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1The Technological Structure and Performance of South African

Manufactured Exports, (2010 – 2014)

W. Flowerday, J.G. Goddard, A. Steenkamp; (2017)

Working Paper Number ######

The Technological Structure and Performance of South African Manufactured

Exports, (2010 – 2014):

Are South African manufactured goods becoming more sophisticated?

Wayde Flowerday; John Gabriel Goddard, Andre Steenkamp

Abstract here:

August 2017




Acknowledgements

-Treasury

-SARS

-Support team

-DST et al.




Abbreviations

BRICS Brazil, Russia, India, China, and South Africa

CIT Company Income Tax

HS Harmonised System

IMF International Monetary Fund

ISIC International Standard Industrial Classification

NDP National Development Plan

NEC Nowhere Else Classified

NT National Treasury

OECD Organisation for Economic Co-Operation and Development

R&D Research and Development

SAEU South African Economic Update

SARS South African Revenue Service

SITC Standard International Trade Classification

VAT Value Added Tax

WBG World Bank Group




ContentsAcknowledgements...................................................................................................................2

Abbreviations............................................................................................................................3

1. Introduction...........................................................................................................................5

2. Background...........................................................................................................................6

3. Data and methodology........................................................................................................10

4. Analysis of technological sophistication of South Africa’s manufactured exports........16

4.1 What is the composition of South African exports in terms of technology?............18

4.2 How diversified are South African exports?...............................................................21

4.3 Where are South African exports going?....................................................................22

4.4 Who are the high technology firms – what do they look like?..................................25

4.5 How Much Do Firm Characteristics Matter For High Technology Exporting?.......29

XXX. References....................................................................................................................31

Appendix A – Description of used variables from SARS-NT dataset................................32




1. Introduction Reducing poverty and inequality by generating better job opportunities remains the

number one priority of development policy in South Africa. The urgency of faster

economic growth, as well as more inclusive economic growth, has long been recognized

by South African policy makers and is well reflected in the National Development Plan

(NDP) of 2012. However, structural and policy bottlenecks make the challenge of

reducing poverty and inequality difficult to resolve. One key area mentioned n the NDP is

“the legacy of highly concentrated industries that have limited competition and efficiency

gains”. The document goes further to note that South Africa suffers from uncompetitive

goods and services as a result of pre-1990s growth patterns and the autarchy of the

economy in earlier years. Whilst this results in higher profit margins, the incentives to

invest and innovate remain weak; new firms are not entering the market or growing to the

point where they can effectively compete with the industrial champions of the past, and as

a result, employment creation is low (Banda et al., 2015).

Cyclical factors and external shocks have impacted aggregate demand and employment,

as well as the fiscal space available to the Government, magnifying the challenges facing

South Africa. South African growth slowed to 1.5 percent in 2014 owing to the end of the

commodity super cycle, which continued throughout 2015. The fall in global commodity

process and the slowdown in China have largely driven the deceleration in South Africa.

Domestic factors and policy missteps have also contributed to the slowdown by deterring

investment. To reactivate growth, the World Bank (2016) South African Economic

Update has underlined several areas for growth and development, one of the more critical

being that of innovation.

Despite greater openness to trade over the past two decades, the South African export

sector remains highly concentrated1. The top 5 percent of South Africa’s exporting firms

account for more than 90 percent of exports. Despite their dominance, these super-

exporters appear to be losing dynamism and competitiveness, particularly after the global

financial crisis, which saw them create fewer new products and enter fewer, new markets

abroad (World Bank, 2016).

1 A fact which holds true even when commodity exports are excluded.




Realising the shortcomings of the commodity-driven growth model, the South African

government committed to programmes and policies that promote industrial development.

Such policy orientation builds on the expectation that industrial development could

potentially generate several positive outcomes (SAEU, 2016). Coupling this sentiment

with the context of increased economic globalisation as well as increased worldwide

technological spill-over, technology emerges as a key factor in enhancing growth and

competitiveness of an economy.

The last decade has shown that products which embody higher technological aspects are

the fastest growing segment of international trade. Furthermore, due to greater openness

of trade, and faster diffusion of new technologies across countries, developing nations

have increased their contribution towards high-tech products in international trade.

Utilising new-to-the-world, highly disaggregated trade data, this paper explores the

linkages between technology embedded in South African exports, innovation, and

economic growth for South Africa.

2. Background There is growing body of evidence that argues that what a firm/country exports is

important for growth – especially within the context of developing nations.

Hatzichronoglou (1997) claims that firms which are technology-intensive, innovate more,

win new markets, and use available resources more productively while generally offering

a higher rate of remuneration to their employees – the end result being an increase in

economic growth of a country.

Hausmann et al., (2007) illustrate that a significant predictor of economic growth within a

developing country is the extent to which the developing nation’s export basket overlaps

with that of developed countries. Not all goods are created equal in terms of their

consequences for economic growth. Certain goods may offer greater potential for forward

and backward linkages, yield higher knowledge spillovers, or even offer an easier

pathway towards products with such characteristics (IMF, 2012). Over time, the




sophistication of a country’s exports may evolve, and hopefully move into newer, more

sophisticated products.

Considering earlier literature only bolsters this argument; Dalum et al. (1999) stresses that

better growth prospects are typically interlinked with exporting products with higher

income elasticity2. Furthermore, Lall (2000) states that low technology products tend to

grow slower than that of high-technology products. Sophisticated sectors are more likely

to act as an engine of growth for the broader economy, rather than turn into isolated

enclaves (IMF, 2012). Effects of this nature in South Africa have already been partially

captured by the work of Rankin and Schöer (2013). The authors illustrate that there is a

link between export destination and product quality in South Africa – interpreting this

another way allows us to state that in order for South African firms to adequately export

into different regions, they must have a product of higher quality (sophistication), thus in

essence implying that some form of product or process innovation must have taken place

at the firm level.

One of the noticeable trends in South Africa is that innovation expenditure has been

falling relative to GDP while other fast-growing emerging countries have forged ahead

(shown in figure 1). The ratio of research and development (R&D) expenditure to GDP in

South Africa – 0.73% (2012) - is the lowest among the BRICS countries (e.g. China

1.93%, Brazil 1.15%). The number of Triad patents is also lower than in the other

BRICS. The exception is the mining and fuels sub-sectors which have patents and

research and development R&D comparable to its competitors, the US, Canada and

Australia.

2 This is often the case with technologically-intensive products.




Figure 1: R&D expenditure as a percentage of GDP

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 20140

0.5

1

1.5

2

2.5

BrazilChinaIndiaRussian FederationSouth Africa

Source: World Bank Group (2017)

The relatively slow absorption of new technologies and limited domestic investments in

innovation are two likely explanations as to why South Africa has experienced declining

productivity at a time when most BRICS peers were experiencing rapid gains in

productivity and overall growth. This slowdown in growth has also been accompanied by

stagnating performance of South Africa’s exports. Poor export performance has resulted

in a 15 percent decline in South Africa’s share of world exports since 2011.

Moreover, South Africa has larger public innovation expenditure relative to private

expenditure, which limits the short-to medium term economic impact. This public

expenditure is allocated largely towards scientific innovation. While South Africa was

known as a global leader in specific industries such as mining, but innovation

expenditures have been shifted to other countries, and firms in mining equipment are less

prone to developing their intellectual property locally. Outside of mining and related

areas, South Africa has few patents, and producers tend to be technological followers

rather than leaders.




Looking at the highly aggregated levels of high-technology exports (as a percentage of

manufacturing export value) over time, it can be seen that South Africa is lagging relative

to comparator BRIC nations (figure 2).

Figure 2: High-technology exports as a percentage of manufactured exports

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 20150

5

10

15

20

25

30

35

BrazilChinaIndiaRussian FederationSouth Africa

Source: World Bank Group (2017); Comtrade Database

Adding to the complexity of the issues highlighted above, is the lack of diversification of

South African exports in the last 20 years. As it can be seen in figure 3, there are only a

few areas of increased diversification, with the majority of South Africa’s exports still

lying within the natural resource class. The areas showing dramatic increases are areas

that require the advancement of scientific knowhow and technological advancement –

however, this has not been truly unpacked and disentangled through the literature up until

this point. The concentration of South Africa’s exports, along with the lack of extensive

innovation, is consistent with the country’s higher concentration of export market share

than that of its peers, and the higher price– cost margins associated with it.




Figure 3: The export basket of South Africa (2005 and 2015)

2005 2015

Source: Atlas of Complexity (2017)

This paper fills that gap by utilising the classification of export transactions to determine

the level of export sophistication of South Africa; furthermore, this paper utilises this

export sophistication of firms as a proxy for firm innovation. The observed level of

sophistication can be classed using various classification schemes which can reflect the

technology and innovation embedded in these specific products.

The following sections of this paper will outline these schemas; the data utilised to

conduct this study; the results obtained; and finally, policy relevant conclusions.

3. Data and methodologyAs previously mentioned, this paper makes use of a new-to-the-world dataset – that of the

South African Revenue Service and National Treasury (SARS-NT) firm level dataset.

Utilising firm-level administrative tax records for research has become an internationally

widespread phenomenon, mostly extending to researchers that aim to do cutting-edge

research. The dataset utilised by this paper is an unbalance panel that was created through

the merging of several sources of administrative tax data. The panel runs from 2008 until

20153, and is comprised of 4 primary sources:

3 2008 and 2015 exist within the panel, however these years are poorly populated relative to the other years within the panel – as a result, this panel restricts analysis to between 2009 and 2015.




(i) Company income tax (CIT) data from registered firm whom submit tax forms;

(ii) Employee data from employee income tax certificates (IRP5s) that are

submitted by employees;

(iii) Value-added tax (VAT) data from registered firms; and

(iv) Customs transaction data from firms which engage in import/export

behaviours.

For the purposes of this paper, the primary focus will is on the data gathered from (iv) -

the customs transaction data – which grants this paper approximately 13,500,000

observations for analysis. Customs data are collected at a transaction level, and each trade

is assigned its own line-item within the dataset. These customs records contain

information about the products imported and exported by traders, including the product

codes4, value and volume of goods, as well as origin and destination information 5

(Pieterse et al., 2016). Utilising unique firm identifiers 6, firm characteristics were

matched to these transactions, allowing this paper to further disaggregate by firm size,

employment, and R&D expenditure.

The aforementioned classification system is crucial to the methodology that this paper

employs. When considering the classification of technological sophistication of a firm’s

exports, there are two generally accepted approaches, (i) the sectoral approach; and (ii)

the product approach. In this context the latter is assumed to supplement the former for a

more detailed analysis.

The sectoral classification system was developed by the Organisation for Economic Co-

Operation and Development (OECD) and utilises R&D intensity as the sole criterion upon

which the classification system is built7.

4 These products are captured using the Harmonised Commodity Description and Coding System (HS), level 4 and 6, which allows this paper to accurately unpack what products each South African firm is trading, in a way that was never possible before.5 A description of the variables from this dataset, which were used for this paper, is offered in Appendix A. 6 Such as tax reference numbers, and customs registration numbers.7 See Hatzichronoglou (1997).




The classification is based on the importance of expenditures on R&D expenditure

relative to the gross output and value added of different types of industries that produce

goods for export. In brief, industries are assigned as either: (i) high technology, (ii)

medium-high technology, (iii) medium-low technology, or (iv) low technology, based on

the industry’s level of R&D intensity.

According to the OECD, high-tech sectors are those with R&D intensity above 5%,

medium-high technology consists of firms with between 3 & 5% R&D intensity, while

medium-low technology sectors have between 0.9 & 3%. Finally, low technology sectors

have less than 0.9% R&D intensity. Following these 4 categories, and utilising the

International Standard Industrial Classification8 (ISIC), the groupings shown in table 1

were determined.

Table 1: Manufacturing industries classified according to OECD sectoral classification system

Classification ISIC Code (Revision 3)

High-technologyAircraft and spacecraft 353Pharmaceuticals 2423Office, accounting and computing machinery 30Radio, TV and communications equipment 32Medical, precision and optical instruments 33

Medium-high-technologyElectrical machinery and apparatus n.e.c. 31Motor vehicles, trailers and semi-trailers 34Chemicals excluding pharmaceuticals 24 excl. 2423Railroad equipment and transport equipment n.e.c. 352+359Machinery and equipment n.e.c. 29

Medium-low-technologyBuilding and repairing of ships and boats 351Rubber and plastics products 25Coke, refined petroleum products and nuclear fuel 23Other non-metallic mineral products 26Basic metals and fabricated metal products 27-28

Low-technologyManufacturing n.e.c., recycling 36-37Wood, pulp, paper, paper products, printing and publishing 20-22

8 Revision 3 – concordance tables are available to map ISIC Rev.3 to the more recent ISIC Rev.4.




Food products, beverages, and tobacco 15-16Textiles, textile products, leather and footwear 17-19

Source: OECD (2017)Note: Based on data for 12 OECD countries: United States, Canada, Japan, Denmark, Finland, France,

Germany, Ireland, Italy, Spain, Sweden, and United Kingdom.

The product approach to classification takes the aforementioned classifications a step

further, with the primary argument being that industrial sectors specialising in a few high-

technology products may also produce low-technology products; thus making the product

approach more appropriate when talking about the high-technology elements of

international trade (Hatzichronoglou, 1997). This classification system leaves the

medium-high-technology, medium-low-technology, and low-technology specifications

the same, but excludes specific products from the high-technology group. This method

takes into account R&D intensity in the same way that the sectoral approach does,

however, it also considers levels of human capital, numbers of scientific personnel, and

technology embedded in patents. The further disaggregation of the high-technology

classification is shown in table 2, below:

Table 2: High-technology manufacturing industries classified according to OECD product classification system 9

Classification SITC Code (Revision 3)

Aerospace [7921+7922+7923+7924+7925+79293+(714-71489-71499)+87411]

Computers; & office machines [75113+75131+75132+75134+(752-7529)+75997]

Electronics; & telecommunications

[76381+76383+(764-76493-76499) +7722+77261+77318+77625+7763+7764+7768+89879]

Pharmacy [5413+5415+5416+5421+5422]

Scientific instruments [774+8711+8713+8714+8719+87211+(874-87411-8742)+88111+88121 +88411+88419+89961+89963+89967]

Electrical machinery [77862+77863+77864+77865+7787+77844]

Chemistry [52222+52223+52229+52269+525+57433+591]

Non-electrical machinery [71489+71499+71871+71877+72847+7311+73131+73135 +73144+73151+73153+73161+73165+73312+73314+73316

9 Full definitions of the SITC codes shown in table 2 are explained in Hatzichronoglou (1997).




+73733+73735]

Armament [891]Source: OECD (2017)Note: Based on data for 12 OECD countries: United States, Canada, Japan, Denmark, Finland, France,

Germany, Ireland, Italy, Spain, Sweden, and United Kingdom.

In order to get the data utilised by this paper to match up with the classifications that have

been explained, correspondence tables have been utilized10. Correspondence tables map

particular product classification schemes to other product classification schemes.

Unfortunately, there is no straightforward combination of correspondence tables that can

be utilised to get from the HS-2012 classification to that of the ISIC Rev.3 – thus it is

required that we utilise a correspondence table to convert from HS-2012 codes to HS1996

codes, and then another correspondence table to convert from HS-1996 to ISIC Rev.3.

Fortunately, SITC codes are more straightforward as correspondence tables exist taking

HS-2012 to SITC Rev.3.

The final classification structure used by this paper is shown in table 3, below. Any

products that are not classified under the system that has been explained here are

categorised as “other” for the purposes of our analysis – the bulk of which is made up of

natural resource exports.

Table 3: Final classification system utilised

High technology industries Medium-high technology industries Aerospace Computers; & office machinery Electronics; & telecommunications Pharmacy Scientific instruments Electrical machinery Chemistry Non-electrical machinery Armament

Electrical machinery and apparatus n.e.c. Motor vehicles, trailers and semi-trailers Chemicals excluding pharmaceuticals Railroad equipment and transport

equipment n.e.c. Machinery and equipment n.e.c.

Medium-low technology industries Low technology industries Building and repairing of ships and boats Rubber and plastics products Coke, refined petroleum products and

nuclear fuel Other non-metallic mineral products Basic metals and fabricated metal products

Manufacturing n.e.c., recycling Wood, pulp, paper, paper products, printing

and publishing Food products, beverages, and tobacco Textiles, textile products, leather and

footwear

10 These tables are available from the United Nations Statistics Division.




The classification system imposed by this paper includes one further category, “other”.

This category is used to account for exports which have not been classified elsewhere.

Before moving to present results based on this classification scheme, this paper offers

definitions of these categories with respect to the levels of technology, labour, and capital

generally included within them.

Low technology (LT): These are products that tend to have stable, well-diffused

technologies (Lall, 2000). The technologies utilised in this area are primarily

embodied in the capital equipment, and the low end of the range has relatively

simple skill requirements in terms of the labour employed. Many traded products

in this category are undifferentiated and are therefore reliant on price structures to

be competitive – hence, labour costs present a significant consideration for firms

operating in this technological area. Scale economies and barriers to entry are

generally low, and the market grows slowly with income elasticities below unity

(Lall, 2000). It is notable that developing countries tend to express great interest in

operating within this technological area, hence a great focus on price competition

rather than quality competition. The majority of goods produced within this

technological band have seen great movement from rich to poor countries over the

past years.

Medium Technology (MT): For the sake of this paper, medium technology is

broken into two sub-groups, (i) medium-low technology, and (ii) medium-high

technology. On aggregate, medium technology represents an area that comprises

the bulk of skill and scale-intensive technologies in capital good and intermediate

products – this is the mainstay of industrial activity in mature economies (Lall,

2000). They tend to have complex technologies with moderately high levels of

R&D, advanced skill needs, and longer learning periods. The distinction between

medium-low technology, and medium-high technology, is based on the difference

between these two sub-groups based on the R&D intensity calculations provided

by the OECD.




High technology: These products tend to have advanced and fast-changing

technologies, with high R&D investment, and an emphasis on product design

(Lall, 2000). The most advanced technologies often require sophisticated

technology infrastructure, high levels of specialised technical skills, and close

interactions between firms, as well as in between firms and research institutions.

Based on the aforementioned specifications, we calculate the following R&D intensity for

South African exporting firms, per technology category.

Figure XX: R&D Intensity Per Technology Category (2014)

L.T M.L.T M.H.T H.T0

0.05

0.1

0.15

0.2

0.25

0.3

R&D Intensity

4. Analysis of technological sophistication of South Africa’s

manufactured exports

To ensure that the SARS-NT dataset provides comparable results with what has already

been widely accepted as a world standard, the OECD methodology was imposed on both

the SARS-NT dataset, as well as that of the Comtrade dataset.




Figure 4 illustrates the value of high technology exports (as a percentage of manufactured

exports) based on both the Comtrade and SARS-NT data. This comparison allows us to

remain confident that the SARS-NT data being utilised is representative enough for our

purposes – especially since similar results are achieved when analysing high technology

export value through a macro lens (Comtrade data), versus a micro lens (SARS-NT data).

Figure 4: High technology export value (as a percentage of manufactured exports)

SARS-NT data versus Comtrade data

The preceding figure illustrates that South Africa clearly has a low percentage of high

tech products making up the value of the South African export basket – even though this

value has been increasing over the 5 year period under review.




4.1 What is the composition of South African exports in terms of technology?

Figure 5 illustrates the composition of the South African manufacturing export market in

2014. As the figure illustrates, “other” and medium technologies comprise the bulk of the

export basket. This is not surprising as South Africa has long been a country that has been

known for its exports of minerals, and resource based products – all of which make up the

category of “other”. Medium-low technology, and medium-high technology account for

almost the same percentage (approximately 47% in total) – implying that South Africa is

mostly locked in the medium technology space (which as was explained earlier is

typically a space that is reflective of complex technologies, with moderate to high levels

of R&D investment).

Figure 5: Export value (as percentage of manufactured exports) of different

technology levels in the South African total export (%) in 2014




Table 4: Export value (as percentage of manufactured exports) of different

technology levels in the South African total export (%)

2010 2011 2012 2013 2014Low Technology 11.67 11.04 11.28 12.86 13.04Medium-Low Technology 27.70 21.95 23.34 24.13 24.91Medium-High Technology 22.35 24.84 23.89 22.62 22.00High Technology 5.10 5.78 6.13 5.96 7.08

Source: Authors’ CalculationsNote: Columns do not sum to 100% due to the fact that some export transactions of the total export

value fall outside of the export classification system that was imposed i.e. Resourced Based exports.

As can be seen in table 4, the export value (as a percentage of manufactured exports) has

remained fairly consistent over time, when disaggregating by technological classification.

The high technology sector has shown moderate growth over this time frame, with slight

trade-offs occurring in the lower technology brackets.

However, none of these changes have been dramatic. Figure 6 illustrates the Kdensity of

customs values when disaggregated by technology type. As shown in the figure below,

the changes appear to be almost negligible in terms of their distribution, with only the

peak of each curve shifting minorly over time.




Over time, exports have been shifting increasingly toward medium- and high-technology sectors,

with corresponding demand for high skills and capital investment. In fact, the only two sectors in

which South Africa has gained in both revealed comparative advantage and global market share

since 2000 are among the most sophisticated—industrial machinery and transport equipment. In

these sectors South Africa is competing largely on quality rather than price in global markets

(World Bank, 2014). By contrast, exports have performed least well in manufacturing sectors that

are labor- and (in some cases) material-intensive (World Bank, 2014). It is plausible that the

discrepancy here occurs due to increasingly strong labour legislation (Flowerday et al., 2017) as

South African firms are quick to adapt to more capital intensive means of production when the

perceived cost of labour is increased (Flowerday et al., 2016).

The factor content of South Africa’s exports reveals that exports are concentrated in products

with human capital and especially physical capital intensity far beyond those in South Africa’s

endowments (World Bank, 2014). For example, South Africa’s export basket is associated with

products produced by countries in which 67 percent of the employed labor force has

postsecondary education, but less than 21 percent of the employed South African labor force has

at least some tertiary education (World Bank, 2014).

4.2 How diversified are South African exports?

The good news is that South Africa is not trapped in low-technology exports, giving it substantial

scope for further upgrading and for competing on quality and price (also reflected by table 4).

The bad news is that the mismatch with endowments suggests that this positioning reflects a

strategic response to domestic constraints rather than a strategy following comparative advantage.

This raises concerns about the sustain-ability of South Africa’s competitiveness in non-mineral

exports, especially over the longer term, and whether the export sector (under its current model of

competitiveness) can contribute much to inclusive growth (World Bank, 2014). However, there

does appear to be a further catch-22 to consider, and this is reflected by the upward trend in the

number of unique exports by firms over time. This is indicative of a growing product

diversification (albeit a slow one), with positive growth shown in the medium, and high

technology spaces. This has positive implications for competitiveness but underutilizes South




Africa’s large pool of low-skilled labor, thus failing to create enough jobs to make the export

sector a major direct contributor to employment growth and poverty reduction.

Figure 7: Average Number Of Unique Hs6 Products Exported Annually Per Firm

(2010 -2014), by technology type

Whilst product diversification does seem to be growing slowly over time, it is apparent

that the main exports per technology group remain the same. The following table

illustrates that the top 5 exports per technology category remain similar (if not entirely the

same) over the 5 year sample period.

Table 4: Top 5 exports (in terms of total export value) (2010-2014) – Low

Technology

Low

Tec

hnol

ogy

2010

Wine; still, in containers holding 2 litres or lessCigarettes; containing tobaccoWood; for fuel, in chips or particles, non-coniferous, whether or not agglomeratedDiamonds; non-industrial, (other than unworked or simply sawn, cleaved or bruted), but not mounted or setCoin; other than coin of item no. 7118.10

2011

Food preparations; n.e.c. in item no. 2106.10Wine; still, in containers holding 2 litres or lessWood; for fuel, in chips or particles, non-coniferous, whether or not agglomeratedWood pulp; chemical wood pulp, dissolving gradesDiamonds; non-industrial, (other than unworked or simply sawn, cleaved or bruted), but not mounted or set

2012

Wine; still, in containers holding 2 litres or lessWood; for fuel, in chips or particles, non-coniferous, whether or not agglomeratedWood pulp; chemical wood pulp, dissolving gradesDiamonds; non-industrial, (other than unworked or simply sawn, cleaved or bruted), but not mounted or setCoin; other than coin of item no. 7118.10

2013

Wine; still, in containers holding 2 litres or lessWood; for fuel, in chips or particles, non-coniferous, whether or not agglomeratedWood pulp; chemical wood pulp, dissolving gradesDiamonds; non-industrial, (other than unworked or simply sawn, cleaved or bruted), but not mounted or setCoin; other than coin of item no. 7118.10




2014

Wine; still, in containers holding 2 litres or lessWine; still, in containers holding 2 litres or lessWood; for fuel, in chips or particles, non-coniferous, whether or not agglomeratedDiamonds; non-industrial, (other than unworked or simply sawn, cleaved or bruted), but not mounted or setCoin; other than coin of item no. 7118.10

Table 5: Top 5 exports (in terms of total export value) (2010-2014) – Medium Low

Technology

Med

ium

Low

Tec

hnol

ogy

2010

Metals; platinum, unwrought or in powder formMetals; platinum, semi-manufacturedMetals; rhodium, unwrought or in powder formFerro-alloys; ferro-manganese, containing by weight more than 2% of carbonFerro-alloys; ferro-chromium, containing by weight more than 4% of carbon

2011

Metals; platinum, unwrought or in powder formMetals; platinum, semi-manufacturedMetals; palladium, unwrought or in powder formFerro-alloys; ferro-chromium, containing by weight more than 4% of carbonAluminium; unwrought, (not alloyed)

2012

Metals; platinum, unwrought or in powder formMetals; platinum, semi-manufacturedMetals; palladium, unwrought or in powder formMetals; rhodium, unwrought or in powder formFerro-alloys; ferro-chromium, containing by weight more than 4% of carbon

2013

Petroleum oils and oils from bituminous minerals, not containing biodiesel, not crude, not waste oils; preparations n.e.c, containing by weight 70% or more of petroleum oils or oils from bituminous minerals; light oils and preparationsMetals; platinum, unwrought or in powder formMetals; platinum, semi-manufacturedMetals; palladium, unwrought or in powder formFerro-alloys; ferro-chromium, containing by weight more than 4% of carbon

2014

Petroleum oils and oils from bituminous minerals, not containing biodiesel, not crude, not waste oils; preparations n.e.c, containing by weight 70% or more of petroleum oils or oils from bituminous minerals; light oils and preparationsMetals; platinum, unwrought or in powder formMetals; platinum, semi-manufacturedMetals; palladium, unwrought or in powder formFerro-alloys; ferro-chromium, containing by weight more than 4% of carbon

Table 6: Top 5 exports (in terms of total export value) (2010-2014) – Medium High

Technology

Med

ium

Hig

h Te

chno

logy 20

10

Phosphoric acid and polyphosphoric acidsAcyclic hydrocarbons; unsaturated, n.e.c. in heading no. 2901Acids; unsaturated acyclic monocarboxylic acids; esters of acrylic acidVehicles; with only spark-ignition internal combustion reciprocating piston engine, cylinder capacity over 1500 but not over 3000ccVehicles; compression-ignition internal combustion piston engine (diesel or semi-diesel), for transport of goods, (of a gvw not exceeding 5 tonnes), n.e.c. in item no 8704.1

2011

Phosphoric acid and polyphosphoric acidsAcyclic hydrocarbons; unsaturated, n.e.c. in heading no. 2901Machinery; for filtering or purifying gases, other than intake air filters for internal combustion enginesVehicles; with only spark-ignition internal combustion reciprocating piston engine, cylinder capacity over 1000 but not over 1500ccVehicles; with only spark-ignition internal combustion reciprocating piston engine, cylinder capacity over 1500 but not over 3000cc

2012

Acyclic hydrocarbons; unsaturated, n.e.c. in heading no. 2901Acids; saturated acyclic monocarboxylic acids; ethyl acetateIndustrial fatty alcoholsMachinery; for filtering or purifying gases, other than intake air filters for internal combustion engines




Vehicles; with only spark-ignition internal combustion reciprocating piston engine, cylinder capacity over 1500 but not over 3000cc

2013

Phosphoric acid and polyphosphoric acidsAcyclic hydrocarbons; unsaturated, n.e.c. in heading no. 2901Machinery; for filtering or purifying gases, other than intake air filters for internal combustion enginesMachinery; parts of machines handling earth, minerals or ores and n.e.c. in heading no. 8431Machines, for sorting, screening, separating, washing, crushing etc mineral substances, for agglomerating, shaping or moulding solid fuels, ceramic pastes etc, for forming foundry moulds of sand; parts

2014

Acyclic hydrocarbons; unsaturated, n.e.c. in heading no. 2901Alcohols; saturated monohydric, butan-1-ol (n-butyl alcohol)Machinery; for filtering or purifying gases, other than intake air filters for internal combustion enginesMachines, for sorting, screening, separating, washing, crushing etc mineral substances, for agglomerating, shaping or moulding solid fuels, ceramic pastes etc, for forming foundry moulds of sand; partsVehicle parts and accessories; n.e.c. in heading no. 8708

Table 7: Top 5 exports (in terms of total export value) (2010-2014) – High

Technology

High

Tec

hnol

ogy

2010

Machinery; parts and accessories (other than covers, carrying cases and the like) of the machines of heading no. 8471Boards, panels, consoles, desks and other bases; for electric control or the distribution of electricity, (other than switching apparatus of heading no. 8517), for a voltage not exceeding 1000 voltsTanks and other armoured fighting vehicles; motorised, whether or not fitted with weapons, and parts of such vehiclesAeroplanes and other aircraft; of an unladen weight exceeding 2000kg but not exceeding 15,000kgAmmunition; cartridges and parts thereof n.e.c. in heading no. 9306

2011

Machinery; parts and accessories (other than covers, carrying cases and the like) of the machines of heading no. 8471Tanks and other armoured fighting vehicles; motorised, whether or not fitted with weapons, and parts of such vehiclesAeroplanes and other aircraft; of an unladen weight exceeding 2000kg but not exceeding 15,000kgMeters; electricity supply or production meters, including calibrating meters thereofAmmunition; n.e.c. in chapter 93

2012

Uranium; natural uranium and its compounds, alloys, dispersions (including cermets), ceramic products and mixtures containing natural uranium or natural uranium compoundsCommunication apparatus (excluding telephone sets or base stations); machines for the reception, conversion and transmission or regeneration of voice, images or other data, including switching and routing apparatusTanks and other armoured fighting vehicles; motorised, whether or not fitted with weapons, and parts of such vehiclesAeroplanes and other aircraft; of an unladen weight exceeding 2000kg but not exceeding 15,000kgMeters; electricity supply or production meters, including calibrating meters thereof

2013

Engines; for aircraft, spark-ignition reciprocating or rotary internal combustion piston enginesTelephones for cellular networks or for other wireless networksTanks and other armored fighting vehicles; motorised, whether or not fitted with weapons, and parts of such vehiclesAeroplanes and other aircraft; of an unladen weight exceeding 2000kg but not exceeding 15,000kgAmmunition; n.e.c. in chapter 93

2014

Engines; for aircraft, spark-ignition reciprocating or rotary internal combustion piston enginesTurbo-jets; of a thrust exceeding 25kNTelephones for cellular networks or for other wireless networksAeroplanes and other aircraft; of an unladen weight exceeding 2000kg but not exceeding 15,000kgAmmunition; n.e.c. in chapter 93




4.3 Where are South African exports going?

At the beginning of the 2000s nearly 60 per-cent of South Africa’s merchandise exports

went to Organization for Economic Co-operation and Development (OECD) countries,

most of that to Europe. But over the next decade exports to EU and OECD markets

stagnated, while exports to BRICs, most notably China, exploded on the back of the

commodities boom. By 2011/12 the EU’s share of South Africa’s exports had fallen to

just 21 percent, while the BRICs’ share had grown from less than 5 percent to more than

19 percent (World Bank, 2014).




But stripping out mineral ores, metals, and fuels reveals that the Sub-Saharan African

market is where South Africa’s real export dynamism lies. In non-mineral sectors the

BRICs’ share of exports has grown only from 5 percent to 9 percent since 2000. By

contrast, Africa’s share has grown from 19 percent to almost 29 percent, overtaking

exports is important because these markets are in many ways substitutes for each other.

The mix of South African exports by sub-sector is very similar across the two markets,

with African markets more important for machinery and chemicals exports and European

markets more important for material-based manufactures. Africa has grown more than

Europe since 2002, but the 2008–09 crisis brought a substantial shift—exports to the

European Union fell 39 percent, but exports to Africa fell only 16 percent. And exports to

Africa (53 percent) have recovered much more than those to Europe (22 percent) since the

crisis (figure 2.8, top panel). As a nearby market, Africa is also a natural entry point for

new exporters. While new firm entry into European markets is down some 40 percent

since 2004–06 (three-year average), entry into African markets has remained robust

(figure 2.8, bottom panel). A new exporter is now more than three times as likely to start

in Africa as in Europe (World Bank, 2014).

The shift to Africa is a positive story for several reasons. It has diversified markets,

reducing aggregate risk from adverse shocks; it cushioned the impact of the decline in

Europe; and Africa’s rapidly growing consumer class and infrastructure needs have

provided ample opportunities for South Africa’s exporters and investors. But the

European market is still 30 times larger than the African market, and South Africa’s

market share in much of Africa is already higher than its share in Europe across most

sectors. So even with a large market share there are some limits to the scale of growth

possible in Africa relative to traditional markets (World Bank, 2014).

In fact, South Africa is already moderately integrated in global value chains. At 51

percent, South Africa is second among its peers on the Global Value Chain Participation

Index, after Thailand (UPDATE THIS). Some sectors have seen major increases in the

foreign content of their exports, including transport (from 22 percent in 1995 to 38

percent in 2009), electrical equipment (from 16 percent to 27 percent), and chemicals and

non-metallic minerals (from 14 percent to 27 percent). In agriculture, food and beverages,




and transport equipment, South Africa’s share of foreign content in exports is near the

highest among its peers (World Bank, 2014).

But despite these increases South Africa remains a minor player on a global scale. It also

operates at a long distance from final demand, not just geographically but also in

production stages, suggesting that substantial scope remains for upgrading South Africa’s

value chain (and value-added) position (World Bank, 2014).

As trade patterns have shifted, Sub-Saharan Africa has emerged as the key destination for

South Africa’s non-mineral exports. This has created greater market opportunities for

newer and smaller exporters. So far, exports to Sub-Saharan Africa have remained

somewhat smaller and shorter lived than exports to traditional markets, suggesting that

lower competitiveness in regional markets is allowing the less efficient firms to enter and

exit opportunistically. This poses challenges in considering how best to support emerging

exports (World Bank, 2014).

This holds true even in the case of high technology export intensity. Looking at figure 8

which expresses the percentage of South African exports per country of destination, it

appears that Sub-Saharan Africa is a popular destination for South African high tech

exports, with each of the countries within this region importing more than 2% of this high

technology contingent. Notably, these countries (and the region in generally) are not

known for being particularly high in technology on a global scale. However, this figure

also shows that Germany, China, Great Britain, France, and the United States of America

import a significant percentage of South Africa’s high technology exports. This is a

particularly good sign as these are countries that are known for technological prowess,

and evidently South Africa is capable of supplying a portion of their respective needs.

Figure 8: Percentage of high technology South African exports by country of

destination (2014)




4.4 Who are the high technology firms – How do they differ from other firms?

In order to make meaningful comment on policies that would be useful in the context of

export sophistication and export promotion, it is only appropriate that a better

understanding of whom the high technology exporting firms who, what characteristics

they share, and what exporting behaviour they exhibit11.

11 For the sake of simplicity, the following descriptive analysis restricts the sample to only firms that

export.




In order to classify these firms as one of the 4 aforementioned categories, specific

measures had to be taken. Unlike other papers, this paper did not have the luxury of being

able to rely on survey or self-reported classification data by firms that would confirm

which ‘space’ they feel they operate in. Instead, this paper took the weighted average

value of each technology category per firm, and calculated what percentage of the total

export value it accounted for (per firm). Each firm was then assigned into a technology

category dependent on what technology was most exported by the firm (in value).

For the sake of simplicity, the following descriptive analysis restricts the sample to only

firms that export.

Figure 9: Breakdown of Each Technology Category By Employment Size (%)


5

10

15

20

25

30

1-4 Emp.5-9 Emp.10-19 Emp.20-49 Emp.50-99 Emp.100-249 Emp.250-999 Emp.1000+ Emp.

When considering firm size by employment (figure 9), a somewhat surprising trend

emerges. For all intents and purposes, the distribution of firms within each technology

category is not surprising, and follows a somewhat typical distribution of firm size within

the economy. What is however surprising is the much sharper contrast within the high

technology industry – after the 20-49 employee bracket, there is a sharp drop off to the

50-99 employee mark. With the weight of the distribution of this category having a

leftward leaning tendency, it appears that South African high technology firms tend to be

smaller in size, fitting the image of a capital intensive space within the economy.




Figure 10: Percentage Of High Tech Firms, Versus Other Technologies By Sales Size

R1-R99k

R100k-R24

R250k-R49

R500k-R99

R1m-R1,9m

R2m-R4,9m

R5m-R9,9m

R10m-R19,

R20m-R49,

R50m-R99,

R100m-R19

R200m +0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

L.TM.L.TM.H.TH.T

Adding to the size argument presented above, we now consider the sales size of firms,

and which firms make up each category. The first observation to emerge is that of a

distinct turning point at the R1 million – R1.9 million mark. It is at this point that low

technology is no longer the dominant technology in the sales size class, and instead

switches to being medium high technology (completely bypassing the medium low

technology aspect).

Figure 11: Percentage Breakdown of Each Technology Category By Geographical Province

(%)


10

20

30

40

50

60

70

80

Western CapeEastern CapeNorthern CapeFree StateKwaZulu-NatalNorth WestGautengMpumalangaLimpopo




The inclusion of province as a descriptor is one of vital importance for understanding the

concentration of technologies in South Africa. It is readily apparent that Gauteng (the

economic hub of South Africa) is the most prominent operating province for technologies

of all types – with 70% of high technology firms being concentrated in the region. There

is an apparent upward trend for firms to be located in Gauteng – as the type of technology

increases, the prevalence of firms operating in the region also increase.

Interestingly, despite the Western Cape’s efforts to brand itself as the “Silicon Valley” of

South Africa, it is clear that low technology exports mostly originate from the region.

Figure 12: Percentage Breakdown of Each Technology Category By Sector (%)





10

20

30

40

50

60

AgricultuMiningManufactuElectriciConstructWholesaleTransportCateringInformatiFinancingProfessioOther

Figure 13: Percentage Breakdown of Sector by Each Technology Category (%)

Agricu

ltuMining

Manufac

tu

Electr

ici

Construct

Wholesale

Transport

Caterin

g

Informati

Finan

cing

Profes

sioOther

0%

10%

20%

30%

40%

50%

60%

70%

80%

L.TM.L.TM.H.TH.T

Finally, this paper moves onto its last critical component when discussing the difference

between high technology firms, and others; that of R&D expenditure.




The Human Sciences Research Council (HSRC) estimated that South Africa spent 0.73

per cent of its GDP on R&D in 2013/14 according to its R&D survey, which compares

unfavourably to an OECD average of 2.4 per cent of GDP. Government recognises the

important role that the innovation process can play in achieving these goals, and as a

result introduced, among other measures, the R&D Tax Incentive in November 2006 to

encourage firms to undertake R&D (Schaffer at al., 2017).

South Africa is one of several countries that use a tax-based incentives to stimulate

private sector R&D. The use of R&D tax incentives has gained popularity globally. About

26 of the 34 OECD member countries currently have some form of R&D tax incentives.

All the BRICS countries, Brazil, the Russian Federation, India, China and South Africa

and other developing countries such as Singapore, Malaysia and Lithuania also offer tax-

based R&D incentives. Countries such as the United States, Japan, South Korea and

Canada, have large outlays in these incentives as part of their overall support for private

sector R&D (OECD, 2010). Several advanced economies have also used their R&D

incentives creatively as part of their response to the recent global economic crisis,

evidenced by specific adjustments introduced between 2009 and 2011 to counter the

weakening private sector R&D and competing for R&D that was migrating to emerging

economies.

Figure 14 shows the upward trend of R&D expenditure when technology type increases –

this is not surprising, as increased R&D is synonymous with higher levels of technology –

there also appears to be an increase in the rate at which firms spend on R&D in the upper

levels of technology. This point is further reiterated by figure 15, whereby kernel density

estimations of R&D expenditure per technology type are plotted against a normal curve.

This approach entails utilising a kernel density plot, whereby (x1, x2,… , xn) is an

independent and identically distributed sample drawn from some distribution with an

unknown density, f. To approximate the probability density function f(x), the following

equation is utilized:




f̂ h( x )=1n∑i=1

n

Kh ( x−xi)=1nh∑i=1

n

K (x−xi

h) (1)

where function K refers to the kernel, which determines the weights of the estimation, and

h is the smoothing parameter (also known as the scale parameter) (Lancaster University,

2017). To ensure smooth estimates, this paper utilized an Epanechnikov kernel for K,

where K(u) takes a value of

34(1−u2 )

if the absolute value of x is smaller than 1, and 0

otherwise.

K (u )={34(1−u2) for |u|≤1

0 otherwise (2)

For all technologies, apart from high technology, the spend tracks a normal distribution

quite well – however, high technology firms exhibit a rightward skewness in their

distribution of R&D expenditure, clearly indicating a higher level of expenditure.

Furthermore, when considering the R&D tax credit, there is a clear shift increase in the

gradient at the medium high technology level, whereby more firms applied for the

incentive, at an increasing rate, yet again reiterating the more serious undertaking of R&D

activity by firms within higher technology classifications.

Figure 14: Percentage Breakdown of Firms Spending on R&D and the R&D Tax Credit by

Each Technology Category (%) – Average (2010-2014)





0.5

1

1.5

2

2.5

3

3.5

4

4.5

R&D ExpenditureR&D Tax Credit

Figure 14: Kernel Density Estimates of R&D Expenditure by Each Technology Category

(%) – Average (2010-2014)

Low Technology Medium Low Technology

Medium High Technology High Technology




4.5 How Much Do Firm Characteristics Matter For High Technology Exporting?

Having unpacked what difference exist between high technology firms, and other firms

via a descriptive analysis, this paper proceeds to its final point of departure- that of a

regression analysis.

The method utilised takes the form of a linear probability model (LPM) regression,

where:

Pr (Y =1|Xki)i=α+ β1 Xki+εi (1)

where Pr(Y=1|Xki)i is the probability of a firm being a high technology firm (Y=1), versus

it being a firm of any other technological classification (Y=0) 12. Xki is a vector containing

control variables such as sector of firm, grade of individual, age of the firm, province,

firm size, and whether or not the firm spends on R&D. The constant and disturbance term

are provided by α and ε respectively.

This model is run under 3 different specifications; (i) utilises the log of R&D expenditure

as a control variable; (ii) utilises a dummy of R&D expenditure (1 if the firm spends on

R&D, 0 otherwise) as a control variable; and (iii) utilises R&D intensity (R&D

expenditure/sales) as a control variable.

Table 8: LPM Regression Results

Pr (High Technology=1) Specification [i]

Specification [ii]

Specification [iii]

Log R&D 0.024***(0.007)

R&D Intensity 0.027***(0.007)

R&D Dummy 0.016(0.016)

Firm Size (Base=Micro)Small 0.119 0.031* 0.009

(0.112) (0.015) (0.007)Medium 0.030 0.022 -0.009

12 This classification is as per the discussion at the beginning of section 4.4.




(0.113) (0.016) (0.008)Large 0.016 0.012 -0.015

(0.114) (0.017) (0.011)Firm Age (Base=0-5 years)

5-20 years 0.037 0.012 0.004(0.056) (0.011) (0.008)

20-100 years -0.006 -0.024* -0.028*(0.060) (0.012) (0.009)

100+ years -0.125 -0.042 -0.004(0.083) (0.055) (0.058)

Province (Base=Gauteng)Western Cape -0.023 -0.046*** -0.048***

(0.042) (0.009) (0.006)Eastern Cape -0.189*** -0.072*** -0.070***

(0.036) (0.016) (0.013)Northern Cape -0.078 -0.072** -0.095***

(0.088) (0.027) (0.017)Free State -0.375*** -0.097*** -0.067***

(0.114) (0.017) (0.016)Kwazulu-Natal -.097* -0.073*** -0.064***

(0.042) (0.009) (0.007)North-West -0.120 -0.098*** -0.097***

(0.074) (0.015) (0.014)Mpumalanga -0.166*** -0.118*** -0.092***

(0.038) (0.011) (0.012) Limpopo -0.151** -0.114*** -0.112***

(0.054) (0.018) (0.016)Sector (Base=Mining)

Agriculture 0.257*** 0.050** 0.046*(0.079) (0.023) (0.020)

Manufacturing 0.198*** 0.016 0.017(0.036) (0.019) (0.017)

Electricity, Gas, Water 0.226* 0.223*** 0.251***(0.115) (0.047) (0.036)

Construction 0.105 0.010 0.019(0.068) (0.024) (0.021)

Wholesale and retail trade 0.198*** 0.080*** 0.067***(0.051) (0.020) (0.018)

Transport and storage 0.194 0.174*** 0.163***(0.148) (0.029) (0.024)

Catering and Accommodation 0.039 0.011 -0.006(0.046) (0.038) (0.031)

Information and Comm. 0.388 0.318*** 0.249***(0.222) (0.073) (0.054)

Financing, Insurance & Real Estate 0.113 0.141*** 0.125***(0.059) (0.024) (0.020)

Professional technical and scientific act. 0.570*** 0.236*** 0.205***

(0.084) (0.028) (0.023)Other services 0.118 0.118 0.065

(0.020) (0.090) (0.048)R&D Tax Credit Dummy 0.151*** 0.188*** 0.218***

(0.043) (0.034) (0.033)Constant -0.392** 0.067** 0.106***

(0.148) (0.025) (0.019)Observations 950 18757 34775

Note: Standard errors are given in parentheses

*p<0.05 ** p<0.01 *** p<0.001




Across all specifications, a few distinct findings emerge. Firstly, firm characteristics such

as firm size, and firm age show no significance when looking at the probability of a firm

being a high technology firm. This is a valid finding, as there are varying degrees of high

technology firms – from the small start-ups, to the multi-product multinational

corporations, clearly implying that the high technology space is not restricted to those

more established/larger firms. Secondly, province and sector are shown to contain

significant effects across the board. This implies that location is essential for high

technology firms, potentially explained by gains from diffusion of knowledge, and better

networking capabilities for firms. The sector effect is fairly self-explanatory as would be

expected – certain sectors are more prone to containing high technology firms than others,

such is the nature of the economy. Thirdly, the R&D Tax Incentive is shown to have a

significant positive impact on a firm being a high technology firm.

The preferred specification for this analysis is that of specification (ii).

Findings from this analysis complement the aforementioned discussion. Under this

specification, it can be seen that a 1 unit increase in R&D intensity (either by (i) increased

R&D, (ii) decreased sales, (iii) or a faster increase in R&D expenditure than the increase

in sales), results in 2.7% increase in the probability of the firm being a high technology

firm. Incidentally, the claiming of the R&D tax incentive is shown to increase the

probability of a firm being a high technology firm by 18.8%.

Utilising a similar interpretation, the sectoral analysis is also quite revealing. Sectors such

as transport and storage; electricity, gas, and water; information and communications; and

professional technical and scientific activities show the greatest likelihood of containing

high technology firms (when compared to the mining industry – the original backbone of

the South African economy). These sectors are 17.4%, 22.3%, 31.8%, and 23.6% more

likely to contain high technology firms (when compared to the mining sector)

respectively.




XXX. References

Pieterse, D, KREUSER, FIRM LEVEL DATA DESCIPRTION

LALL

DIOP 2012

SAEU

WB 2014 EXPORTING

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