.4 LFPRC 70.2 58.6 64.5 73.6 54.9 63.7 72.0 34.6 52.9 71.4 43.9 57.0 72.2 56.2 64.0 68.7 50.0 59.1 72.0 53.0 62.2 Plettenberg Bay UC 19.6 44.8 29.7 13.7 27.1 20.2 37.5 50.0 41.7 3.2 4.4 3.7 17.5 36.9 25.9 26.9 28.9 15.1 31.0 22.1 LFPRC 83.4 69.1 77.0 77.4 65.5 71.2 66.7 80.0 70.6 72.1 51.4 61.1 81.0 67.5 74.5 52.0 51.4 51.7 78.9 63.7 71.5

SE Coast UC 28.8 50.1 37.8 18.2 28.0 22.8 20.5 12.5 17.5 5.2 6.5 5.8 23.0 37.2 29.4 17.5 17.3 17.4 19.1 30.7 24.3 LFPRC 74.6 61.7 68.5 74.3 58.2 65.8 60.9 51.1 56.8 70.1 47.6 58.3 74.4 59.6 66.9 67.3 55.5 61.4 73.3 56.7 64.8

Table A8.9.8. Conventional unemployment (UC) and labour force participation rates (LFPRC): Eastern Cape coastal harbours.


M F T M F T M F T M F T M F T M F T M F T Jeffreys Bay UC 21.7 35.6 27.0 6.5 17.6 11.5 0.0 0.0 0.0 7.2 6.5 6.9 14.5 25.5 19.1 6.7 0.0 4.3 11.8 18.3 14.5 LFPRC 80.7 69.2 76.0 75.5 58.7 66.8 100.0 100.0 100.0 60.0 38.6 48.4 78.2 62.9 71.0 83.3 34.8 56.1 70.5 50.7 60.6

Port Elizabeth UC 45.8 56.8 51.3 27.5 31.5 29.4 13.5 12.9 13.3 4.7 4.8 4.8 39.7 49.4 44.4 24.5 27.2 25.8 32.2 41.3 36.6 LFPRC 63.4 55.8 59.3 69.4 54.1 61.3 75.1 47.2 61.0 75.6 56.9 66.1 65.3 55.2 59.9 65.0 54.1 59.3 67.2 55.5 61.0

Port Alfred UC 42.2 50.9 46.8 24.3 42.0 32.3 75.0 0.0 37.5 5.3 3.3 4.3 40.5 50.1 45.6 46.2 63.6 54.2 34.9 43.5 39.4 LFPRC 53.9 50.1 51.8 60.1 47.5 53.6 50.0 66.7 57.1 56.3 45.6 50.5 54.4 49.9 52.0 30.2 26.8 28.6 54.5 49.1 51.6

East London UC 41.3 45.4 43.4 27.4 28.0 27.7 9.7 9.7 9.7 3.9 5.0 4.4 38.2 42.2 40.2 16.1 20.4 18.2 28.4 33.2 30.7 LFPRC 65.5 55.0 59.7 69.8 56.7 62.9 79.4 58.0 68.9 79.0 61.6 70.2 66.5 55.3 60.4 58.0 52.2 55.0 69.6 56.7 62.7

Eastern Cape UC 44.3 53.4 48.9 27.1 30.8 28.8 12.4 11.8 12.2 4.5 4.9 4.7 39.1 47.4 43.2 23.0 26.1 24.5 31.0 39.0 34.8 LFPRC 63.9 55.5 59.4 69.5 54.5 61.6 76.3 50.3 63.3 76.1 57.7 66.7 65.5 55.2 60.0 63.1 53.0 57.8 67.7 55.6 61.3


246

Table A8.9.9: Conventional unemployment (UC) and labour force participation rates (LFPRC): KwaZulu-Natal coastal harbours.



Port Shepstone

UC 15.3 20.7 18.4 18.5 15.7 17.2 5.8 8.0 6.6 6.3 7.3 6.7 11.9 17.8 14.9 11.9 13.4 12.7 9.9 14.5 12.2

LFPRC 74.1 69.3 71.3 74.3 53.0 62.6 81.0 41.7 60.4 67.6 50.3 58.4 76.6 60.3 67.3 70.5 48.9 57.8 73.1 56.7 64.0

Durban

UC 37.9 35.7 36.9 24.1 18.0 21.2 10.3 12.0 11.0 5.4 5.5 5.5 27.8 27.6 27.7 23.5 18.5 21.2 20.8 20.1 20.5

LFPRC 80.0 70.0 75.2 75.3 59.4 66.8 76.9 42.2 58.7 79.3 62.8 70.8 78.5 59.0 68.7 73.9 57.4 65.4 78.7 60.2 69.3

Mthunzini

UC 46.0 59.8 53.5 0.0 0.0 0.0 0.0 0.0 2.3 4.6 3.3 45.6 59.5 53.2 0.0 33.3 20.0 27.3 42.0 34.7

LFPRC 61.9 50.9 55.4 100.0 60.0 71.4 100.0 0.0 25.0 82.0 59.4 70.3 62.0 50.8 55.4 100.0 50.0 62.5 69.2 53.2 60.1

Richards Bay

UC 5.9 10.3 7.9 13.2 19.9 16.2 6.1 8.2 6.8 3.4 7.7 5.1 6.6 10.0 7.9 5.9 14.5 10.4 4.6 8.6 6.1

LFPRC 86.0 68.4 76.7 78.2 60.1 68.9 85.8 47.6 66.9 84.3 57.0 71.3 85.2 53.9 69.5 68.9 57.9 62.7 84.5 55.9 70.5

St Lucia

UC 7.3 0.0 3.4 0.0 0.0 5.9 4.0 5.1 7.3 0.0 3.3 0.0 0.0 6.3 2.1 4.3

LFPRC 90.2 82.9 86.1 100.0 100.0 77.9 51.7 64.1 90.2 83.5 86.4 40.0 0.0 13.3 80.7 60.3 69.6

Natal

UC 36.0 33.9 35.0 23.7 17.9 20.9 9.7 11.5 10.4 5.3 5.8 5.5 26.1 26.4 26.3 22.5 18.1 20.5 19.2 19.3 19.3

LFPRC 79.5 69.7 74.6 75.3 59.2 66.7 77.7 42.4 59.2 78.6 61.1 69.6 78.5 58.9 68.5 73.5 56.7 64.7 78.5 59.6 68.8


247

Conventional labour force (LFPRC) in whole numbers Table A8.10.1. Conventional labour force (LFPRC) in whole numbers: South African harbours



Northern Cape 194 207 401 964 700 1664 1 4 5 123 68 191 1159 911 2070 8 4 12 1290 983 2273

Eastern Cape 118677 121091 239768 43879 38865 82744 3516 2321 5837 50218 39746 89964 166072 162277 328349 1252 1141 2393 217542 203164 420706

KwaZulu-Natal 65574 56487 122061 11253 10254 21507 37546 22403 59949 57140 46883 104023 114373 89144 203517 1656 1402 3058 173169 137429 310598

Western Cape 19147 12925 32072 34819 32450 67269 1148 764 1912 38762 32000 70762 55114 46139 101253 2665 2263 4928 96541 80402 176943

SA Coast 203592 190710 394302 90915 82269 173184 42211 25492 67703 146243 118697 264940 336718 298471 635189 5581 4810 10391 488542 421978 910520

Table A8.10.2. Conventional labour force (LFPRC) in whole numbers: Northern Cape harbours



Port Nolloth 181 198 379 886 641 1527 1 4 5 122 65 187 1068 843 1911 8 4 12 1198 912 2110

Hondeklip Bay 13 9 22 78 59 137 0 0 0 1 3 4 91 68 159 0 0 0 92 71 163

Northern Cape 194 207 401 964 700 1664 1 4 5 123 68 191 1159 911 2070 8 4 12 1290 983 2273 Table A8.10.3. Conventional labour force (LFPRC) in whole numbers: Western Cape harbours



NW Coast 610 263 873 3173 2814 5987 9 4 13 774 449 1223 3792 3081 6873 30 25 55 4596 3555 8151

West Coast 5388 4028 9416 16789 16629 33418 981 674 1655 27564 23428 50992 23158 21331 44489 2083 1791 3874 52805 46550 99355

SW Coast 6932 5130 12062 8175 7205 15380 39 24 63 4183 3158 7341 15146 12359 27505 292 237 529 19621 15754 35375

SE Coast 6217 3504 9721 6682 5802 12484 119 62 181 6241 4965 11206 13018 9368 22386 260 210 470 19519 14543 34062

Western Cape 19147 12925 32072 34819 32450 67269 1148 764 1912 38762 32000 70762 55114 46139 101253 2665 2263 4928 96541 80402 176943 Table A8.10.4. Conventional labour force (LFPRC) in whole numbers: NW coast harbours



Doringbaai 4 1 5 181 143 324 0 0 0 0 0 0 185 144 329 0 2 2 185 146 331

Lambert's Bay 69 16 85 729 560 1289 5 0 5 169 120 289 803 576 1379 11 13 24 983 709 1692

Elandsbaai 168 88 256 995 910 1905 1 1 2 178 79 257 1164 999 2163 3 5 8 1345 1083 2428

Velddrif 3 2 5 36 18 54 0 0 0 238 148 386 39 20 59 0 0 0 277 168 445

St Helenabaai 357 152 509 1019 1005 2024 3 3 6 178 93 271 1379 1160 2539 5 3 8 1562 1256 2818

Paternoster 9 4 13 213 178 391 0 0 0 11 9 20 222 182 404 11 2 13 244 193 437

NW Coast 610 263 873 3173 2814 5987 9 4 13 774 449 1223 3792 3081 6873 30 25 55 4596 3555 8151 Table A8.10.5. Conventional labour force (LFPRC) in whole numbers: west coast harbours.


M F T M F T M F T M F T M F T M F T M F T Saldanha 949 372 1321 2595 2253 4848 40 20 60 923 498 1421 3584 2645 6229 120 92 212 4627 3235 7862 Yzerfontein 0 4 4 0 2 2 0 3 3 63 36 99 0 9 9 0 0 0 63 45 108 Cape Town-(MD) 4250 3597 784713100 13362 264629106301540250582168846746182601758935849 1858 1599 3457 451764087686052

Hout Bay Harbour 149 14 163 1029 965 1994 7 4 11 134 96 230 1185 983 2168 84 85 169 1403 1164 2567

Kommetjie 7 11 18 22 9 31 0 0 0 464 389 853 29 20 49 5 4 9 498 413 911 Simons Town 33 30 63 43 38 81 24 17 41 922 721 1643 100 85 185 16 11 27 1038 817 1855 West Coast 5388 4028 941616789 16629 334189816741655275642342850992231582133144489 2083 1791 3874 528054655099355


248

Table A8.10.6. Conventional labour force (LFPRC) in whole numbers: SW coast harbours.



Kalk Bay 9 16 25 101 81 182 2 0 2 178 143 321 112 97 209 4 4 8 294 244 538

Strand 3772 2342 6114 4361 3768 8129 90 56 146 3406 2811 6217 8223 6166 14389 222 170 392 11851 9147 20998

Gordons Bay 36 29 65 587 523 1110 13 3 16 1213 831 2044 636 555 1191 7 7 14 1856 1393 3249

Kleinmond 313 133 446 415 316 731 0 0 0 210 182 392 728 449 1177 14 18 32 952 649 1601

Hermanus 1822 888 2710 732 695 1427 12 3 15 969 869 1838 2566 1586 4152 6 4 10 3541 2459 6000

Gansbaai 265 96 361 486 419 905 2 0 2 265 129 394 753 515 1268 7 7 14 1025 651 1676

SW Coast 6217 3504 9721 6682 5802 12484 119 62 181 6241 4965 11206 13018 9368 22386 260 210 470 19519 14543 34062 Table A8.10.7. Conventional labour force (LFPRC) in whole numbers: SE coast harbours.



Struisbaai 8 4 12 297 204 501 0 4 4 83 63 146 305 212 517 3 0 3 391 275 666

Arniston 2 8 10 209 134 343 0 0 0 17 10 27 211 142 353 0 0 0 228 152 380

Knysna 2796 2018 4814 2721 2626 5347 13 7 20 1397 1220 2617 5530 4651 10181 184 158 342 7111 6029 13140

Mossel Bay 2703 2145 4848 4111 3467 7578 18 9 27 2212 1479 3691 6832 5621 12453 79 60 139 9123 7160 16283

Plettenberg Bay 1423 955 2378 837 774 1611 8 4 12 474 386 860 2268 1733 4001 26 19 45 2768 2138 4906

SE Coast 6932 5130 12062 8175 7205 15380 39 24 63 4183 3158 7341 15146 12359 27505 292 237 529 19621 15754 35375 Table A8.10.8. Conventional labour force (LFPRC) in whole numbers: Eastern Cape harbours.



Jeffreys Bay 893 539 1432 804 678 1482 4 1 5 974 737 1711 1701 1218 2919 15 8 23 2690 1963 4653

Port Elizabeth 83170 84674 167844 36347 32132 68479 2424 1560 3984 32810 25786 58596 121941 118366 240307 1006 911 1917 155757 145063 300820

Port Alfred 1962 2204 4166 202 169 371 4 4 8 418 397 815 2168 2377 4545 13 11 24 2599 2785 5384

East London 32652 33674 66326 6526 5886 12412 1084 756 1840 16016 12826 28842 40262 40316 80578 218 211 429 56496 53353 109849

Eastern Cape 118677 121091 239768 43879 38865 82744 3516 2321 5837 50218 39746 89964 166072 162277 328349 1252 1141 2393 217542 203164 420706 Table A8.10.9. Conventional labour force (LFPRC) in whole numbers: KwaZulu-Natal harbours.



Port Shepstone 4692 6490 11182 421 364 785 3128 1782 4910 4576 3884 8460 8241 8636 16877 67 67 134 12884 12587 25471

Durban 59741 48831 108572 10611 9708 20319 32418 19538 51956 47142 39570 86712 102770 78077 180847 1534 1277 2811 151446 118924 270370

Mthunzini 422 508 930 2 3 5 1 0 1 309 238 547 425 511 936 2 3 5 736 752 1488

Richards Bay 664 595 1259 219 176 395 1999 1083 3082 5011 3116 8127 2882 1854 4736 51 55 106 7944 5025 12969

St Lucia 55 63 118 0 3 3 0 0 0 102 75 177 55 66 121 2 0 2 159 141 300

KwaZulu-Natal 65574 56487 122061 11253 10254 21507 37546 22403 59949 57140 46883 104023 114373 89144 203517 1656 1402 3058 173169 137429 310598


249

The number of people in the extended labour force (ELF) Table A8.11.1. The number of people in the extended labour force (ELF), South African harbours.



Northern Cape 196 218 414 968 738 1706 1 4 5 123 72 195 1165 960 2125 8 4 12 1296 1036 2332

Eastern Cape 124746 131172 255918 45346 40768 86114 3553 2386 5939 50619 40436 91055 173645 174326 347971 1313 1192 2505 225577 215954 441531

KwaZulu-Natal 67354 59050 126404 11464 10557 22021 37998 23222 61220 57666 47885 105551 116816 92829 209645 1692 1432 3124 176174 142146 318320

Western Cape 19533 13529 33062 35416 33383 68799 1175 794 1969 39154 32539 71693 56124 47706 103830 2684 2317 5001 97962 82562 180524

SA Coast 211829 203969 415798 93194 85446 178640 42727 26406 69133 147562 120932 268494 347750 315821 663571 5697 4945 10642 501009 441698 942707

Table A8.11.2. The number of people in the extended labour force (ELF), Northern Cape harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Port Nolloth 183 209 392 890 675 1565 1 4 5 122 66 188 1074 888 1962 8 4 12 1204 958 2162 Hondeklip Bay 13 9 22 78 63 141 0 0 0 1 6 7 91 72 163 0 0 0 92 78 170 Northern Cape 196 218 414 968 738 1706 1 4 5 123 72 195 1165 960 2125 8 4 12 1296 1036 2332 Table A8.11.3. The number of people in the extended labour force (ELF), Western Cape harbours.



NW Coast 614 270 884 3215 2896 6111 9 4 13 782 465 1247 3838 3170 7008 30 25 55 4650 3660 8310

West Coast 5525 4186 9711 17065 17036 34101 1003 689 1692 27831 23794 51625 23593 21911 45504 2098 1832 3930 53522 47537 101059

SW Coast 6313 3696 10009 6777 5981 12758 121 71 192 6310 5049 11359 13211 9748 22959 263 217 480 19784 15014 34798

SE Coast 7081 5377 12458 8359 7470 15829 42 30 72 4231 3231 7462 15482 12877 28359 293 243 536 20006 16351 36357

Western Cape 19533 13529 33062 35416 33383 68799 1175 794 1969 39154 32539 71693 56124 47706 103830 2684 2317 5001 97962 82562 180524 Table A8.11.4. The number of people in the extended labour force (ELF), NW coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total


Doringbaai 4 1 5 185 145 330 0 0 0 0 0 0 189 146 335 0 2 2 189 148 337

Lambert's Bay 69 16 85 735 571 1306 5 0 5 169 120 289 809 587 1396 11 13 24 989 720 1709

Elandsbaai 171 88 259 1019 953 1972 1 1 2 183 81 264 1191 1042 2233 3 5 8 1377 1128 2505

Velddrif 3 2 5 36 18 54 0 0 0 240 148 388 39 20 59 0 0 0 279 168 447

St Helenabaai 358 159 517 1024 1029 2053 3 3 6 178 107 285 1385 1191 2576 5 3 8 1568 1301 2869

Paternoster 9 4 13 216 180 396 0 0 0 12 9 21 225 184 409 11 2 13 248 195 443

NW Coast 614 270 884 3215 2896 6111 9 4 13 782 465 1247 3838 3170 7008 30 25 55 4650 3660 8310 Table A8.11.5. The number of people in the extended labour force (ELF), west coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total


Saldanha 949 373 1322 2615 2298 4913 40 20 60 925 527 1452 3604 2691 6295 120 96 216 4649 3314 7963

Yzerfontein 0 4 4 0 2 2 0 3 3 65 40 105 0 9 9 0 0 0 65 49 114

Cape Town-(MD) 4384 3753 8137 13331 13683 27014 928 645 1573 25300 21998 47298 18643 18081 36724 1872 1627 3499 45815 41706 87521

Hout Bay Harbour 152 15 167 1054 1005 2059 7 4 11 137 99 236 1213 1024 2237 85 91 176 1435 1214 2649

Kommetjie 7 11 18 22 9 31 0 0 0 473 399 872 29 20 49 5 4 9 507 423 930

Simons Town 33 30 63 43 39 82 28 17 45 931 731 1662 104 86 190 16 14 30 1051 831 1882

West Coast 5525 4186 9711 17065 17036 34101 1003 689 1692 27831 23794 51625 23593 21911 45504 2098 1832 3930 53522 47537 101059


250

Table A8.11.6. The number of people in the extended labour force (ELF), SW coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total

M F T M F T M F T M F T M F T M F T M F T Kalk Bay 9 16 25 102 81 183 2 0 2 180 149 329 113 97 210 4 4 8 297 250 547 Strand 3832 2437 6269 4441 3915 8356 90 62 152 34412850 6291 8363 6414 14777 225 174 399 12029 9438 21467 Gordons Bay 36 32 68 588 528 1116 13 4 17 1222 842 2064 637 564 1201 7 7 14 1866 1413 3279 Kleinmond 314 138 452 417 318 735 0 0 0 214 185 399 731 456 1187 14 21 35 959 662 1621 Hermanus 1855 944 2799 740 711 1451 14 5 19 984 891 1875 2609 1660 4269 6 4 10 3599 2555 6154 Gansbaai 267 129 396 489 428 917 2 0 2 269 132 401 758 557 1315 7 7 14 1034 696 1730 SW Coast 6313 3696 10009 6777 5981 12758 121 71 192 63105049 11359 13211 9748 22959 263 217 480 19784 15014 34798

Table A8.11.7. The number of people in the extended labour force (ELF), SE coast harbours.

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T

Struisbaai 12 4 16 310 218 528 0 4 4 84 66 150 322 226 548 3 0 3 409 292 701 Arniston 2 8 10 209 136 345 0 0 0 17 12 29 211 144 355 0 0 0 228 156 384 Knysna 2856 2144 5000 2776 2712 5488 16 10 26 142012422662 5648 4866 10514 184 160 344 7252 6268 13520 Mossel Bay 2768 2234 5002 4216 3606 7822 18 12 30 223415203754 7002 5852 12854 80 60 140 9316 7432 16748 Plettenberg Bay 1443 987 2430 848 798 1646 8 4 12 476 391 867 2299 1789 4088 26 23 49 2801 2203 5004 SE Coast 7081 5377 12458 8359 7470 15829 42 30 72 423132317462 15482 12877 28359 293 243 536 20006 1635136357

Table A8.11.8. The number of people in the extended labour force (ELF), Eastern Cape harbours.



Jeffreys Bay 904 585 1489 826 734 1560 4 1 5 985 753 1738 1734 1320 3054 15 9 24 2734 2082 4816

Port Elizabeth 86987 91105 178092 37599 33732 71331 2451 1604 4055 33071 26181 59252 127037 126441 253478 1057 951 2008 161165 153573314738

Port Alfred 2111 2441 4552 213 175 388 4 6 10 425 402 827 2328 2622 4950 13 11 24 2766 3035 5801

East London 34744 37041 71785 6708 6127 12835 1094 775 1869 16138 13100 29238 42546 43943 86489 228 221 449 58912 57264 116176

Eastern Cape 124746 131172 255918 45346 40768 86114 3553 2386 5939 50619 40436 91055 173645 174326 347971 1313 1192 2505 225577 215954441531 Table A8.11.9. The number of people in the extended labour force (ELF), KwaZulu-Natal harbours.



Port Shepstone 4765 6672 11437 423 376 799 3149 1842 4991 4643 3978 8621 8337 8890 17227 70 69 139 13050 12937 25987

Durban 61427 51169 112596 10820 9992 20812 32843 20285 53128 47579 40386 87965 105090 81446 186536 1567 1303 2870 154236 123135 277371

Mthunzini 437 537 974 2 3 5 1 0 1 313 239 552 440 540 980 2 3 5 755 782 1537

Richards Bay 670 609 1279 219 183 402 2005 1095 3100 5027 3204 8231 2894 1887 4781 51 57 108 7972 5148 13120

St Lucia 55 63 118 0 3 3 0 0 0 104 78 182 55 66 121 2 0 2 161 144 305

Kwa-Natal 67354 59050 126404 11464 10557 22021 37998 23222 61220 57666 47885 105551 116816 92829 209645 1692 1432 3124 176174 142146 318320


251

Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR) Tables A8.12.1. Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR): South African harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T KwaZulu-Natal ELFR 81.7 72.9 77.3 76.7 60.9 68.2 78.6 43.9 60.5 79.4 62.4 70.7 80.1 61.4 70.6 75.1 58.0 66.1 79.8 61.7 70.6 CLFR 79.5 69.7 74.6 75.3 59.2 66.7 77.7 42.4 59.2 78.6 61.1 69.6 78.5 58.9 68.5 73.5 56.7 64.7 78.5 59.6 68.8 Eastern Cape ELFR 67.2 60.1 63.4 71.8 57.2 64.1 77.1 51.7 64.4 76.7 58.7 67.5 68.5 59.3 63.6 66.1 55.4 60.5 70.2 59.2 64.3 CLFR 63.9 55.5 59.4 69.5 54.5 61.6 76.3 50.3 63.3 76.1 57.7 66.7 65.5 55.2 60.0 63.1 53.0 57.8 67.7 55.6 61.3 Northern Cape ELFR 81.7 77.6 79.5 79.4 55.4 66.8 100.0 50.0 55.6 86.6 50.7 68.7 79.8 59.2 68.9 66.7 44.4 57.1 80.3 58.4 68.9 CLFR 80.8 73.7 77.0 79.1 52.5 65.2 100.0 50.0 55.6 86.6 47.9 67.3 79.4 56.2 67.2 66.7 44.4 57.1 79.9 55.4 67.1 Western Cape ELFR 81.2 67.1 74.8 78.2 63.1 70.1 75.3 52.4 64.0 77.7 60.3 68.7 79.2 63.9 71.4 78.5 61.2 69.4 78.6 62.4 70.2 CLFR 79.6 64.1 72.6 76.9 61.3 68.5 73.6 50.4 62.2 76.9 59.3 67.8 77.8 61.8 69.6 77.9 59.7 68.4 77.4 60.7 68.8 SA Coast ELFR 72.5 63.9 68.0 75.4 60.2 67.3 78.4 44.8 60.9 78.0 60.6 69.1 73.9 60.7 67.0 74.4 58.8 66.2 75.1 60.6 67.6 CLFR 69.6 59.7 64.5 73.6 58.0 65.3 77.4 43.2 59.6 77.3 59.4 68.2 71.6 57.4 64.1 72.9 57.2 64.7 73.2 57.9 65.2 Tables A8.12.2. Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR): Northern Cape harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Port Nolloth ELFR 81.7 78.0 79.7 79.0 55.0 66.5 100.0 50.0 55.6 86.5 50.0 68.9 79.4 59.0 68.7 66.7 44.4 57.1 80.0 58.2 68.6 CLFR 80.8 73.9 77.0 78.6 52.2 64.8 100.0 50.0 55.6 86.5 49.2 68.5 79.0 56.1 66.9 66.7 44.4 57.1 79.6 55.4 67.0 Hondeklip Bay ELFR 81.3 69.2 75.9 84.8 60.0 71.6 100.0 60.0 63.6 84.3 61.0 72.1 84.4 60.9 71.7 CLFR 81.3 69.2 75.9 84.8 56.2 69.5 0.0 0.0 0.0 100.0 30.0 36.4 84.3 57.6 70.4 0.0 0.0 0.0 84.4 55.5 68.8 Northern Cape ELFR 81.7 77.6 79.5 79.4 55.4 66.8 100.0 50.0 55.6 86.6 50.7 68.7 79.8 59.2 68.9 66.7 44.4 57.1 80.3 58.4 68.9 CLFR 80.8 73.7 77.0 79.1 52.5 65.2 100.0 50.0 55.6 86.6 47.9 67.3 79.4 56.2 67.2 66.7 44.4 57.1 79.9 55.4 67.1 Tables A8.12.3. Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR): Western Cape African harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T NW Coast ELFR 91.5 65.4 81.5 80.3 65.7 72.7 100.0 100.0 100.0 69.8 39.5 54.3 79.4 56.6 67.5 75.0 51.0 61.8 79.6 60.5 69.9 CLFR 90.9 63.7 80.5 79.3 63.9 71.2 100.0 100.0 100.0 69.0 38.2 53.2 77.7 55.8 66.3 75.0 51.0 61.8 78.6 58.8 68.5 West Coast ELFR 82.0 71.6 77.1 77.8 63.9 70.2 75.5 52.3 63.9 80.9 66.1 73.3 78.6 64.8 71.3 80.7 63.3 71.6 79.9 65.4 72.3 CLFR 80.0 68.9 74.8 76.5 62.4 68.8 73.8 51.1 62.5 80.1 65.1 72.4 77.2 63.1 69.7 80.1 61.9 70.6 78.8 64.0 71.1 SW Coast ELFR 86.0 66.4 77.5 81.4 62.9 71.5 76.6 48.3 63.0 70.8 49.6 59.5 83.5 64.0 73.9 76.0 51.7 62.7 78.8 58.1 68.3 CLFR 84.7 62.9 75.3 80.2 61.0 70.0 75.3 42.2 59.3 70.0 48.7 58.7 82.2 61.5 72.1 75.1 50.0 61.4 77.8 56.3 66.9 SE Coast ELFR 76.2 64.6 70.7 76.0 60.4 67.7 65.6 63.8 64.9 70.9 48.7 59.2 76.1 62.1 69.0 67.5 56.9 62.3 74.8 58.8 66.6 CLFR 74.6 61.7 68.5 74.3 58.2 65.8 60.9 51.1 56.8 70.1 47.6 58.3 74.4 59.6 66.9 67.3 55.5 61.4 73.3 56.7 64.8 Western Cape ELFR 81.2 67.1 74.8 78.2 63.1 70.1 75.3 52.4 64.0 77.7 60.3 68.7 79.2 63.9 71.4 78.5 61.2 69.4 78.6 62.4 70.2 CLFR 79.6 64.1 72.6 76.9 61.3 68.5 73.6 50.4 62.2 76.9 59.3 67.8 77.8 61.8 69.6 77.9 59.7 68.4 77.4 60.7 68.8


252

Tables A8.12.4. Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR): NW coast harbours.



Doringbaai

ELFR 100.0 100.0 100.0 79.1 56.4 67.2 79.4 56.6 67.5 100.0 100.0 79.4 56.9 67.7

CLFR 100.0 100.0 100.0 77.4 55.6 66.0 0.0 0.0 0.0 0.0 0.0 0.0 77.7 55.8 66.3 0.0 100.0 100.0 77.7 56.2 66.5

Lambert's Bay

ELFR 89.6 47.1 76.6 76.5 56.9 66.5 100.0 100.0 71.9 43.3 56.4 77.6 56.6 67.1 61.1 54.2 57.1 76.3 53.8 64.9

CLFR 89.6 47.1 76.6 75.9 55.8 65.6 100.0 0.0 100.0 71.9 43.3 56.4 77.0 55.5 66.3 61.1 54.2 57.1 75.8 53.0 64.2

Elandsbaai

ELFR 91.9 66.2 81.2 80.5 69.1 74.6 100.0 100.0 100.0 81.0 34.5 57.3 82.0 68.9 75.3 75.0 71.4 72.7 81.8 64.3 72.9

CLFR 90.3 66.2 80.3 78.6 66.0 72.0 100.0 100.0 100.0 78.8 33.6 55.7 80.1 66.0 72.9 75.0 71.4 72.7 79.9 61.7 70.6

Velddrif

ELFR 100.0 40.0 62.5 90.0 81.8 87.1 60.8 35.5 47.8 90.7 74.1 84.3 63.7 37.8 50.7

CLFR 100.0 40.0 62.5 90.0 81.8 87.1 0.0 0.0 0.0 60.3 35.5 47.5 90.7 74.1 84.3 0.0 0.0 0.0 63.2 37.8 50.5

St Helenabaai

ELFR 92.7 70.4 84.5 82.9 71.2 76.6 100.0 100.0 100.0 72.1 47.3 60.3 85.3 71.1 78.1 71.4 50.0 61.5 83.5 68.3 75.8

CLFR 92.5 67.3 83.2 82.5 69.6 75.5 100.0 100.0 100.0 72.1 41.2 57.3 84.9 69.3 77.0 71.4 50.0 61.5 83.2 65.9 74.5

Paternoster

ELFR 60.0 28.6 44.8 80.9 60.0 69.8 66.7 42.9 53.8 79.8 58.6 68.6 100.0 20.0 61.9 79.7 56.5 67.5

CLFR 60.0 28.6 44.8 79.8 59.3 69.0 61.1 42.9 51.3 78.7 58.0 67.8 100.0 20.0 61.9 78.5 55.9 66.6

NW Coast

ELFR 91.5 65.4 81.5 80.3 65.7 72.7 100.0 100.0 100.0 69.8 39.5 54.3 82.0 65.7 73.7 75.0 51.0 61.8 79.6 60.5 69.9

CLFR 90.9 63.7 80.5 79.3 63.9 71.2 100.0 100.0 100.0 69.0 38.2 53.2 81.0 63.9 72.3 75.0 51.0 61.8 78.6 58.8 68.5 Tables A8.12.5. Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR): west coast harbours.



Saldanha

ELFR 93.4 72.1 86.2 79.1 64.9 71.8 83.3 54.1 70.6 82.7 51.8 68.0 82.5 65.7 74.4 81.6 59.6 70.1 82.5 62.8 73.0

CLFR 93.4 72.0 86.2 78.5 63.6 70.8 83.3 54.1 70.6 82.6 48.9 66.5 82.0 64.6 73.6 81.6 57.1 68.8 82.1 61.3 72.1

Yzerfontein

ELFR 100.0 100.0 66.7 66.7 100.0 100.0 52.4 29.2 40.2 90.0 90.0 52.4 33.3 42.1

CLFR 100.0 100.0 66.7 66.7 100.0 100.0 50.8 26.3 37.9 0.0 90.0 90.0 50.8 30.6 39.9

Cape Town-(MD)

ELFR 79.5 71.6 75.6 77.8 63.9 70.1 75.6 52.7 64.1 81.3 67.2 74.1 78.1 64.8 70.9 81.0 63.5 71.8 79.9 66.0 72.6

CLFR 77.0 68.6 72.9 76.5 62.4 68.6 74.1 51.4 62.8 80.5 66.3 73.2 76.5 63.1 69.3 80.4 62.4 70.9 78.8 64.7 71.4

Hout Bay Harbour

ELFR 95.0 41.7 85.2 74.5 62.8 68.3 35.0 40.0 36.7 77.4 63.5 70.9 76.1 62.2 69.0 72.6 65.0 68.5 76.0 62.5 69.1

CLFR 93.1 38.9 83.2 72.7 60.3 66.1 35.0 40.0 36.7 75.7 61.5 69.1 74.3 59.7 66.9 71.8 60.7 65.8 74.3 59.9 67.0

Kommetjie

ELFR 100.0 100.0 100.0 88.0 56.3 75.6 79.5 63.3 71.2 90.6 74.1 83.1 62.5 66.7 64.3 79.8 63.8 71.6

CLFR 100.0 100.0 100.0 88.0 56.3 75.6 78.0 61.7 69.6 90.6 74.1 83.1 62.5 66.7 64.3 78.4 62.3 70.2

Simons Town

ELFR 84.6 76.9 80.8 79.6 58.2 67.8 84.8 39.5 59.2 74.5 55.6 64.8 82.5 57.7 69.1 94.1 66.7 78.9 75.5 56.0 65.4

CLFR 84.6 76.9 80.8 79.6 56.7 66.9 72.7 39.5 53.9 73.8 54.9 64.1 79.4 57.0 67.3 94.1 52.4 71.1 74.6 55.1 64.5

West Coast

ELFR 82.0 71.6 77.1 77.8 63.9 70.2 75.5 52.3 63.9 80.9 66.1 73.3 78.6 64.8 71.3 80.7 63.3 71.6 79.9 65.4 72.3

CLFR 80.0 68.9 74.8 76.5 62.4 68.8 73.8 51.1 62.5 80.1 65.1 72.4 77.2 63.1 69.7 80.1 61.9 70.6 78.8 64.0 71.1


253

Tables A8.12.6. Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR): SW coast harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Kalk Bay ELFR 39.1 80.0 58.1 81.0 55.9 67.5 100.0 100.0 82.6 72.3 77.6 74.8 58.8 66.5 66.7 26.7 38.1 79.2 64.8 71.9 CLFR 39.1 80.0 58.1 80.2 55.9 67.2 100.0 0.0 100.0 81.7 69.4 75.7 74.2 58.8 66.1 66.7 26.7 38.1 78.4 63.2 70.7 Strand ELFR 85.6 65.7 76.6 79.0 59.6 68.6 72.6 48.1 60.1 71.6 51.8 61.0 81.8 61.7 71.6 77.1 50.7 62.8 78.5 58.1 68.0 CLFR 84.2 63.2 74.7 77.6 57.4 66.7 72.6 43.4 57.7 70.8 51.1 60.3 80.4 59.3 69.8 76.0 49.6 61.7 77.3 56.3 66.5 Gordons Bay ELFR 76.6 72.7 74.7 83.2 65.3 73.7 92.9 57.1 81.0 79.7 53.6 66.5 82.9 65.7 73.8 63.6 50.0 56.0 80.7 57.8 68.9 CLFR 76.6 65.9 71.4 83.0 64.7 73.3 92.9 42.9 76.2 79.1 52.9 65.8 82.8 64.6 73.2 63.6 50.0 56.0 80.2 57.0 68.3 Kleinmond ELFR 98.1 91.4 96.0 92.1 71.3 81.8 43.4 29.6 35.7 94.6 76.4 86.6 63.6 65.6 64.8 74.5 52.8 63.8 CLFR 97.8 88.1 94.7 91.6 70.9 81.3 0.0 0.0 0.0 42.6 29.1 35.1 94.2 75.2 85.9 63.6 56.3 59.3 73.9 51.8 63.0 Hermanus ELFR 84.9 64.6 76.8 82.1 68.8 75.0 100.0 55.6 82.6 66.1 47.3 55.6 84.2 66.3 76.2 75.0 57.1 66.7 78.3 58.2 68.5 CLFR 83.4 60.8 74.3 81.2 67.3 73.8 85.7 33.3 65.2 65.1 46.1 54.5 82.8 63.4 74.1 75.0 57.1 66.7 77.0 56.0 66.7 Gansbaai ELFR 92.7 69.4 83.5 93.5 84.1 88.9 50.0 0.0 33.3 71.9 33.4 52.1 93.0 79.9 87.0 100.0 77.8 87.5 86.5 63.2 75.3 CLFR 92.0 51.6 76.2 92.9 82.3 87.7 50.0 0.0 33.3 70.9 32.7 51.2 92.4 73.9 83.9 100.0 77.8 87.5 85.7 59.1 73.0 SW Coast ELFR 86.0 66.4 77.5 81.4 62.9 71.5 76.6 48.3 63.0 70.8 49.6 59.5 83.5 64.0 73.9 76.0 51.7 62.7 78.8 58.1 68.3 CLFR 84.7 62.9 75.3 80.2 61.0 70.0 75.3 42.2 59.3 70.0 48.7 58.7 82.2 61.5 72.1 75.1 50.0 61.4 77.8 56.3 66.9 Tables A8.12.7. Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR): SE coast harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Struisbaai ELFR 80.0 26.7 53.3 86.4 58.9 72.4 66.7 66.7 40.2 27.5 33.4 86.1 57.8 71.6 75.0 0.0 27.3 69.7 45.8 57.2 CLFR 53.3 26.7 40.0 82.7 55.1 68.7 0.0 66.7 66.7 39.7 26.3 32.5 81.6 54.2 67.6 75.0 0.0 27.3 66.6 43.1 54.4 Arniston ELFR 66.7 100.0 90.9 75.5 52.3 64.2 65.4 41.4 52.7 75.4 53.7 64.8 74.5 52.5 63.7 CLFR 66.7 100.0 90.9 75.5 51.5 63.9 0.0 0.0 0.0 65.4 34.5 49.1 75.4 53.0 64.4 0.0 0.0 0.0 74.5 51.2 63.0 Knysna ELFR 76.8 66.0 71.8 75.0 63.9 69.1 59.3 100.0 70.3 72.0 55.4 63.2 75.9 64.8 70.3 69.4 60.8 65.2 74.9 62.6 68.7 CLFR 75.2 62.1 69.1 73.5 61.8 67.3 48.1 70.0 54.1 70.8 54.4 62.1 74.3 62.0 68.1 69.4 60.1 64.8 73.5 60.2 66.7 Mossel Bay ELFR 71.9 61.0 66.6 75.5 57.1 65.7 72.0 46.2 58.8 72.1 45.1 58.0 74.0 58.5 66.0 69.6 50.0 59.6 73.5 55.1 64.0 CLFR 70.2 58.6 64.5 73.6 54.9 63.7 72.0 34.6 52.9 71.4 43.9 57.0 72.2 56.2 64.0 68.7 50.0 59.1 72.0 53.0 62.2 Plettenberg Bay ELFR 84.5 71.4 78.6 78.4 67.6 72.8 66.7 80.0 70.6 72.5 52.1 61.6 82.1 69.6 76.1 52.0 62.2 56.3 79.9 65.6 72.9 CLFR 83.4 69.1 77.0 77.4 65.5 71.2 66.7 80.0 70.6 72.1 51.4 61.1 81.0 67.5 74.5 52.0 51.4 51.7 78.9 63.7 71.5 SE Coast ELFR 76.2 64.6 70.7 76.0 60.4 67.7 65.6 63.8 64.9 70.9 48.7 59.2 76.1 62.1 69.0 67.5 56.9 62.3 74.8 58.8 66.6 CLFR 74.6 61.7 68.5 74.3 58.2 65.8 60.9 51.1 56.8 70.1 47.6 58.3 74.4 59.6 66.9 67.3 55.5 61.4 73.3 56.7 64.8


254

Tables A8.12.8. Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR): Eastern Cape harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Jeffreys Bay ELFR 81.7 75.1 79.0 77.6 63.5 70.3 100.0 100.0 100.0 60.7 39.4 49.2 79.7 68.2 74.3 83.3 39.1 58.5 71.6 53.8 62.7 CLFR 80.7 69.2 76.0 75.5 58.7 66.8 100.0 100.0 100.0 60.0 38.6 48.4 78.2 62.9 71.0 83.3 34.8 56.1 70.5 50.7 60.6 Port Elizabeth ELFR 66.3 60.0 63.0 71.8 56.8 63.8 75.9 48.5 62.0 76.2 57.8 66.8 68.0 59.0 63.2 68.3 56.4 62.1 69.6 58.7 63.8 CLFR 63.4 55.8 59.3 69.4 54.1 61.3 75.1 47.2 61.0 75.6 56.9 66.1 65.3 55.2 59.9 65.0 54.1 59.3 67.2 55.5 61.0 Port Alfred ELFR 58.0 55.5 56.6 63.4 49.2 56.1 50.0 100.0 71.4 57.2 46.2 51.3 58.5 55.0 56.6 30.2 26.8 28.6 58.0 53.5 55.6 CLFR 53.9 50.1 51.8 60.1 47.5 53.6 50.0 66.7 57.1 56.3 45.6 50.5 54.4 49.9 52.0 30.2 26.8 28.6 54.5 49.1 51.6 East London ELFR 69.7 60.5 64.7 71.7 59.0 65.0 80.1 59.5 70.0 79.6 62.9 71.1 70.3 60.3 64.8 60.6 54.7 57.6 72.6 60.8 66.3 CLFR 65.5 55.0 59.7 69.8 56.7 62.9 79.4 58.0 68.9 79.0 61.6 70.2 66.5 55.3 60.4 58.0 52.2 55.0 69.6 56.7 62.7 Eastern Cape ELFR 67.2 60.1 63.4 71.8 57.2 64.1 77.1 51.7 64.4 76.7 58.7 67.5 68.5 59.3 63.6 66.1 55.4 60.5 70.2 59.2 64.3 CLFR 63.9 55.5 59.4 69.5 54.5 61.6 76.3 50.3 63.3 76.1 57.7 66.7 65.5 55.2 60.0 63.1 53.0 57.8 67.7 55.6 61.3 Tables A8.12.9. Conventional labour force participation rates (CLFR) and extended labour force participation rates (ELFR): KwaZulu-Natal harbours.



Port Shepstone

ELFR 75.3 71.3 72.9 74.6 54.7 63.7 81.6 43.1 61.4 68.6 51.5 59.5 77.5 62.1 68.7 73.7 50.4 59.9 74.1 58.3 65.3

CLFR 74.1 69.3 71.3 74.3 53.0 62.6 81.0 41.7 60.4 67.6 50.3 58.4 76.6 60.3 67.3 70.5 48.9 57.8 73.1 56.7 64.0

Durban

ELFR 82.3 73.4 78.0 76.8 61.1 68.4 77.9 43.8 60.0 80.0 64.1 71.8 80.3 61.5 70.8 75.4 58.6 66.7 80.2 62.3 71.1

CLFR 80.0 70.0 75.2 75.3 59.4 66.8 76.9 42.2 58.7 79.3 62.8 70.8 78.5 59.0 68.7 73.9 57.4 65.4 78.7 60.2 69.3

Mthunzini

ELFR 64.1 53.8 58.0 100.0 60.0 71.4 100.0 0.0 25.0 83.0 59.6 71.0 64.2 53.7 58.0 100.0 50.0 62.5 71.0 55.3 62.1

CLFR 61.9 50.9 55.4 100.0 60.0 71.4 100.0 0.0 25.0 82.0 59.4 70.3 62.0 50.8 55.4 100.0 50.0 62.5 69.2 53.2 60.1

Richards Bay

ELFR 86.8 70.0 77.9 78.2 62.5 70.2 86.1 48.1 67.3 84.6 58.7 72.2 85.6 54.9 70.1 68.9 60.0 63.9 84.8 57.2 71.3

CLFR 86.0 68.4 76.7 78.2 60.1 68.9 85.8 47.6 66.9 84.3 57.0 71.3 85.2 53.9 69.5 68.9 57.9 62.7 84.5 55.9 70.5

St Lucia

ELFR 90.2 82.9 86.1 100.0 100.0 79.4 53.8 65.9 90.2 83.5 86.4 40.0 0.0 13.3 81.7 61.5 70.8

CLFR 90.2 82.9 86.1 0.0 100.0 100.0 0.0 0.0 0.0 77.9 51.7 64.1 90.2 83.5 86.4 40.0 0.0 13.3 80.7 60.3 69.6

Natal

ELFR 81.7 72.9 77.3 76.7 60.9 68.2 78.6 43.9 60.5 79.4 62.4 70.7 80.1 61.4 70.6 75.1 58.0 66.1 79.8 61.7 70.6

CLFR 79.5 69.7 74.6 75.3 59.2 66.7 77.7 42.4 59.2 78.6 61.1 69.6 78.5 58.9 68.5 73.5 56.7 64.7 78.5 59.6 68.8


255

Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect Table A8.13.1. Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect (EU-CU): South African harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Natal EU ( of ELF) 37.6 36.8 37.2 25.1 20.3 22.8 10.8 14.6 12.3 6.1 7.8 6.9 27.7 29.4 28.4 24.1 19.8 22.2 20.6 22.0 21.2 CU ( of CLF) 36.0 33.9 35.0 23.7 17.9 20.9 9.7 11.5 10.4 5.3 5.8 5.5 26.1 26.4 26.3 22.5 18.1 20.5 19.2 19.3 19.3 (EU-CU) 1.7 2.9 2.2 1.4 2.4 1.8 1.1 3.1 1.9 0.9 2.0 1.4 1.5 2.9 2.2 1.6 1.7 1.7 1.4 2.7 2.0 Eastern Cape EU ( of ELF) 47.0 57.0 52.1 29.4 34.0 31.6 13.3 14.2 13.7 5.3 6.5 5.8 41.7 51.0 46.4 26.6 29.3 27.9 33.5 42.6 37.9 CU ( of CLF) 44.3 53.4 48.9 27.1 30.8 28.8 12.4 11.8 12.2 4.5 4.9 4.7 39.1 47.4 43.2 23.0 26.1 24.5 31.0 39.0 34.8 (EU-CU) 2.7 3.6 3.2 2.4 3.2 2.8 0.9 2.4 1.5 0.8 1.6 1.1 2.7 3.6 3.2 3.6 3.2 3.4 2.5 3.6 3.1 Northern Cape EU ( of ELF) 42.9 66.1 55.1 23.3 44.9 32.6 0.0 100.0 80.0 5.7 29.2 14.4 26.6 49.9 37.1 0.0 75.0 25.0 24.5 48.6 35.2 CU ( of CLF) 42.3 64.3 53.6 23.0 41.9 30.9 0.0 100.0 80.0 5.7 25.0 12.6 26.2 47.2 35.5 0.0 75.0 25.0 24.1 45.8 33.5 (EU-CU) 0.6 1.8 1.5 0.3 3.0 1.7 0.0 0.0 0.0 0.0 4.2 1.8 0.4 2.7 1.7 0.0 0.0 0.0 0.4 2.8 1.7 Western Cape EU ( of ELF) 24.0 42.7 31.6 15.6 19.2 17.3 11.3 14.7 12.7 5.7 6.6 6.1 18.4 25.8 21.8 11.5 13.0 12.2 13.1 17.9 15.3 CU ( of CLF) 22.4 40.1 29.5 14.1 16.9 15.5 9.2 11.4 10.1 4.7 5.0 4.9 16.9 23.3 19.8 10.8 11.0 10.9 11.8 15.7 13.6 (EU-CU) 1.5 2.7 2.1 1.4 2.3 1.9 2.1 3.3 2.6 1.0 1.6 1.2 1.5 2.5 2.0 0.6 2.1 1.3 1.3 2.2 1.7 SA Coast EU ( of ELF) 41.9 50.2 46.0 23.6 26.6 25.0 11.0 14.6 12.4 5.7 7.1 6.3 18.7 19.0 18.8 24.9 31.4 27.9 33.2 40.9 36.8 CU ( of CLF) 39.6 46.8 43.1 22.8 25.1 23.9 9.9 11.6 10.6 5.0 5.4 5.2 17.2 16.8 17.0 23.3 28.5 25.7 31.3 37.8 34.4 (EU-CU) 2.3 3.4 2.9 0.8 1.6 1.2 1.1 3.0 1.8 0.8 1.6 1.2 1.5 2.1 1.8 1.6 2.9 2.3 1.8 3.0 2.5 Table A8.13.2. Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect (EU-CU): Northern Cape harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Port Nolloth EU ( of ELF) 45.4 66.0 56.4 23.9 42.5 31.9 0.0 100.0 80.0 5.7 27.3 13.3 27.6 48.3 37.0 0.0 75.0 25.0 25.2 47.0 34.8 CU ( of CLF) 44.8 64.1 54.9 23.6 39.5 30.3 0.0 100.0 80.0 5.7 26.2 12.8 27.2 45.6 35.3 0.0 75.0 25.0 24.8 44.3 33.2 (EU-CU) 0.6 1.9 1.5 0.3 3.0 1.7 0.0 0.0 0.0 0.0 1.1 0.5 0.4 2.8 1.7 0.0 0.0 0.0 0.4 2.7 1.6 Hondeklip Bay EU ( of ELF) 7.7 66.7 31.8 16.7 69.8 40.4 0.0 50.0 42.9 15.4 69.4 39.3 15.2 67.9 39.4 CU ( of CLF) 7.7 66.7 31.8 16.7 67.8 38.7 0.0 0.0 0.0 0.0 0.0 0.0 15.4 67.6 37.7 0.0 0.0 0.0 15.2 64.8 36.8 (EU-CU) 0.0 0.0 0.0 0.0 2.0 1.7 0.0 0.0 0.0 0.0 50.0 42.9 0.0 1.8 1.5 0.0 0.0 0.0 0.0 3.2 2.6 Northern Cape EU ( of ELF) 42.9 66.1 55.1 23.3 44.9 32.6 0.0 100.0 80.0 5.7 29.2 14.4 26.6 49.9 37.1 0.0 75.0 25.0 24.5 48.6 35.2 CU ( of CLF) 42.3 64.3 53.6 23.0 41.9 30.9 0.0 100.0 80.0 5.7 25.0 12.6 26.2 47.2 35.5 0.0 75.0 25.0 24.1 45.8 33.5 (EU-CU) 0.6 1.8 1.5 0.3 3.0 1.7 0.0 0.0 0.0 0.0 4.2 1.8 0.4 2.7 1.7 0.0 0.0 0.0 0.4 2.8 1.7


256

Table A8.13.3. Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect (EU-CU): Western Cape harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T NW Coast EU ( of ELF) 14.2 18.9 15.6 17.1 17.7 17.4 22.2 0.0 15.4 4.5 9.9 6.5 16.7 17.8 17.2 10.0 36.0 21.8 14.6 16.9 15.6 CU ( of CLF) 13.6 16.7 14.5 16.0 15.3 15.7 22.2 0.0 15.4 3.5 6.7 4.7 15.7 15.4 15.6 10.0 36.0 21.8 13.6 14.5 14.0 (EU-CU) 0.6 2.2 1.1 1.1 2.4 1.7 0.0 0.0 0.0 1.0 3.2 1.8 1.0 2.4 1.6 0.0 0.0 0.0 1.0 2.5 1.6 West Coast EU ( of ELF) 22.2 29.2 25.2 14.8 14.7 14.8 9.5 13.1 10.9 5.4 6.2 5.7 16.3 17.4 16.9 10.8 11.1 10.9 10.4 11.6 11.0 CU ( of CLF) 20.2 26.5 22.9 13.4 12.6 13.0 7.4 11.1 8.9 4.4 4.7 4.6 14.8 15.2 15.0 10.2 9.0 9.7 9.2 9.7 9.4 (EU-CU) 2.0 2.8 2.3 1.4 2.1 1.7 2.0 1.9 2.0 0.9 1.5 1.2 1.6 2.2 1.9 0.6 2.0 1.3 1.2 1.9 1.5 SW Coast EU ( of ELF) 19.5 45.7 29.1 11.2 18.6 14.7 20.7 25.4 22.4 6.8 7.0 6.9 15.3 28.9 21.1 9.9 19.8 14.4 12.5 21.4 16.3 CU ( of CLF) 18.2 42.7 27.0 10.0 16.1 12.8 19.3 14.5 17.7 5.7 5.4 5.6 14.0 26.1 19.0 8.8 17.1 12.6 11.3 18.9 14.5 (EU-CU) 1.2 3.0 2.1 1.3 2.5 1.9 1.3 10.8 4.7 1.0 1.6 1.3 1.3 2.9 2.0 1.0 2.7 1.8 1.2 2.5 1.8 SE Coast EU ( of ELF) 30.2 52.4 39.8 20.0 30.6 25.0 26.2 30.0 27.8 6.3 8.6 7.3 24.7 39.7 31.5 17.7 19.3 18.5 20.7 33.3 26.3 CU ( of CLF) 28.8 50.1 37.8 18.2 28.0 22.8 20.5 12.5 17.5 5.2 6.5 5.8 23.0 37.2 29.4 17.5 17.3 17.4 19.1 30.7 24.3 (EU-CU) 1.5 2.3 2.0 1.8 2.6 2.2 5.7 17.5 10.3 1.1 2.1 1.5 1.7 2.5 2.1 0.3 2.0 1.1 1.6 2.5 2.0 Western Cape EU ( of ELF) 24.0 42.7 31.6 15.6 19.2 17.3 11.3 14.7 12.7 5.7 6.6 6.1 18.4 25.8 21.8 11.5 13.0 12.2 13.1 17.9 15.3 CU ( of CLF) 22.4 40.1 29.5 14.1 16.9 15.5 9.2 11.4 10.1 4.7 5.0 4.9 16.9 23.3 19.8 10.8 11.0 10.9 11.8 15.7 13.6 (EU-CU) 1.5 2.7 2.1 1.4 2.3 1.9 2.1 3.3 2.6 1.0 1.6 1.2 1.5 2.5 2.0 0.6 2.1 1.3 1.3 2.2 1.7


257

Table A8.13.4. Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect (EU-CU): NW coast harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Doringbaai EU ( of ELF) 0.0 100.0 20.0 40.5 48.3 43.9 39.7 48.6 43.6 100.0 100.0 39.7 49.3 43.9 CU ( of CLF) 0.0 100.0 20.0 39.2 47.6 42.9 38.4 47.9 42.6 100.0 100.0 38.4 48.6 42.9 (EU-CU) 0.0 0.0 0.0 1.3 0.7 1.0 1.3 0.7 1.0 0.0 0.0 1.3 0.7 1.0 Lambert's Bay EU ( of ELF) 23.2 43.8 27.1 33.1 32.2 32.7 40.0 0.0 40.0 4.1 3.3 3.8 32.3 32.5 32.4 9.1 46.2 29.2 27.2 27.9 27.5 CU ( of CLF) 23.2 43.8 27.1 32.5 30.9 31.8 40.0 0.0 40.0 4.1 3.3 3.8 31.8 31.3 31.5 9.1 46.2 29.2 26.8 26.8 26.8 (EU-CU) 0.0 0.0 0.0 0.6 1.3 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.3 0.8 0.0 0.0 0.0 0.4 1.1 0.7 Velddrif EU ( of ELF) 0.0 0.0 0.0 0.0 0.0 0.0 5.0 7.4 5.9 0.0 0.0 0.0 4.3 6.5 5.1 CU ( of CLF) 0.0 0.0 0.0 0.0 0.0 0.0 4.2 7.4 5.4 0.0 0.0 0.0 3.6 6.5 4.7 (EU-CU) 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.5 0.0 0.0 0.0 0.7 0.0 0.4 Elandsbaai EU ( of ELF) 7.6 6.8 7.3 10.8 13.7 12.2 0.0 0.0 0.0 6.6 6.2 6.4 10.3 13.1 11.6 66.7 0.0 25.0 9.9 12.6 11.1 CU ( of CLF) 6.0 6.8 6.3 8.6 9.7 9.1 0.0 0.0 0.0 3.9 3.8 3.9 8.2 9.4 8.8 66.7 0.0 25.0 7.8 9.0 8.3 (EU-CU) 1.6 0.0 1.1 2.2 4.1 3.1 0.0 0.0 0.0 2.6 2.4 2.5 2.1 3.7 2.9 0.0 0.0 0.0 2.1 3.6 2.8 St Helenabaai EU ( of ELF) 15.9 22.6 18.0 10.0 8.1 9.0 0.0 0.0 0.0 1.7 24.3 10.2 11.5 10.0 10.8 0.0 0.0 0.0 10.3 11.1 10.7 CU ( of CLF) 15.7 19.1 16.7 9.5 5.9 7.7 0.0 0.0 0.0 1.7 12.9 5.5 11.1 7.6 9.5 0.0 0.0 0.0 10.0 8.0 9.1 (EU-CU) 0.2 3.6 1.3 0.4 2.2 1.3 0.0 0.0 0.0 0.0 11.4 4.6 0.4 2.4 1.3 0.0 0.0 0.0 0.3 3.2 1.6 Paternoster EU ( of ELF) 11.1 25.0 15.4 9.7 25.0 16.7 0.0 8.3 0.0 4.8 9.8 25.0 16.6 0.0 50.0 7.7 9.3 24.1 15.8 CU ( of CLF) 11.1 25.0 15.4 8.5 24.2 15.6 0.0 0.0 0.0 0.0 8.6 24.2 15.6 0.0 50.0 7.7 7.8 23.3 14.6 (EU-CU) 0.0 0.0 0.0 1.3 0.8 1.1 0.0 8.3 0.0 4.8 1.2 0.8 1.0 0.0 0.0 0.0 1.5 0.8 1.2 NW Coast EU ( of ELF) 14.2 18.9 15.6 17.1 17.7 17.4 22.2 0.0 15.4 4.5 9.9 6.5 16.7 17.8 17.2 10.0 36.0 21.8 14.6 16.9 15.6 CU ( of CLF) 13.6 16.7 14.5 16.0 15.3 15.7 22.2 0.0 15.4 3.5 6.7 4.7 15.7 15.4 15.6 10.0 36.0 21.8 13.6 14.5 14.0 (EU-CU) 0.6 2.2 1.1 1.1 2.4 1.7 0.0 0.0 0.0 1.0 3.2 1.8 1.0 2.4 1.6 0.0 0.0 0.0 1.0 2.5 1.6


258

Table A8.13.5. Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect (EU-CU): west coast harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Saldanha EU ( of ELF) 26.7 50.4 33.4 13.1 14.7 13.8 10.0 0.0 6.7 1.7 13.7 6.1 16.6 19.5 17.9 16.7 18.8 17.6 13.7 18.6 15.7 CU ( of CLF) 26.7 50.3 33.3 12.4 13.0 12.7 10.0 0.0 6.7 1.5 8.6 4.0 16.2 18.1 17.0 16.7 15.2 16.0 13.3 16.6 14.6 (EU-CU) 0.0 0.1 0.1 0.7 1.7 1.2 0.0 0.0 0.0 0.2 5.0 2.0 0.5 1.4 0.9 0.0 3.5 1.6 0.4 2.0 1.1 Yzerfontein EU ( of ELF) 0.0 0.0 0.0 0.0 0.0 0.0 13.8 15.0 14.3 0.0 0.0 13.8 12.2 13.2 CU ( of CLF) 0.0 0.0 0.0 0.0 0.0 0.0 11.1 5.6 9.1 0.0 0.0 11.1 4.4 8.3 (EU-CU) 0.0 0.0 0.0 0.0 0.0 0.0 2.7 9.4 5.2 0.0 0.0 2.7 7.8 4.8 Hout Bay Harbour EU ( of ELF) 50.0 26.7 47.9 17.2 21.7 19.4 28.6 0.0 18.2 5.8 11.1 8.1 21.4 21.7 21.5 12.9 7.7 10.2 19.4 19.8 19.6 CU ( of CLF) 49.0 21.4 46.6 15.2 18.4 16.8 28.6 0.0 18.2 3.7 8.3 5.7 19.5 18.4 19.0 11.9 1.2 6.5 17.5 16.3 17.0 (EU-CU) 1.0 5.2 1.3 2.0 3.2 2.6 0.0 0.0 0.0 2.1 2.8 2.4 1.9 3.3 2.5 1.0 6.5 3.7 1.8 3.4 2.6 Cape Town-(MD) EU ( of ELF) 20.4 27.5 23.7 15.0 14.2 14.6 9.2 13.5 10.9 5.4 6.0 5.7 16.0 17.0 16.5 10.4 10.6 10.5 9.9 10.9 10.4 CU ( of CLF) 17.9 24.4 20.9 13.5 12.2 12.8 7.4 11.4 9.0 4.5 4.6 4.6 14.3 14.6 14.4 9.7 9.1 9.4 8.7 9.1 8.9 (EU-CU) 2.5 3.1 2.8 1.5 2.1 1.8 1.8 2.1 1.9 0.9 1.3 1.1 1.8 2.3 2.0 0.7 1.6 1.1 1.3 1.8 1.5 Kommetjie EU ( of ELF) 0.0 0.0 0.0 9.1 0.0 6.5 5.5 6.3 5.8 6.9 0.0 4.1 40.0 0.0 22.2 5.9 5.9 5.9 CU ( of CLF) 0.0 0.0 0.0 9.1 0.0 6.5 3.7 3.9 3.8 6.9 0.0 4.1 40.0 0.0 22.2 4.2 3.6 4.0 (EU-CU) 0.0 0.0 0.0 0.0 0.0 0.0 1.8 2.4 2.1 0.0 0.0 0.0 0.0 0.0 0.0 1.7 2.3 2.0 Simons Town EU ( of ELF) 0.0 0.0 0.0 2.3 15.4 8.5 14.3 0.0 15.6 6.2 6.3 6.3 4.8 10.5 7.4 0.0 35.7 16.7 6.0 7.2 6.5 CU ( of CLF) 0.0 0.0 0.0 2.3 13.2 7.4 0.0 0.0 7.3 5.3 5.0 5.2 1.0 9.4 4.9 0.0 18.2 7.4 4.8 5.6 5.2 (EU-CU) 0.0 0.0 0.0 0.0 2.2 1.1 14.3 0.0 8.2 0.9 1.3 1.1 3.8 1.1 2.5 0.0 17.5 9.3 1.2 1.6 1.4 West Coast EU ( of ELF) 22.2 29.2 25.2 14.8 14.7 14.8 9.5 13.1 10.9 5.4 6.2 5.7 16.3 17.4 16.9 10.8 11.1 10.9 10.4 11.6 11.0 CU ( of CLF) 20.2 26.5 22.9 13.4 12.6 13.0 7.4 11.1 8.9 4.4 4.7 4.6 14.8 15.2 15.0 10.2 9.0 9.7 9.2 9.7 9.4 (EU-CU) 2.0 2.8 2.3 1.4 2.1 1.7 2.0 1.9 2.0 0.9 1.5 1.2 1.6 2.2 1.9 0.6 2.0 1.3 1.2 1.9 1.5


259

Table A8.13.6. Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect (EU-CU): SW coast harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Kalk Bay EU ( of ELF) 44.4 0.0 16.0 6.9 8.6 7.7 0.0 0.0 6.7 6.7 6.7 9.7 7.2 8.6 0.0 0.0 0.0 7.7 6.8 7.3 CU ( of CLF) 44.4 0.0 16.0 5.9 8.6 7.1 0.0 0.0 5.6 2.8 4.4 8.9 7.2 8.1 0.0 0.0 0.0 6.8 4.5 5.8 (EU-CU) 0.0 0.0 0.0 0.9 0.0 0.5 0.0 0.0 1.0 3.9 2.3 0.8 0.0 0.4 0.0 0.0 0.0 0.9 2.3 1.6 Strand EU ( of ELF) 21.8 44.5 30.6 13.0 20.4 16.5 21.1 24.2 22.4 6.2 6.8 6.5 17.1 29.6 22.5 11.6 19.5 15.0 13.9 22.5 17.7 CU ( of CLF) 20.5 42.2 28.8 11.4 17.3 14.1 21.1 16.1 19.2 5.2 5.5 5.3 15.7 26.8 20.4 10.4 17.6 13.5 12.6 20.1 15.8 (EU-CU) 1.2 2.3 1.8 1.6 3.1 2.3 0.0 8.1 3.2 1.0 1.3 1.1 1.4 2.8 2.1 1.2 1.9 1.5 1.3 2.5 1.8 Kleinmond EU ( of ELF) 32.8 40.6 35.2 8.9 15.4 11.7 8.4 7.0 7.8 19.2 23.0 20.6 0.0 28.6 17.1 16.5 18.7 17.4 CU ( of CLF) 32.6 38.3 34.3 8.4 14.9 11.2 6.7 5.5 6.1 18.8 21.8 20.0 0.0 16.7 9.4 15.9 17.1 16.4 (EU-CU) 0.2 2.2 0.9 0.4 0.5 0.5 1.7 1.5 1.6 0.3 1.2 0.7 0.0 11.9 7.8 0.6 1.6 1.0 Gordons Bay EU ( of ELF) 11.1 18.8 14.7 8.8 13.1 10.8 30.8 25.0 29.4 8.5 7.8 8.2 9.4 13.5 11.3 0.0 0.0 0.0 8.8 10.0 9.3 CU ( of CLF) 11.1 10.3 10.8 8.7 12.2 10.4 30.8 0.0 25.0 7.8 6.6 7.3 9.3 12.1 10.6 0.0 0.0 0.0 8.3 8.8 8.5 (EU-CU) 0.0 8.4 3.9 0.2 0.8 0.5 0.0 25.0 4.4 0.7 1.2 0.9 0.1 1.4 0.7 0.0 0.0 0.0 0.5 1.3 0.8 Hermanus EU ( of ELF) 13.9 48.7 25.6 7.0 11.3 9.1 14.3 40.0 21.1 6.4 6.4 6.4 11.9 32.7 20.0 0.0 0.0 0.0 10.4 23.4 15.8 CU ( of CLF) 12.3 45.5 23.2 6.0 9.2 7.6 0.0 0.0 0.0 5.0 4.0 4.5 10.4 29.5 17.7 0.0 0.0 0.0 8.9 20.5 13.7 (EU-CU) 1.6 3.2 2.4 1.0 2.0 1.5 14.3 40.0 21.1 1.4 2.4 1.9 1.5 3.1 2.3 0.0 0.0 0.0 1.5 3.0 2.2 Gansbaai EU ( of ELF) 9.7 64.3 27.5 7.8 25.7 16.1 0.0 0.0 0.0 5.9 10.6 7.5 8.4 34.6 19.5 0.0 42.9 21.4 7.7 30.2 16.8 CU ( of CLF) 9.1 52.1 20.5 7.2 24.1 15.0 0.0 0.0 0.0 4.5 8.5 5.8 7.8 29.3 16.6 0.0 42.9 21.4 6.9 25.3 14.1 (EU-CU) 0.7 12.3 7.0 0.6 1.6 1.1 0.0 0.0 0.0 1.4 2.1 1.6 0.6 5.3 3.0 0.0 0.0 0.0 0.8 4.8 2.7 SW Coast EU ( of ELF) 19.5 45.7 29.1 11.2 18.6 14.7 20.7 25.4 22.4 6.8 7.0 6.9 15.3 28.9 21.1 9.9 19.8 14.4 12.5 21.4 16.3 CU ( of CLF) 18.2 42.7 27.0 10.0 16.1 12.8 19.3 14.5 17.7 5.7 5.4 5.6 14.0 26.1 19.0 8.8 17.1 12.6 11.3 18.9 14.5 (EU-CU) 1.2 3.0 2.1 1.3 2.5 1.9 1.3 10.8 4.7 1.0 1.6 1.3 1.3 2.9 2.0 1.0 2.7 1.8 1.2 2.5 1.8


260

Table A8.13.7. Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect (EU-CU): SE coast harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Struisbaai EU ( of ELF) 33.3 0.0 25.0 8.7 19.7 13.3 0.0 0.0 4.8 6.1 5.3 9.6 19.0 13.5 0.0 0.0 8.6 16.1 11.7 CU ( of CLF) 0.0 0.0 0.0 4.7 14.2 8.6 0.0 0.0 3.6 1.6 2.7 4.6 13.7 8.3 0.0 0.0 4.3 10.9 7.1 (EU-CU) 33.3 0.0 25.0 4.0 5.5 4.7 0.0 0.0 1.1 4.5 2.6 5.0 5.3 5.2 0.0 0.0 4.2 5.2 4.6 Arniston EU ( of ELF) 0.0 50.0 40.0 7.2 14.7 10.1 17.6 33.3 24.1 7.1 16.7 11.0 7.9 17.9 12.0 CU ( of CLF) 0.0 50.0 40.0 7.2 13.4 9.6 17.6 20.0 18.5 7.1 15.5 10.5 7.9 15.8 11.1 (EU-CU) 0.0 0.0 0.0 0.0 1.3 0.5 0.0 13.3 5.6 0.0 1.2 0.5 0.0 2.2 0.9 Mossel Bay EU ( of ELF) 45.8 61.3 52.7 23.6 34.7 28.7 5.6 33.3 16.7 7.4 10.7 8.7 32.3 44.8 38.0 23.8 20.0 22.1 26.3 37.6 31.3 CU ( of CLF) 44.5 59.7 51.2 21.6 32.0 26.4 5.6 11.1 7.4 6.5 8.2 7.2 30.6 42.6 36.0 22.8 20.0 21.6 24.7 35.3 29.4 (EU-CU) 1.3 1.6 1.5 2.0 2.6 2.3 0.0 22.2 9.3 0.9 2.5 1.6 1.7 2.3 2.0 1.0 0.0 0.6 1.6 2.4 2.0 Knysna EU ( of ELF) 20.0 45.9 31.1 18.2 27.2 22.6 43.8 30.0 38.5 5.4 7.0 6.2 19.2 35.5 26.7 14.1 15.6 14.8 16.4 29.3 22.4 CU ( of CLF) 18.3 42.6 28.5 16.5 24.8 20.6 30.8 0.0 20.0 3.9 5.3 4.5 17.5 32.5 24.3 14.1 14.6 14.3 14.7 26.5 20.1 (EU-CU) 1.7 3.4 2.7 1.7 2.4 2.0 13.0 30.0 18.5 1.6 1.7 1.6 1.7 3.0 2.4 0.0 1.1 0.5 1.7 2.8 2.2 Plettenberg Bay EU ( of ELF) 20.7 46.6 31.2 14.9 29.3 21.9 37.5 50.0 41.7 3.6 5.6 4.5 18.6 38.9 27.5 26.9 43.5 34.7 16.1 33.0 23.6 CU ( of CLF) 19.6 44.8 29.7 13.7 27.1 20.2 37.5 50.0 41.7 3.2 4.4 3.7 17.5 36.9 25.9 26.9 31.6 28.9 15.1 31.0 22.1 (EU-CU) 1.1 1.8 1.5 1.1 2.2 1.7 0.0 0.0 0.0 0.4 1.2 0.8 1.1 2.0 1.6 0.0 11.9 5.8 1.0 2.0 1.5 SE Coast EU ( of ELF) 30.2 52.4 39.8 20.0 30.6 25.0 26.2 30.0 27.8 6.3 8.6 7.3 24.7 39.7 31.5 17.7 19.3 18.5 20.7 33.3 26.3 CU ( of CLF) 28.8 50.1 37.8 18.2 28.0 22.8 20.5 12.5 17.5 5.2 6.5 5.8 23.0 37.2 29.4 17.5 17.3 17.4 19.1 30.7 24.3 (EU-CU) 1.5 2.3 2.0 1.8 2.6 2.2 5.7 17.5 10.3 1.1 2.1 1.5 1.7 2.5 2.1 0.3 2.0 1.1 1.6 2.5 2.0 Table A8.13.8. Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect (EU-CU): Eastern Cape harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Jeffreys Bay EU ( of ELF) 22.7 40.7 29.8 9.0 23.8 16.0 0.0 0.0 0.0 8.2 8.5 8.3 16.1 31.3 22.7 6.7 11.1 8.3 13.2 23.0 17.4 CU ( of CLF) 21.7 35.6 27.0 6.5 17.6 11.5 0.0 0.0 0.0 7.2 6.5 6.9 14.5 25.5 19.1 6.7 0.0 4.3 11.8 18.3 14.5 (EU-CU) 1.0 5.1 2.8 2.5 6.3 4.4 0.0 0.0 0.0 1.0 2.0 1.4 1.6 5.8 3.6 0.0 11.1 4.0 1.4 4.7 2.9 Port Elizabeth EU ( of ELF) 48.1 59.8 54.1 29.9 34.8 32.2 14.4 15.3 14.8 5.5 6.3 5.8 42.1 52.6 47.3 28.1 30.3 29.1 34.5 44.6 39.4 CU ( of CLF) 45.8 56.8 51.3 27.5 31.5 29.4 13.5 12.9 13.3 4.7 4.8 4.8 39.7 49.4 44.4 24.5 27.2 25.8 32.2 41.3 36.6 (EU-CU) 2.4 3.1 2.8 2.4 3.2 2.8 1.0 2.4 1.5 0.8 1.4 1.1 2.4 3.2 2.9 3.6 3.1 3.4 2.3 3.3 2.8 Port Alfred EU ( of ELF) 46.2 55.6 51.3 28.2 44.0 35.3 75.0 33.3 50.0 6.8 4.5 5.7 44.6 54.8 50.0 46.2 63.6 54.2 38.8 48.2 43.7 CU ( of CLF) 42.2 50.9 46.8 24.3 42.0 32.3 75.0 0.0 37.5 5.3 3.3 4.3 40.5 50.1 45.6 46.2 63.6 54.2 34.9 43.5 39.4 (EU-CU) 4.1 4.8 4.5 3.9 2.0 3.0 0.0 33.3 12.5 1.6 1.2 1.4 4.1 4.7 4.5 0.0 0.0 0.0 3.9 4.7 4.4 East London EU ( of ELF) 44.8 50.4 47.7 29.4 30.8 30.1 10.5 11.9 11.1 4.7 6.9 5.7 41.5 47.0 44.3 19.7 24.0 21.8 31.3 37.7 34.5 CU ( of CLF) 41.3 45.4 43.4 27.4 28.0 27.7 9.7 9.7 9.7 3.9 5.0 4.4 38.2 42.2 40.2 16.1 20.4 18.2 28.4 33.2 30.7 (EU-CU) 3.5 5.0 4.3 2.0 2.8 2.4 0.8 2.2 1.4 0.7 2.0 1.3 3.3 4.8 4.1 3.7 3.6 3.6 2.9 4.6 3.8 Eastern Cape EU ( of ELF) 47.0 57.0 52.1 29.4 34.0 31.6 13.3 14.2 13.7 5.3 6.5 5.8 41.7 51.0 46.4 26.6 29.3 27.9 33.5 42.6 37.9 CU ( of CLF) 44.3 53.4 48.9 27.1 30.8 28.8 12.4 11.8 12.2 4.5 4.9 4.7 39.1 47.4 43.2 23.0 26.1 24.5 31.0 39.0 34.8 (EU-CU) 2.7 3.6 3.2 2.4 3.2 2.8 0.9 2.4 1.5 0.8 1.6 1.1 2.7 3.6 3.2 3.6 3.2 3.4 2.5 3.6 3.1


261

Table A8.13.9. Conventional unemployment (CU), extended unemployment (EU) and the discouraged worker effect (EU-CU): KwaZulu-Natal harbours.

% African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T Port Shepstone EU ( of ELF) 16.6 22.8 20.2 18.9 18.4 18.6 6.4 11.0 8.1 7.6 9.5 8.5 12.9 20.2 16.6 15.7 15.9 15.8 11.0 16.9 13.9 CU ( of CLF) 15.3 20.7 18.4 18.5 15.7 17.2 5.8 8.0 6.6 6.3 7.3 6.7 11.9 17.8 14.9 11.9 13.4 12.7 9.9 14.5 12.2 (EU-CU) 1.3 2.2 1.8 0.4 2.7 1.5 0.6 3.0 1.5 1.4 2.2 1.7 1.0 2.3 1.7 3.8 2.5 3.1 1.1 2.3 1.7 Durban EU ( of ELF) 39.6 38.7 39.2 25.6 20.3 23.0 11.5 15.3 12.9 6.3 7.5 6.8 29.4 30.6 29.9 25.1 20.1 22.9 22.2 22.9 22.5 CU ( of CLF) 37.9 35.7 36.9 24.1 18.0 21.2 10.3 12.0 11.0 5.4 5.5 5.5 27.8 27.6 27.7 23.5 18.5 21.2 20.8 20.1 20.5 (EU-CU) 1.7 2.9 2.3 1.5 2.3 1.9 1.2 3.2 2.0 0.9 1.9 1.3 1.6 3.0 2.2 1.6 1.6 1.6 1.4 2.7 2.0 Richards Bay EU ( of ELF) 6.7 12.3 9.4 13.2 23.0 17.7 6.3 9.2 7.4 3.7 10.2 6.3 6.9 11.6 8.8 5.9 17.5 12.0 4.9 10.8 7.2 CU ( of CLF) 5.9 10.3 7.9 13.2 19.9 16.2 6.1 8.2 6.8 3.4 7.7 5.1 6.6 10.0 7.9 5.9 14.5 10.4 4.6 8.6 6.1 (EU-CU) 0.8 2.1 1.4 0.0 3.1 1.5 0.3 1.0 0.5 0.3 2.5 1.2 0.4 1.6 0.9 0.0 3.0 1.7 0.3 2.2 1.1 Mthunzini EU ( of ELF) 47.8 62.0 55.6 0.0 0.0 0.0 0.0 0.0 3.5 5.0 4.2 47.5 61.7 55.3 0.0 33.3 20.0 29.1 44.2 36.8 CU ( of CLF) 46.0 59.8 53.5 0.0 0.0 0.0 0.0 0.0 2.3 4.6 3.3 45.6 59.5 53.2 0.0 33.3 20.0 27.3 42.0 34.7 (EU-CU) 1.9 2.2 2.1 0.0 0.0 0.0 0.0 0.0 1.2 0.4 0.9 1.9 2.2 2.1 0.0 0.0 0.0 1.8 2.2 2.1 St Lucia EU ( of ELF) 7.3 0.0 3.4 0.0 0.0 7.7 7.7 7.7 7.3 0.0 3.3 0.0 0.0 7.5 4.2 5.9 CU ( of CLF) 7.3 0.0 3.4 0.0 0.0 5.9 4.0 5.1 7.3 0.0 3.3 0.0 0.0 6.3 2.1 4.3 (EU-CU) 0.0 0.0 0.0 0.0 0.0 1.8 3.7 2.6 0.0 0.0 0.0 0.0 0.0 1.2 2.0 1.6 Natal EU ( of ELF) 37.6 36.8 37.2 25.1 20.3 22.8 10.8 14.6 12.3 6.1 7.8 6.9 27.7 29.4 28.4 24.1 19.8 22.2 20.6 22.0 21.2 CU ( of CLF) 36.0 33.9 35.0 23.7 17.9 20.9 9.7 11.5 10.4 5.3 5.8 5.5 26.1 26.4 26.3 22.5 18.1 20.5 19.2 19.3 19.3 (EU-CU) 1.7 2.9 2.2 1.4 2.4 1.8 1.1 3.1 1.9 0.9 2.0 1.4 1.5 2.9 2.2 1.6 1.7 1.7 1.4 2.7 2.0


262

Number of people unemployed by race and gender Table A8.14.1. Number of people unemployed by race and gender: South African harbours.



Northern Cape 82 133 215 222 293 515 0 4 4 7 17 24 304 430 734 0 3 3 311 450 761

Eastern Cape 52556 64691 1172471188411971 23855 435 275 710 2264 1943 4207 64875 76937 141812 288 298 586 67427 79178 146605

Kwa-Zulu Natal23578 56487 1220611125310254 21507375462240359949571404688310402334449441320948192316561402 3058173169137429310598

Western Cape 4296 5178 9474 4916 5485 10401 106 87 193 1826 1611 3437 9318 10750 20068 289 248 537 11433 12609 24042

SA Coast 80512126489 2489972827528003 56278380872276960856612375045411169141899150132664453722331951 4184252340229666482006 Table A8.14.2. Number of people unemployed by race and gender: Northern Cape harbours.


M F T M F T M F T M F T M F T M F T M F T Port Nolloth 81 127 208 209 253 462 0 4 4 7 17 24 290 384 674 0 3 3 297 404 701 Hondeklip Bay 1 6 7 13 40 53 0 0 0 0 0 0 14 46 60 0 0 0 14 46 60 Northern Cape 82 133 215 222 293 515 0 4 4 7 17 24 304 430 734 0 3 3 311 450 761

Table A8.14.3. Number of people unemployed by race and gender: Western Cape harbours.

African/Black Coloured Indian/Asian White HRG Unspecified Total M F T M F T M F T M F T M F T M F T M F T NW Coast 83 44 127 509 431 940 2 0 2 27 30 57 594 475 1069 3 9 12 624 514 1138 West Coast 1088 1066 21542256 2100 4356 73 75 148 12241106 23303417 3241 6658 212 162 374 4853 4509 9362 SW Coast 1132 1497 2629 667 935 1602 23 9 32 357 269 626 1822 2441 4263 23 36 59 2202 2746 4948 SE Coast 1993 2571 45641484 2019 3503 8 3 11 218 206 424 3485 4593 8078 51 41 92 3754 4840 8594 Western Cape 4296 5178 94744916 5485 10401 106 87 193 18261611 343793181075020068 289 248 537 114331260924042

Table A8.14.4. Number of people unemployed by race and gender: NW coast harbours.


M F T M F T M F T M F T M F T M F T M F T Doringbaai 0 1 1 71 68 139 0 0 0 0 0 0 71 69 140 0 2 2 71 71 142 Lambert's Bay 16 7 23 237 173 410 2 0 2 7 4 11 255 180 435 1 6 7 263 190 453 Elandsbaai 10 6 16 86 88 174 0 0 0 7 3 10 96 94 190 2 0 2 105 97 202 Velddrif 0 0 0 0 0 0 0 0 0 10 11 21 0 0 0 0 0 0 10 11 21 St Helenabaai 56 29 85 97 59 156 0 0 0 3 12 15 153 88 241 0 0 0 156 100 256 Paternoster 1 1 2 18 43 61 0 0 0 0 0 0 19 44 63 0 1 1 19 45 64 NW Coast 83 44 127 509 431 940 2 0 2 27 30 57 594 475 1069 3 9 12 624 514 1138

Table A8.14.5. Number of people unemployed by race and gender: west coast harbours.


M F T M F T M F T M F T M F T M F T M F T Saldanha 253 187 440 323 292 615 4 0 4 14 43 57 580 479 1059 20 14 34 614 536 1150 Yzerfontein 0 0 0 0 0 0 0 0 0 7 2 9 0 0 0 0 0 0 7 2 9 Cape Town-(MD) 762 876 16381774 1625 3399 67 72 139 113210022134260325735176 180 145 325 391537207635 Hout Bay Harbour 73 3 76 156 178 334 2 0 2 5 8 13 231 181 412 10 1 11 246 190 436 Kommetjie 0 0 0 2 0 2 0 0 0 17 15 32 2 0 2 2 0 2 21 15 36 Simons Town 0 0 0 1 5 6 0 3 3 49 36 85 1 8 9 0 2 2 50 46 96 West Coast 1088 1066 21542256 2100 4356 73 75 148 122411062330341732416658 212 162 374 485345099362


263

Table A8.14.6. Number of people unemployed by race and gender: SW coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total

M F T M F T M F T M F T M F T M F T M F T Kalk Bay 4 0 4 6 7 13 0 0 0 10 4 14 10 7 17 0 0 0 20 11 31 Strand 774 989 1763 496 652 1148 19 9 28 178 154 332 1289 1650 2939 23 30 53 1490 1834 3324 Gordons Bay 4 3 7 51 64 115 4 0 4 95 55 150 59 67 126 0 0 0 154 122 276 Kleinmond 102 51 153 35 47 82 0 0 0 14 10 24 137 98 235 0 3 3 151 111 262 Hermanus 224 404 628 44 64 108 0 0 0 48 35 83 268 468 736 0 0 0 316 503 819 Gansbaai 24 50 74 35 101 136 0 0 0 12 11 23 59 151 210 0 3 3 71 165 236 SW Coast 1132 1497 2629 667 935 1602 23 9 32 357 269 626 1822 2441 4263 23 36 59 2202 2746 4948 Table A8.14.7. Number of people unemployed by race and gender: SE coast harbours.


M F T M F T M F T M F T M F T M F T M F T Struisbaai 0 0 0 14 29 43 0 0 0 3 1 4 14 29 43 0 0 0 17 30 47 Arniston 0 4 4 15 18 33 0 0 0 3 2 5 15 22 37 0 0 0 18 24 42 Knysna 511 859 1370 450 651 1101 4 0 4 54 65 119 965 15102475 26 23 49 1045 1598 2643 Mossel Bay 1203 1280 2483 890 1111 2001 1 1 2 143 121 264 209423924486 18 12 30 2255 2525 4780 Plettenberg Bay 279 428 707 115 210 325 3 2 5 15 17 32 397 640 1037 7 6 13 419 663 1082 SE Coast 1993 2571 45641484 2019 3503 8 3 11 218 206 424 348545938078 51 41 92 3754 4840 8594 Table A8.14.8. Number of people unemployed by race and gender: Eastern Cape harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total


Jeffreys Bay 194 192 386 52 119 171 0 0 0 70 48 118 246 311 557 1 0 1 317 359 676

Port Elizabeth 38052 48081 86133 9993 10133 20126 327 202 529 1542 1247 2789 4837258416106788 246 248 494 5016059911 110071

Port Alfred 827 1121 1948 49 71 120 3 0 3 22 13 35 879 1192 2071 6 7 13 907 1212 2119

East London 13483 15297 28780 1790 1648 3438 105 73 178 630 635 1265 1537817018 32396 35 43 78 1604317696 33739

Eastern Cape 52556 64691 11724711884 11971 23855 435 275 710 2264 1943 4207 6487576937141812 288 298 586 6742779178 146605 Table A8.14.9. Number of people unemployed by race and gender: KwaZulu-Natal harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total


Port Shepstone 719 1341 2060 78 57 135 181 142 323 286 282 568 978 1540 2518 8 9 17 1272 1831 3103

Durban 22622 17439 40061 2561 1743 4304 3353 2352 5705 2547 2193 4740 28536 21534 50070 361 236 597 31444 23963 55407

Mthunzini 194 304 498 0 0 0 0 0 0 7 11 18 194 304 498 0 1 1 201 316 517

Richards Bay 39 61 100 29 35 64 121 89 210 172 239 411 189 185 374 3 8 11 364 432 796

St Lucia 4 0 4 0 0 0 0 0 0 6 3 9 4 0 4 0 0 0 10 3 13

Natal 23578 56487122061 11253 10254 215073754622403599495714046883104023344494413209481923 1656 14023058173169137429310598


264

Skills levels by number of people Table A8.15.1. Skills levels by number of people: South African harbours.

African/Black Coloured Indian/Asian White HRG Unspecified Total Skill level M F T M F T M F T M F T M F T M F T M F T

1 28236 3980 32216 13320 5285 18605 1812 446 2258 8774 1162 9936 43368 9711 53079 520 178 698 52662 11051 637132 20348 2353 22701 13522 5486 19008 5117 1434 6551 14500 1430 15930 38987 9273 48260 676 235 911 54163 10938 651013 39714 69558 109272 19898 29854 49752 9447 9036 18483 27451 46475 73926 69059 108448 177507 1258 1904 3162 97768 156827 2545954 3376 2125 5501 4967 2308 7275 1447 635 2082 3546 1277 4823 9790 5068 14858 232 102 334 13568 6447 200155 11846 16555 28401 10185 12570 22755 11594 7367 18961 59970 47200 107170 33625 36492 70117 1519 1395 2914 95114 85087 180201

Table A8.15.2. Skills levels by number of people: Northern Cape harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total Skill level M F T M F T M F T M F T M F T M F T M F T

1 44 2 46 246 31 277 0 0 0 11 0 11 290 33 323 0 0 0 301 33 3342 20 1 21 159 5 164 0 0 0 19 6 25 179 6 185 9 0 9 207 12 2193 46 53 99 156 310 466 2 0 2 20 38 58 204 363 567 0 1 1 224 402 6264 9 3 12 127 15 142 0 0 0 5 0 5 136 18 154 3 0 3 144 18 1625 7 19 26 69 55 124 5 0 5 57 21 78 81 74 155 4 0 4 142 95 237

Table A8.15.3. Skills levels by number of people: Western Cape harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total Skill level M F T M F T M F T M F T M F T M F T M F T

1 5978 593 6571 7520 3367 10887 74 26 100 2035 291 2326 13572 3986 17558307 111 418 15914 4388 203022 1487 119 1606 4401 1765 6166 96 22 118 3193 384 3577 5984 1906 7890 278 121 399 9455 2411 118663 4705 5695 10400 8805 15134 23939 327 367 694 8221 1318121402138372119635033661 1024 1685 22719 35401581204 658 140 798 3043 954 3997 46 13 59 1399 403 1802 3747 1107 4854 166 55 221 5312 1565 68775 1189 989 2178 4036 4314 8350 482 294 776 186971392632623 5707 5597 11304854 708 1562 25258 2023145489

Table A8.15.4. Skills levels by number of people: NW coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total Skill level M F T M F T M F T M F T M F T M F T M F T

1 246 121 367 792 938 1730 0 0 0 59 7 66 1038 1059 2097 11 4 15 1108 1070 21782 73 8 81 337 195 532 3 0 3 103 8 111 413 203 616 2 0 2 518 211 729 3 89 61 150 630 856 1486 2 6 8 148 282 430 721 923 1644 5 21 26 874 1226 21004 116 4 120 688 76 764 0 0 0 179 3 182 804 80 884 13 0 13 996 83 10795 11 4 15 174 197 371 5 1 6 277 154 431 190 202 392 0 2 2 467 358 825

Table A8.15.5. Skills levels by number of people: west coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total Skill level M F T M F T M F T M F T M F T M F T M F T

1 1271 105 1376 2700 1412 4112 48 25 73 1144 183 1327 4019 1542 5561 192 65 257 5355 1790 71452 351 59 410 2431 1149 3580 73 22 95 2011 289 2300 2855 1230 4085 227 108 335 5093 1627 67203 1500 2037 3537 4257 7606 11863 268 300 568 5643 9125 14768 6025 9943 15968 503 791 1294 12171 19859320304 214 39 253 1258 613 1871 34 10 44 825 285 1110 1506 662 2168 119 39 158 2450 986 34365 673 627 1300 2536 2732 5268 426 247 673 140501059524645 3635 3606 7241 701 577 1278 18386 1477833164


265

Table A8.15.6. Skills levels by number of people: SW coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total Skill level M F T M F T M F T M F T M F T M F T M F T

1 2416 166 2582 1995 538 2533 19 1 20 490 53 543 4430 705 5135 65 25 90 4985 783 57682 495 16 511 891 281 1172 19 0 19 623 57 680 1405 297 1702 33 4 37 2061 358 24193 1646 1673 3319 1771 3082 4853 30 39 69 1451 2415 3866 3447 4794 8241 90 106 196 4988 7315 123034 148 30 178 395 92 487 12 3 15 250 58 308 555 125 680 16 9 25 821 192 10135 214 146 360 655 755 1410 37 38 75 2644 1881 4525 906 939 1845 60 42 102 3610 2862 6472

Table A8.15.7. Skills levels by number of people: SE coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total Skill level M F T M F T M F T M F T M F T M F T M F T

1 2045 201 2246 2033 479 2512 7 0 7 342 48 390 4085 680 4765 39 17 56 4466 745 52112 568 36 604 742 140 882 1 0 1 456 30 486 1311 176 1487 16 9 25 1783 215 19983 1470 1924 3394 2147 3590 5737 27 22 49 979 1359 2338 3644 5536 9180 63 106 169 4686 7001 116874 180 67 247 702 173 875 0 0 0 145 57 202 882 240 1122 18 7 25 1045 304 13495 291 212 503 671 630 1301 14 8 22 1726 1296 3022 976 850 1826 93 87 180 2795 2233 5028

Table A8.15.8. Skills levels by number of people: Eastern Cape harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total Skill level M F T M F T M F T M F T M F T M F T M F T

1 13864 2322 16186 4369 1683 6052 153 41 194 3030 445 3475 18386 4046 22432 96 30 126 21512 4521 260332 12253 1211 13464 6773 3290 10063 368 82 450 5977 541 6518 19394 4583 23977 156 60 216 25527 5184 307113 20655 38165 58820 8854 10743 19597 733 8941627 9883 1602525908302424980280044 273 396 669 40398 662231066214 1966 1210 3176 1604 1117 2721 115 55 170 1073 415 1488 3685 2382 6067 39 26 65 4797 2823 7620 5 6429 9518 15947 4762 5741 10503 13507842134207571594936706125411604328584 318 304 622 33616 32296 65912

Table A8.15.9. Skills levels by number of people: KwaZulu-Natal harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total

Skill level M F T M F T M F T M F T M F T M F T M F T

1 8350 1063 9413 1185 204 1389 1585 379 1964 3698 426 4124 11120 1646 12766 117 37 154 14935 2109 17044

2 6588 1022 7610 2189 426 2615 4653 1330 5983 5311 499 5810 13430 2778 16208 233 54 287 18974 3331 22305

3 14308 25645 39953 2083 3667 5750 8385 7775 16160 9327 17231 26558 24776 37087 61863 324 483 807 34427 54801 89228

4 743 772 1515 193 222 415 1286 567 1853 1069 459 1528 2222 1561 3783 24 21 45 3315 2041 5356

5 4221 6029 10250 1318 2460 3778 9757 6289 16046 20459 17304 37763 15296 14778 30074 343 383 726 36098 32465 68563


266

Education levels by number of people Table A8.16.1. Education levels by number of people: South African harbours.


Level M F T M F T M F T M F T M F T M F T M F T

I 37440 32714 70154 7672 8412 16083 872 2141 3013 910 961 1871 45983 43267 89249 313 353 666 47205 44581 91786

II 115306 119482234788 45976 53927 99903 7644 11344 18988 9214 9530 18743 1689261847523536781569 1794 3363 179708 196076 375783

III 52501 63243 115744 25105 29721 54826 11280 11456 22736 27192 35777 62969 88886 1044201933061402 1628 3030 117480 141825 259305

IV 39476 42828 82304 18686 21124 39810 17284 16902 34186 55387 68696 124083 75446 80854 1563001603 1812 3415 132436 151362 283798

V 11279 15240 26519 7279 7803 15082 7900 6810 14710 46450 39688 86138 26458 29853 56311 1241 1131 2372 74149 70672 144821

Total 281576 296266577842114905 12902624393148872 520971009691629401685313314714453534773899227427986 791815904616279 6538381270117 Table A8.16.2. Education levels by number of people: Northern Cape harbours.

African/Black Coloured Indian/Asian White HRG Unspecified Total Level M F T M F T M F T M F T M F T M F T M F T

I 43 37 80 126 121 247 0 0 0 2 2 4 169 158 327 2 0 2 172 160 332 II 117 162 278 593 666 1259 2 2 4 19 18 37 712 830 1541 7 0 7 737 848 1585III 45 55 100 184 202 386 0 0 0 28 29 57 229 257 486 9 2 11 266 288 554 IV 12 11 23 114 124 238 2 4 6 66 62 128 128 139 267 7 0 7 201 201 402 V 4 2 6 25 20 45 0 0 0 19 17 36 29 22 51 0 0 0 48 39 87

Total 269 286 555 1110 1209 2319 4 6 10 151 141 292 1383 1501 2884 26 2 28 1560 1644 3204 Table A8.16.3. Education levels by number of people: Western Cape harbours.


I 3944 2238 6181 3559 4014 7572 41 39 80 266 254 520 7543 6290 13833 122 144 266 7931 6688 14618II 10094 8396 18490 17398 2084038237 219 243 462 2183 2239 4422 2771129478 57189 701 828 1529 30594 32545 63139III 3160 3614 6774 8125 9804 17929 273 279 552 5680 7490 131701155813697 25255 558 718 1276 17796 21905 39701IV 2973 2575 5548 6595 7163 13758 497 477 974 1531018616339261006510215 20280 710 823 1533 26085 29654 55739V 1005 906 1911 2609 2658 5267 351 293 644 144631319127654 3965 3857 7822 661 557 1218 19089 17605 36694

Total 24086 19662 43748 42442 4801590457 1557 1463 3020441814609190272680856914013722533363615 6951 115602 118846234448 Table A8.16.4. Education levels by number of people: NW coast harbours.


I 136 70 205 423 466 888 0 0 0 10 2 11 558 535 1093 4 11 15 572 547 1119 II 325 225 550 1994 2180 4173 1 2 3 88 71 159 2319 2407 4726 23 40 63 2430 2517 4947 III 72 78 150 563 614 1177 3 0 3 173 255 428 638 692 1330 19 5 24 830 952 1782 IV 29 29 58 327 378 705 5 4 9 286 359 645 361 411 772 13 8 21 660 778 1438 V 4 1 5 95 131 226 0 0 0 205 172 377 99 132 231 0 0 0 304 304 608

Total 628 440 1068 3623 4013 7636 9 6 15 897 939 1836 4260 4459 8719 69 69 138 5226 5467 10693 Table A8.16.5. Education levels by number of people: west coast harbours.


I 972 528 1500 1124 1482 2606 26 28 54 174 186 360 2122 2037 4159 63 82 144 2358 2305 4663 II 2174 1999 4173 7104 9296 16399 154 184 338 1434 1521 2955 9432 1147820909 439 564 1002 11304 13562 24866III 744 921 1665 4257 5344 9601 220 239 459 3629 4691 8320 5221 6504 11725 439 544 983 9289 11739 21028IV 1746 1224 2970 3763 4331 8094 425 414 839 106021255323155 5934 5969 11903 565 647 1212 17101 19169 36270V 714 655 1369 1676 1710 3386 311 251 562 10731 9719 20450 2701 2616 5317 539 456 995 13971 12791 26762

Total 7484 6050 13534 20238 23998 44236 1286 121425003111831850629682900831262602702448 2708 5156 62574 65820128394


267

Table A8.16.6. Education levels by number of people: SW coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total Level M F T M F T M F T M F T M F T M F T M F T

I 1437 690 2127 735 824 1559 8 9 16 48 44 91 2180 1523 3702 22 23 45 2249 1589 3838II 3365 2626 5990 3470 4084 7554 48 32 80 378 368 746 6883 6741 13623 133 133 266 7393 7241 14634III 830 994 1824 1455 1625 3080 27 32 59 1093 1418 2511 2312 2651 4963 42 93 135 3447 4162 7609IV 396 424 820 1044 1028 2072 46 34 80 2682 3402 6084 1486 1486 2972 51 73 124 4219 4961 9180V 93 96 189 326 409 735 31 26 57 2194 2108 4302 450 531 981 43 30 73 2687 2669 5356

Total 7025 5413 12438 7684 8655 16339 183 158 341 7139 7782 14921 14892 14226 29118 358 402 760 22389 22410 44799 Table A8.16.7. Education levels by number of people: SE coast harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total Level M F T M F T M F T M F T M F T M F T M F T

I 1400 950 2350 1277 1243 2520 7 3 10 35 23 58 2684 2196 4879 34 29 62 2752 2247 4999II 4231 3547 7778 4831 5281 10112 16 26 42 284 280 563 9078 8854 17931 107 92 199 9468 9225 18693III 1514 1621 3135 1850 2221 4071 23 8 31 785 1126 1911 3387 3850 7237 58 76 134 4230 5052 9282IV 802 898 1700 1461 1426 2887 21 25 46 1740 2302 4042 2284 2349 4633 81 95 176 4105 4746 8851V 194 154 348 512 408 920 9 16 25 1333 1192 2525 715 578 1293 79 71 150 2127 1841 3968

Total 8949 7759 16708 10897 11349 22246 79 85 164 5027 5520 10547 19925 19193 39118 461 436 897 25413 25149 50562 Table A8.16.8. Education levels by number of people: Eastern Cape harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total


I 22649 21593 44241 3668 3938 7606 71 81 152 267 280 547 26387 25611 51998 107 108 215 26760 25999 52759

II 76365 84416 160781 24471 28137 52608 651 789 1440 4190 4312 8502 101487 113341 214828 493 517 1010 106170 118170 224339

III 33813 43838 77651 13280 15394 28674 1010 1077 2087 11555 14226 25781 48103 60309 108412 394 430 824 60052 74965 135017

IV 22360 25921 48281 9126 10231 19357 1283 1345 2628 19467 23927 43394 32769 37497 70266 395 432 827 52631 61856 114487

V 5594 8386 13980 3263 3548 6811 761 548 1309 14419 12031 26450 9618 12482 22100 229 240 469 24266 24753 49019

Total 173743 197497 371240 57914 64572 122486 4169 4127 8296 57231 58651 115882 235826 266196 5020221906 1966 3872 294963 326813 621776 Table A8.16.9. Education levels by number of people: KwaZulu-Natal harbours. African/Black Coloured Indian/Asian White HRG Unspecified Total


I 10805 8847 19652 319 340 659 761 2021 2782 376 425 801 11884 11208 23092 83 102 184 12343 11735 24077

II 28730 26510 55240 3515 4284 7799 6772 10310 17082 2822 2961 5783 39017 41104 80121 368 449 817 42207 44514 86721

III 15483 15736 31219 3516 4321 7837 9997 10100 20097 9929 14032 23961 28996 30157 59153 441 478 919 39366 44667 84033

IV 14131 14321 28452 2851 3606 6457 15502 15076 30578 20544 26091 46635 32484 33003 65487 491 557 1048 53519 59651 113170

V 4676 5946 10622 1382 1577 2959 6788 5969 12757 17549 14449 31998 12846 13492 26338 351 334 685 30746 28275 59021

Total 83478 78821 162299 13439 15230 28669 43142 46501 89643 61377 63648 125025 140059 140552 280611 2718 2335 5053 204154 206535 410689

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 9. User Charges

268


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9. USER CHARGES AND REVENUE COLLECTION

SUMMARY

The role of Levies in the South African fishing microeconomy is graphically represented in Figure 2.1: A

Simple Economic System for South African Fishing as a flow of revenue from primary sector fishing

activities to MCM. This pays for a flow of services provided by MCM to the fishing industry. It stands to

reason that being an intervention in the microeconomic system, it influences how the system behaves.

The calculation of fees and levies for fishing in South Africa has lacked a quantitative basis and one of the

tasks of the ESS was, therefore, to review the system of fees and levies and to suggest a rational

approach to their determination.

The basis of implementation of fees and levies was the “user-pays” principle, whereby there are certain

specialised and specific services that directly benefit the user and that only the State can provide due to

one form or another of market failure. It is important to note that the user-pays principle is not a fishery

specific cost recovery approach. Charges and levies are based on an “ability to pay” in the respective

fisheries and used to fund a portion of the total basket of the fishery services provided by the State. The

economics of the various fisheries dictate that some have a greater ability to pay than others, and that

levies will be determined accordingly.

The different instruments available for revenue collection were defined and evaluated in terms of

economic efficiency, equity and practicality. These included fees, royalty charges, levies and the

proportional user charge (PUC). It was advised that the proportional user charge, coupled with a penalty

charge system, should replace (preferably in 2003) the current pay-as-you-catch levy system in TAC

based fisheries. The PUC and penalty charge system still, however, requires a fair amount of additional

data, workshopping and some analysis to make it feasible.

A model for the determination of levies was developed by the ESS, based on an array of income

statements (convertible into a short-run Cobb-Douglas production and cost functions) for a functional

group of vessels, based on size or some other distinguishing characteristic. Because this method of

analysis is vessel based, it necessitated a calculation of a minimum viable quota (MVQ) per vessel class

based on vessel operating costs (including proposed levy charges).

The ESS was faced with two unforeseen problems that made it unfeasible to implement this system in

2002, namely:

¶ Because fishing rights are not attached to vessels (see Part 3: The Economics of Allocations), the

concept of an MVQ becomes an abstract concept and was not used as a basis for rights allocation.

Furthermore, there was aversion by many established industry players to accept the concept of an

MVQ.


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¶ The collection of standard and comparable income statements for each size group of vessel proved

to be beyond the scope of the ESS study.

Levy charges were however reviewed with primary regard to President Mbeki’s vision of an economically

integrated southern Africa. It is important to bear in mind that ‘a feature of deeper economic integration

beyond a mere customs union, would be the harmonisation of fiscal instruments’ (Catteneo, 2001).

Simply, this would include harmonising, where possible, user charges on primary economic activities to

avoid a competitive bias. Comparison of levies revealed that South African fishing levies are substantially

lower than Namibia’s for equivalent fisheries. Levy recommendations for commercial fisheries were then

developed, fishery by fishery, assuming the current pay-as-you-catch system of collecting levies and that

100% of TAC/TAE is levied. Additional revenue from over-catch both in directed species and by-catch

were not included in revenue calculations. In all cases it is assumed that the legal liability, but not

necessarily the final burden, for paying the levy falls on the rights holder.

9.1. DEFINING TERMS

In order to avoid ambiguity and unnecessary misunderstanding, it is important to carefully define the

terms used in this report. An attempt is made to be as consistent as possible with the current terminology

used in the fishing industry and MCM.

9.1.1 Instruments: Fees, Royalty Taxes and Charges MCM has at its disposal several instruments it can use to collect revenue. It may also use one or a

number of these fiscal instruments to influence economic decision making in the fishing industry.

The selected instrument may be:

¶ Non-price distorting (for example, per unit charges) or

¶ Price distorting (for example, royalty taxes).

Price distorting and non-price distorting instruments affect markets and business decision making in

different ways.

In addition these instruments may have a progressive, flat or regressive structure.

¶ A progressive instrument will increase revenue collected per unit, that is, with the quantum of rights

applied for or allocated. For example, it might charge R1 on the first 100 tons, R1.20 on the second

hundred tons and so on.

¶ A flat rate instrument keeps the revenue collected per unit constant. An example of this is a set

charge, or levy, per ton of catch allocated or harvested. This is the current structure used by MCM.


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¶ A regressive structure results in a lower amount per unit collected as the quantum of rights applied

for, or awarded, increases.

A progressive tax affects economic efficiency143. However, where progressive taxes fall down on

economic efficiency, they score better on equity issues, that is, the ability-to-pay criteria. They are also

more difficult to implement and police.

Flat rate instruments tend to be economically efficient as they do not affect relative prices, but fall down

with respect to the ability-to-pay criteria (equity). Flat rate charges are relatively easy to administer.

Regressive instruments are generally avoided as they score very badly on the ability-to-pay criterion

(equity).

9.1.1.1 Fees

In the South African fishing situation, a fee is usually applied as a permit fee in recreational fishing, an

application fee for rights to fish commercially or a myriad of other fees charged for services rendered by

government.

The logic of a permit fee, in the recreational sense, may be either

¶ To increase the costs of fishing and thus reduce effort.

¶ As a user charge.

¶ A combination of the above.

Recreational permit fees have by their nature and general purpose a flat rate structure.

In the commercial sense, an application fee is

¶ Normally set to cover the average administrative costs of processing a rights application, that is a

cost recovery based charge.

¶ It may also be used as a deterrent to opportunistic applications when the structure of fishing

rights allows for non-attachment.

An application fee may be regressive, flat or progressive.

143 See any introductory text on tax structures, public finance or public policy.


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9.1.1.2 Royalty Taxes

A royalty tax is a charge that attempts to tax away excess profits, or expected excess profits. It thus has

to be based on revenue and profit (the South African gold mining royalty tax was based on the ratio of

profit to revenue, including a tax tunnel). Royalty taxes were phased out of the gold mining system of

taxation around the turn of the last century, as they were deemed unfeasible and difficult to administer. In

accordance with the received wisdom, royalty taxes, or taxes on economic rent, will not be considered in

this report.

9.1.1.3 Charges (levies)

A charge, or levy, is a fixed amount per unit payable to the State when it awards a fishing right. It may be

in the form of a pay-as-you-catch charge or, for want of a better term, a proportional user charge.

Namely,

¶ The pay-as-you-catch charge levies a certain fixed amount on the user based on the amount caught.

It may be progressive, flat or regressive.

¶ The proportional user charge levies the proportion of TAC that is allocated. It is similar to a per unit

fee, except that it is charged only to successful applicants and usually at a higher rate. It may be

collected yearly, bi-yearly, quarterly or monthly depending on the circumstances of the fishery in

question.

Two additional charges are contemplated, that is, a by-catch charge and a penalty charge.

¶ The by-catch charge is levied on by-catch, usually at different rates depending on the species caught.

The by-catch charge can take the form of a pay-as-you-catch charge or a proportional user charge.

The pay-as-you-catch charge is the current system in place.

¶ A penalty charge is levied on over-catch with respect to both by-catch and directed catch. It can be

used as a tool to discourage over-fishing.

A flat rate pay-as-you-catch charge on directed catch and by-catch is the existing system used in the

South African commercial fishery. No penalty charges exist.

9.1.2 The User-Pays Principle The user-pays principle to revenue collection can only be invoked where there are specialised and

specific services that directly benefit the user and that only the State can provide due to one form or

another of market failure. The common property nature of the fishing industry leads to a form of market

failure manifest as an under-investment in fisheries management.


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Under the user-pays system, services that do not directly benefit the user should not be funded out of the

revenue collections; rather income should come from central government if the activity is deemed to

benefit society as a whole. It is important to note that the user-pays principle is not a fishery specific cost

recovery approach. Charges and levies are based on an “ability to pay” in the respective fisheries and

used to fund a portion of the total basket of the fishery services provided by the State. The economics of

the various fisheries dictate that some have a greater ability to pay than others and that levies will be

determined accordingly. A good practice would be to set the budget requirements of the Marine Living

Resources Fund according to the expected revenue received through a user-pays system and not the

other way around.

Definition of Specialist Services. In order to institute a user charge (levy) based on the user-pays

principle, it is necessary to define the specialised services that will be provided by the State:

¶ Research. Biological research is used to determine TAC, TAE and other important elements that are

necessary to ensure the sustainable use of LMRs (resource management). As the fishery has to be a

managed microeconomic system, social and economic research is necessary to determine the

analytics of the system.

¶ Rights allocations. Because the market fails to allocate LMRs, the State must allocate user rights

with the purpose of maximising economic and social benefit.

¶ Compliance services. After allocations have been made, incentives still exist to over-exploit and for

individuals without rights to use the resource. The State should thus undertake the management and

compliance of catch and entry.

¶ Administration. The specialised services require control and organisation, and thus administration.

These are further illustrated on Figure 2.1: A Simple Economic System for South African Fishing.

Definition of Users. The user must be defined in terms of those who benefit materially and directly from

the services provided. Depending on the circumstances, these may be:

¶ Rights holders.

¶ Fishing boat owners.

¶ Vertically integrated companies.

¶ Processing companies.

¶ Recreational fishers.

¶ Subsistence fishers.


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In the case of the South African commercial fishing, where rights are not attached to vessels, the

distinction between rights holders, fishing boat owners, processing companies and vertically integrated

companies is not always clear. In essence they are all beneficiaries of the service as they all rely to a

greater or lesser extent on the health of the fish stocks and the good management of the microeconomic

system.

Subsistence fishers by virtue of their socio-economic situation cannot be expected to pay a user charge;

rather the commercial or recreational fisheries should subsidise management of fisheries144.

Use of levy income. Expenditure items that do not directly benefit the fishery (commercial, recreational

or subsistence) should not be financed out of the revenue collected from fees and levies. For example,

coastal zone management services benefit society as a whole and should be financed out of central

government transfers145.

Incidence of Charge. It is important to know the distinction between who is liable for the payment and

who actually pays. To use economic jargon, the incidence of the charge or fee must, in the absence of a

good analysis of the market rights and output markets, be reasonably estimated. That is, the incidence is

the ability for one group to shift the burden of the charge or fee to another. For example, if the rights

holder is not a boat owner, it is usually possible for the unattached rights holder to shift the burden of a

charge to the vessel owner. Likewise if the rights holder is a vertically integrated company with the ability

to set prices at least in the domestic market, it can shift the burden of the charge to the consumer through

higher prices.

9.2. CONSIDERATIONS FOR IMPLEMENTING CHARGES FOR SOUTH AFRICAN

COMMERCIAL FISHING Charges on a fishery-by-fishery basis should be considered in terms of:

¶ The ease of administration. In other words, the collection and recording of charges should be easily

administered from both industry and MCM’s point of view. Difficult administration brings with it

additional costs146.

¶ The incidence of the charge should be known. A precise measure of the incidence of the charge

requires good data and rigorous analysis. However, some indication of the incidence is possible in

most cases.

144 Specifically, the recreational fishery will usually be in direct conflict with the subsistence users and it would be a good principle to use, at least part of, the fees collected from recreational users for protecting and developing the subsistence fishery. Also, at least a portion of the money raised should be spent on the preservation and development of the recreational fishery. 145 The distinction between almost pure public goods, like those provided by coastal services, and semi-public goods, for example where the market fails to provide a sufficient amount of marine and economic research that directly benefits the user, is covered in all entry level public finance text books. 146 One of the additional costs incurred through a difficult administrative system are inefficiencies in levy collection.


275

¶ The effect on the economic behaviour of the fishing firm should be anticipated. This would include

the expected incentives that arise from fiscal instruments on the exploitation, capitalisation and

employment. The concept of MVQs and representative costs can provide a fairly good indication of

expected responses to charges being levied. However, given the serious problems associated with

MVQs, and the inability to collect standardised representative costs, this analysis could not be

completed.

¶ The ability-to-pay criterion is important from an equity point of view. It is obviously linked to the

incidence of the charge. Failing the availability of other measures, Part 6: ESS Survey: Classification

(Size and Shape) can provide certain insights into this criterion.

In the interests of administrative ease and resource management, it is recommended that the current

“pay-as-you-catch” system of levy collections could be reconsidered in favour of a proportional user

charge (PUC) coupled with a system of penalty charges.

The PUC is briefly described below:

¶ The PUC is levied on the proportion of TAC that is awarded to a rights holder. Note that a PUC

cannot be used for an effort-based fishery.

¶ A PUC can be levied

- before a permit to catch is issued, or

- after a certain period of time from when the permit to fish is awarded.

The word permit is used here in favour of the word right because of the regulations and sometimes

ambiguities pertaining to the Marine Living Resources Act 1998.

Depending on the choices of the resource manager, a penalty charge is levied at a single rate or different

rates. For example, a 10% premium can be added as a penalty charge for the first 20% of over-catch

and increased progressively as the proportion of over-catch increases. In other words, the penalty

charge can work on a simple progressive system analogous in construct to the South African personal

income tax structure.

Using a system where the PUC and penalty charges are levied after a certain period of harvesting, for

example six months, the following advantages are realised:

¶ Users can be made to pay their levy and provide credible catch returns before the permit to renew a

licence to fish is awarded for the next period. This provides a good check on levy compliance that is

particularly important with the awarding of longer-term rights.

¶ Compulsory catch returns also provide a vehicle for better data collection and analysis needed for the

task of resource management.


276

¶ The PUC system should be a lot easier to administer than the pay-as-you-catch system, thus less

costly both to MCM and industry.

¶ Income forcasting and hence budgeted expenditure from the MLRF will be more accurate.

¶ The rate at which the penalty charge is set can be used to strongly, or weakly, discourage over-

fishing or targeting by-catch species. For example, a very high penalty charge substantially increases

the risk and reward profile of being caught over-catching – analogous to the death penalty without the

moral considerations. This obviously provides an important tool for the resource manager.

¶ Additional revenue to MCM can be realised in the case of catch non-compliance and the targeting of

by-catch species.

There are, however, certain requirements that are necessary to establish the effectiveness and efficiency

of the PUC system. The most important is to establish suitable periods for collecting the levy. It does not

make sense to collect four times a year if there are only two peak seasons.

9.3. A MODEL FOR DETERMINATION OF CHARGES AND FEES BASED ON VESSEL COSTS A quantitative model based on vessel income and operating costs was developed (by adapting the short-

run Cobb-Douglas production function) for the calculation of user charges and fees for commercial

fishing, and is presented in Appendix 9.1. Comments are made on the implications of the charges for the

minimal viable amount of fish required to operate a class of fishing vessel.

Implementation of the vessel cost based system of levy detemination would require an agreed system for

classifying functional classes of vessels, and the establishment of a mechanism for the fair determination

of the “representative costs” for operating each vessel class. The magnitude of implementing this

administrative exercise to a point where a legally robust, and equitable set of levies could be calculated

for 2002 was beyond the scope of the ESS. However, analysis of representative cost data from the ESS

survey showed that the calculation of charges was relatively straightforward for fisheries where very

distinct vessel classes operate in a largely standard way. For example, single species fisheries, such as

abalone, squid, hake handline and longline, and rock lobster, producing a single or very limited product

range. The model is difficult to apply in more complex fisheries such as deep-sea trawl and small pelagics

because:

ü The large companies which dominate the industry operate mixed fleets with complex multi-

species and multi-product fishing strategies, which are very flexible depending on environmental

and market conditions. Generalisation of the costs of operation per vessel class is thus more

complex.


277

ü Vertical integration of the larger fishing and processing operations means that there is no record

of a market based “quayside” landed price for the raw product. Calculation of income and profit

margins for the primary fishing activity is thus a virtual accounting exercise. A concept of a

“notional trawler” as a cost based model for the determination of levies in the deep-sea industry

was developed by Mr Roy Bross of the Deep Sea Trawling Industry Association and is included

as Appendix 9.2.

In summary:

ü The abandonment of the mooted Minimum Viable Quota system of allocations weakened the

legal and accounting rationale for implementing a levy system based on standardised vessel

class operating costs. Individual rights holders will thus have to be uniformly levied on their

allocated quantum of fish, regardless of economic viability based on the vessel type catching

their fish.

ü The ESS survey data did not provide a sufficient basis for recommending an implementable set

of levies based on the operating costs of vessel classes.

ü The model does, however, provide a useful tool for evaluating the “ability to pay” of user groups

within specific fisheries.

9.4. A PRAGMATIC ALTERNATIVE

In view of the complexity of implementing a levy calculation system based on the operating costs of

vessel classes, the pragmatic route to take in the short term is one based on a national agenda and

international trends in world markets.

¶ Bearing in mind President Mbeki’s vision of an economically integrated southern Africa, MCM could

take the view of harmonised fiscal instruments among potential economic partner countries. Our

nearest fishing neighbour with potential for economic integration is Namibia. It makes sense then to

have similar charges to avoid competitive bias. An exchange rate of 1/1 is used for the calculations.

¶ Rising prices in the world seafood market and falling R/$ exchange rates increase the ability of export

orientated fishing companies to pay the higher levies.

The current (2001) levy charges for the commercial species are examined in Appendix 9.3, with proposed

changes. It is evident that South African levies are significantly lower than Namibia’s and that processes

should be implemented to achieve parity. Comment is made on the expected incidence, and ability-to-pay

criteria. Revenue calculations were based on the best available TAC and catch estimates at the time of

writing. Linkage of the proposed increases in levies to the nature, quality and cost of the services

rendered by Government, specifically DEAT / MCM, should be made if the increases are to be justified to

the users.


278

With the advent of medium and long term rights, and the need to tighten up the economic management of

the fishery, it is advised that a Proportional User Charge system (PUC) coupled with a penalty charge

system replace the current pay-as-you-catch system, preferably in 2003. The PUC and penalty charge

system still, however, requires a fair amount of additional data, workshopping and some analysis to make

it feasible.


279

APPENDIX 9.1: Model of the Calculation of Charges and Fees based on the Short-Run Cobb-Douglas Production Function

Due to the underlying accounting identity of the well-established Cobb-Douglas production function (Q =

ALaKb where a and b are elasticities of output with respect labour ‘L’ and capital ‘K’) is used as the base

production model for fishing vessels. The short-run form where capital is held constant and the elasticity

of output with respect to labour (variable costs) is equal to one, converts into a linear cost function which

is consistent with accounting income statements or cash flow projections. This very simple model is

sufficiently flexible to allow both effort based and quota based fishing vessels to be examined. It also

allows the inclusion of by-catch and additive, grouped or single cost, items.

The short-run Cobb-Douglas cost function (analytical form of the income statement) can be written as

follows.

TC = (a + rK) + H(b + c) a: fixed cost to vessel

r: discount rate (rate of return on capital)

K: capital value of the vessel

H: catch

b: proportion of variable costs

c: proportion of wages

Various other additive expressions can be included, for example, a distinction can be made between

several types of labour and their respective wage rates without affecting the linearity of the model. In

reality, this linear model is only useful if the fishing firm falls within the breakeven levels of catch given

certain cost assumptions. It still, however, remains useful to illustrate the effects potential revenue raising

instruments might have on the fishing firm.

Average costs, or the cost per unit caught (tons, kilograms), are the costs used to make some statement

on expected behaviour of fishing vessels to proposed revenue raising instruments. The average cost

function, total cost divided by-catch, is as follows.

AC = TC = (a + rK) + (b + c)

H H

This average cost identity shows that as catch (H) increases, the average cost falls. It is in the form of a

rectangular hyperbola.


280

Briefly, if firms want to maximise profits, they will harvest an amount where the difference between total

costs and total revenue is maximised. That is, solving for an unconstrained optimisation problem, they

will select a level of catch where the cost of harvesting an additional unit (marginal cost) is just equal to

the revenue received from that additional unit (marginal revenue). At this stage it is important to state that

the average cost approaches the marginal cost (marginal cost = b + c) asymptotically. In other words, the

fishing vessel believes that it can maximise profits by-catching at a level of maximum capacity.

MINIMUM VIABLE QUOTAS (MVQs)

A minimum quota allocation, or minimum viable quota (MVQ), in this sense is the breakeven catch for a

typical fishing vessel in its specific class group within a particular species directed fishery. The breakeven

catch (Hmvq) occurs where the average cost per unit of the catch (AC) is equal to the price (P) received

from that catch, as follows,

Hmvq = (a + rK) P: price per unit catch

P – (b + c)

The above expression shows that:

¶ As price increases the minimum viable quota (Hmvq) will of necessity fall.

¶ Alternatively, if the proportion of variable costs and/or fixed costs and/or the discount rate increases,

the Hmvq must rise.

Assuming that fishing companies do not target by-catch147, incidental catch should not add to the costs of

directed catch. That is, it does not affect the cost and break-even catch expressions. All that by-catch

does, in this model at any rate, is to add to total revenue and profits. In economic terms, targeting by-

catch is pure rent-seeking behaviour. For economic efficiency, the targeted by-catch species should be

controlled and allocated accordingly to ensure maximum temporal economic benefit.

At this point, it should be noted that the concept of a MVQ is an artificial construct, as rights are not

attached to vessels. That is, a market for rights exists, enabling people to buy and sell rights and then,

for example, to charter vessels to harvest the appropriate amount of the resource. However, the concept

of an MVQ does bring out the effect that the various revenue raising instruments may have on efficiency,

equity and fishing firm behaviour.

147 In fact by-catch is incidental catch and the ability to target incidental catch points to inaccurate catch/incidental catch ratios, poor regulation or a misnaming of the term.


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FEES (v)

A fee is usually a permit fee in the recreational sense or an application fee in the commercial fishing

sense.

¶ The logic of a permit fee, in the recreational sense, is simply to increase the costs of fishing and thus

reduce effort in this fishery.

¶ An application fee is normally set to cover the administrative costs of processing that particular

application. It may also be used as a deterrent to opportunistic applications when the structure of

fishing rights allows for non-attachment.

Care should be taken to ensure that if the application fee is inflated to filter out opportunistic applications,

the excess (that amount left over after the administration costs of processing the application) should be

returned to the applicant to ensure fairness. A problem arises in that, if the State (MCM) suffers from

bounded rationality (due to for example, inefficiency, established practice inherited from apartheid, lack of

information etc.), the administrative costs may already be artificially high.

1. A fixed regressive fee (vr) A fixed regressive fee is in effect a fixed cost. It does not vary with the amount applied for. It may be

included as an additive constant into the fixed costs component of total costs and affect the average cost

function as follows.

AC = (a + rK + vr) + (b + c) vr: fixed regressive fee

H

Similarly, it must increase the minimum viable quota or break-even level of harvesting.

Hmvq = a + rK + vr

P – (b + c)

The fixed regressive fee is included into the expression along with the fixed cost component. It will

increase the minimum viable quota. The important realisation, however, is that because it is included into

fixed costs, the size of the fixed cost component is important. The lower the fixed cost, or smaller the

company or vessel, the greater the overall effect the fixed fee will have on relative minimum viable quota

changes. To illustrate,

1. A SME vessel with a low fixed cost. For example, fixed costs = R 12 000; a = R 10 000, rK = R 2

000, variable cost = R 2 per kilogram; b + c = 2 and price = R4 gives a MVQ = 6 000 kilograms.

Introducing a R 5 000 fixed levy will increase the MVQ to 8 500 kilograms - almost a 42% increase in

MVQ.


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2. A vessel with a high fixed cost of, for example, R 30 000 and similar variable costs (R 2 per kilogram)

facing a similar price would have a MVQ of 15 000 kilograms. Placing a similar levy of R 5 000

would increase the MVQ to 17 500 kilograms, an increase of almost 17%. This is a considerably

lower than the percentage change than for a smaller vessel.

Total revenue collected will be the number of applicants multiplied by the fixed fee per application.

2. A per unit flat rate fee (vf) The per unit flat rate fee structure varies with the quantum of rights applied for. In essence, it can be

added to variable costs for successful applicants and a fixed cost for unsuccessful ones. That is, it adds

to the per-unit costs of the quantum of rights allocated. It is an additive constant in the proportion of total

costs and enters the average cost (or cost per unit) expression as follows.

AC = (a + rK) + H(b + c) + vf

It also changes the minimum viable quota to,

Hmvq = a + rK

P – (b + c + vf)

The minimum viable allocation increases as a result of vf. Using the example numbers from 3.1, and a

fee of 10c per kilogram,

¶ The smaller vessel would have a MVQ of 6 316 kilograms and paying R0.10 per kilogram, that is a

total fee of R631.58.

¶ The larger vessel’s new MVQ is 15 789 kilograms. It would pay R0.10 per kilogram fee with a total of

R1 578.94. In general for every 10c fee per kilogram an additional 5.3% of the original quota needed.

Total revenue will be the number of units (tons, kilograms etc) applied for multiplied by the fee per unit. In

general, the higher the proportion of variable costs to fixed costs the greater the impact a flat rate per unit

fee will have on the behaviour (required MVQ) of the firm.

CHARGES (u)

A charge is a fixed amount payable to the State when it awards a fishing right. It is linked to the

proportion of TAC that is given to the right holder (not the value of the catch) and is thus strictly speaking

a fixed output tax and is not price distorting. It is similar to a per unit fee, except that it is charged only to

successful applicants and usually at a higher rate. It may be collected yearly, bi-yearly, quarterly or

monthly depending on the circumstances of the fishery in question.


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¶ The advantage to the State is that revenue collection is based on the TAC or TAE and can therefore

be predetermined allowing for administrative ease and careful budgetary planning.

¶ A disadvantage falls on the fishing firm in that, when the price of the product drops (profits decline),

the proportion to profits to charge decreases, making them even worse off than just absorbing a fall in

price.

Because it is a cost item, these revenue collections do not enter into the normal budgetary procedures of

central government. The downside, however, is that it charges only on an amount allocated and not the

amount caught. Fishing firms will therefore not pay charges on over catching. To counter the negative

effects of this, it is possible to build in a penalty charge, which could be higher than the basic charge, for

each unit that the fishing company harvest above its allocated amount. In essence, the closer the penalty

charge per unit approaches the average cost per unit, the greater the incentive becomes not to over-

catch.

The charge is thus an ear-marked extra-budgetary fixed output (based on TAC or TAE) non-price

distorting tax. Without penalties, it appears in the average cost and minimum viable quota expressions in

exactly the same way as a per unit fee.

AC = (a + rK) + H(b + c) + u

and

Hmvq = a + rK

P – (b + c + u)

In general and in the absence of economic rents, the higher the proportion of variable costs to fixed costs,

the greater the impact a charge will have on the behaviour (required MVQ) of the firm. However, a

penalty charge can be used as an incentive to overcome the adverse effects of harvesting above normal

allocation levels. This assumes, of course that compliance control is effective and that fishing enterprises

correctly record and report catch.

LEVIES (l)

A levy is defined as a fixed amount per unit of catch payable to the State. It may be collected yearly, bi-

yearly, quarterly, monthly or per fishing trip. It is not linked to the TAC or TAE, but is dependent on the

catch rates of the fishery in question. Catch rates are obviously functionally related to the TAC and TAE.

¶ The advantage to the State is that without penalty charges revenue collections will tend to be higher.

¶ The disadvantages to the State are that revenue flows can be erratic resulting in difficult budgetary

procedures. Because levies are linked to catches the administrative costs of collection could also be

high.


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This may result in an administrative cost trade-off between using charges without penalties and levies.

These revenue collections do not enter into the normal budgetary procedures of central government if

they are a tax-deductible cost.

Levies, so defined, are included into the model in an identical way to charges and per unit fees.

AC = (a + rK) + H(b + c + l)

and

Hmvq = a + rK

P – (b + c + l)

Similarly, then, and in the absence of economic rents, the higher the proportion of variable costs to fixed

costs, the greater the impact a charge will have on the behaviour (required MVQ) of the firm.

ROYALTY TAXES (tr)

A royalty tax is in effect a rent tax. An analogy may be drawn to the times when the King owned all land

and tenants paid rent, or royalty taxes. The assumption being that the excess harvest belongs to the

King, or in the modern fisheries case the State. However, on marginal land or marginal fishing grounds148

there might not be a surplus. Firms may just cover their costs and get a reasonable return on capital. It

is also a price distorting tax which in the absence of economic rents, or royalties, influences the rational

decision making of the firm. The administrative costs of collections may be prohibitively high as MCM

would have to simulate the tax collection mechanisms of the South African Revenue Services. A royalty

tax enters the basic model as follows.

AC = [(a + rK) + H(b + c)] - tr and

Hmvq = a + rK

(P + tr) – (b + c)

As a royalty tax necessitates the presence of economic rents; is price distorting in the absence of rents;

would have prohibitively high administrative costs to MCM; and may be in contention with the directives of

the Department of finance, it will not be considered further.

148 Marginal fishing grounds usually occur through over-capitalisation (that is, over-capacity), which leads to over fishing, or marginal fishing, and may result in less fish, lower profits and higher costs.


285

APPENDIX 9.2: “Cost And Revenue” Model for Catch Levy Determination for the Deep-Sea Hake Trawling Sector

Meeting levy expectations has been rather more difficult than initially envisaged. This happens to have

turned out true not only for trawling but also for all fishery sectors covered by the ESS. The Terms of

Reference for the ESS imply that the State intends to achieve revenue objectives through the capture of a

proportion of the ensuing economic rent. It is assumed that catch levies, calibrated on the classical

ability to pay criterion, will be the main policy instrument for achieving the purpose. Market dynamics pose

conceptual, practical and technical difficulties in the measurement of rent and in its capture by the

administrative arm. Rent based charges turn out to be problematical in reality so that it is generally

agreed that the best to hope for is partial capture – rents almost always have to shared between State

and the private sector.

The methodology for the Economic Sectoral Study specifies that the levy base be restricted to the primary

sector; in other words the ambit of levies should not extend to economic activity beyond the quayside. It

also implies that imposts should relate to the standard costs and revenues associated with exploitation of

the specific fish resources expressed in unit fishing rights (e.g. Rands per nominal ton of hake rights.)

The costs and revenues in question are somewhat more broadly based than the fishing operation itself as

they would be associated also with bringing the primary product to point of sale.

The ESS Team stumbled on an obstacle, a surprising one considering that hake trawling is a long

established mature industry. Briefly, no quay level operational standard exists for the industry as a whole.

All fleet operations are integrated into different business models with varying emphasis on different

markets, end products and intervening processes. The solution to the problem was to create a realistic

notional standard and it was generally agreed that this entailed an opportunity cost approach to the

problem.

The answer to the question “what alternative mode of production would be adopted if current business

models embracing value added product were to be aborted?” proved to be relatively simple. The parties

concurred that HACCP compliant, hake directed fishing for export quality, blast frozen, headed and gutted

trawler packs is the underlying common denominator for trawler owning rights holders. It was also

considered significant that some trawler operators deploy H&G type hake freezers. 149

The basic approach to the levy determination aspects will therefore be to take a vessel with fishing

capacity roughly consonant with the operator’s current fishing rights and apply comprehensive current

budget data (where possible) to frozen H&G operations. The idea is to establish costs from first principles

,150 where possible, rather than to resort directly to records and averages.

149 To enlarge, 65% of the fish in question is landed in fresh form. Other than a transfer price, which is meaningless in context, none of this product has a real quay, or landing, price. It is therefore impossible to undertake meaningful economic calculation. 150 Insurance can be taken as a case in point. Trawler operators are known to use very different insurance strategies and insurers usually assess variable risk worthiness in their clients. These kinds of differences are not reflected by the cost classification used. The notional exercise is intended to arrive at an economically “correct” cost. Whatever happens in practise the “correct” answer is to cover all categories of insurance and to insure at realistic replacement values at neutral rates.


286

THE STANDARD MODEL

Input specifications for model FISHING CYCLE – STANDARD H&G FREEZER Trip length 30 days Shore Leave 6 days Trip Cycle 36 days Average lost days/ season, refits, slippping etc 25 days Effective Trawling season 340 days Trips 9.4 per cycle Sea Days 283 days/cycleWeather losses 1.5 days/trip Sea repairs and towing 0.4 days/trip Lost sea days 17.9 days/cycleEffective fishing 265 days/cycle Parameters used for data generation Vessel Size 750 grt Vessel Length 49 m Vessel Power 1350 kw Complement 40 persons No of trips completed 9.4 per year Average sea days per trip 30 days Gross sea days per year 283 days Net fishing days per year 265 days Hake Landing per fishing day 7.31 H&G By-catch landing as % of H&G hake 12.2% % Total Landing per day 8.2 tons Depreciation guideline 7yrs x 10%

REPLACEMENT COSTS OF CAPITAL

All ROI calculations turn on the question of capital valuation and in fishing this means in effect the cost of

hulls. This is no easy matter. An argument exists for using new buildings but to do so distorts the

calculation to the point of meaninglessness. It was ultimately decided to use the industry consensus on

what constituted a conservative, rational and realistic replacement strategy. The European market price

of a seven-year-old diesel burning stern trawler to specification fully equipped and modified for blast

frozen H&G production under RSA conditions. The actual hull cost used amounted to 38% of the price ex

Spanish yards.

Other Fixed Capital: Account also has to be taken of replacement cost of property necessary for the

operation, whether leased or owned. The costs of leased property are excluded from the model, which

means that a capital allowance must be included. Where fleets are deployed this allowance is subdivided

between hulls.


287

APPORTIONMENT OF OVERHEAD

Overheads are apportioned on the basis of a standard number of ships deployed within an enterprise.

Overheads extend to internally generated primary product only.

Output specification

CATCHES

Catch composition also raises the business plan problem. Each fleet operator uses a distinctive catch

strategy both in regard to size and species. Some operations employ what may be called semi-directed

hake strategies for a variety of commercial species, while others are strictly hake directed. Because of

this complexity it was ultimately decided that the average species composition achieved by major hake

fleets provide the basis for assessing by-catch levels for the notional industry. It should be borne in mind

that this is notional and represents a way of doing business which may be feasible but which few put into

practice.

REALISATIONS

Wherever feasible average FOB export price is used for hake and average domestic ex warehouse

realisations are used for the majority of by-catch items.

Return on investment based leviability calculations

Note: The calculations below were made at an exchange rate of $1 = R8. Given the current rate of

exchange of $1 to ca. R11.50 obviously makes the viability of the calculations less favourable. In essence

this illustrates the difficulty of defining a fixed levy.

Modelling catching operations makes it possible to calculate the degree to which the industry is leviable

and the notional trawler approach has been used for this purpose. For the most part leviability outcomes

depend on three important variables:-

1. conventional rate of return employed for the calculation

2. the valuation of capital

3. hypothetical earnings

1. Conventional rate of return

Regarding a system of levies founded on the partial capture of economic rent, one of the important

matters that has to be decided is the appropriate risk enhanced rate of return that should accrue to private

enterprise in the fishery sector. Once this issue is settled it becomes possible to get a reasonable fix on

the scope of the tax base. Purely for the purpose of the argument it is suggested that the threshold below

which the industry is intrinsically unleviable is an after tax return of 20%, which we believe to be

relatively conservative. The 20% rate would include a 5% allowance for the universally acknowledged

risks of the fishing business.


288

2. The valuation of capital

Originally the cost of new hulls was intensively researched and, although it was agreed that the standard

model cannot envisage new buildings, construction prices provide the basis for valuation. The second

hand hull cost used for the standardised notional H&G freezer trawler is that of a seven year old vessel.

The annual rate of depreciation applied to the delivery cost of a new building is 10% declining balance. A

trawler of standard dimensions would be brought into reckoning at °51% the new cost. The vessel cost

formulae were also applied to a ship half as old again and to one twice as old in order to test sensitivity to

vessel valuation (see Note i ). Ageing the hull in this way enhances ROI by about 6% in each case at

current rates of levy.

3. Earnings

Earnings constitute the least controversial part of the model (see Note ii). Earnings for the calculation are

as per the standard model. The effect on catch rates on the ROI was also calculated.

RESULTS

Given the assumptions used for the standard notional trawler the deep-sea trawling industry does not

reach the 20% ROI hurdle (nor the non risk discounted 15% criterion). It is interesting to note that a

vessel half as old again at acquisition still does not do so unless the after tax criterion is relaxed. Under

those conditions the current rate of levies would be appropriate. Doubling the age at acquisition (reducing

capital employed by 40%) would allow a doubling of the current rate of levy on the assumption that the

State would want it all. (Note iii)

An examination of hypothetical catch rate variation revealed that the ROI threshold would be reached at a

25% increase in the current catch rate.

The cost and revenue model tables reveal no realistic potential for increasing the present economic rent

based levies. Even under less stringent capital postulates, the industry provides little to no scope and the

same appears to be true of variation in the catch rate.


289

________________________ i Vessel valuation was varied in the analysis because it is the predominant element of capital and the formulation used in the model is more arguable than the others. A 7-year old vessel would constitute more than 80% of total capital employed. ii Even earnings are not so simple. For instance the very high short run fixed cost of trawling means that the ROI will be sensitive to catch rates and there are other conceptual complications with earnings. The calculation of levy potential over time becomes distorted unless all prices remain constant relative to each other in the longer run - something that rarely happens in practice. The price of the currency is a similar case in point. Depending on when the particular foreign investment has been secured, the systematic erosion of the local currency in recent years has cranked up returns in the deep-sea trawling industry, with its relatively high propensity to import ships and export outputs. The future financial effects of such phenomena cannot be measured with any accuracy nor, generally speaking, is it considered desirable to bring them into account. Such things, real as they may be after the event, have to be disregarded for leviability assessment purposes. iiiAll rent-based taxation is problematical and the many conceptual difficulties involved invariably lead to a compromise. It is customary and practical not to be too ambitious by trying to capture all the rent. A prudent State always shares whatever surpluses the calculations may show.

A disk copy of the operational model was provided with the final report and is available on request. The data and the model are presented on four interactive Microsoft Excel Spreadsheets.

1. Levy model inputs which shows the model and the inputs and the basis for the calculations of the inputs.

2. Trawler building and depreciation costs. 3. The operational model for three different size classes of fresher and freezer trawlers. 4. Sensitivity analysis to capital costs and catch rate.

SENSITIVITY TO

CAPITAL EMPLOYED

Relative Before Tax After Tax Before Tax After Tax Before Tax After Tax Rate of LevyCurrent = 1

0 16.3% 13.9% 22.2% 17.8% 29.1% 22.5%1 = R339916 15.4% 13.2% 21.0% 17.0% 27.6% 21.4%

2 14.5% 12.6% 19.8% 16.2% 26.1% 20.3%3 13.7% 12.0% 18.7% 15.3% 24.6% 19.3%4 12.8% 11.4% 17.5% 14.5% 23.1% 18.3%5 11.9% 10.8% 16.4% 13.7% 21.7% 17.2%6 11.0% 10.1% 15.2% 12.9% 20.2% 16.2%7 10.2% 9.5% 14.1% 12.1% 18.7% 15.2%8 9.3% 8.9% 13.0% 11.3% 17.3% 14.1%9 8.4% 8.3% 11.8% 10.5% 15.8% 13.1%10 7.6% 7.7% 10.7% 9.7% 14.4% 12.1%

CAPITAL EMPLOYED

SENSITVITY TOCATCH RATE

standard daily catch ratevariance tons per sea day Before Tax After Tax

Current = 0

-2 7.3% 7.6%-1 11.4% 10.4%

0 = 11.93tons/sea day 15.4% 13.2%1 19.5% 16.0%2 23.4% 18.8%3 27.4% 21.6%4 31.4% 24.4%5 35.3% 27.1%

CAPITAL EMPLOYED

Return on Investment Return on Investment

7 years old at acquisition 10.5 years old at acquisition 14 years old at acquisition

39,330,991

Deep-Sea Trawling ROI based leviability calculationsread in conjunction with the standard notional h&g freezer trawler cost and revenue model

Return on Investment

7 years old at acquisition

10% annual depreciation on new building price throughout

39,330,991 30,215,355 23,685,364

Return on Investment


290

Appendix 9.3: Calculation of Possible Income from South African Commercial Fisheries Based on Harmonisation of Fees and Charges Within SADC, with Namibia as a Reference Point.

Deep-sea Hake Trawl

Liability:

Rights holder.

Incidence:

The incidence of the charge may take one of the following forms

¶ The rights holder, if not a boat owner, can usually shift the burden of the charge to the boat owners.

¶ The rights holder, if not part of a vertically integrated company, can usually shift the burden of

payment onto the boat owner.

¶ If the boat owner is not a vertically integrated company, but sells their catch to a vertically integrated

company with some oligopolistic power (ability to set input fish prices), the boat owner cannot easily

shift the burden on to the processing and marketing company. That is, the boat owner of a small

company bears a large part of the burden.

¶ If a vertically integrated company owns the boat, the burden of the charge can usually be shifted to

the processing and marketing divisions. Indeed, most of the larger vertically integrated fishing

companies involved in hake trawl use a cost centre approach where all profits accrue to the

marketing division (Bross, 2001).

¶ If the vertically integrated company has some power to set prices in the domestic market, they can

shift the burden of a charge to the consumer through higher prices. Around 50% of hake marketed

through the big vertically integrated companies is for domestic consumption (Hecht, 2001) and they

do have market power (Bross, 2001).

Basically, the burden of the charge will rest largely with the boat owner if they are a small non-vertically

integrated company, or in the case of the vertically integrated company, it will be partly shifted to the

domestic consumer.

Ability to pay:

Increasing world seafood prices coupled with downward pressure on the R/$ exchange rate does

increase the ability of the fishing industry to pay higher levies. In addition, increasing world prices will put

pressure on local prices to increase as South Africa has adopted an open economy approach to

macroeconomic management through GEAR. This further increases the ability of the fishing industry to

pay additional levies.


291

Current Charge and Proposed Charge:

The current levy on hake is R115 per ton in South Africa. It is R300 per ton for wetfish and R550 per ton

for frozen fish in Namibia. The Deep-sea Hake Trawl fishery freezes 16% of its catch at sea for the

export market and the rest is used elsewhere (Hecht, 2001).

Taking into consideration concessions for exempt export charges from Namibia (12%) and the more

remote location of the South African fishing industry centre in the Western Cape, a charge equivalent to

80% of the Namibian charge is recommended. Averaging out the 16% of frozen export quality fish

harvested in South Africa at R440 per ton charge for frozen fish (80% of the Namibian levy of R550 per

ton) and the rest at a charge of R240 per ton (80% of the Namibian levy of 30 c per kilogram) gives a

recommended single charge of R272 per ton of hake. At a TAC of 139 000 tons, the expected revenue

collection from the Deep-sea Hake Trawl Fishery is around R37.8 million.

Inshore Hake Trawl

Liability:

Rights holder.

Incidence:

The incidence of the charge may take one of the following forms:


¶ The rights holder, if not part of a vertically integrated company can usually shift the burden of


¶ If the boat owner is not a vertically integrated company, but sells their catch to a vertically integrated

company with some oligopolistic power (ability to set input fish prices), the boat owner cannot easily

shift the burden on to the processing and marketing company. That is, the boat owner of a small

company bears a large part of the burden.

¶ If the boat owner is a vertically integrated company with some domestic market power, it can shift the

burden to the consumer.

Ability to pay:

The ability-to-pay is much the same as that of the Deep-sea Hake Trawl Fishery. That is, increasing

world seafood prices coupled with downward pressure on the R/$ exchange rate does increase the ability

of the fishing industry to pay higher levies. In addition, increasing world prices will put pressure on local

prices to increase as South Africa has adopted through GEAR an open economy approach to

macroeconomic management. This further increases the ability of the fishing industry to pay additional

levies.


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Current and Proposed Charges:

For hake:

As most of the landings are wetfish, an increase to 80% of the Namibian wetfish charge is recommended,

that is, an increase from R115 per ton to single charge of R240 per ton. The expected revenue from a

TAC of 10 200 tons is about R2.5 million.

For sole:

The current charge is R185 per ton of sole. An equivalent increase to that of Inshore Hake Trawl is

recommended, namely to R388 per ton. For a TAC of 871 tons, the expected revenue is R338 000.

Hake Trawl By-catch

Six by-catch species are currently levied, namely Kingklip, Horse Mackerel, Monk Fish, Ribbon Fish,

Snoek and Squid.

Liability:

Rights holder.

Incidence:

The incidence of by-catch charges is dependent on the market for the specific species, the level of

vertical integration and the amount of market power that exists within the harvesting companies.

Current and Proposed Charges

Below is a table of proposed increases. The increases, rounded to the nearest Rand, for kingklip, ribbon

fish and snoek are equivalent increases to that of the inshore hake and sole trawl. The monk fish levy is

substantially below that of Namibia and the recommended rate is adjusted to 80% of the Namibian

charge.


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Table 1. Hake Trawl by-catch charges

Current charge

(per ton) % Increase

RecommendedCharge

(per ton) Expected

catch Revenue

Kingklip

R 187.50 110% R 394.00 3 845 tons R 1.5 million

Monk Fish R 100.00 80% of

Namibian levy

R 320.00 7 400 tons R 2.4 million

Ribbon Fish

R 12.00 108% R 25.00 3 500 tons R 87 000

Snoek

R 14.00 107% R 29.00 5 200 tons R 151 000

Horse mackerel1

R 12.00 0% R 12.00 19 000 tons R 228 000

Squid2

R 291.00 10% R 320.00 350 tons R 112 000

Total revenue from by-catch rounded to nearest R100 000 R 4.5 million 1As Horse mackerel is not being fully utilised even though it is also a directed catch species, there are subsequently no proposed increases in charges. 2See recommendation on the Squid Fishery.

Hake Longline Liability:

Rights holder.

Incidence:

The incidence of the charge may take one of the following forms:


¶ The rights holder, if not part of a vertically integrated company, can usually shift the burden of


¶ Boat owners can shift at least part of the burden onto the marketing company.

Ability-to-pay

Compared to similar products in the hake trawl fisheries, longline hake commands a relatively high value.

A large portion is exported which means that, if the burden is shifted - at least partly - to the marketing

company, this industry gets advantage from a declining R/$ exchange and increasing world prices. The

increasing terms of trade also hold for the domestic market as local prices are bid up in response to world

prices.


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For hake:

Due to the high value and positive terms of trade it is recommended that the charge be increased in

accordance with that of the Deep-sea Hake Fishery, namely, an increase from R115 per ton to R272 per ton. With a TAC of 10 500 tons, the expected revenue is in the region of R2.9 million.

For kingklip by-catch:

The recommended charge for kingklip by-catch is in accordance with that in the Hake Trawl fisheries. A

charge of R394 per ton for an approximated 300 tons of kingklip by-catch is expected to generate about

R118 000.

Hake Handline Liability:

Rights holder.

Incidence:

The rights holder is usually the boat owner. It is likely that the burden of the charge will lie with the rights

holder.

Ability to pay:

Most hake handline rights holders and boat owners operate in the micro or small scale of part of the

fishery (see CLASSIFICATION: ESS SURVEY). Their ability to shift the burden of the charge is limited.

These characteristics indicate that the ability-to-pay criterion, being an equity one, necessitates that a

lower charge be levied in this fishery. However, it is imperative that serious attention be given to the

racial distribution of rights in this fishery.


Based on a limited ability to pay, a 10% increase in the basic charge is recommended. That is an

increase from R115 per ton to R126 per ton. For a TAC of 5 500 tons the revenue raised is R693 000.

The Pelagic Fishery

Liability:

Rights holder.

Incidence:

¶ Small boat owners carry most of the burden.

¶ The vertically integrated companies operating in this fishery can also, to some extent, shift the burden

to the consumer.


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The processed product from this fishery is largely sold (85%) in the domestic market to poorer people as

a relatively low cost protein source – it is not a table fish. It is, therefore, undesirable to shift an increased

burden to the consumer.

Ability-to-pay:

Most of the product from the pelagic fishery is consumed in the domestic market either as processed

edible fish products or for industrial use. This fishery cannot easily take advantage of the increasing

terms of trade in the world fisheries market and the favourable exchange rate for exporters.


The current charge per ton for human consumption is R31.40 and R8.20 for industrial use. The Namibian

charge for quota allocated to Namibian boats is R110 per ton for human consumption and R27.50 per ton

for industrial use. A 250% levy increase for consumptive catch and a 235% increase in non-consumptive

catch are required to create equivalence with Namibia. Based on the fact that catches are highly variable

– often not dependent on TAC – and that the processed consumptive product is still a form of low cost

protein, these charge increases are inappropriate, even at the 80% level. A percentage increase, more in

line with that in the other TAC fisheries, is suggested. Recommended increases are: pilchard directed to R64 per ton and to R15.40 per ton for industrial use. The revenue from the pilchard directed TAC

of 150 000 tons is R9.6 million and that for industrial use, with a total tonnage of 340 000 tons, is R5.2 million. A total income of about R14.8 million would be collected from the TAC.

West Coast Rock Lobster Liability:

Rights holder.

Incidence: The burden of the charge lies either with the boat owner or the marketing company, depending on the

ability of the boat owner to shift the burden.

Ability to pay:

Rock lobster commands a high price in the international market. As a large proportion of the product is

exported, the marketing companies can take almost full advantage of the increasing terms of trade in the

international seafood market as well as a favourable exchange rate. A declining R/$ value makes

exported products more valuable in local currency.


The charge on tails is a formulaic adjustment based on the whole mass charge. By applying a consistent

80% of the Namibian rate, the charge is recommended to increase to R4 000 per ton of whole mass.

The expected revenue collection on a TAC of 2 151 tons is R8.6 million.


296

South Coast Rock Lobster

Liability:

Rights holder.

Incidence:

If the rights holder is not the boat owner, the burden of the charge is probably shifted to the boat owner.

As the product is almost exclusively exported, the incidence of the charge is with the domestic company

that harvests (boat owner) and/or markets the resource.

Ability to pay:

The ability-to-pay principle is similar to that in the West Coast Rock Lobster fishery, namely, a high

international price and favourable terms of trade. The fact that quota sizes and personal profits are

smaller in the West Coast Rock Lobster Fishery is offset by the capital intensive and more costly

operations in the South Coast Rock Lobster fishery. This is not withstanding the fact that South Coast

Rock Lobster is a different species of rock lobster.


The current charge on South Coast Rock Lobster is R2 250 per ton of whole mass. The charge on tails is

a formulaic adjustment based on the whole mass charge. The Namibian charge is R5 000 per ton of

whole mass for a Namibian vessel. Applying a consistent 80% of the Namibian rate, the charge is

recommended to increase to R4 000 per ton of whole mass. For a TAC of 784 tons, the expected

minimum revenue collected is about R3.1 million.

Abalone

Liability:

Rights holder.

Incidence: Because the product is largely exported, the incidence of the charge lies with the rights holder or the

processing company.

Ability to pay:

The value of abalone products is high and the cost of harvesting is low (Pulfrich, ESS report, indicates

that the average profit is between R140 and R165 per kilogram). Companies harvesting, processing and

marketing abalone can take advantage of the favourable terms of trade in the world seafood market. In

addition, they also gain through a beneficial exchange rate to exporters.


297


Based on a strong ability to pay it is recommended that the abalone charge be increased in accordance

with that of South Coast Rock Lobster, namely by 78%. That is, an increase from R4 800 per ton to R8 500 per ton. The expected revenue collection from the TAC of 431 tons is R3.7 million.

The Effort-Based Fisheries

Because catching capacity is strictly regulated, the effort-based fisheries are on the whole more risky than

the TAC ones. As a precautionary measure, it is suggested that an across-the-board 10% increase in

levies is implemented. The table 2 below illustrates proposed charges and expected revenue collections.

Table 2: Effort-based fishery charges

Current

charge (per ton)

% increase Recommended

Charge (per ton)

Expected catch Revenue

Shark Longline

R 100 10% R 110 156 tons R 17 160

Tuna baitboat

R 100 10% R 110 6 500 ton R 715 000

Squid

R 291 10% R 320 6 000 tons R 1 920 000

Prawn trawl

R 115 10% R 126 400 tons R 50 400

Total revenue from effort-based fisheries rounded to the nearest R 100 000 R 2.7 million SUMMARY OF PROPOSED CHARGES FOR 2002

These recommendations are based on equalising the rates to those of Namibia and from here they

should not be increased beyond the South African rate of inflation in the future, unless under exceptional

circumstances. It is further suggested that the increased revenue be applied to improve the service

provision by MCM to the fishing industry – this would include establishing economic research. The table

3 below indicates the proposed charges for the year 2002.


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Table 3. Proposed charges for 2001/2002.

2000

(R/ton) 2001

(R/ton)

% Change from 2000

to 2001

2002 (R/ton)

% Change from 2001

to 2002

Expected revenue for

2002 TRAWL Deep-sea hake 33 115 248% 272 137% R 37.8 million Inshore hake 33 115 248% 240 109% R 2.5 million Inshore sole 100.2 185 85% 388 110% R 0.3 million

Sub-total: 50% R 40.6 million BY-CATCH Kingklip* 97 187.5 93% 394 110% R 1.5 million Monk Fish 20 100 400% 320 220% R 2.4 million Ribbon Fish 10.5 12 14% 25 108% R 0.09 million Snoek 14 14 0% 29 107% R 0.15 million Horse mackerel 12 12 0% 12 0% R 0.23 million Squid 291 291 0% 320 10% R 0.11 million

Sub-total: 6% R4.5 million HAKE LINE Hake longline 48 115 140% 272 137% R 2.9 million Hake handline 115 126 10% R 0.7 million

Sub-total: 4% R 3.6 million PELAGIC Pilchard 20.8 31.2 50% 64 105% R 9.6 million Industrial 8.2 8.2 0% 15.4 89% R 5.2 million

Sub-total: 18% R 14.8 million OTHERS SCRL1 454 2250 396% 4000 78% R 3.1 million WCRL2 711 3000 322% 4000 33% R 8.6 million Abalone 1305 4800 268% 8500 77% R 3.7 million

Sub-total: 19% R 15.4 million EFFORT BASED Squid 291 291 0% 320 10% R 1.9 million Shark longline 0 100 110 10% R 0.02 million Tuna baitboat 0 100 110 10% R 0.7 million Prawn trawl 95 115 21% 126 10% R 0.05 million

Sub-total: 3% R 2.7 million TOTAL REVENUE COLLECTED R 81.6 million

* Kingklip by-catch is the total for both trawl and hake longline.1 South Coast Rock Lobster (whole mass). 2 West Coast Rock Lobster (whole mass).

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 10. Future Management

299

10. FUTURE MANAGEMENT: THE NEED FOR INFORMATION

The general policy change of MCM from a “resource centred” approach to marine resource management

to a “people centred” one, has had huge implications, and management policies surrounding the use of

the coast, including utilisation of living marine resources in South Africa can best be described as in a

state of flux. The development of fishery specific management policies continues to be constrained by a

lack of information. This has severely hampered the ability of the Department to implement processes to

achieve the goals of the MLRA, specifically the restructuring of the fishing industry to address historical

imbalances and to achieve equity within all branches of the fishing industry.

The ESS was initially designed to provide a first realistic overview of the main commercial fishing sectors

and was envisaged to play a role in the development of a sound policy framework and the revised

process for the allocation of rights in 2002. The lack of clear policy guidelines meant that the terms of

reference for this study were of necessity, based on the anticipated policy and management requirements

of the Department, particularly those relevant to the 2001/2 rights allocation process.

Not all key questions were addressed as first envisaged, and in consultation with the Department, the

approach to specific issues was modified. For example, the idea of a Minimum Viable Quota per vessel

category was found to be too simplistic a concept to implement for many fisheries. Due to the late

commencement of the study, and legal opinion on the type of information presented to the allocation

teams, only the industry overview documents (ESS Volume 2) were forwarded for inclusion in the

allocation process. The detailed analysis and interpretation of the survey data (ESS Volume 1) was

subsequently presented to the Department of Marine and Coastal Management and the industry in a

series of workshops, and feedback was included in the final analysis.

One of the key results of the ESS survey was that it highlighted the almost complete lack of prior

information on the size and shape of the South African fishing fleets. The fact that the ESS survey

database, which contains a vast amount of recent information, provides a very useful foundation upon

which to build a future data gathering and management system was discussed at length in the feedback

workshops. Such a system (Fisheries Information System – FIS) should be developed as a matter of

urgency and must be able to provide current information and track changes in the different fishing

sectors. Since the ESS survey data and data captured from the rights applications are already dating,

this needs immediate attention. The short term requirements of the FIS would be to support the

development of the new policy framework, and the monitoring and management of the four-year interim

rights period, with a view to the successful implementation of long term rights. Given that the new policy

will have to include a closer working relationship with the fishing industry, the FIS can play a valuable role

in providing a platform for information flow between the Department and the different fishery sectors.

A suggested outline for creating such a baseline FIS is set out below. A crucial aspect of such a system

will be to design a flexible framework able to be easily developed and expanded as the new management

systems are put in place and further data requirements are crystalised.

An Economic and Sectoral Study of the South African Fishing Industry: Volume 1 10. Future Management

300

FIS Coordinator

Database Manager

Data typists

FIS Database

INITIAL INCOMING DATA: INITIAL INCOMING DATA: -- RIGHTS APPLICATIONSRIGHTS APPLICATIONS-- VERIFICATION UNIT VERIFICATION UNIT -- ESS DATAESS DATA-- PERMIT INFORMATIONPERMIT INFORMATION

FISHERIES MANAGEMENT FISHERIES MANAGEMENT SUMMARIES & DATA SUMMARIES & DATA REQUESTS FROM MCMREQUESTS FROM MCMFISHING INDUSTRY,FISHING INDUSTRY,MEDIA &MEDIA &OTHER LIASIONSOTHER LIASIONS

F isheriesF isheriesI nformationI nformationU nitU nit

Database Administ.

FUTURE DATA INPUTS: FUTURE DATA INPUTS: -- VMS DATAVMS DATA-- INDUSTRY REPORTING INDUSTRY REPORTING -- INDUSTRY SURVEYSINDUSTRY SURVEYS-- INPUTS FROM INPUTS FROM RESEARCH AND OTHER RESEARCH AND OTHER MCM DATABASESMCM DATABASES

Figure 10.1. Schematic outline of the proposed Fisheries Information Unit.

prepared for: marine and coastal management department of environmental affairs and tourism private...

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