R&D Intensity and the Classification of Manufacturing Technology in South Africa:

An Indigenous Taxonomy

Abstract

This paper determines technology classes relevant for South Africa using source data, as

opposed to adopting technology classes developed specifically for OECD countries. It

establishes a concordance between OECD and SA technology classes; and goes on to

determine the industry groupings that fall into various technology classes directly from this

concordance. One outcome of this is a ranking of SA industries in terms of their technology

intensiveness, within a classification scheme consisting of high, medium-high, medium-low

and low technology classes different to that obtained from OECD technology classes. The

‘flavour’ of the SA high-tech industry profile is thus made apparent quantitatively and is not

based purely on individual expert opinion as has been common practice up to now. The point

is made that, since its conception, technology class has been context and country specific -

dependent on the choice of industrial subsectors, which may vary, especially between

developing and developed world contexts. An indigenous determination of technology classes

allows for the identification of future R&D performers especially among the lower-

technology enterprises; something which the application of the standard OECD technology

classes does not allow. The results afford policymakers with the opportunity to track South

Africa's competitive knowledge economy subsectors with respect to potentially any macro-

economic quantity.

JEL O32, O38

Keywords: technology classes, policy tools, developing economy

Mustapha, Nazeem; Blankley, William; Leiberum, Vaughan; Rumbelow, Julien;Kondlo, LwandoHuman Sciences Research Council, South Africa

Introduction

Science, technology and innovation indicators purport to map and measure the state of the

conceptual knowledge economy of a nation. Measurable components of the knowledge

economy comprise diverse activities such as Research and (Experimental) Development

(R&D) performance, creative practices like inventions and innovations, knowledge diffusion

(including technology transfer) and science-related human resource development. These

indicators cut across bureaucratically disparate policy making bodies in the science, economic

development, higher education, and human and social development sectors. Of the various

science, technology and innovation indicators, the expenditure on R&D in the economy

quantified by R&D intensity (where R&D expenditure is expressed as a percentage of GDP)

is the most widely used indicator for setting, monitoring and evaluating vital components of

national science policy. The R&D expenditure by the business sector in South Africa grew

relatively robustly from R8.244 billion in 2005 to R 12.332 billion in 2008 or 49.6% over 3

years, and business has in the last decade remained the largest contributor to R&D

expenditure, or performance (OECD, 2010), making up 57.6% of all R&D expenditure on

average between 2005 and 2008. Gault has noted that if more than half of a country’s R&D is

performed by the business sector, this is indicative of a maturing knowledge economy (Gault,

2010). An understanding of the composition of the business sector in terms of R&D

performers and industrial sectors of performance is thus particularly important given the

possibility that certain high technology sectors in South Africa may be under-utilised

(Walwyn, 2008) in that they do not match up to OECD levels of expected R&D expenditure.

Thus the identification of high and low R&D intensive industries in the country is very

important, and the business R&D activities themselves should form a key focus in

understanding the composition of the components of the knowledge based economy in a

country. However, possible constraints due to the shortage of supply of skilled human

resources in general and R&D expertise and skills in particular, as well as infrastructure

shortcomings also need to be taken into account. This information and the associated

indicators should thus inform science policy in most settings, including that of a developing

country like South Africa where they are actively used by decision makers (DST, 2008).

Mani (2004) dealt with the matter of definition of technology intensity and applied this to

determine the true technology intensity exporting abilities of developing countries. The

sometimes vociferous discussion on technology intensity and its use with regards especially to

3

the developing country context has perhaps overshadowed its potential uses to decision

makers. This paper concerns itself with the use of technology intensity as an instrument to aid

decision makers and thus consider the taxonomy most popularly employed by policy makers,

that of the OECD. The OECD Secretariat’s initial work on high technology industries used a

classification system that was originally developed in the United States. In 1984 the

Secretariat used a sample of eleven countries to develop a new classification based on R&D

intensity (R&D expenditure in relation to production output). The classification produced a

list placing industries in three groups of high, medium and low technology. A re-evaluation of

this scheme by Hatzichronoglou (1997) resulted in four technology class groups of high,

medium-high, medium-low and low technology. These lists have been widely used since then.

The OECD classification of technology intensity has been applied to developing countries in

studies by United Nations Conference on Trade and Development by Basu and Das (2011);

and, following on from Mani (2004), Srholec (2007) looked at high technology exports from

developing countries, particularly regarding electronic products and cautions that high

specialisation in certain high technology fields in some developing countries may be the result

of the rapid dispersion of global networks across national borders and not necessarily

associated with indigenous technological capabilities. The OECD classification has also been

tested in India by Mukherjee (2009) who applied the classification directly to Indian exports

and concluded that although Indian exports have increased considerably since 2001, this was

mainly in low technology goods with little discernible increase in high technology exports.

The OECD itself indicates that this correspondence of industries as R&D intensive or non-

R&D intensive may not be suitable for other countries due to the fact that countries tend to

specialise in different forms of research. These differences in technology classification are

particularly important for developing countries and in this respect

broad indicators are fairly accurate but may be biased if there are major differences in

the economic structure of the countries compared. For example, the activities of big

R&D-intensive multinationals may influence the GERD/GDP ratio in a particular

country quite significantly (OECD, 2002).

Notwithstanding this, the OECD technology classes by industry are often taken at face value

and applied directly to detailed subsectors in other countries and settings. This has led to the

practice of identifying gaps in technology intensity in South Africa relative to the US or other

developed OECD countries; followed by recommendations of increasing expenditure on R&D

in identified subsectors to try and match the performance of developed economies (see, for

4

example Walwyn (2008)). If this argument is applied simplistically, it can lead to fruitless

conclusions and an under-appreciation of domestic capability. Take, for example, the simple

case of manufacturing of aircraft and spacecraft. This is a sector that is often mentioned as

high technology as it appears in the OECD taxonomy. In South Africa, sales in this sector are

invariably non-existent because the income of companies in this sector is mainly due to

maintenance, repairs and modification of aircraft and not the production of technologically

intensive aerospace products. By extension of this argument, there is no guarantee that the

profile of industries classified in the OECD will have a similar profile in South Africa.

Marcato and Malfi (2007) tested the validity of the OECD taxonomy of technology intensity

to the Italian manufacturing industry and found that while the application of the classification

to Italy was plausible there were specific country differences between Italy and the overall

OECD profile that emerged. Generally, Italian manufacturing industry had lower levels of

technological intensity than the OECD average but fifteen of the nineteen Italian industries

matched the OECD technology intensity groupings. The authors cautioned against the

wholesale application of the OECD technology intensity groupings to individual countries

without taking into account their specific structural and technological characteristics. This is

the point we wish to emphasise. In principle, the approach may well be a valid route to follow

towards mapping a path towards a more knowledge based economy for South Africa.

However, if it does not take into account the particular developmental strengths and

weaknesses of South Africa, it might not be particularly useful. That is because, on the one

hand, it would require considerable effort for the economy to advance in areas where there is

only a small reservoir of knowledge, skills and technology, and which would therefore require

substantial effort and resources to build up from a low base. On the other hand, the

establishment of a baseline classification scheme that is detailed enough at subsector level to

identify the R&D intensive subsectors in the economy, while still broad enough to allow for

international comparisons, would provide insight into the areas of technological strength and

weakness within the SA economy. This would also allow a more pertinent choice of

countries, at appropriate stages of development, for use in comparisons with South Africa.

Having the results of similar analyses that have been conducted for other countries would then

allow for the identification of successful or unsuccessful strategies, and open up possibilities

for policy tools like evidence-based scenario building or backcasting. The point is that the

taxonomy is context and era specific, and it needs to be viewed from this vantage point for it

to be utilised as a meaningful policy instrument. For the first time, this paper derives

technology classes indigenous to South Africa using data at the lowest disaggregation of

5

industrial classification from the National Survey of Research and Experimental Development

(R&D) series.

Methodology

According to Peneder (2003), classification of individuals in a collection into generic types is

a process of reducing heterogeneity. The aim is to order items according to their similarity. It

was from this principle that the OECD (Hatzichronoglou, 1997) developed the technology

classes that have now become commonplace. The classification across ten OECD countries

for 1980 and 1990 using 22 manufacturing sectors was decided using the principle that

industry intensities remained stable across three indicators of R&D intensity: two direct

intensity indicators and one a sum of direct and indirect intensity. This paper follows this

classification principle, but concerns itself only with direct intensity; using it to group

industries into technology classes appropriate for South Africa. The corresponding OECD

technology classes are compared. The contribution of technology sectors on the economy is

evaluated using sales by technology class.

The method used to determine the intrinsically South African technology classes and rankings

was a combination of statistical cluster analysis methods and expert judgement using visual

inference from time series data.

In South Africa, the Centre for Science, Technology and Innovation at the Human Sciences

Research Council has been producing the National Survey of Research and Experimental

Development (R&D) since 2001 on an annual basis (excluding 2002, when the survey was

not done). The survey is conducted according the OECD Frascati Manual (2002) guidelines

and results have been published as official statistics and submitted to the OECD for

international publication in the OECD Main Science and Technology Indicators. The survey

is effectively a census of all R&D performing industries. A key advantage of this method of

surveying is that the survey frame is not constructed from OECD or other prescriptions of

R&D intensity. This allows for an opportunity to re-examine R&D technology classes, since

the R&D intensive industries for South Africa may be picked out as such from the data

collected by adapting the method described by Hatzichronoglou (1997). Thus the

classification of high, medium-high, medium-low and low technology may be done in a

bottom-up fashion based on evidence. The synthesis in this work considered CeSTII (2011)

data over the five-year period 2004 to 2008. This selection was due to the relatively large

6

decline in manufacturing sales that was experienced in 2009, which would have complicated

the analysis.

Findings

Technology Intensiveness in the SA Manufacturing Industry

The South African manufacturing industry historically has been a big contributor to GDP —

in 2008, 15% of GDP at current prices was due to the manufacturing division. Within

manufacturing, the greatest proportion of value added is from petroleum products, chemicals,

rubber and plastic sector, and the smallest from the radio, TV, instruments, watches and

clocks sector (Loschky, 2010). Figure 1 shows that these are also two of the three most

technology-intensive industries.

Figure 1: Contribution to sales or value added of each manufacturing subsector at 2-digit SIC

(cf. Stats SA (2004) for descriptions of codes) in 2008. The size of each grey bubble indicates

R&D expenditure as a percentage of manufacturing sales relative to other industries. The size

of each black bubble indicates the relative size of R&D expenditure as a percentage of

manufacturing value added relative to other industries. The distance between grey and black

bubbles is the percentage point absolute difference in contribution to sales and contribution to

value added by industry.

7

Manufacturing sales per sector in 2008 were on average 4.4 times larger than manufacturing

value added. Whereas R&D intensity at the level of the firm is calculated as the ratio of the

firm’s R&D expenditure to turnover (or sales), a discussion of R&D intensity in a national

sense determines it as the ratio of GERD to GDP. In a consideration of technology class

determination, the difference between the two indicators is negligible as was shown by

Hatzichronoglou (1997) in the case of OECD countries in the 70s, 80s and 90s — the

technology classes constructed are consistently composed of the same subsectors. This has

been largely confirmed here in Figure 1. A choice of production or value added as

denominator makes no real difference to the separation of the technology classes, although it

does affect the level of R&D intensity. The two measures of technology intensity have a

correlation of 0.939 (p< 0.001). An explanation for this is that it is reasonable to assume that

value added approximates production on infra-annual scales where the effect of intermediate

consumption on the economy may be approximated as a small contribution to value added.

On supra-annual levels of comparison this assumption would break down. For example, in

value chains for products with a lifecycle that includes diffusion across firms, sectors and

possibly countries that takes place over natural time scales larger than a year, this will not be

true. This is an important point to note as there may be industries with large intermediate

consumption, an example of this in the OECD case being petroleum refining, for which

special attention would be required (Hatzichronoglou, 1997).

The SA profile of technology intensity in terms of R&D expenditure by sectoral gross value

added compared with that in terms of R&D expenditure by manufacturing sales are quite

similar. The differences are firstly that the largest R&D intensive sector contributes less to the

whole when R&D intensity is measured by expenditure as a proportion of gross value added.

Secondly, the motor vehicles, parts and accessories and other transport equipment subsector

swops ranking places with the petroleum, chemical products, rubber and plastic products

subsector. The choice adopted by this study to determine technology classes was to use

manufacturing sales — instead of manufacturing value added — in the denominator of this

indicator, partly due to availability of data.

SA Technology Classification using OECD Characteristics

In this section, the technology classes inherited from the OECD were investigated directly

from manufacturing sales and R&D expenditures from two approaches, and then compared.

The first method took the set of industries that the OECD classified into technology classes as

the same ones to be used for the four corresponding South African technology classes. This

8

required the establishment of a concordance between OECD technology classes that use ISIC

classifications and South African technology classes that use SIC classifications. The second

method determined the technology classes that arise for South African industries by

classifying them relative to the OECD technology thresholds. In this case, it will be shown

that only three distinct technology classes arise in the South African case.

SA Technology Classes Based on OECD Industrial Groupings

The method the OECD used to classify industries into four groups was according to the R&D

expenditure as a proportion of production or value added, where the group selections were

done in such a way as to have the same set of industries for the sample OECD area in any one

group remaining in that group for an extended period of time. The groups (Hatzichronoglou,

1997) that were identified are listed in Table 1, where the current South African concordant

Standard Industrial Code is available from Stats SA (2004).

Table 1: Technology class concordance between OECD and SA manufacturing industries.

A recent more comprehensive review of the OECD classification using 25 OECD developed

and developing countries concluded that the determination of high-tech and medium-high-

tech sectors was unchanged, with only the ranking of industries within these classes being

Technology Class OECD Description ISIC Rev. 2 Code SIC Code

1. Aerospace 3845 386

2. Computers, office machinery 3825 359

3. Electronics-communications 3832 371+372+373

4. Pharmaceuticals 3522 3353

5. Scientific instruments 385 374+375+376

6. Motor vehicles 3843 381

7. Electrical machinery 383-3832 36

8. Chemicals 351+352+3522 334+335+336-3353

9. Other transport equipment 3842+3844+3849 382+383+387

10.Non-electrical machinery 382-3825 356+357+358

11. Rubber and plastic products 355+356 337+338

12. Shipbuilding 3841 3841

13. Other manufacturing 39 392

14. Non-ferrous metals 372 352

15. Non-metallic minerals 36 342

16. Fabricated metal products 381 354+355

17. Petroleum refining 351+354 332

18. Ferrous metals 371 351

19. Paper printing 34 323+324+325+326

20. Textiles and clothing 32 31

21. Food, beverages, and tobacco 31 30

22. Wood and furniture 33 321+322+391

High

Medium-high

Medium-low

Low

9

affected (Loschky, 2010). The thresholds of R&D intensity determining the technology

classes were the same as those used in previous studies and are displayed in Table 3.

The technology classes obtained by direct transcription OECD technology classes were

determined using the SIC codes in Table 1. These are the technology classes with which SA

manufacturing industries are often implicitly compared. The underlying assumption is that the

SA industrial profile in terms of technology intensity is similar to that of the OECD average.

Time series of manufacturing sales and R&D intensity for these technology classes formed as

a collection of the corresponding OECD industries in South Africa were plotted between 2004

and 2008 in Figure 2.

Figure 2: SA manufacturing sales for the four technology classes based on OECD groupings

of industrial classification (left inset) and corresponding R&D expenditure as a percentage of

sales show a marginal separation of the classes, an undesirable feature in a taxonomy.

The time series in Figure 2 show that, when OECD industrial groupings are used to define

technology classes, medium-low technology industries account for the largest value of sales

and that the sales accruing to this class has rapidly increased over the years 2005 to 2008.

Furthermore, sales that accrued from high technology industries have remained level from

2004 to 2008, compared to sales from other technology classes. Figure 2 shows that the low

technology and medium-low technology classes were not too dissimilar from each other. This

is contrary to the purpose of classifying items into different classes, which is to clearly

delineate such items according to a chosen criterion (Peneder, 2003).

SA Technology Classes Based on OECD R&D Intensity Thresholds

A somewhat more reasonable classification scheme with clear demarcation between classes

were obtained when the OECD technology class thresholds in Table 3 were used to pick out

10

SA industries and group them into technology classes. In this case, only three technology

classes emerged. The industries that compose these three technology classes are given in

Table 2. The aerospace and shipbuilding industries were not included as no sales were made

from the manufacture of products of this nature.

Table 2: SA manufacturing industries classified according the levels of OECD technology

classes in Table 3.

The contribution to manufacturing sales and the R&D intensities for each technology class

between 2004 and 2008 are illustrated in Figure 3.

Figure 3: The inset on the left of the panel depicts manufacturing sales for the three

technology classes that arise when OECD technology class thresholds are used to determine

Technology class SA description of manufacturing SIC code

Medium-high Electronics-communications 371+372+373

Other chemical products including pharmaceuticals 335

Scientific instruments 374+375+376

Motor vehicles 381

Electrical machinery 36

Chemicals (excluding other chemical products)† 334

Other transport equipment 382+383+387

Non-electrical machinery 356+357+358

Rubber and plastic products 337+338

Other manufacturing 392

Non-ferrous metals 352

Non-metallic mineral products 342

Fabricated metal products 354+355

Coke oven products, petroleum refineries and processing of nuclear fuel 331+332+333

Ferrous metals 351

Paper printing 323+324+325+326

Textiles and clothing 31

Food, beverages, and tobacco 30

Wood and furniture 321+322+391

† Sales data for manufacture of man-made fibres (SIC=336) were not available.

Medium-low

Low

11

SA technology classes. The right inset depicts the corresponding R&D intensities. This is a

reasonable taxonomy, but is not suitable for comparison with the four OECD classes in use

currently.

The time series in Figure 3 show that the lowest technology industries have been the greatest

contributors to manufacturing sales and consequently to overall GDP, and that this was

persistent. The time series also show that the higher technology industries have had

consistently the lowest contributions to manufacturing sales. This classification into three

technology intensive sectors may arguably be appropriate for South Africa at this stage of the

development of the economy. It may be that a signature of rising sophistication in technology

intensiveness is the number of ‘natural’ divisions of industry. It is interesting to note that the

number of OECD classes in the 80s was also three, suggesting that the technology

sophistication of SA is akin to an OECD average of twenty years ago. However, the

composition of each technology class would still not be based on an identification that draws

on the indigenous nature of SA industries.

SA Technology Classification using Classification Principles

Neither of the two approaches above is suitable for a comprehensive understanding of the

dynamics of the SA manufacturing division with respect to technology intensity that still

allows for comparison with the OECD technology classes. This is because both use a fairly

arbitrary means of establishing the technology classes from parameters more suited to some

OECD countries. Even though these methods do provide some means of indicating those

industries with propensity to drive R&D expenditure, they do not do so from this principle at

the outset, which was the original purpose behind technology classes. Thus they do not have

the ability to tease out the particular technology profile of the South African manufacturing

industry.

The other investigation this study conducted followed the original principles of OECD

methodology and not its determination of industry classes. The separation of groups was

determined by visual observation of the stability of groups of industries between 2004 and

2008. The number of technology groups was taken as four so as to compare with the OECD

groups. This is useful for comparison, but may not be the most natural number of classes to

use for South Africa, as mentioned before. In contrast to the OECD approach, which took

account of the trade practices between member states, the approach we followed here did not

consider trading partners. A more comprehensive study would ideally include the main

12

trading partners of SA, and also consider the effect of indirect R&D intensity in this approach;

but, following Hatzichronoglou (1997), the expectation is that this would not change the

technology classes except for the ranking of industries within classes.

The clustering behaviour of SA manufacturing industries was first investigated using

statistical cluster analysis methods. The investigation considered Ward’s minimum variance

by Ward (1963); average linkage and centroid ( (Sokal & Michener, 1958), as discussed in

Ward (1963)); complete linkage (cf. Sorensen (1948)); density linkage (Wong & Lane, 1983)

using k-th nearest neighbour with k =2, 3,4; flexible-beta (Lance & Williams, 1967) with β=-

0.5, -0.25; McQuitty’s similarity analysis (Sokal & Michener, 1958); median (Gower J. C.,

1967); and single linkage ( (Florek, Lukaszewicz, Perkal, & Zubrzycki, 1951), see for

example (Gower & Ross, 1969)) methods. The method chosen as most appropriate for our

purposes was Ward’s minimum variance method, where the distance between any two

clusters is the ANOVA sum of squares between the two clusters added up over all the

variables. The variables were the R&D expenditure as a proportion of sales for 2004, 2005,

2006, 2007 and 2008 for thirty-five manufacturing industries listed in Figure 4.

13

Figure 4: Wards minimum variance cluster analysis results of R&D expenditure as a

percentage of sales for thirty-five manufacturing industries.

The number of clusters was taken as four for the purposes of this paper. At the start, each

observation starts of as clusters with one item. Each following generation of the tree creates

new clusters by merging two clusters from the previous generation, such that the within-

cluster sum of squares is minimized over all possible partitions (indicated by the semi-partial

R-squared when viewed as a fraction of the total sum of squares in Figure 4). When the data

are grouped into four clusters, the proportion of variance accounted for by the clusters is 95%.

No ties in the similarity of clusters formed during the analysis, signifying no degeneracy in

the final results. The results (Figure 4) demonstrated the separation of electronics-

communications as a cluster of one item and scientific instruments and other chemicals

including pharmaceuticals as a cluster of two (as did all the cluster analysis methods used in

preliminary investigations listed above). Another cluster was composed of other electrical

machinery, non-ferrous metals, electricity distribution apparatus, special purpose machinery,

motor vehicles, electric motors, non-metallic minerals, petroleum products, other transport

equipment and publishing. The final cluster was composed of the remaining industries. The

cluster analysis is a good method for determining the composition of the different technology

groups, but it does not determine the level of technology intensity or a ranking of the

industries within classes. This was done using time series plots of the levels of technology

intensity.

14

Figure 5: Spaghetti plot of R&D expenditure as a percentage of sales for twenty-nine

manufacturing industries. The separation of individual industries is not as important as the

separation of technology classes; the latter appearing as four groups from visual examination.

An expert view of Figure 5 identifies the four technology classes for South African

manufacturing industries. These have been demarcated by horizontal lines indicating

estimated threshold values of R&D expenditure as a percentage of sales. The SIC codes for

the industry descriptions are available from Stats SA.15 Below the threshold value of 0.2%,

the group of industries comprising the low technology class appears. Drilling down below the

threshold value of 0.2% to ascertain whether there may be further clustering yields none that

are easily apparent by eye.

The R&D intensity limits that emerged from this approach for the indigenous technology

classes in manufacturing are in Table 3.

Table 3: R&D intensity thresholds defining technology classes for manufacturing industries

within South Africa.

15

The subsectors were determined using the criterion that industries classified in a higher

category have a higher average technology intensity (between 2004 and 2008) than those in a

lower one, using R&D expenditure as a percentage of manufacturing sales as a proxy

indicator for technology intensiveness. The taxonomy in Table 4 emerged from investigation

of the R&D intensities of industries in Figure 5, combined with considerations of the cluster

analysis, as the four technology classes for South Africa.

Since manufacturing sales values at 3-digit SIC were not available between 2004 and 2008,

R&D intensity (R&D expenditure as a percentage of value added) values at 3-digit SIC have

been estimated for each technology class by dividing R&D expenditure as a percentage of

sales at 3-digit SIC by the ratio of R&D intensity per industry at 2-digit SIC and R&D

expenditure as a percentage of sales at 2-digit SIC. These are useful for comparison with

OECD classes (see Table 4).

Table 4: Technology classes for twenty-three SA manufacturing industries based on

technology intensity characteristics of the SA manufacturing division, with industries ranked

by their mean estimated R&D intensity.

Technology class R&D expenditure as a percentage of sales OECD R&D intensity thresholds

High Above 2.2% Above 8%

Medium-high Between 0.7% and 2.2% Between 2.5% and 8%

Medium-low Between 0.2% and 0.7% Between 1.0% and 2.5%

Low Less than 0.2% Less than 1.0%

Technology Class Type of manufacturing SIC codeMean R&D expenditure

as a fraction of sales (%)

Mean estimated

R&D intensity (%)

High 1. Electronics-communications 371+372+373 2.8 10.1

2. Scientific instruments 374+375+376 2 7.4

3. Other chemical products including pharmaceuticals 335 1.8 6.7

4. Other electrical machinery 366 0.8 3.9

5. Motor vehicles 381 0.4 3.1

6. Other transport equipment (excluding motor parts and bodies) 387 0.4 2.8

7. Special purpose machinery 357 0.5 2.6

8. Basic electrical machinery 36-366 0.5 2.2

9. Petroleum products 331+332+333 0.5 2

10. Non-ferrous metals 352 0.3 1.4

11. Non-metallic minerals 342 0.4 1.3

12. Publishing 324 0.3 1

13. Motor parts and bodies 382+383 0.1 0.8

14. Food, beverages, and tobacco 30 0.1 0.4

15. Ferrous metals 351 0.1 0.4

16. Paper printing (excluding publishing) 323+325+326 0.1 0.3

17. Fabricated metal products 354+355 0.1 0.2

18. Non-electrical machinery (excluding special purpose machinery) 356+358 0 0.2

19. Textiles and clothing 31 0 0.2

20. Rubber and plastic products 337+338 0 0.2

21. Chemicals (excluding other chemical products)† 334 0 0.1

22. Other manufacturing 392 0 0.1

23. Wood and furniture 321+322+391 0 0† Sales data for manufacture of man-made fibres (SIC=336) were not available.

Medium-low

Low

Medium-high

16

The South African industries classified into SA technology classes show some similarities but

also a number of differences with those of the OECD technology classes (cf. Table 1). The

industries are ranked using approximate R&D intensities as a means of comparison with the

OECD taxonomy. Overall, the SA R&D intensities are low compared with corresponding

industries in average OECD countries, as expected. Because South Africa has a lower

(overall) R&D intensity than more developed countries, the expectation was that, even in the

disaggregation, the R&D intensities would be relatively lower.

Some lumpiness in the data is evident. Non-ferrous metals manufacturing had fell within the

medium-low range between 2004 and 2005, but between 2006 and 2008, it lay within the

limits for the low technology class. The reason for this lumpiness may be due to the

infrequent nature of large R&D projects within this industry. Non-ferrous metals

manufacturing was classified as medium-low technology, but arguably would better belong in

the low technology class. Publishing was classified as medium-low technology since it

attained values within this technology range consistently between 2005 and 2008. The rise of

publishing from low to medium-low may be an indication of rising technology-intensiveness,

perhaps an early indication of increased productivity in information technology and

communication services.

The cluster analysis (Figure 4) had earlier classified electricity distribution apparatus and

electric motors under industries that had medium-high R&D intensity values. However, upon

visual examination it was decided to include these with industries in the low technology class.

These were collectively included under basic electrical machinery. Similarly, other electrical

machinery was separated from electrical machinery due to its relatively high R&D intensity

and also due to an outcome of the statistical cluster analysis that seemed to indicate that other

electrical machinery may be an industry on the cusp of medium-low and medium-high

technology. This industry often came out as an industry that form a single item cluster in the

various cluster analyses performed, but based on visual inspection of its mean estimated R&D

intensity between 2004 and 2008, it was decided to classify this under medium-high

technology, in line with the OECD threshold R&D intensity values.

One effect of using R&D intensity — that is R&D expenditure as a proportion of value added

— as a ranking variable was that the R&D intensities for some industries were very different

from what they would be if R&D expenditure as a percentage of manufacturing sales were

used. For example, manufacture of motor vehicles move to the top of the medium-low

17

technology class because the sales from manufacture of motor vehicles are 7.8 times that of

its manufacturing value added, whereas the average manufacturing sales is only 4.4 times that

of manufacturing value added. The rankings of industries are most glaringly different perhaps

for other chemicals including pharmaceuticals, which in South Africa is classified as medium-

high technology and ranked lower than scientific instruments in that technology class. This

may partially be due to the fact that the level of disaggregation used to classify

pharmaceuticals (3-digit SIC) is the same level as those for the other industries considered

here. Using a greater level of disaggregation would likely yield a higher R&D intensity for

pharmaceuticals, but that would most likely be true for any other industry. The industries of

special purpose machinery and publishing were classified as medium-low technology. This is

in contrast to the OECD classifications where they were combined with other low technology

groupings. This was expected for the special purpose machinery sector, which includes

manufacturing of machinery for mineral resource extraction and also machinery for other

manufacturing industries including the manufacture of weapons and ammunition, which are

areas where South Africa has historically produced high technology products. Petroleum

refining is ranked much higher in South Africa at number 9 in the medium-low technology

class, compared to 16 in OECD rankings of technology-driven industries. This may be

expected given the presence of influential actors in South Africa such as Petrosa with their

gas-to-liquids research, and SASOL with their gas-to-liquids and coal-to-liquids research

within this sector. An industry that does not appear in the OECD rankings but over 2004 to

2008 does emerge as a medium-low technology industry in South Africa is that of other

transport equipment (excluding parts and accessories for motor vehicles).

Figure 6: Both manufacturing sales for the four South African technology classes based on

Table 4 (left inset) and corresponding R&D expenditure as a percentage of sales (right inset)

18

show clear separations between classes signifying a reasonable taxonomy. They also

demonstrate the stationary nature of R&D investment and economic impact of the technology

intensive sectors.

The time series in Figure 6 demonstrate that the effect of industries on manufacturing sales

levels increase monotonically as the level of technology decreases. This is in contrast to the

results obtained when OECD-inspired technology classes were used as in Figure 2 and Figure

3, where this relationship is not as smooth – indeed the effect of the medium-low technology

class on the economy is very different between the two approaches. It is clear from Figure 6

that the low and medium-low technology classes experienced steady nominal growth in sales,

whereas the high and medium-high technology classes did not. The four technology classes

are clearly delineated in Figure 6, whereas they are not that clear and provide less, even

misleading, insight when OECD-inspired characteristics are used as in Figure 2 and Figure 3.

Conclusion and Discussion

The identification of technology classes by industry classification suitable for the South

African manufacturing sector is necessary to the placement of South Africa in terms of its

economic development particularly in considerations of the national system of innovation. It

is justifiably accepted that interest and resources must be devoted to strategic areas of

international importance such as biotechnology and nanotechnology, and perhaps also

quantum coherence phenomena as Marburger (2011) has suggested; given the potentially

huge returns such investments may have in the long term. However, a fundamental question

for policy makers in Science and Technology, Trade and Industry, Higher Education,

Economic Development and other departments concerned with socio-economic development

and planning is whether, in the shorter to long term, to execute plans to achieve strategic goals

relative to the technological performance of advanced OECD countries, or to do so based on

the existent capabilities of the South African nation. The argument here is for the latter,

requiring evaluation of plans against the organic development of technology strong industries.

These may then be benchmark against soundly identified comparable countries or success

stories. Even in OECD member states, although the original OECD classification scheme has

been updated at several stages, it may very well not be applicable anymore, since the nature of

intense innovation activity is such that it may transform the R&D profile of an economy over

possibly relatively short periods. This is so provided the nature of innovation takes place in

19

such a way that there is significant spill-over industry created as a result of the increased

technological nature of a particular sub-industry.

The findings arguably have confirmed what many experts would consider to be the R&D

intensive sectors are in the SA manufacturing division, but in a systematic manner which can

now be reproduced and used by decision makers in planning South Africa’s transition to a

knowledge based economy, for example. An interesting finding outcome from the systematic

approach applied here is the possible emergence of R&D intensiveness in the publishing

industry, perhaps a sign of rising technology intensiveness in the information technology and

communications economic sector. The results also do indicate (as expected) that there is still

propensity for R&D investment in various sectors when compared with corresponding OECD

intensities. Most notably, the high technology sectors were investing at levels that are still

below levels attained in other more developed countries. One reason for this may be the

existence of high-technology sectors where there is not enough high-technology competition

to drive R&D expenditure at a greater rate within those subsectors. This, along with a

shortage of skilled knowledge workers and other framework conditions, may have a

hindrance on the growth of the technology-intensiveness of the whole subsector, since there is

still great propensity for R&D in such situations.

Arguably the most useful application of the work presented here is to track the changing

economic profile of South African industry with regards to technology-intensive industries. It

follows from the technology classes identified by industry groupings in the body of this paper

that the impact of technology intensity on any macro-economic quantity within the

manufacturing division may be tracked and assessed. These quantities include all major

macro-economic variables like employment, gross domestic product and fixed capital

formation. The transition of the South African manufacturing industry towards production of

goods of greater or lower technology intensiveness may thus be mapped out by traditional

macro-economic quantities and the future national economic competitiveness may be

evaluated in a timely manner allowing for effective policy monitoring and intervention if

required. Evidence of the positive impact of trade in technology-intensive products combined

with high skill on GDP per capita for developing countries has been found. For example,

Basu and Das (2011) determined that with a sophisticated financial market system (combined

with favourable framework conditions in place such as institutional and human capacity) and

market access, it is generally the case that the higher the technology content of exports, the

greater the beneficial impact on GDP per capita. The usefulness of the technology

20

classifications developed for South Africa above is important in this context. It would now be

possible to track the organic growth and decline of technology-intensive exports in

manufacturing that takes account of the true R&D intensive industrial sectors in South Africa.

The existence of technology classes by industry naturally poses the question as to which

product classifications can be differentiated in a similar manner. It is becoming more

important to determine the path of technology-oriented products with regards to diffusion and

uptake in different sectors; the extent of the involvement of the services sector in this value

chain is of topical interest. This paper has not concerned itself with the technological

intensities of export products, since the study by Hatzichronoglou (1997) showed that

technology class is not qualitatively changed by including considerations of indirect R&D

intensity; implying that the product technology classes may be well approximated by the

industry classifications determined here and a consideration of the trading partners and

intensity of trade with these. Whilst a classification of technology class by product is more

appropriate for considerations of trade, this has not been addressed here and is left as a

discussion for the future.

This work has considered the manufacturing industry only, for reasons of comparison and

ready availability of suitably disaggregated data. A full consideration of all industries is

arguably the ideal terrain within which to conduct a full determination of technology classes

for South Africa, perhaps using the methodology outlined in the body, since the most highly

R&D intensive industries may not be just in manufacturing as those considered here. There is

also an expectation of growing R&D intensiveness in the services industry, given the nature

of the SA economy. A consideration of technology classes incorporating all sectors is

arguably necessary, but outside the scope of this article. This is not to deny the importance of

the classification scheme for manufacturing produced here, given the traditional importance

and labour intensiveness of the manufacturing sector in South Africa.

Bibliography

BASU, S. R. & DAS, M., 2011. Export Structure and Economic Performance in Developing

Countries: Evidence from Nonparametric Methodology, New York and Geneva: United

Nations.

CESTII, 2011. National Survey of Research and Experimental Development (2008/09 Fiscal

Year), Pretoria: Department: Science and Technology, Republic of South Africa.

21

DST, 2008. Ten Year Plan – Innovation towards a Knowledge Economy, Pretoria:

Department: Science and Technology, Republic of South Africa.

FLOREK, K., LUKASZEWICZ, J., PERKAL, J. & ZUBRZYCKI, S., 1951. Sur la Liaison et

la Division des Points d’un Ensemble Fini. Colloquium Mathematicae, Issue 2, p. 282–285.

GAULT, F., 2010. Innovation Strategies for a Global Economy: Development,

Implementation, Measurement and Management. Ottawa (ON): International Development

Research Centre.

GOWER, J. C., 1967. A Comparison of Some Methods of Cluster Analysis. Biometrics, Issue

23, p. 623–637.

GOWER, J. C. & ROSS, G., 1969. Minimum spanning trees and single linkage cluster

analysis. Journal of the Royal Statistical Society Series C (Applied Statistics), 18(1), pp. 64-

64.

HATZICHRONOGLOU, T., 1997. Revision of the High-Technology Sector and Product

Classification, Revision of the High-Technology Sector and Product Classification, OECD

STI Working Paper 1997/2, Paris: Organisation for Economic Cooperation and Development.

LANCE, G. & WILLIAMS, W. T., 1967. A General Theory of Classificatory Sorting

Strategies. I. Hierarchical Systems. Computer Journal, Issue 9, p. 373–380.

LOSCHKY, A., 2010. Reviewing the Technology for High-Technology - the Sectoral

Approach, Luxembourg: European Commission.

MANI, S., 2004. Exports of high technology products from developing countries; Are the

figures real or are they statistical artefacts? In: S. Mani & H. Romijn, eds. Innovation,

Learning, and Technological Dynamism of Developing Countries. Hong Kong: United

Nations University Press, pp. 12-47.

MARBURGER III, J. H., 2011. Science, Technology and Innovation in a 21st Century

Context. Policy Sciences, 27 Sep, 44(3), pp. 209-213.

MARCATO, G. & MALFI, L., 2007. Testing the OECD Classification of Manufacturing

Industries based on Technology. [Online]

Available at: http://www.decon.unipd.it/personale/curri/marcato/testing-OECD.pdf [Accessed

26 June 2012].

22

MUKHERJEE, S., 2009. Technology intensity of Indian merchandise exports. [Online]

Available at: http://mpra.ub.uni-muenchen.de/35694/ [Accessed 26 June 2012].

OECD, 2002. Frascati Manual: Proposed Standard Practice for Surveys on Research and

Experimental Development, Paris: Organisation for Economic Cooperation and Development.

OECD, 2010. OECD Science, Technology and Industry Outlook 2010, Paris: Organisation for

Economic Cooperation and Development.

PENEDER, M., 2003. Industry classifications: Aim, scope and techniques. Journal of

Industry, Competition and Trade, Issue 3, p. 109–129.

SOKAL, R. R. & MICHENER, C. D., 1958. A Statistical Method for Evaluating Systematic

Relationships. University of Kansas Science Bulletin, Issue 38, p. 1409–1438.

SORENSEN, T., 1948. A Method of Establishing Groups of Equal Amplitude in Plant

Sociology Based on Similarity of Species Content and Its Application to Analyses of the

Vegetation on Danish Commons. Biologiske Skrifter, Issue 5, p. 1–34.

SRHOLEC, M., 2007. High-tech exports from developing countries: A symptom of

technology spurts or optical illusion? Review of World Economics, 143(2), pp. 227-255.

STATS SA, 2004. Standard Industrial Classification. [Online]

Available at: http://www.statssa.gov.za/additional_services/sic/sic.htm [Accessed 26 June

2012].

WALWYN, D., 2008. A Target for South Africa’s Business Expenditure on Research and

Development Based on Industry Structure. South African Journal of Science,

September/October, Volume 104, pp. 340-344.

WARD, J. H., 1963. Hierarchical Grouping to Optimize an Objective Function. Journal of the

American Statistical Association, Issue 58, p. 236–244.

WONG, M. A. & LANE, T., 1983. A k-th Nearest Neighbour Clustering Procedure. Journal

of the Royal Statistical Society. 45(3), pp. 362-368.