The significance of the paper is twofold. Firstly, it adds to the small but growing body of literature focusing on the decomposition of South Africa’s export growth. Secondly, it identifies the determinants of the intensive and extensive margins of South Africa’s exports – a topic that (as far as the authors are concerned) has not been explored before.
This paper aims to investigate a wide range of market access determinants that affect South Africa’s export growth along the intensive and extensive margins.
Export diversification has been identified as one of the critical pillars of South Africa’s much-hoped-for economic revival. Although recent years have seen the country’s export product mix evolving, there is still insufficient diversification into new markets with high value-added products. This is putting a damper on export performance as a whole and, in turn, hindering South Africa’s economic growth.
A Heckman selection gravity model is applied using highly disaggregated data. The first stage of the process revealed the factors affecting the probability of South Africa exporting to a particular destination (extensive margin). The second stage, which modelled trade flows, revealed the variables that affect export volumes (intensive margin).
The results showed that South Africa’s export product mix is relatively varied, but the number of export markets is limited. In terms of the extensive margin (or the probability of exporting), economic variables such as the importing country’s GDP and population have a positive impact on firms’ decision to export. Other factors affecting the extensive margin are distance to the market (negative impact), cultural or language fit (positive impact), presence of a South African embassy abroad (positive impact), existing free trade agreement with Southern African Development Community (positive impact) and trade regulations and costs (negative impact). In terms of the intensive margin (or the factors influencing the volume of exports), there are strong parallels with the extensive margin, with the exception being that the time involved in exporting has more of an impact than documentary requirements.
Among the factors contributing to South Africa’s exports having largely developed in the intensive margin are a general lack of market-related information, infrastructural weaknesses (both of a physical and technological nature) and a difficult regulatory environment – all of which add to the cost and time involved in exporting. Policymakers have long spoken about the need for the country to diversify its export basket, but now talk about needs to give way to action. The government and its economic partners need to arrive at a common vision of an export sector that will be able to expand into new products and markets, be an active participant in global value chains and deliver sustainable jobs.
Since the first democratic elections were held in 1994, the South African government has been intent on boosting employment in the country by encouraging higher and more inclusive economic growth. Industrialisation and export diversification have been part and parcel of this goal (Viviers et al.
… the growth and diversification of South African exports has been weak, with over half of all exports derived from the mining value chain. In order to stabilise growth it is important to diversify exports, including into higher value-added activities, and to improve overall competitiveness.
The World Bank (
The above scenario is the result of both broad structural problems in the South African economy and largely uncontrollable global influences, such as the continuous decline in commodity prices in recent years and waning demand in traditional markets. In addition, various market access barriers, including the distance to export markets and high transport costs, have conspired to erode South Africa’s export competitiveness and weakened the country’s export growth potential (Steenkamp, Grater & Viviers.
This paper aims to investigate a wide range of market access determinants that affect South Africa’s export growth. The significance of the paper is twofold. Firstly, it adds to the small but growing body of literature focusing on the decomposition of South Africa’s export growth. Secondly, it identifies the determinants of the intensive and extensive margins of South Africa’s exports – a topic that (as far as the authors are concerned) has not been explored before.
The study’s empirical framework is derived from the influential Melitz (
This paper uses the gravity model to analyse the pattern of South African exports at the product-level. Detailed product-level data are used to determine the impact of trade costs and barriers on the number of firms exporting to different markets and the volume of exports to each market. To this end, the product-level data are decomposed per industry, and the impact of different aspects relating to market access (i.e. market capacity, trade facilitation and trade barriers) are assessed in terms of the authors’ gravity model definition. This specification is estimated using the two-stage sample selection procedure proposed by Heckman (
The rest of the paper is structured as follows: The next section discusses a brief literature overview. The ‘Empirical specification’ section presents the data and methodology used in the study. The ‘Extensive and intensive margins of South African exports’ section summarises the results of the empirical analysis and the ‘Summary of key findings and concluding remarks’ section summarises the key findings and provides some concluding remarks.
The heterogeneous nature and performance of firms has become a key focus area in international trade research (Melitz & Redding
The Melitz (
A gravity model approach is used in the empirical analysis presented in this paper. The gravity model has been used in a plethora of empirical studies involving trade margins (see, e.g. Amurgo-Pacheco & Pierola
Finally, there are a few papers that have studied the determinants of African trade using a gravity model. However, none of them has explored the role of extensive and intensive trade margins. Eita (
This paper uses South African exports disaggregated by product in 2012, with the data sourced from the United Nations’ Comtrade Database. This database provides export data from a particular exporting country to an importing country disaggregated by product up to Harmonised System 6-digit level (HS6). Export data are classified per HS cluster, similar to the approach in Smet (
Varieties of products exported by industry (2012).
Industry (HS chapters) | Total products | Products with positive exports | % | Products with exports > US$100 000 | % | Products with exports > US$1 000 000 | % |
---|---|---|---|---|---|---|---|
Animal and animal products (0100–0599) | 194 | 177 | 91.2 | 95 | 49.0 | 48 | 24.7 |
Vegetable products (0600–1599) | 323 | 285 | 88.2 | 159 | 49.2 | 82 | 25.4 |
Foodstuffs (1600–2499) | 181 | 173 | 95.6 | 132 | 72.9 | 79 | 43.6 |
Mineral products (2500–2799) | 170 | 131 | 77.1 | 84 | 49.4 | 55 | 32.4 |
Chemicals and allied industries (2800–3899) | 760 | 664 | 87.4 | 372 | 48.9 | 172 | 22.6 |
Plastics and rubbers (3900–4099) | 189 | 185 | 97.9 | 139 | 73.5 | 65 | 34.4 |
Raw hides, skins, leather and furs (4100–4399) | 74 | 51 | 68.9 | 36 | 48.6 | 15 | 20.3 |
Wood and wood products (4400–4999) | 228 | 189 | 82.9 | 128 | 56.1 | 54 | 23.7 |
Textiles (5000–6399) | 809 | 640 | 79.1 | 201 | 24.8 | 37 | 4.6 |
Footwear and headgear (6400–6799) | 55 | 47 | 85.5 | 28 | 50.9 | 4 | 7.3 |
Stone and glass (6800–7199) | 190 | 175 | 92.1 | 117 | 61.6 | 52 | 27.4 |
Metals (7200–8399) | 587 | 508 | 86.5 | 373 | 63.5 | 198 | 33.7 |
Machinery and electrical (8400–8599) | 762 | 709 | 93.0 | 548 | 71.9 | 264 | 34.6 |
Transportation (8600–8999) | 132 | 123 | 93.2 | 106 | 80.3 | 70 | 53.0 |
Miscellaneous (9000–9799) | 385 | 320 | 83.1 | 186 | 48.3 | 55 | 14.3 |
5039 | 4377 | 86.9 | 2704 | 53.7 | 1250 | 24.8 |
Percentage of non-zero export flows by industry (2012).
Industry | Flows | Positive | % |
---|---|---|---|
Animal and animal products | 38 024 | 2 073 | 5.5 |
Chemicals and allied industries | 148 960 | 10 460 | 7.0 |
Foodstuffs | 35 476 | 4771 | 13.4 |
Footwear/headgear | 10 780 | 1246 | 11.6 |
Machinery/electrical | 149 352 | 21 384 | 14.3 |
Metals | 115 052 | 12 142 | 10.6 |
Mineral products | 33 320 | 1920 | 5.8 |
Miscellaneous | 75 460 | 8651 | 11.5 |
Plastics/rubbers | 37 044 | 5031 | 13.6 |
Raw hides, skins, leather and furs | 14 504 | 1285 | 8.9 |
Stone/glass | 37 240 | 3478 | 9.3 |
Textile | 158 564 | 8674 | 5.5 |
Transportation | 25 872 | 3107 | 12.0 |
Vegetable products | 63 308 | 5049 | 8.0 |
Wood and wood products | 44 688 | 4321 | 9.7 |
987 644 | 93 592 | 9.5 |
Detailed results of export flows in terms of products and destinations show that other African countries such as Zimbabwe, Zambia, Mozambique and the Democratic Republic of Congo are the recipients of more than 67% of South Africa’s exports, while countries such as Yemen, Puerto Rico, Sudan and Palau are not among South Africa’s export destinations.
The gravity model has been the empirical approach to analysing the determinants of bilateral trade flows. The basic form of this model assumes that trade between countries can be equated to the gravitational pull between two objects because it is directly related to countries’ size and inversely related to the distance between them.
Similar to the approach in Greenaway et al. (
The first stage in the model consists of a probit regression which explains the probability that South Africa will export to country
The selection (
Included in vector
For exporting firms, the conditional expectation of the volume of exports can be derived as follows:
Variable definitions and sources.
Variable | Definition | Source |
---|---|---|
Log of GDPpc | Logarithm of real GDP per capita of importer country i | World Development Indicators ( |
Ln of Pop | Logarithm of population of importing country i | |
LnDist | Logarithm of distance (in km) between South Africa and importing country | GeoDist Database (Mayer & Zignago |
Border | Dummy variable: value of 1 if South Africa shares a common land border with importing country, 0 otherwise | Rose ( |
Landl | Dummy variable: value of 1 if importing country is landlocked, 0 otherwise | |
Island | Dummy variable: value of 1 if importing country is an island, 0 otherwise | |
Language | Dummy variable: value of 1 if importing country has English as one of its official languages, 0 otherwise | Data from World FactBook by the Central Intelligence Agency ( |
Religion | Religious similarity index | |
Colony | Dummy variable that takes the value 1 if the importer country has a colonial relationship with South Africa, 0 otherwise | GeoDist Database (Mayer & Zignago |
Political stability | Political stability Indicator of destination/origin country. Ranges from -4 (less political stability) to 2 (more political stability) | Worldwide Governance Indicators (Kaufmann, Kraay & Mastruzzi |
Embassies | Dummy variable: value of 1 if South Africa has an embassy in importing country, 0 otherwise | Rose ( |
EFTA | Dummy variable: value of 1 for European Free Trade Association (EFTA) countries since the RTA entered into force, 0 otherwise | RTA database by World Trade Organization ( |
EU | Dummy variable: value of 1 for European Union (EU) countries since the RTA entered into force, 0 otherwise | |
SADC | Dummy variable: value of 1 for Southern African Development Community (SADC) countries since the RTA entered into force, 0 otherwise | |
Log of cost | Logarithm of official, administrative fees in dollars per imported container in country i in year t | World Bank’s Doing Business Survey (Djankov, Freund & Pham |
Log of time | Logarithm of number of calendar days required to move a shipment from South Africa through importing country i’s port in year t | |
Log of document | Logarithm of number of documents required to move a shipment from South Africa through importing country i’s port in year t | |
North America | Dummy variable: value of 1 if importing country is in North America, 0 otherwise | United Nations’ Classification. |
South America | Dummy variable: value of 1 if importing country is in South America, 0 otherwise | |
North Africa | Dummy variable: value of 1 if importing country is in North Africa, 0 otherwise | |
South Africa | Dummy variable: value of 1 if importing country is in South Africa, 0 otherwise | |
East Europe | Dummy variable: value of 1 if importing country is in East Europe, 0 otherwise | |
Asia | Dummy variable: value of 1 if importing country is in Asia, 0 otherwise | |
Oceania | Dummy variable: value of 1 if importing country is in Oceania, 0 otherwise | |
Cost | Dummy variable: value of 1 if relative cost to start a business is greater than the median for importing country i, 0 otherwise | World Bank’s Doing Business Survey (Djankov et al. |
Days and Documents | Dummy variable: value of 1 if sum of number of days and procedures to start a business is greater than the median for importing country i, 0 otherwise |
RTA, Regional trade agreements; EFTA, European Free Trade Association; EU, European Union; SADC, Southern African Development Community.
Finally, the Heckman procedure depends on a prior assumption of the validity of the exclusion restriction which is included in
The Heckman procedure used in this paper, as in Helpman et al. (
As mentioned in an earlier section, products are classified by HS cluster, giving rise to 15 different sectors. Given a total of 987 644 country-pair observations (3059 products × 196 countries), 93 592 of these present positive export flows (9.5% of the sample). Although South Africa exports around 87% of the products, these are concentrated in just a few countries that vary depending on the type of product exported. So, a challenge for South Africa is to increase the number of export destination countries.
As a starting point, the Heckman procedure is applied to estimate export flows from South Africa disaggregated by industry. The model is estimated by maximum likelihood, and robust standard errors are computed.
Extensive margins of trade (selection equation).
Variables | All | Animal and animal products | Vegetable products | Food stuffs | Mineral products | Chemicals and allied industries | Plastics/Rubbers | Raw hides, skins, leather and furs | Wood and wood products | Textiles | Footwear /Headgear | Stone /Glass | Metals | Machinery/Electrical | Transport | Miscellaneous |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ln of GDPpc | 0.034 |
0.026 |
0.033 |
0.046 |
0.019 |
0.020 |
0.045 |
0.039 |
0.033 |
0.024 |
0.056 |
0.045 |
0.034 |
0.047 |
0.041 |
0.045 |
Ln of Population | 0.038 |
0.026 |
0.031 |
0.041 |
0.030 |
0.029 |
0.051 |
0.041 |
0.039 |
0.026 |
0.051 |
0.045 |
0.043 |
0.052 |
0.044 |
0.048 |
Ln of Distance | -0.007 |
-0.009 |
-0.006 | -0.018 |
-0.006 | 0.005 | 0.018 |
-0.002 | -0.005 | 0.005 |
-0.012 | 0.006 | 0.001 | 0.034 | -0.009 | 0.018 |
Landlocked | -0.019 |
-0.017 |
-0.032 |
-0.054 |
-0.019 |
-0.015 |
-0.031 |
-0.021 |
-0.021 |
-0.010 |
-0.017 |
-0.021 |
-0.021 |
-0.018 |
-0.015 |
-0.012 |
Island | 0.002 |
0.008 |
0.003 | 0.018 |
-0.001 | 0.000 | -0.003 | 0.000 | 0.003 | 0.002 | 0.01 | 0.001 | 0.002 | -0.001 |
0.012 |
0.002 |
Language | 0.029 |
0.001 |
0.024 |
0.058 |
0.015 |
0.023 |
0.037 |
0.040 |
0.031 |
0.026 |
0.044 |
0.032 |
0.028 |
0.034 |
0.025 |
0.036 |
Colony | -0.021 |
-0.033 |
-0.013 |
-0.014 | -0.010 | -0.017 |
-0.027 |
-0.033 |
-0.013 | -0.039 |
-0.028 | -0.019 |
-0.008 | -0.029 |
0.026 |
-0.028 |
Religion | 0.069 |
0.122 |
0.145 |
0.126 |
0.020 | 0.051 |
0.055 |
0.029 | 0.109 |
0.084 |
0.116 |
0.075 |
0.032 |
0.042 |
0.048 |
0.070 |
Political stability | -0.007 |
-0.008 |
-0.005 |
-0.007 |
-0.001 | -0.010 |
-0.009 |
-0.005 | -0.002 | -0.002 |
-0.012 |
-0.009 |
-0.006 |
-0.011 |
-0.012 |
-0.006 |
Embassies | 0.010 |
-0.011 |
0.001 | 0.012 |
0.003 | 0.009 |
0.014 |
0.005 | 0.010 |
0.006 |
-0.014 | -0.010 |
0.018 |
0.027 |
0.002 | 0.009 |
EFTA | -0.007 |
-0.001 | 0.015 | 0.052 |
-0.009 | -0.016 |
-0.062 |
0.034 | 0.004 | -0.012 |
0.034 | -0.004 | -0.028 |
-0.041 |
0.005 | 0.014 |
EU | 0.001 | -0.006 | -0.024 |
0.009 | -0.009 | 0.007 | 0.012 | 0.026 | 0.007 | -0.021 |
-0.005 | -0.012 | 0.003 | 0.013 |
0.042 |
0.006 |
SADC | 0.096 |
0.084 |
0.106 |
0.106 |
0.077 |
0.096 |
0.135 |
0.051 |
0.079 |
0.067 |
0.071 |
0.099 |
0.091 |
0.119 |
0.109 |
0.096 |
North America | 0.001 | -0.009 | -0.029 |
0.018 | -0.008 | -0.008 | 0.000 | -0.027 | -0.018 | -0.008 | -0.03 | -0.013 | 0.021 |
0.014 | 0.062 |
0.002 |
South America | 0.021 |
-0.023 |
-0.053 |
0.016 | 0.014 | 0.015 |
0.065 |
-0.030 | 0.006 | 0.001 | -0.015 | -0.01 | 0.048 |
0.059 |
0.089 |
0.017 |
North Africa | 0.038 |
0.011 | 0.041 |
0.007 | -0.002 | 0.034 |
0.081 |
-0.027 | 0.045 |
0.033 |
0.066 |
0.019 | 0.026 |
0.058 |
0.070 |
0.046 |
South Africa | 0.256 |
0.158 |
0.193 |
0.315 |
0.133 |
0.178 |
0.371 |
0.186 |
0.266 |
0.178 |
0.345 |
0.268 |
0.280 |
0.382 |
0.310 |
0.330 |
North Europe | 0.050 |
0.029 |
0.038 |
0.037 |
0.043 |
0.029 |
0.075 |
0.013 |
0.039 |
0.053 |
0.046 |
0.059 |
0.058 |
0.071 |
0.060 |
0.062 |
Asia | 0.055 |
0.056 |
0.058 |
0.097 |
0.036 |
0.040 |
0.086 |
0.011 | 0.062 |
0.037 |
0.054 |
0.037 |
0.060 |
0.069 |
0.088 |
0.057 |
Oceania | 0.095 |
0.071 |
0.044 |
0.115 |
0.072 |
0.077 |
0.143 |
0.044 | 0.088 |
0.072 |
0.108 |
0.088 |
0.119 |
0.123 |
0.177 |
0.105 |
Ln of time | -0.013 |
0.004 | -0.006 |
-0.025 |
-0.004 | -0.011 |
-0.010 |
-0.023 |
-0.026 |
-0.010 |
-0.018 |
-0.013 |
-0.013 |
-0.016 |
-0.020 |
-0.023 |
Log of Document | -0.004 |
-0.010 |
-0.009 |
0.015 |
-0.019 |
-0.007 |
-0.001 | -0.019 |
-0.004 | -0.005 |
-0.001 | -0.006 | -0.008 |
-0.001 | -0.006 | 0.011 |
Time and document | -0.013 |
-0.012 |
-0.013 |
-0.014 |
-0.007 |
-0.011 |
-0.024 |
-0.013 |
-0.003 | -0.011 |
-0.021 |
-0.014 |
-0.011 |
-0.017 |
0 | -0.018 |
Observations | 861 669 | 33 174 | 55 233 | 30 951 | 29 070 | 129 960 | 32 319 | 12 654 | 38 988 | 138 339 | 9405 | 32 490 | 100 377 | 130 302 | 22 572 | 65 835 |
RTA, Regional trade agreements; EFTA, European Free Trade Association; EU, European Union; SADC, Southern African Development Community.
Robust standard errors are computed:
Marginal effects are reported.
In terms of cultural variables, cultural proximity (expressed in terms of language and religion) has, for almost all sectors, a positive impact on the extensive margin. However, having a shared colonial history has no impact or even a negative impact on the probability of South Africa exporting goods from a range of sectors. When it comes to political variables, political stability in an importing country has a negative effect on the probability of exporting to that country. Such results can be somewhat controversial because they imply that South Africa is more likely to export to countries that are perceived to present a lower likelihood of political instability and/or politically motivated violence. However, on closer inspection of the data, it is evident that some of South Africa’s main trading partners are in fact countries that have political instability episodes, including China, India, Zambia, Zimbabwe and Mozambique.
An analysis of the impact of RTAs on the extensive margin of South African exports yields interesting results. Only the SADC Free Trade Agreement has had a significant and positive impact on the probability of South Africa exporting to other SADC members. This is not surprising, given their close proximity. Trade agreements with the EU and EFTA, in contrast, have not had a positive effect on the extensive margin. Considering that the excluded category is East Europe, countries located in Asia and Oceania show a higher probability of importing from South Africa.
Trade regulation variables generally have a negative impact on the probability of exporting. The more time-consuming and costly it is to export, the more difficult it is for local companies to be competitive and to access international markets. The time it takes to export is a decidedly negative factor for almost all industries, yet documentary requirements are not a significant obstacle for many industries. Moreover, it can be observed how the exclusion restriction in terms of time and documentation has the expected extremely negative impact on export-extensive margins in almost all industries. A trade facilitation drive aimed at shortening the time to export and reducing the documentary burden would generate new trading partners for South Africa.
The estimation results for the second stage, the outcome equation, are presented in
Intensive margins of trade (outcome equation).
Variable | All | Animal and animal products | Vegetable product | Foodstuffs | Mineral products | Chemicals and allied industries | Plastics/Rubbers | Raw hides, skins, leather and furs | Wood and wood products | Textiles | Footwear/Headgear | Stone/Glass | Metals | Machinery/Electrical | Transport | Miscellaneous |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ln of GDPpc | 0.199 |
0.540 |
0.0209 | 0.286 |
-0.667 |
0.222 |
-0.222 |
0.271 | 0.161 |
0.0710 | 0.118 | 0.126 | 0.0366 | -0.337 |
0.209 |
0.103 |
Ln of Population | 0.396 |
0.371 |
0.0851 | 0.323 |
-0.762 |
0.327 |
-0.108 | 0.350 | 0.369 |
0.0756 | 0.272 |
0.0724 | 0.346 |
-0.128 |
0.476 |
0.191 |
Ln of Distance | -0.505 |
-1.488 |
-0.0703 | -0.406 | -0.350 | -0.297 |
-0.913 |
-0.135 | -0.691 |
-0.266 |
-0.376 | -0.704 |
-0.595 |
-0.915 |
-0.879 |
-0.433 |
Landlocked | -0.129 |
-0.562 |
0.513 |
0.250 | 0.176 | -0.0458 | 0.0409 | 0.0144 | -0.167 | -0.108 | -0.189 | 0.0518 | -0.254 |
0.00834 | -0.332 |
-0.0832 |
Island | -0.305 |
0.0377 | -0.549 |
-0.289 |
-0.0659 | -0.274 |
-0.455 |
-0.419 | -0.540 |
-0.366 |
-0.587 |
0.0864 | -0.317 |
-0.452 |
-0.242 | -0.287 |
Language | 0.125 |
0.166 | -0.755 |
-0.303 |
-1.002 |
0.217 |
-0.0921 | 0.186 | 0.312 |
0.0708 | 0.161 | -0.0316 | -0.0141 | -0.0830 | 0.267 |
0.0227 |
Colony | -0.124 |
-1.042 |
0.991 |
-0.299 | 1.293 |
0.00381 | -0.700 |
0.538 | -0.0187 | 0.0361 | -0.161 | -0.342 | -0.599 |
0.416 |
-0.576 |
0.0686 |
Religion | 0.327 |
0.247 | -2.192 |
0.224 | -2.671 |
0.202 | -0.00364 | -0.0830 | 1.552 |
-0.211 | 0.273 | 1.333 |
1.128 |
-0.314 | 0.168 | 0.474 |
Political stability | -0.0812 |
-0.0727 | -0.134 |
-0.0562 | 0.322 | -0.0211 | -0.203 |
-0.166 | -0.283 |
-0.160 |
-0.0201 | -0.157 | -0.113 |
0.0270 | 0.0211 | -0.0242 |
Embassies | -0.0991 |
-0.612 |
-0.291 | -0.0753 | -0.148 | 0.151 | -0.539 |
-0.897 | -0.122 | 0.180 | -0.263 | -0.624 |
-0.0390 | -0.464 |
-0.578 |
-0.128 |
EFTA | -0.562 |
0.00107 | -1.442 |
-0.853 | 0.952 | -0.881 |
-0.683 | -0.290 | -1.506 |
-0.821 |
-1.755 |
-0.409 | -1.452 |
-0.215 | 0.646 | 0.0153 |
EU | -0.506 |
0.0863 | -0.184 | -0.570 | 0.312 | 0.0344 | -0.716 |
-0.0855 | -0.910 |
-0.788 |
-1.507 |
-0.617 | -0.967 |
-0.452 |
-0.287 | -0.428 |
SADC | 1.247 |
0.730 |
0.866 |
1.777 |
-2.422 |
0.914 |
0.220 | 1.427 | 1.926 |
0.696 |
1.559 |
0.924 |
1.515 |
0.0322 | 1.095 |
0.794 |
North America | -0.277 |
0.941 | -0.414 | -0.766 | 0.137 | 0.554 | -1.093 |
-0.954 | 0.290 | -1.246 |
-0.495 | -0.295 | -0.407 | -0.375 | -1.020 | -0.129 |
South America | -0.361 |
0.0716 | -1.134 |
0.550 | -0.320 | 1.806 |
-1.202 |
-0.407 | 0.211 | -0.707 | -1.253 | -1.444 |
-0.581 | -1.092 |
-2.369 |
-0.892 |
North Africa | -0.515 |
1.554 | -1.200 |
-1.635 |
-0.143 | 1.308 |
-1.181 |
-1.456 | 0.937 | -1.580 |
-0.506 | -1.692 |
-1.990 |
-1.297 |
-0.472 | -0.699 |
South Africa | 0.0836 | 0.219 | -3.055 |
-0.372 | -6.914 |
0.773 |
-3.701 |
-0.970 | 1.386 |
-1.397 |
-0.329 | -3.013 |
-0.839 |
-3.901 |
-1.367 |
-0.824 |
North Europe | 0.141 | 0.790 | -0.242 | 0.168 | -2.414 |
0.294 | -0.992 |
-1.282 | 1.370 |
-0.722 |
0.403 | -0.420 | 0.433 | -0.950 |
-0.586 | -0.176 |
Asia | -0.00644 | 1.326 |
-0.658 | 0.0572 | -0.846 | 1.379 |
-1.893 |
-0.279 | 1.316 |
-1.127 |
-0.642 | -0.545 | 0.165 | -1.223 |
-1.342 |
-0.628 |
Oceania | 0.454 |
1.347 |
-1.182 |
0.424 | -2.914 |
1.233 |
-1.147 |
-0.565 | 1.768 |
-0.970 |
0.238 | -1.246 |
0.249 | -0.900 |
-0.639 | -0.0789 |
Log of time | -0.478 |
-1.086 |
-0.738 |
-0.271 | -0.183 | -0.640 |
-0.198 | -0.845 | -0.726 |
-0.269 | -0.434 | 0.0827 | -0.385 |
-0.323 |
-0.334 | -0.211 |
Log of documents | 0.0361 | 0.702 |
0.409 |
-0.00668 | 0.378 | 0.219 |
-0.0903 | 0.177 | -0.0246 | 0.174 |
0.187 | 0.331 |
-0.0627 | 0.243 |
0.00801 | 0.126 |
ρ (rho) | 0.0637 |
0.0851 | -0.254 | 0.0356 | -1.240 |
0.0355 | -0.789 |
-0.415 | 0.0185 | -0.169 |
-0.00371 | -0.367 |
-0.0854 |
-0.987 |
0.0166 | -0.236 |
lnσ (sigma) | 1.089 |
1.087 |
1.147 |
1.144 |
1.757 |
1.127 |
1.271 |
1.061 | 1.139 |
0.956 |
0.898 |
1.144 |
1.146 |
1.259 |
1.097 |
1.005 |
λ (Mills) | 0.189 | 0.252 | -0.783 | 0.112 | -4.899 | 0.110 | -2.344 | -1.135 | 0.058 | -0.437 | -0.009 | -1.102 | -0.268 | -2.663 | 0.050 | -0.633 |
Observations | 92 519 | 2042 | 5010 | 4671 | 1896 | 10 349 | 4946 | 1262 | 4284 | 8608 | 1234 | 3450 | 12 014 | 21 130 | 3043 | 8580 |
RTA, Regional trade agreements; EFTA, European Free Trade Association; EU, European Union; SADC, Southern African Development Community.
Robust standard errors are computed:
Marginal effects are reported.
In general, the variables that affect the extensive margin of trade also affect the intensive margin, although many of these variables are not significant, depending on the industry considered. As predicted by the gravity model, the economic size of the importing country, measured in terms of GDP per capita and population, is an important factor in explaining the volume of South Africa’s exports. However, these variables produce a negative impact on some sectors, such as
When all industries are considered, geographical variables present the expected negative sign; however, differences in the significance of the coefficients can be observed by industry. As for the extensive margin, distance and being an island present the expected negative sign when the variables are significant, while being a landlocked country (which rules out sea transport) negatively affects the volume of exports. Regarding cultural variables, having a common language or religion has a positive effect on the volume of exports if the variables are significant. Sharing the same colonial background has a negative impact when all industries are considered, but interestingly, the sign of the coefficients changes for some industries when the sector classification is used.
Political instability has a negative effect on all industries when the variable is significant, while having an embassy in the importing country has no effect or even a negative effect on export volumes. In similar vein to the extensive margin, the only trade agreement that delivers a positive effect on export volumes is the SADC Free Trade Agreement, and only in respect of some industries. Jordaan and Eita (
The analysis reveals the most relevant determinants of exports by industry, offering a useful platform from which policymakers can formulate appropriate strategies for what they consider to be high-priority sectors and products.
South Africa’s DTI has long been of the view that South Africa needs to boost and diversify its exports – in other words, expand exports in both the intensive and extensive margins. The paper set out to reveal the key determinants influencing South Africa’s extensive and intensive trade margins, thereby highlighting key opportunity areas and overarching shortcomings in the country’s policy, regulatory and physical environments. This was done by employing a Heckman selection gravity model, using highly disaggregated data for 2012 (at HS6 level). The first stage of the process revealed the factors affecting the probability of South Africa exporting to a particular destination (extensive margin). The second stage, which modelled trade flows, revealed the variables that affect export volumes (intensive margin).
The key results from the study indicate that South Africa exports an extensive range of products to a limited number of countries, which reinforces the benefit of performing a trade (and especially export) margin analysis. The specific results, in turn, reveal how a wide range of market access determinants affect South Africa’s export growth and potential exporter profitability. In terms of the probability to export, or the extensive margin, economic variables such as the importing country’s GDP and population have a positive impact on the firms’ decision to export. This highlights the importance of firms focusing their exporting endeavours on export markets with growing demand. In order to do so, it is important that they have access to reliable and affordable information about export opportunities in growing markets. Here information support systems such as the Decision Support Model, which identifies realistic export opportunities for South African exports, is a useful tool for both regional and national export promotion agencies (for details, see Cuyvers, Steenkamp & Viviers
Other factors affecting the extensive margin are distance to the market (negative impact), cultural/language fit (positive impact), presence of a South African embassy abroad (positive impact), existing free trade agreement with SADC (positive impact) and trade regulations and costs (negative impact). These results firstly emphasise the importance of investing in transport infrastructure in order to reduce the transport cost burden of exporting to distant markets. At the same time, trade facilitation initiatives (e.g. more streamlined trade regulations) should be rolled out to stimulate export growth in South Africa. This would also contribute to the deepening of trade within SADC and help to exploit ‘the untapped potential to develop a system of regional value chains’, as proposed by the World Bank (
In terms of the intensive margin (or factors influencing the volume of exports), there are strong parallels with the extensive margin, with the exception that the time involved in exporting has more of an impact than documentary requirements. This is also heavily dependent on the state of the infrastructure, the complexity of the regulatory apparatus and other factors such as congestion at ports and borders. This is in line with the World Bank’s recommendations in 2014 that South Africa needed to seriously tackle its infrastructural bottlenecks [both of a physical and ICT (information and communication technology) nature] if it was to enhance its export competitiveness from a time and cost perspective and provide an environment in which small and medium-sized exporters could flourish and grow.
In conclusion, the dearth of adequate market-related information and other noted shortcomings in South Africa’s infrastructure and regulatory environment (which add to the cost and time to export) could explain why the country’s exports have largely developed in the intensive margin. If South Africa is to make sustainable inroads into more markets and expand its product offerings, the government and its economic partners need to seriously address the obstacles standing in the way, while also adopting an industry-based approach to export policymaking and promotion. Dissecting the industry-specific results would be an important part of this process and future research would involve developing counterfactual scenarios to assess the expected reaction of potential exporting firms’ trade flows to changes in key exogenous determinants. Additionally, industry-specific research (that focuses on obtaining firm-level information) on market access and trade barriers would help industry-based approaches in policymaking as suggested above.
This work is based on the research supported in part by the National Research Foundation of South Africa (Grant Number 90709). Any opinion, finding and conclusion or recommendation expressed in this material is that of the authors and the NRF does not accept any liability in this regard.
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
M.M. constructed the introduction, literature review and conclusion. M.S-G. constructed the empirical specification, methodology and results.
List of importing countries.
Afghanistan | Denmark | Kuwait | Puerto Rico |
Albania | Djibouti | Kyrgyz Republic | Qatar |
Algeria | Dominica | Lao PDR | Romania |
American Samoa | Dominican Republic | Latvia | Russian Federation |
Andorra | Ecuador | Lebanon | Rwanda |
Angola | Egypt, Arab Republic | Lesotho | Samoa |
Antigua and Barbuda | El Salvador | Liberia | San Marino |
Argentina | Equatorial Guinea | Libya | Sao Tome and Principe |
Armenia | Eritrea | Liechtenstein | Saudi Arabia |
Australia | Estonia | Lithuania | Senegal |
Austria | Ethiopia | Luxembourg | Seychelles |
Azerbaijan | Faeroe Islands | Macao | Sierra Leone |
Bahamas, The | Fiji | Madagascar | Singapore |
Bahrain | Finland | Malawi | Slovak Republic |
Bangladesh | France | Malaysia | Slovenia |
Barbados | French Polynesia | Maldives | Solomon Islands |
Belarus | Gabon | Mali | Somalia |
Belgium | Gambia, The | Malta | Spain |
Belize | Georgia | Marshall Islands | Sri Lanka |
Benin | Germany | Mauritania | Sudan |
Bermuda | Ghana | Mauritius | Suriname |
Bhutan | Greece | Mexico | Swaziland |
Bolivia | Greenland | Micronesia | Sweden |
Bosnia and Herzegovina | Grenada | Moldova | Switzerland |
Botswana | Guam | Monaco | Syrian Arab Republic |
Brazil | Guatemala | Mongolia | Tajikistan |
Brunei | Guinea | Morocco | Tanzania |
Bulgaria | Guinea-Bissau | Mozambique | Thailand |
Burkina Faso | Guyana | Myanmar | Togo |
Burundi | Haiti | Namibia | Tonga |
Cambodia | Honduras | Nepal | Trinidad and Tobago |
Cameroon | Hong Kong | The Netherlands | Tunisia |
Canada | Hungary | New Caledonia | Turkey |
Cape Verde | Iceland | New Zealand | Turkmenistan |
Cayman Islands | India | Nicaragua | Turks and Caicos |
Central African Republic | Indonesia | Niger | Tuvalu |
Chad | Iran | Nigeria | Uganda |
Chile | Iraq | Northern Mariana | Ukraine |
China | Ireland | Norway | UAE |
Colombia | Israel | Oman | Ukraine |
Comoros | Italy | Pakistan | USA |
Congo | Jamaica | Palau | Uruguay |
Congo, Democratic Republic | Japan | Panama | Uzbekistan |
Costa Rica | Jordan | Papua New Guinea | Vanuatu |
Cote d’Ivoire | Kazakhstan | Paraguay | Venezuela |
Croatia | Kenya | Peru | Vietnam |
Cuba | Kiribati | Philippines | Yemen |
Cyprus | Korea | Poland | Zambia |
Czech Republic | Korea, Democratic Republic | Portugal | Zimbabwe |
Main importing countries by industry.
China | 10 485 | Spain | 2548 | Mexico | 53 049 | Niger | 3094 |
Japan | 7812 | Italy | 2433 | Bangladesh | 6299 | Algeria | 2755 |
India | 3892 | Cameroon | 2120 | The Netherlands | 5865 | Japan | 2672 |
USA | 3818 | Portugal | 2082 | Russia | 5353 | Sweden | 2612 |
Korea | 3488 | Fiji | 1838 | UK | 3401 | Germany | 2229 |
The Netherlands | 2416 | Hong Kong | 1674 | Malaysia | 2983 | UK | 2140 |
Germany | 2360 | Australia | 1069 | Hong Kong | 2791 | Syria | 2024 |
China | 214 858 | USA | 3289 | Brazil | 2431 | Italy | 2577 |
India | 61 668 | Belgium | 3052 | China | 1627 | China | 1543 |
Finland | 48 581 | Brazil | 2395 | Paraguay | 1396 | Korea | 1402 |
Korea | 42 897 | The Netherlands | 2019 | Zambia | 1176 | Bulgaria | 1366 |
Israel | 40 964 | Thailand | 1894 | Venezuela | 1015 | Thailand | 925 |
Japan | 38 113 | Japan | 1673 | Zimbabwe | 965 | Vietnam | 793 |
The Netherlands | 33 291 | Lithuania | 1656 | Congo, Democratic Republic | 824 | Brazil | 740 |
Indonesia | 11 852 | Czech Republic | 3763 | Argentina | 307 | Japan | 72 918 |
Japan | 5956 | China | 2876 | Brazil | 287 | Switzerland | 34 228 |
China | 4789 | Bangladesh | 1013 | Zimbabwe | 206 | Hong Kong | 17 385 |
Thailand | 3832 | Indonesia | 962 | Hong Kong | 204 | USA | 16 502 |
Belgium | 1538 | India | 531 | Zambia | 187 | Belgium | 15 007 |
India | 1234 | Italy | 452 | Lebanon | 179 | UK | 14 325 |
Argentina | 952 | Bulgaria | 412 | Indonesia | 173 | Israel | 12 791 |
Japan | 10 564 | Georgia | 2555 | USA | 35 635 | Bulgaria | 7141 |
China | 7848 | Germany | 2438 | Algeria | 23 192 | Germany | 1020 |
Korea | 7697 | Hungary | 1453 | Russia | 23 002 | USA | 812 |
USA | 4393 | Czech Rep. | 1420 | Germany | 19 180 | Brunei | 396 |
India | 4053 | USA | 1395 | Japan | 10 497 | Czech Republic | 286 |
Malaysia | 3566 | Poland | 1227 | Tunisia | 6918 | Spain | 272 |
The Netherlands | 3361 | Zambia | 1177 | Belgium | 6618 | Zambia | 252 |
These results are available on request.
Santos Silva and Tenreyro (
List of importing countries are presented in
The main difficulty in this approach is to find an exclusion variable for the probit model (selection equation) that is exogenous to the trade value. Alternatively, religious similarity has also been considered as exclusion restriction and results are very similar. Estimates are available upon request.
Religion Similarity Index is