productivity analysis of brazilian seaports

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Productivity analysis of BrazilianseaportsCarlos Pestana Barros a , J. Augusto Felício a & Renato LeiteFernandes ba Instituto Superior de Economia e Gestão, Technical University ofLisbon , Rua Miguel Lupi 20, 1249-078 Lisboa , Portugalb Centro de Apoio a Sistemas Operativos, Brazilian Navy , Ilha deMocanguê S/n° Niterói-RJ, CEP 24040-300 , BrazilPublished online: 02 Aug 2012.

To cite this article: Carlos Pestana Barros , J. Augusto Felício & Renato Leite Fernandes (2012)Productivity analysis of Brazilian seaports, Maritime Policy & Management: The flagship journal ofinternational shipping and port research, 39:5, 503-523, DOI: 10.1080/03088839.2012.705033

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MARIT. POL. MGMT., SEPTEMBER 2012,VOL. 39, NO. 5, 503–523

Productivity analysis of Brazilian seaports

CARLOS PESTANA BARROS*y, J. AUGUSTO FELICIOy

and RENATO LEITE FERNANDESz

yInstituto Superior de Economia e Gestao, Technical University ofLisbon, Rua Miguel Lupi 20, 1249-078 Lisboa, PortugalzCentro de Apoio a Sistemas Operativos, Brazilian Navy,Ilha de Mocangue S/n� Niteroi-RJ, CEP 24040-300, Brazil

This paper analyses the productivity of Brazilian seaports over the period 2004–2010, using a Malmquist index with technological bias. During this period,Brazilian seaports, on average, became less productive with improvements inefficiency change and deterioration in technological change. Our results indicatethat the traditional growth accounting method, which assumes Hicks-neutraltechnological change, is not appropriate for analysing the productivity changes ofBrazilian seaports. Policy implications are derived.

1. Introduction

Seaport productivity is an important issue in contemporary economics due to theincreasing importance of international trade in the contemporary world. Twomethods have been used to analyse productivity and efficiency in seaports, theparametric techniques [1–8] and non-parametric methods of Data EnvelopmentAnalysis (DEA) [8–15]. The advantage of using non-parametric frontier techniques isthat they impose no a priori functional form on technology, nor any restrictiveassumptions regarding input remuneration. Furthermore, the frontier nature of thesetechniques permits us to identify a ‘benchmarking’ perspective. While several DEAmodels have been used in seaport research, this paper innovates in this context byanalysing Brazilian seaports for the first time, which have not been previouslyanalysed, and adopting a DEA model not previously used in seaports analysis, theMalmquist index with technological bias [16]. While the Malmquist index istraditionally used in seaport productivity studies [13], the Malmquist index withtechnological change is innovative in this context.

The motivations for this research are as follows. First, there is no publishedresearch that solely analyses Brazil seaports, despite their importance to LatinAmerican trade. The only analysis found that incorporates Brazil seaports focuses onthe container terminals of the Mercosur—the Southern Latin American commonmarket [17]. Second, there are some papers analysing seaport productivity with eitherthe Malmquist index [13] or the Luenberger indicator [18, 19]; however, these papersassume implicitly Hicks-neutral technological change, which may not be identified inthe data sample. Third, increasing competition in the seaports market related toglobalization and growing trade is in evidence in Brazil, obliging the ports to be more

*To whom correspondence should be addressed. E-mail: [email protected]

Maritime Policy & Management ISSN 0308–8839 print/ISSN 1464–5254 online � 2012 Taylor & Francishttp://www.tandfonline.com/TMPM

http://dx.doi.org/10.1080/03088839.2012.705033

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competitive in order to attract more traffic [20]. With maritime transportationaccounting for 95% of its exports, Brazil faces a major task to improve its seaports’efficiency and productivity. Fourth, there are concerns on Brazilian seaports tariffsand as seaports are local monopolies, productivity analysis is currently used toinvestigate the monopoly price regulation [21]. Fifth, there is also concern onBrazilian seaports delays on handling containerized exports and delays increase costsand results in inefficiency, which is analysed in the present research. Finally, theinvestigation of the technological bias permits a clear analysis of the causes ofproductivity change in Brazilian seaports.

This study is structured as follows. Section 2 presents the institutional setting ofBrazilian seaports. Section 3 presents a survey of the literature on the efficiency andproductivity of seaports. Section 4 explains the methodology. Section 5 presents thedata, followed by the results. Section 6 discusses our findings and concludingremarks.

2. Background

Brazil’s seaports are a strategic asset in the country’s development in the twenty-firstcentury. The seaports were privatized in 1869, and later nationalized in 1930s by themilitary dictatorship. A public company, Portobras was created for the purpose ofmanaging the seaports through concessions from 1930 to 1990. In 1993, thepublication of the Seaport Modernization Law established the present framework,overseen by a new public agency ANTAQ—the Agencia Nacional de TransportesAquaviarios (National Maritime Transport Agency), which was set up as theregulatory body in 2001 [22]. At present, the sector in Brazil comprises both publicinternational seaports and specialized seaports, such as those that belong to publicoil company, Petrobras (specialized terminal of Sao Sebastiao), and the porthandling the minerals of the company, Vale do Rio Doce (specialized terminal ofPonta da Madeira). However, the present study examines only the publicly ownedseaports displayed in Figure 1.

The Brazilian seaports presently face two major problems that are attractingmedia interest. First, the Brazilian seaports display slowness on the export process,which takes an average of 18 days for containerized exports, of which 14 days arespent solely on bureaucratic matters. Second, as a result, the cost to export acontainer from Brazil is US$895, compared with India, where the cost is US$425.Moreover, the knock-on effect of this cost is to decrease the competitiveness ofBrazilian products in the international market. Therefore, these two complementaryproblems (costs and delays) are an indication of inefficiency.

The government aims presently to attract private investment in premises [23] withthe goal of establishing infrastructural connected networks linking the seaports,railways and roads, improving the accessibility of ports and decreasing the heavyweight of bureaucracy and the cost of seaports in the export process. However, thesuccess of this aim is uncertain due to the public and bureaucratic nature of Brazilianseaports. Some characteristics of the seaport sample analysed are presented in Table 1.

Seaports are local monopolies, so prices of these companies should be regulatedaccording to costs. However, if the company is allowed to allocate all costs on prices,it has an incentive to behave inefficiently. Therefore to solve this problem inducingmonopolies to behave more efficiently, parametric and non-parametric methods

504 C. P. Barros et al.

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have been used in tariff regulation [21]. The results of these studies have been used intariff setting process so that inefficient seaports are compelled to operate efficiently.

3. Literature survey

While there is extensive literature on benchmarking, applied to a wide diversity ofeconomic fields, the scarcity with regard to Latin American seaports bears testimonyto the fact that that this is a relatively under-researched topic. Investigation of theliterature found that existing research embraces the three scientific methods ofquantitative efficiency analysis, namely ratio analysis, the econometric frontierand DEA.

Song and Cullinane [24] apply ratio analysis to Asian container separates. Amongthe papers using DEA are Roll and Hayuth [9], who present a theoretical expositionand propose the use of cross-sectional data from financial reports in order to renderthe DEA approach operational; however, they did not use actual data but ratherhypothetical data. Tongzon [11] uses cross-sectional data from 1996 covering 4Australian ports and 12 other ports around the world; Martinez et al. [10] estimatethe efficiency of Spanish ports; Barros [12] analyses the technical and allocativeefficiency of Portuguese seaports and Barros [13] evaluates the total productivity

Figure 1. Location of the seaports analysed in the paper.

Productivity analysis of Brazilian seaports 505

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change in the same seaports, using two stages. In the first stage, a Malmquist index isestimated, followed by Tobit regression model estimation in the second stage. Barrosand Athanassiou [14] compare the efficiency of Portuguese and Greek seaports,applying DEA. Finally, Park and De [26] analyse the efficiency of 11 Koreanseaports with DEA.

Papers using the econometric frontier include Banos et al. [27], who apply atranslog function to Spanish ports. Liu [1] compares the efficiency of public andprivate ownership in Britain with a translog function. Coto Millan et al. [2] estimatea translog cost frontier for Spanish ports. Estache et al. [3] estimate a Cobb–Douglasand a translog production frontier for Mexican seaports. Cullinane et al. [4] estimatea Cobb–Douglas production function for major Asian container terminals.Cullinane and Song [5] estimate a production function for Korean containerterminals. Venus et al. [28] focus on organizational growth.

The variables used in the literature cited are listed in Table 2.The general conclusions that emerge from this body of research is first that almost

all papers focus on efficiency and only a short number of papers focus on seaport

Table 1. Characteristics of the Brazilian seaports analysed (2010).

Seaport

Containerized

cargo

(tons)

Breakbulk

cargo

(tons)

Dry

bulk

(tons)

Liquid

bulk

(tons)

Total

tons

Large seaports

1 Itaqui 55 257.00 53 428.00 98 548 694.00 6 529 560.00 105 186 939.00

2 Itaguaı (Sepetiba) 3 597 003.00 558 663.00 80 732 666.00 0 84 888 332.00

3 Santos 29474 858.00 4 042 963.00 35 419 219.00 14 379 147.00 83 316 187.00

Average seaports

4 Sao Sebastiao 0 416 970.00 549 103.00 47 413 053.00 48 379 126.00

5 Paranagua 5 449 554.00 4 349 959.00 20 246 890.00 3 971 813.00 34 018 216.00

6 Aratu 0 0 4 108 981.00 27 492 056.00 31 601 037.00

7 Rio Grande 5 520 013.00 1 275 266.00 13 917 770.00 3 839 167.00 24 552 216.00

8 Belem 396 378.00 695 322.00 18 311 944.00 2 050 668.00 21 454 312.00

9 Vila de Conde 209 468.00 2 300 758.00 15 664 343.00 2 297 183.00 20 471 752.00

10 Rio de Janeiro 4 787 345.00 1 655 461.00 1 985 727.00 11 375 049.00 19 803 582.00

11 Sao Francisco do Sul 2 389 813.00 1 066 076.00 4 181 015.00 9 346 877.00 16 983 781.00

Small seaports

12 Manaus 0 1 045 856.00 4 467 483.00 7 752 411.00 13 265 750.00

13 Suape 3 688 423.00 261 724.00 637 093.00 4 067 802.00 8 655 042.00

14 Itajai 4 565 508.00 2 206 784.00 0 49 048.00 6 821 340.00

15 Salvador 2 596 135.00 523 925.00 2 713 684.00 3515.00 5 837 259.00

16 Maceio 88 109.00 352 392.00 2 400 074.00 1 939 006.00 4 779 581.00

17 Fortaleza 722 178.00 119 212.00 1 101 239.00 1 812 883.00 3 755 512.00

18 Natal 143 296.00 76 283.00 100 421.00 2 912 143.00 3 232 143.00

19 Recife 0 321 080.00 1 588 439.00 75 614.00 1 985 133.00

20 Imbituba 303 268.00 104 552.00 1 332 454.00 122 228.00 1 862 502.00

21 Ilheus 0 1 409 099.00 113 678.00 0 1 522 777.00

22 Cabedelo 0 64 298.00 292 285.00 531 693.00 888 276.00

23 Vitoria 3 135 192.50 2 434 860.50 2 394 603.90 222 018.20 8 186 675.10

Mean 2 918 339.00 1 101 519.00 13 513 383.00 6 442 736.00 23 975 977.00

Median 396 378.00 558 663.00 2 400 074.00 2 297 183.00 5 652 298.00

Stdev 6 129 226.00 1 236 070.00 25 718 632.00 10 970 588.00 44 054 516.00

Source: Ref. [25].


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Table

2.Literature

review

.

Papers

Method

Units

Inputs

Outputs

Rolland

Hayuth

[9]

DEA–CCR

model

Hypotheticalnumerical

example

of20ports

Manpower,capital,cargouniform

ity

Cargothroughput,level

service,

con-

sumer

satisfaction,ship

calls

Martinez

Budria

etal.[10]

DEA–BCC

model

26Spanishports,

1993–1997

Labourexpenditure,depreciation

charges,other

expenditure

Totalcargomoved

throughdocks,

revenueobtained

from

rentofport

facilities

Tongzon[11]

DEA–CCR

additivemodel

4Australianand12other

internationalportsfor

1996

Number

ofcranes,number

ofcontainer

berths,number

oftugs,term

inal

area,delaytime,

labour

Cargothroughput;ship

workingrate

Barros[12]

DEA-allocativeand

TechnicalEfficiency

FivePortugueseseaports,

1999–2000

Number

ofem

ployees,bookvalueof

assets

Outputs:Ships,movem

entoffreight,

gross

tonnage,

market

share,break-

bulk

cargo,containerized

cargo,Ro–

Rotraffic,dry

bulk,liquid

bulk,net

incomePrices:Price

oflabourmea-

suredbysalaries

andbenefitsdivided

bythenumber

ofem

ployees;price

of

capitalmeasuredbyexpenditure

on

equipmentandpremises

divided

by

thebookvalueofphysicalassets

Barros[13]

DEA–Malm

quistindex

andaTobitmodel

10Portugueseseaports,

1990–2000

Number

ofem

ployeesandbookvalue

ofassets

Ships,movem

entoffreight,break-bulk

cargo,containerized

freight,solid

bulk,liquid

bulk

Park

andDe[26]

DEA–CCR

andBCC

11Koreanseaportsfor

theyear1999

Berthingcapacity

(number

ofships)

andcargohandling(tons)

Cargothroughputs,number

ofship

calls,revenueandconsumer

satisfaction

Barrosand

Athanassiou[14]

DEA–CCR

andBCC

TwoGreek

andfour

Portugueseseaports,

1998–2000

Labourandcapital

Number

ofships,movem

entoffreight,

cargohandled,container

handled

Liu

[1]

Translogproduction

function

28British

portauthorities,

1983–1990

Movem

entoffreight(tons)

Turnover

Coto

Millan

etal.[2]

Translogcost

model

27SpanishPorts,

1985–1989

Cargohandled(tons)

Aggregate

port

output(includes

total

goodsmoved

intheportin

thousand

(continued

)


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Table

2.Continued.

Papers

Method

Units

Inputs

Outputs

tons,thepassenger

embarked

and

disem

barked

andthenumber

of

vehicleswithpassengers)

Estacheet

al.[3]

TranslogandCobb–Douglas

productionfrontier

model

14Mexicanports

1996–1999.

Containershandled(tons)

Volumeofmerchandizehandled

Cullinaneet

al.[4]

Stochastic

Cobb–Douglas

productionfrontier:half-

norm

al,exponential,and

truncatedmodels

15Asiancontainer

ports

observed

in10years,

1989–1998.

Number

ofem

ployees

Annualcontainer

throughputin

TEUs

Cullinaneand

Song[5]

Stochastic

Cobb–Douglas

productionfrontier:half-

norm

al,exponential,and

truncatedmodels

Fivecontainer

term

inals,

KoreanandUK,dif-

ferentyearofobserva-

tions(65observations)

Fixed

capitalin

euro(1998¼100)

Turnover

derived

from

theprovisionof

container

term

inalservices,but

excludingproperty

sales

Cullinaneet

al.[15]DEA–CCR,DEA–BCC

and

DEA–FHD

models

57internationalcontainer

seaportsin

1999

Terminallength,term

inalarea,quay-

sidegantry,yard

gantryandstraddle

carries

Container

throughput

Tongzonand

Heng[29]

Stochastic

Cobb–Douglas

model

andacompetitive-

nessregression.Analysis

restricted

tothefrontier

equation

25internationalcontainer

seaports

Terminalquaylength,number

ofquay

cranes,port

size

measuredbya

dummywhichisoneforportswhich

exceed

onemillionTEU

andprivate

participationin

theport

Container

throughput

Barros[7]

SFA–Stochastic

translogcost

frontier

10Portugueseseaports,

1990–2000

Price

oflabour,price

ofcapital,ships,

cargo,trend.

Totalcost

(endogenousvariable

ofthe

cost

function)

Barrosand

Peypoch

[19]

Luenberger

productivity

indicator

34ItalianandPortuguese

seaports,2002–2004

Totaloperationalcost,personnel

and

investm

ent

Totalcontainers,sales,liquid

bulk,

solidbulk

andships

Cullinaneet

al.[8]

Stochastic

Cobb–Douglas

andDEA

models

28Internationalcontainer

seaports,observed

from

1983to

1990.

Container

throughput

Terminallength,term

inalarea,quay-

sidegantry,yard

gantryandstraddle

carries

Managi[18]

Luenberger

productivity,

indicator,unbalanced

panel

data

1966–2005

11Japaneseshipping

firm

sCapital,labour,expenditure

Revenue

Sim

oes

and

Marques

[30]

DEA

congestioninefficiency

41Europeanseaports,

2005

Operationalexpenses,capitalexpenses

Generalcargo,Ro–Rocargo,dry

bulk

cargo,liquid

bulk

cargo,passengers


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Sim

oes

etal.[31]

DEA–CRSandbootstrapped

VRS

33Iberianseaports,

2005and2006

Quays,staff,cranes

Liquid

bulk,solidbulk,generalcargo,

Ro–Rocargo,TEU,passengers

Sim

oes

and

Marques

[32]

FDH;order-m

DEA

efficiency

41Europeanseaports,

2005

Totaloperatingexpenses

Container,dry

bulk

cargo,liquid

bulk

cargo,passengers

Oliveira

and

Cariou[33]

DEA–CCR

andDEA–BCC

122ironore

andcoal

ports,2005

Draught,length,stockpileandloading

rates

Throughput

Barroset

al.[34]

Malm

quistandLuenberger

DEA

models

48observationsfrom

16

Middle

Easternand

East

Africanseaports

fortheperiod

2005–2007

Employees,totalcostsandcranes

Throughputandships

Bichou[35]

Chain

DEA

model,CCR

and

BCC

models

10container

term

inalsfor

theperiod2002–2008

Quaysite

inputs

(draft,LOA;length

over

allforvesselto

berth,Crane

index,term

inalarea,trucksand

vehicles)

Yard

Site(yard

stacking

index,yearfee,

storagetime,

trucks

andvehicles)

Terminal(theabove

inputs)

Quaysite

(crane/movehour).Yard

site

(cargodwelltime)

Terminal

(throughputin

TEU)

Bergantinoand

Musso[36]

DEA

infirststageandSFA

insecondstage

18SouthernEuropean

ports,locatedin

the

Mediterranean.

1995–2007.

Dim

ensionofquay,number

ofterm

i-nals,Areaoftheport

forhandling,

handlingequipment,GDPper

person,em

ploymentrate,total

movem

ents,portsize,involvem

entin

container

traffic

Totalmovem

ents

Barros[37]

Luenberger

productivity

indicator

23Africanseaportsfrom

Nigeria,Angola

and

Mozambique,

2004–2010

Quaylength

Seaport

areaLabour

TEU

20-footequivalentunit,Dry

bulk,

Liquid

bulk,andDelaysin

handling

ship

cargo

MeddaandLiu

[38]SFA–Stochastic

frontier

model

165Worldcontainer

term

inals,2006

Berth

depth;quaylength;yard

space;

number

ofgantrycranes

spacing;

term

inaltype(container

vs.multi-

purpose);operationtype(globalvs.

local)

TEU

Source:

Author’selaboration.


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productivity [13, 18, 19]. Second, the variables used in the analysis are small based onthe available data and seaport activity characteristics. In particular, fixed inputs(number of quays or quay length, terminal area) are traditionally used alongsidevariable inputs. Third, there are some seaport characteristics that increase efficiency,such as seaport dimension and location [1]. Moreover, small ports are more efficientthan larger ones [2, 11]. Fourth, other characteristics decrease efficiency, such ascapital intensity that has no significant impact and there is no significant advantagein private ownership [1]. Additional, seaport autonomy does not make any difference[2, 11]. Furthermore, there is over-capitalization in Spanish seaports [27]. Moreover,action intended to improve the rate of total productivity growth is to be welcomed,as long as it is focused on capital accumulation and the rate of innovation to shift thefrontier of technology, i.e. technical change [13]. Finally, economies of scale and non-neutral progress contribute to decrease seaport costs, while pure technical changecontributes to increase costs. Therefore, there are contradictory results as well asconfirmatory results [39].

Comparing the above-mentioned research with that undertaken in other fields,this is one of the main fields in economics in which frontier models have beenapplied, with such diverse methods that range from DEA to econometrics, displayingan openness to different approaches that is not so apparent in other fields. However,there are too many papers that replicate previous research, while offering scantmethodological improvements. Therefore, the present research innovates in thiscontext.

4. Methodology

This paper estimates the efficiency and total factor productivity change for Brazilianseaports, using DEA estimating a productivity Malmquist index [13]. The Malmquistindex (MALM) is traditional decomposed in technical efficiency change (EFFCH)and technological change (TECH). The technical efficiency change (EFFCH) isfurther decomposed into pure efficiency change (PURE) and scale efficiency change(SCALE), Malmquist [40]. Fare and Grosskopf [16] propose the decomposition ofthe technical efficiency change in its components: OBTECH-output-biased techno-logical progress, IBTECH-input-biased technological progress and MATECH—magnitude of technological progress. Therefore, the aim of this research is todecompose the technological change in the Malmquist index. The results of theMalmquist index are invariably relative to the decomposition of their components.The decomposition of TECH in its constituents permits to identify the type ofchanges observed in OBTECH, IBTECH and MATECH. In traditional microeco-nomics [41], the technological progress with respect to outputs is Hicks-neutral if themarginal rate of transformation between two outputs is constant, holding the mix ofoutputs constant. Hicks-neutral technological progress is illustrated by a parallelshift of the production possibility set. In the real world, the shift may not be paralleland the change in the production frontier will not be homothetic. In this case therewill be some ports shifting up the production possibility set while others will shiftdown. Therefore, this decomposition is important to open up the black box of theproductivity. The input-biased technological progress between the inputs is analysedholding the input mix constant and the output-biased technological progress betweenthe two outputs is analysed holding output mix constant. More explanations arepresented below.


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The reciprocal of the Shephard [42] input distance function serves as a measure ofthe Farrell [43] input efficiency. Linking input efficiency indexes across time allowsus to estimate the Malmquist productivity index. This index can be decomposed intochange in resource use due to efficiency change and change in resource use due totechnological change. Furthermore, this paper adopts the approach of Fare andGrosskopf [16] and decomposes technological change into an index of output-biasedtechnological change, an index of input-biased technological change and an index ofthe magnitude of technological change, see the appendix for details on themethodology.

In the following section, the Malmquist input-based productivity index for theseaports are estimated and the bias in the use of inputs and outputs is estimated.

5. Data and results

A balanced panel comprising 25 Brazilian seaports during seven years from 2004 to2010 is used. The data set was obtained from several sources: first, data obtainedfrom the website of ANTAQ, second, directly from ports, and, finally, from the bookTerminais Marıtimos e Portos Brasileiros [25]. The proportional rule required byDEA is achieved by the number of observations higher than three times the sum ofinputs and outputs: 17543� (3þ 3) [44, 45].

The variables are presented in Table 3. The outputs used are TEUS—20-footequivalent unit (a measure used for capacity in container transportation), dry andliquid bulks. The inputs are quay length, the number of cranes and the number ofemployees.

5.1. Empirical resultsThe Malmquist indices of the Brazil seaports are presented in Table 4. Values ofMalmquist index, efficiency change, technological change, output-biased technolog-ical progress, input-biased technological progress and magnitude of technologicalprogress less than one indicate productivity gains, increases in efficiency ortechnological progress. Values of Malmquist index, efficiency change, technologicalchange, output-biased technological progress, input-biased technological progress,

Table 3. Descriptive statistics of the data.

Variable Description Minimum Maximum MeanStandarddeviation

OutputsTEU 20-foot equivalent unit,

a measure used forcapacity in containertransportation

0.00 2 677 839.00 253 458.63 491 495.69

Dry bulk Dry bulk in tonsLiquid bulk Liquid bulk in tons 0.00 98 548 694.00 12 011 532.90 22 450 770.05

InputsQuay length Quay length in metres 432.00 11 042.00 2 081.74 2 386.98Cranes Number of cranes 1 62 8 11Labour Number of workers 32 050.00 7 700 000.00 950 966.76 1 637 858.68


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and magnitude of technological progress greater than one indicate productivity loss,decreases in efficiency or technological regression.

In Table 4, a MALM51 signifies productivity improvement and a MALM41signifies productivity degradation. It can be seen that the total productivity changescore (the Malmquist index presented in column 1) is on average 0.992 (MALM41)signifying that there was a slight improvement in seaport productivity in the period.The improvement is 1�0.992¼ 0.008. However, there are seaports displayingimprovements in productivity, namely Ilheus, Imbituba, Itaguaı, Paranagua, RioGrande, Salvador, Santos and Suape. These comprise large seaports (Itaguaı andSantos), medium seaports (Rio Grande and Paranagua) and small seaports (Ilheus,Imbituba and Salvador).

The change in the technical efficiency score EFFCH (column 2) presents a meanvalue of 0.941, signifying that pure and scale efficiency improved during the periodfor many seaports. The only seaports that present a decrease in EFFCH are Suape,Manaus, Natal and Maceio.

Table 4. Malmquist Index average for Brazilian seaports for 2004–2010 ranked according theirproductivity.

Seaport MALM EFFCH TECH OBTECH IBTECH MATECH

Seaports with productivity improvementIlheus 0.696 0.852 1 0.694 1.547 0.695Suape 0.723 1.175 0.753 0.961 1.523 0.725Imbituba 0.764 0.858 0.887 1.246 1.214 1.107Santos 0.951 0.964 1 0.952 1.913 0.95Salvador 0.958 0.893 0.967 0.993 1.911 0.959Rio Grande 0.972 0.982 1 0.972 1.952 0.97Paranagua 0.981 0.942 1 0.988 1.931 0.989Itaguaı (Sepetiba) 0.997 0.931 1 0.997 1.923 0.998

Seaports without any productivity improvement neither productivity degradationAratu 1 0.972 1 1 1.971 1Itajai 1 0.961 1 1 1.962 1Itaqui 1 0.917 1 1 1.916 1Manaus 1 1.008 1 1 1.007 1Sao Sebastiao 1 0.951 1 1 1.952 1Vila de Conde 1 0.745 1 1 1.746 1

Seaports with a productivity degradationRio de Janeiro 1.018 0.959 1 1.018 1.977 1.018Vitoria 1.021 0.99 1.007 1.015 1.998 1.021Sao Francisco do Sul 1.044 0.982 1.044 1 1.941 1.044Natal 1.048 1.042 1 1.048 1.09 1.048Maceio 1.052 1.018 1.027 1.025 1.016 1.052Recife 1.093 0.955 1.096 0.996 1.867 1.093Cabedelo 1.098 0.81 1.312 0.84 1.457 1.098Belem 1.151 0.816 1.145 1.005 1.718 1.151Fortaleza 1.242 0.928 1.236 1.005 1.755 1.242Mean (arithmetic) 0.992 0.941 1.021 0.989 1.708 1.007Median 1.000 0.955 1.000 1.000 1.911 1.000Standard deviation 0.124 0.089 0.107 0.092 0.333 0.115

Notes: MALM¼EFFCH�TECH, TECH¼OBTECH� IBTECH�MATECH. Numbers may notmultiply because of rounding error.


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The change in technological score TECH (column 3) presents a mean value of1.021, signifying that there was degradation on TECH efficiency in the period, whichsignifies that investment was scarce in the period.

The breakdown of the MALM index into efficiency change and technologicalchange identifies several combinations of seaports. First, there are those seaportsthat combine MALM51, EFFCH51 and TECH51, which are the best Brazilianports in the period in terms of productivity improvement, since their productivitychange is due to simultaneous improvement of the two components (TECHand EFFCH). The sole ports in this group are the small ports of Imbituba in thesoutheast and the seaport of Salvador.

The second group of seaports combine MALM51 with EFFCH51 andTECH41, where the productivity improvement results from efficiency improvement,but is offset by a deterioration in TECH. This group includes the large seaports ofItaguaı (Sapetiba) and Santos, the medium seaports of Paranagua, Rio Grande andthe small seaports of Ilheus. The main cause of inefficiency of this group is that inputsstayed the same in the period signifying missing investment in Brazilian seaports.

Then there is the third group, with MALM41, EFFCH51 and TECH41,signifying that the increase in efficiency change was offset by regressions intechnological change that result in overall productivity regression. This groupincludes the large seaport of Itaqui, the medium seaports of Aratu, Belem, Recife,Rio de Janeiro, Sao Francisco do Sul and Sao Sebastiao and the small seaports ofCabedelo, Fortaleza, Itajai, Vila Conde and Vitoria.

Finally, the fourth group consists of the seaports that combine MALM41,EFFCH41 and TECH41, which includes only small seaports Maceio, Manaus andNatal. These are the most inefficient Brazilian seaports in the sample and itcomprises remotely located seaports.

Overall, there are almost all possible combinations between MALM and EFFCHand TECH in Brazilian seaports and the major cause of productivity regression istechnological regression.

The breakdown of the score for the change in technical efficiency into output-biased technological progress, input-biased technological progress and magnitude oftechnological progress shows mixed results, with almost all seaports displayingscores higher than one, signifying that Hicks-neutral technological change cannot beassumed for the seaports analysed.

Almost all seaports had OBTECH41, indicating technological regression in theproduction of outputs, while some seaports had OBTECH51, indicating techno-logical progress in the production of outputs. Based on the input-bias index, againalmost all seaports experienced technological regression in the use of inputs(IBTECH41) used to produce the 2004/2008 vector of outputs. However, based onthe magnitude of technological change, some seaports experienced technologicalimprovement (MATECH51), while some others experienced technological regres-sion driven by the magnitude of technological change. This result can be explainedby the isoquant for period 1 and the isoquant for period 2 (Figure 1). In period 1, aseaport produces on the isoquant at point A, and in period 2, the seaport produceson the isoquant at point B. The magnitude of technological change is given by theratio MATECH ¼ 0A=0A

0A=0C ¼ 0C=0A4 1, indicating technological regression. Clearly,this indicates a technological bias in the use of inputs.

Table 5 presents the inputs, which are quay length (x1), cranes (x2) and labour(x3). A majority of seaports experienced a labour-saving/quay length-using input


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bias and labour-saving/cranes-using input bias, signifying that there is labour savingsin the period that causes the improvement of technological change. In the firstcolumn of Table 5 it is verified that the ratio quay length/labour in period tþ 1 ishigher than in period t for 13 seaports which signify that as quay length is a fixedasset, the labour use decreases in the period for these seaports. The second columnpresents two seaports where the labour is used relative to quay length. The thirdcolumn presents the relation between cranes and labour and nine seaports presentcrane improvement relative to labour and in the fourth column none seaport increaselabour relative to cranes. Therefore, the conclusion relative this result is that therelabour saving and cranes using during the period. Despite this change the averageTECH is positive and display regression.

Table 6 shows the number of seaports that experienced a bias in the production ofthe relative outputs. Recall that the outputs are TEU (y1), dry bulk (y2) and liquidbulk (y3). Based on column 1, it is verified that the ratio TEU/liquid bulk increasesfor eight seaports and does not decrease for any seaport, signifying the increasing useof containers. In the second column no seaport changes TEU/liquid bulk decrease inthe period for any seaport. In the third column the ratio dry/liquid bulk increasesonly for two seaports and decreases also for two. Therefore, it is verified that amajority of seaports experienced bias in favour of producing TEU relative to dry andliquid bulk. Seven seaports experienced neutral technological change in theproduction of these two outputs (OBTECH¼ 1).

From this decomposition it emerges that labour saving and TEU use are the mainchanges observed in Brazilian seaports. Furthermore, despite the decrease in labourand increase in TEU the TECH has regression. As containers are, on average, morecostly to move than bulk shipments, perhaps this output change is a cause of TECHdeclines at the Brazilian seaports.

6. Discussion and conclusion

This paper estimates the Malmquist input-based index of total factor productivityfor 23 Brazilian seaports operating from 2004 to 2010. Productivity change ispartitioned first into an index of efficiency change and an index of technologicalchange. Then, the index of technological change is partitioned into output-biased

Table 5. Input-biased technological change.

Seaportsfor which

x1x3

� �tþ1

4x1x3

� �tx1x3

� �tþ1

5x1x3

� �tx2x3

� �tþ1

4x2x3

� �tx1x3

� �tþ1

5x2x3

� �t

IBTECH41 13 (x1-saving) 2 (x1-using) 9 (x2-saving) 0 (x2-using)IBTECH51 0 (x1-using) 0 (x1-saving) 0 (x2-using) 0 (x2-saving)Neutral 0 0

Table 6. Output-biased technological change.

Seaportsfor which

y1y3

� �tþ1

4y1y3

� �ty1y3

� �tþ1

5y1y3

� �ty2y3

� �tþ1

4y2y3

� �ty2y3

� �tþ1

5y2y3

� �t

OBTECH41 8 (y1-producing) 0 (y1-producing) 2 (y2-producing) 2 (y2-producing)OBTECH51 0 (y1-producing) 0 (y1-producing) 2 (y2-producing) 3 (y2-producing)Neutral 6 0


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technological change, input-biased technological change and the magnitude oftechnological change. It is found that on the basis of the Malmquist index theaverage productivity (0.992) increases slightly in the period for the sample ofBrazilian seaports analysed. This change (1–0.992¼ 0.008) is due to an averageincrease in efficiency change (0.941) that does not compensate the decrease intechnological change (1.021). However, among these average values there areseaports that display productivity improvement while others display productivitydeterioration. Furthermore and as a distinctive characteristic of this paper,IBTECH, OBTECH and MATECH show that there is no Hicks-neutral technolog-ical change in Brazilian seaports. From this result, the policy implication is thatBrazilian seaports need to improve their productivity and this improvement shouldfocus on TECH improvement. TECH improvement signifies investment and newprocedures and methods that increase technological change. The EFFCH should bemaintained and nurtured in order for Brazilian seaports to compete in world trade.EFFCH improvement is based on managerial practices and scale effects. The resultsfrom IBTECH, OBTECH and MATECH signify that there is no common policy onthe Brazilian seaports and that each seaport is driven by its local context. In thiscontext, we may observe some seaports decreasing employment while others increaseit. This heterogeneous behaviour signifies that a common policy is needed to improvethe productivity of all seaports together.

The use of this methodology is in line with the resource-based theory of Barney[46] and Teece et al. [47], which justifies that seaports are heterogeneous in terms ofthe resources and capabilities on which they base their managerial practices, and thusheterogeneity is expected to interfere with efficiency. Given that this methodology isused for the first time in this area, it is difficult to have a direct comparison betweenthe efficiency results of this study and other related studies in the area. However, it ispossible to discuss whether related studies have converged to similar conclusions interms of the impact of the selected variables on efficiency. DEA does not identify thecauses of efficiency, but identifies the inefficient units and therefore permits to derivesome conclusions. Three reasons are identified as causes of efficiency, namely seaportremoteness, lack of commensurate investment and inadequate managerial practices.Some small remote seaports are inefficient because their location attracts scarcetraffic and therefore do not permit the efficient use of the resources available. Otherseaports with positive TECH are inefficient because of lack of investment along theperiod. Seaports regularly need some investment in new tools and new proceduresand without it their performance is negatively affected. Finally, other seaports areinefficient due to a positive TECH. In this condition, policy implications have to bedistinct among the seaports. For those remote seaports, cost control is the mostpromising policy since the resources have to be tailored by the traffic available. Thereis a tendency for public companies in remote areas to serve as an agency ofemployment which affects their efficiency. For the seaports with positive TECH thepolicy implication is to increase the investment in seaports. For the managerialinefficient seaports the privatization is the most sensible policy implication since itwill overcome political management orientation. Relative to the decomposition ofthe technological change, it is found that labour saving and containerization ofseaports is what is inducing the improvement in technological change. Furthermore,the estimates of productivity change and technological bias indicate that thetraditional growth-accounting method, which assumes Hicks-neutral technologicalchange, is not appropriate for analysing changes in the productivity of seaports.


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The impact of size on seaport productivity is identified in this study. For instance,two large seaports out of the three identified in Table 1 present productivityimprovement (Itaguaı and Santos); only two medium seaports out of eight identifiedpresent productivity improvement (Rio Grande and Paranagua) and four smallseaports out of twelve identified present productivity improvement (Ilheus, Suape,Imbituba and Salvador). The idea of linking the variable of firm size to profit andperformance is traced back to pioneering research in the area of performance studies[48–50]. Different researchers have tested whether larger firms increase market shareand achieve economies of scale and as a result experience better performance. Morerecently, Taymaz [51] has found that a larger manufacturing firm size has a positiveeffect on technical efficiency, and on the ability of the firm to expand its operations. Inthe present paper, this result on seaport size is inconclusive. Therefore, in general, it ishypothesized that larger size has a positive relationship with firm profits and firmsuccess. However, in seaports it seems that location is very important since bigseaports are located in strategic rich locations that attract traffic [29].

To sum up, what are the main benefits of the results of this study? The findings arebased on a more accurate methodology and thus can be used as a starting point forfuture investigations based on best practices adopted by the best performingseaports. In particular, this method identifies the existence or non-existence of acommon seaport policy.

The limitations of this study are the relatively short data used in the analysis.However, when comparing with many published papers that use a sole year, thiscritic is not relevant. An additional limitation is the use of an invariant input.However, the input varies along seaports but not along the year. This is a traditionalapproach on seaports’ efficiency based in the literature review.

However, future studies might select some efficient and inefficient propertiesacross several areas and conduct case studies to determine the difference in practicesadopted by these different properties. Such investigation might also be extended todevelop a series of best practices that can be adopted by the Brazilian seaports. Thefindings could also be of interest to the Brazilian Government, especially in theprocess of adopting improvement strategies to the whole industry. Therefore, it iscrucial that the Brazilian Government adopts policies that suit each individualseaport group. For example, policies towards remote seaports might need to bedifferent from seaports with positive TECH.

With regard to the comparisons with previous findings, since there is no otherpublished research on Brazilian seaports the present paper breaks new ground in thiscontext. Furthermore, there are no published papers on seaports that disentangle thetechnological productivity change in input-bias, output-bias and magnitude oftechnological change

Future similar studies on ports in other countries should incorporate data ondistance travelled into the output data, with the results expressed in terms of volume.More research is needed to confirm the present results.

References1. LIU, Z., 1995, The comparative performance of public and private enterprises. Journal of

Transport Economics and Policy, 29, 263–274.2. COTO MILLAN, P., BANOS PINO, J. and RODRIGUES ALVAREZ, A., 2000, Economic

efficiency in Spanish ports: Some empirical evidence. Maritime Policy & Management,27, 169–175.


Dow

nloa

ded

by [

Uni

vers

ity o

f T

enne

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, Kno

xvill

e] a

t 10:

54 2

1 D

ecem

ber

2014

3. ESTACHE, A., GONZALEZ, M. and TRUJILLO, L., 2001, Efficiency gains from port reformsand the potential for yardstick competition: Lessons from Mexico. World Development,30, 545–560.

4. CULLINANE, K. P. B., SONG, D. W. and GRAY, R., 2002, A stochastic frontier model of theefficiency of major container terminals in Asia: Assessing the influence of administrativeand ownership structures. Transportation Research Part A, 36, 734–762.

5. CULLINANE, K. P. B. and SONG, D. W., 2003, A stochastic frontier model of the productiveefficiency of Korean container terminals. Applied Economics, 35, 251–267.

6. TURNER, H., WINDLE, R. and DRESNER, M., 2004, North America container portproductivity, 1984–1997. Transportation Research Part E, 40, 339–356.

7. BARROS, C. P., 2005, Decomposing growth in Portuguese seaports: A frontier costapproach. Maritime Economics & Logistics, 7, 297–315.

8. CULLINANE, K., WANG, T. F., SONG, D. W. and JI, P., 2006, The technical efficiency ofcontainer ports: Comparing data envelopment analysis and stochastic frontier analysis.Transportation Research Part A, 40, 354–374.

9. ROLL, Y. and HAYUTH, Y., 1993, Port performance comparison applying dataenvelopment analysis (DEA). Maritime Policy & Management, 20, 153–161.

10. MARTINEZ BUDRIA, E., DIAZ ARMAS, R., NAVARRO IBANEZ, M. and RAVELO MESA, T.,1999, A study of the efficiency of spanish port authorities using data envelopmentanalysis. International Journal of Transport Economics, 26(2), 237–253.

11. TONGZON, J., 2001, Efficiency measurement of selected Australian and other interna-tional ports using data envelopment analysis. Transportation Research Part A, 35,107–122.

12. BARROS, C. P., 2003, Incentive regulation and efficiency of Portuguese port authorities.Maritime Economics & Logistics, 5, 55–69.

13. BARROS, C. P., 2003, The measurement of efficiency of Portuguese seaport authoritieswith DEA. International Journal of Transport Economics, 30, 335–354.

14. BARROS, C. P. and ATHANASSIOU, M., 2004, Efficiency in European seaports with DEA:Evidence from Greece and Portugal. Maritime Economics & Logistics, 6, 122–140.

15. CULLINANE, K., SONG, D. W. and WANG, T., 2005, The application of mathematicalprogramming approaches to estimating container port production efficiency. Journal ofProductivity Analysis, 24, 73–92.

16. FARE, R. and GROSSKOPF, S., 1996, Inter-temporal Production Frontiers: With DynamicDEA (Boston/London/Dordrecht: Kluwer Academic Publishers).

17. RIOS, L. R. and MACADA, A. C. G., 2006, Analysing the relative efficiency of containerterminals of Mercosur using DEA. Maritime Economics & Logistics, 9, 138–147.

18. MANAGI, S., 2007, Maritime shipping industry and productivity in Japan. MaritimeEconomics and Logistics, 9, 291–301.

19. BARROS, C. P. and PEYPOCH, N., 2007, Comparing productivity change in Italian andPortuguese seaports using the Luenberger indicator approach. Maritime Economics &Logistics, 9(2), 138–147.

20. TALLEY, W. K., 2006, An economic theory of port. In: Port Efficiency. Vol. 16. (Researchin Transport Economics), edited by K. Cullinane and W. K. Talley (Oxford, UK: JAIPress), pp. 43–66.

21. CARRINGTON, R., COELLI, T. and GROOM, E., 2002, International benchmarking formonopoly price regulation: The case of Australian gas distribution. Journal ofRegulatory Economics, 21(2), 191–216.

22. ANTAQ – BRAZILIAN NATIONAL MARITIME TRANSPORT AGENCY, 2008, Port performance.http://www.antaq.gov.br/Portal/DesempenhoPortuario/Index.asp [Accessed 10November 2009].

23. CAMPOS NETO, C. A. S., PEGO FILHO, B., ROMMINGER, A. E., FERREIRA, I. M. andVASCONCELOS, L. F. S., 2009, Gargalos e demanda da infraestrutura portuaria e osinvestimentos do PAC: Mapeamento IPEA de obras portuarias. Texto para discussao n�

1423 (London: IPEA—Instituto de Pesquisa Economica Aplicada).24. SONG, D. W. and CULLINANE, K., 2001, The administrative and ownership structure of

Asian container ports. International Journal of Maritime Economics, 3, 175–197.25. BERGER, A. and BERGER, F. R., 2009, Portos e Terminais Marıtimos do Brasil (Joinville,

SC: Bela Catarina).


Dow

nloa

ded

by [

Uni

vers

ity o

f T

enne

ssee

, Kno

xvill

e] a

t 10:

54 2

1 D

ecem

ber

2014

26. PARK, R. K. and DE, P., 2004, An alternative approach to efficiency measurement ofseaports. Maritime Economics & Logistics, 6, 53–69.

27. BANOS PINO, J., COTO MILLAN, P. and RODRIGUEZ ALVAREZ, A., 1999, Allocativeefficiency and over-capitalisation: An application. International Journal of TransportEconomics, 26(2), 181–199.

28. VENUS LUN, Y. H., PANG, K. W. and PANAYDES, P. M., 2010, Organisational growth andfirm performance in the international container shipping industry. International Journalof Shipping and Transport Logistics, 2(2), 206–223.

29. TONGZON, J. and HENG, W., 2005, Port privatization, efficiency and competitiveness:Some empirical evidence from container ports (terminal). Transportation Research PartA, 39, 405–424.

30. SIMOES, P. and MARQUES, R. C., 2010, Influence of congestion efficiency on the Europeanseaports performance: Does it matter? Transport Reviews, 30(4), 517–539.

31. SIMOES, P. and MARQUES, R. C., 2010, Seaport performance analysis using robust non-parametric efficiency estimators. Transportation Planning and Technology, 33(5),435–451.

32. SIMOES, P., CARVALHO, P., FONSECA, A. and MARQUES, R. C., 2010, Governance andcomparative performance of Iberian Peninsula seaports: An application of non-parametric techniques. International Journal of Transport Economics, 37(1), 32–51.

33. OLIVEIRA, G. F. and CARIOU, P., 2011, A DEA study of the efficiency of 122 iron ore andcoal ports of 15/17 countries in 2005. Maritime Policy & Management, 38(7), 727–743.

34. BARROS, C. P., HARALAMBIDES, H., HUSSAIN, M. and NICOLAS PEYPOCH, N., 2011,Seaport efficiency and productivity growth. In: International Handbook of MaritimeEconomics, edited by K. Cullinane (Northampton, MA: Edward and Elgar), Chap. 17,pp. 363–383.

35. BICHOU, K., 2011, A two-stage supply chain DEA model for measuring container-terminal efficiency. International Journal of Shipping and Transport Logistics, 3(1), 6–26.

36. BERGANTINO, A. S. and MUSSO, E., 2011, The role of external factors versus managerialability in determining seaports’ relative efficiency: An input-by-input analysis through amulti-step approach on a panel of Southern European ports. Maritime Economics &Logistics, 13(2), 121–141.

37. BARROS, C. P., 2012, Productivity assessment of African airports. African DevelopmentReview, 24(1), 1–13.

38. MEDDA, F. and LIU, Q., 2012, Determinants and strategies for the development ofcontainer terminals. Journal of Productivity Analysis (forthcoming).

39. PANAYIDES, P. M., MAXOULIS, C. N., WANG, T. F. and NG, K. Y. A., 2009, A criticalanalysis of DEA applications to seaport economic efficiency measurement.Transportation Reviews, 29(2), 183–206.

40. MALMQUIST, S., 1953, Index numbers and difference surfaces. Trabajos de Estatistica, 4,209–242.

41. VARIAN, H., 1987, Intermediate Microeconomics: A Modern Approach (New York: W.W.Norton & Co.).

42. SHEPHARD, R. W., 1970, Theory of Cost and Production Functions (Princeton: PrincetonUniversity Press).

43. FARRELL, M. J., 1957, The measurement of productive efficiency. Journal of the RoyalStatistical Society Series A, General, 120(3), 253–281.

44. VASSILOGLOU, M. and GIOKAS, D., 1990, A study of the relative efficiency of banks’branches: An application of data envelopment analysis. The Journal of OperationalResearch Society, 41(7), 591–597.

45. DYSON, R. G., ALLEN, R., CAMANHO, A. S., PODIMOVSKI, V. V., SARRICO, C. and SHALE,S., 2001, Pitfalls and protocols in DEA. European Journal of Operational Research,132(2), 245–259.

46. BARNEY, J., 1986, Strategic factor markets: Expectations, luck and business strategy.Management Science, 32, 1231–1241.

47. TEECE, D., PISANO, G. and SHUEN, A., 1997, Dynamic capabilities and strategicmanagement. Strategic Management Journal, 18, 509–533.

48. LALL, S., 1980, Monopolistic advantages and foreign involvement by US manufacturingindustry. Oxford Economic Papers, 32, 102–122.


Dow

nloa

ded

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Uni

vers

ity o

f T

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, Kno

xvill

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54 2

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ber

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49. WOLF, B., 1977, Industrial diversification and internationalization: Some empiricalevidence. Journal of Industrial Economics, 26, 177–191.

50. PUGH, D. S., HICKSON, D. J., HININGS, C. R. and TURNER, R. C., 1969, The context oforganization structure. Administrative Science Quarterly, 14, 91–114.

51. TAYMAZ, E., 2005, Are small firms really less productive? Small Business Economics,25(10), 429–445.

52. MANAGI, S. and KAREMERA, D., 2004, Input and output biased technological change inUS agriculture. Applied Economics Letters, 11(5), 283–286.

53. FARE, R. and PRIMONT, D., 1995, Multi-Output Production and Duality: Theory andApplications (Boston/London/Dordrecht: Kluwer Academic Publishers).

Appendix

MethodologyThe Malmquist index is defined as holding outputs constant, the reciprocal of theinput distance function provides the ratio of minimum inputs to actual inputsemployed and serves as a measure of technical efficiency. Let xt ¼ ðxt1, . . . , xtNÞrepresent a vector of N non-negative inputs in period t, and let yt ¼ ð yt1, . . . , ytMÞrepresent a vector of M non-negative outputs produced in period t. The inputrequirement set in period t represents the feasible input combinations that canproduce outputs and is represented as:

Ltð ytÞ ¼ fxt : xt can produce ytg: ðA:1Þ

The isoquant for the input requirement set is defined as

ISOQ Ltð ytÞ ¼ xt :xt

�=2Ltð yÞ, for �4 1

� �: ðA:2Þ

The Shephard input distance function is defined as

Dtið y

t, xtÞ ¼ max � :xt

�2 Ltð ytÞ

� �: ðA:3Þ

The reciprocal of the Shephard input distance function equals the ratio ofminimum inputs to actual inputs employed and serves as a measure of Farrell inputtechnical efficiency. Efficient Decision-Making Units (DMUs) use inputs that arepart of the ISOQ Ltð ytÞ and have Dt

ið yt, xtÞ ¼ 1. Inefficient DMUs have

Dtið y

t, xtÞ4 1.The paper estimates the reciprocal of the Shephard input distance function using a

linear programming method called DEA. It is assumed that there are k¼ 1, . . . ,KDMUs. The DEA piece-wise linear constant returns-to-scale input requirement settakes the form:

Ltð ytÞ ¼

(xt :

XKk¼1

ztkxtkn � xn, n ¼ 1, . . . ,N,

XKk¼1

ztkytkm � ym,m ¼ 1, . . . ,M,

ztk � 0, k ¼ 1, . . . ,K

): ðA:4Þ

The DEA input requirement set uses linear combinations of the observed inputsand outputs of the K DMUs, using the K intensity variables ztk to construct a best-practice technology. The NþM inequality constraints associated with inputs and


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outputs imply that no less input can be used to produce no more output than a linear

combination of observed inputs and outputs of the K DMUs. Constraining the K

intensity variables to be non-negative allows for constant returns to scale.To compute input technical efficiency for DMU ‘o’, The following linear

programming problem is solved:

1=Dtið y

t, xtÞ ¼ maxz,�

��1 :XKk¼1

ztkxtkn � �

�1xton, n ¼ 1, . . . ,N,

(

XKk¼1

ztkytkm � ytom,m ¼ 1, . . . ,M, ztk � 0, k ¼ 1, . . . ,K

): ðA:5Þ

Following the approaches of Fare and Grosskopf [16] and Managi and Karemera

[52], total factor productivity growth can be estimated using the Malmquist input-

based index of total factor productivity growth. This index can be decomposed into

separate indexes measuring efficiency change and technological change. Efficiency

change measures ‘catching up’ to the frontier isoquant, while technological change

measures the shift in the frontier isoquant from one period to another. The

Malmquist input-based productivity index (MALM) takes the form:

MALM ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiDtþ1

i ð ytþ1,xtþ1Þ

Dtþ1i ð y

t,xtÞ�

Dtið y

tþ1, xtþ1Þ

Dtið y

t, xtÞ

s: ðA:6Þ

Rearranging (6) yields:

MALM ¼Dtþ1

i ðxtþ1, ytþ1Þ

Dtiðx

t, ytÞ�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiDt

iðxt, ytÞ

Dtþ1i ðx

t, ytÞ�

Dtiðx

tþ1, ytþ1Þ

Dtþ1i ðx

tþ1, ytþ1Þ

s, ð1Þ

where efficiency change is represented by EFFCH ¼Dtþ1

ið ytþ1, xtþ1Þ

Dtið yt, xtÞ and technological

progress is represented by

TECH ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiDt

i ð yt, xtÞ

Dtþ1ið yt,xtÞ

�Dt

i ð ytþ1, xtþ1Þ

Dtþ1ið ytþ1, xtþ1Þ

r:

Values of MALM EFFCH, or TECH less (greater) than one indicate productivity

growth (decline), gains (losses) in efficiency and technological progress (regression).Fare and Grosskopf [16] show how the technological index can be further

decomposed into the product of the three separate indexes of output-biased

technological progress (OBTECH), input-biased technological progress (IBTECH)

and magnitude of technological progress (MATECH). These indexes take the

following forms:

OBTECH ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiDt

ið ytþ1, xtþ1Þ

Dtþ1i ð y

tþ1, xtþ1Þ�

Dtþ1i ð y

t, xtþ1Þ

Dtið y

t, xtþ1Þ

s,

IBTECH ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiDtþ1

i ð yt, xtÞ

Dtið y

t, xtÞ�

Dtið y

t,xtþ1Þ

Dtþ1i ð y

t, xtþ1Þ

sand

MATECH ¼Dt

ið yt, xtÞ

Dtþ1i ð y

t, xtÞ, where TECH ¼ OBTECH� IBTECH�MATECH:

ðA:8Þ

Figure A1 illustrates the construction of the input distance function and the

components of the Malmquist input-based productivity index. The input


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requirement set in period 1 includes all points to the northeast of the isoquant L1(y).

It is assumed that technological progress occurs from period 1 to period 2, with the

input requirement set in period 2 including all points to the northeast of the isoquant

L2(y). The DMU for which efficiency and productivity change is calculated employs

input vector A in period 1 and input vector E in period 2. In both periods, the DMU

produces the same level of output (y), but uses excessive inputs and is technically

inefficient. The input distance function in period 1 is D1i ð y, x

1Þ ¼ 0A0B, and in period 2,

the input distance function is D2i ð y, x

2Þ ¼ 0E=0D: The two inter-period input

distance functions are calculated as D1i ð y, x

2Þ ¼ 0E0F and D2

i ð y,x1Þ ¼ 0A

0C. The

Malmquist index is calculated as MALM ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0E=0D0A=0C�

0E=0F0A=0B

q. Efficiency change is

calculated as EFFCH ¼ 0E=0D0A=0B, and technological change is calculated as

TECH ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0A=0B0A=0C�

0E=0F0E=0D

q¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0C0B �

0D0F

q.

Figure A2 illustrates the construction of the index of input-biased technological

change. The isoquant in period 1 is represented by L1(y). Again it is assumed that

there is technological progress and draw two alternative isoquants, represented by

L21(y) and L22(y). Technological progress is Hicks-neutral if the Marginal Rate of

Substitution (MRS) between two inputs remains constant, holding the input mix

constant. Technological progress is x1-saving and x2-using if the MRS between the

two inputs increases, holding the input mix constant. Technological progress is x1-

using and x2-saving if the MRS between the two inputs decreases, holding the input

mix constant. The isoquant L21(y) represents an x1-saving and x2-using bias. The

isoquant L22(y) represents an x1-using and x2-saving bias. From period 1 to period 2,

the ratio of the two inputs change such that x1x2

� �tþ14 x1

x2

� �t. If technological progress

shifts the isoquant to L21(y) in period 2, the input-bias index is

IBTECH ¼ffiffiffiffiffiffiffiffiffiffi0B0C

0D0F

q¼

ffiffiffiffiffiffiffiffiffiffi0B=0C0F=0D

q. Given that 0B=0C4 0F=0D, then IBTECH41, and

the technology exhibits an x1-saving and x2-using bias. If instead, technological

progress shifted the isoquant to L22(y) in period 2, the input-bias index is

IBTECH ¼ffiffiffiffiffiffiffiffiffi0B0C

0G0F

q¼

ffiffiffiffiffiffiffiffiffiffi0B=0C0F=0G

q. In this case, there is 0B=0C5 0F=0G so that

x1

x2

L1(y)

L2(y)

A

B

C

D

E

F

0

Figure A1. Input requirement sets and the Malmquist input-based productivity index.


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IBTECH51, and the technology exhibits an x1-using and x2-saving bias. Thepossible alternatives for input bias between inputs k and n are summarized inTable A1.

The output distance function takes the form:

Dtoðx

t, ytÞ ¼ min � : ð yt=�Þ 2 PtðxtÞ

, ðA:9Þ

where Pt(x) is the output possibility set for period t. Under constant returns to scale,

the Shephard input distance function equals the reciprocal of the Shephard output

distance function [53]. In other words, Dtið y

t, xtÞ ¼ Dtoðx

t, ytÞ�1. Therefore, given

constant returns to scale, OBTECH can be written as the index of output-biased

technological change as

OBTECH ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiDtþ1

o ðxtþ1, ytþ1Þ

Dtoðx

tþ1, ytþ1Þ�

Dtoðx

tþ1, ytÞ

Dtþ1o ðx

tþ1, ytÞ

s: ðA:10Þ

Figure A2 illustrates the construction of the index of output-biased technological

change, assuming technological progress between periods 1 and 2. The output

possibility set in period 1 is given by P1(x). Technological progress with respect to

outputs is Hicks-neutral if the marginal rate of transformation between two outputs

is constant, holding the mix of outputs constant. Hicks-neutral technological

progress is illustrated by a parallel shift of the production possibility set to PHN(x).

Technological progress is biased in favour of output 1 (y1-producing) if the marginal

x1

x2

L1(y)

L21(y)

L22(y)

A

B

C

G

D E

F

0

LHN(y)

H

Figure A2. Input requirement sets (L(y)) and input-biased technological change.

Table A1. Input-biased technological change and changes in the input mix.

Input mix IBTECH41 IBTECH51

xjxk

� �tþ1

4xjxk

� �t

xj-saving, xk-using xj-using, xk-saving

xjxk

� �tþ1

5xjxk

� �t

xj-using, xk-saving xj-saving, xk-using


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rate of transformation between outputs 1 and 2 increases, holding the mix of outputs

constant. Technological progress is biased in favour of output 2 (y2-producing) if the

marginal rate of transformation between the two outputs is less in period 2 than in

period 1, holding the output mix constant. The output possibility set given by P21(x)

illustrates a y1-producing output bias, and the output possibility set given by P22(x)

illustrates a y2-producing output bias. The possible alternatives for output bias

between outputs k and m are summarized in Table A2.In period 1, a DMU is observed to produce an output vector represented by point

A. The output distance function is calculated as D1oðx, y

1Þ ¼ 0A0B. In period 2, the

DMU is observed to produce output vector E. If the technology shifts to P21(x) in

period 2, the output distance function in period 2 is D2oðx, y

2Þ ¼ 0E0F, and the index of

output-biased technological change is OBTECH ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0E=0F0E=0D�

0A=0B0A=0C

q¼

ffiffiffiffiffiffiffiffiffiffi0D=0F0B=0C

q4 1.

Thus, sinceytþ11

ytþ12

5 yt1

yt2and OBTECH41, the technology is y1-producing. If the

technology shifts to P22(x) in period 2, the output distance function is D2oðx, y

2Þ ¼ 0E0G,

and output-biased technological change is OBTECH ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0E=0G0E=0D�

0A=0B0A=0C

q¼ffiffiffiffiffiffiffiffiffiffi

0D=0G0B=0C

q5 1. Given that

ytþ11

ytþ12

5 yt1

yt2and OBTECH51, the technology is y2-producing.

The possible alternatives for output bias between outputs m and q are summarized inFigure A3.

PHN(x) P1(x)

B A

C

D E F

G

y1=output 1

y2=output 2

H

0

P 21(x )

P 22(x)

Figure A3. Production possibility sets (P(x)) and output-biased technological change.

Table A2. Output-biased technological change and changes in the output mix.

Output mix OBTECH41 OBTECH51

ytþ1m

ytþ1q

5ytmytq

ym-producing yq-producing

ytþ1m

ytþ1q

4ytmytq

yq-producing ym-producing


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productivity analysis of brazilian seaports

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