103070 A Capability-Based Assessment of GVC Competitiveness for the SACU Region Vilas Pathikonda1 and Thomas Farole February 20, 2015 1 Corresponding author: please contact on: vpathikonda@gmail.com This is a Working Paper of the World Bank – it is being issued in an effort to share ongoing research. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent Introduction The emergence of global value chains (GVCs) and their rapid expansion over the past two decades has transformed the global trade environment. GVCs involve task-based trade across multiple stages of the production process that take place across a number of different countries, in which multiple inputs and exports of intermediate goods and services are necessary to produce a final good, which may also be exported. This “second unbundling” of global trade2 was made possible by a combination of improved shipping technology, revolutionary changes in ICT, and global trade liberalization which enabled multinational firms to take advantage of differences in comparative advantage across locations to establish integrated networks of intra and inter-firm production and trade. GVC-oriented trade is seen to offer significant opportunities for developing countries, especially smaller ones, to benefit from global integration by changing the nature of competitiveness. In the past, for a country to become an apparel exporter, for example, it would need design capabilities and textile mills; to export in the automotive sector it would need to produce engines and all subcomponents, as well as having the scale to carry out assembly. Under the new GVC dynamics, a developing country can specialize in certain activities (sewing, specific components or subassemblies) within the chain and trade in intermediates. In this sense, GVCs denationalize comparative advantage3, as global lead firms construct global production networks by exploiting the most competitive locations for specific activities. Given this situation, and in an environment where developing countries are urged to “join” and “upgrade” in GVCs4, policymakers in developing countries rightly seek to understand what it takes to do so. And in practice this means understanding what it takes attract lead firms to place stages of the production value-adding process in your country5. Here, the advice remains a bit less clear in several ways. First, identifying what specific aspects of a country’s competitiveness matter most for GVC trade remains a question. Policy advice points to things like trade facilitation, trade agreements, NTMs, contract enforcement, and property rights protection6. On the other hand, the emergence of countries like Bangladesh and Vietnam as major players in global production networks suggests that it may be all about globally low wages and large labor forces; while the development of automotive value chains in Central and Eastern Europe points more to relative wages, technology, and proximity. This points to a second practical challenge – the fact that what drives competitiveness in GVCs is likely to vary across GVC sectors as well as across GVC positions (upstream or downstream). Finally, with competition for GVC investment taking place in a truly global market, factor competitiveness relative to other countries matters a lot. In this context, the purpose of this note is to shed some light for policymakers, in this case specifically in the Southern African Customs Union (SACU) countries, on where to focus efforts to drive competitiveness for GVC participation. We do this by generating “revealed factor intensity” 2 Baldwin (2011) 3 Taglioni and Winkler (2014) 4 Cattaneo et al (2013) 5 Either directly through investment or indirectly by contracting with a supplier in the country 6 See, for example, OECD, WTO, and UNCTAD (2013), OECD ET AL (2014), Cattaneo et al (2013) 1 measurements of traded goods, extending the traditional theory of factor-content of trade to account for the factors that would be most relevant in task-based trade, and utilizing these measurements to illustrate how underlying capabilities shape participation of SACU economies in global value chains7. II. The factor content methodology and its extension We draw on the methodology outlined by Shiritori, Tumurchudur and Cadot (2010), which generated revealed factor intensities for all traded goods from 1971 to 2003 with respect to physical capital, human capital and natural resource endowment. In this way, for example, a product exported predominantly by countries that are richly endowed with physical capital is “revealed” to be intensive in physical capital. Then we use these measurements to assess SACU’s GVC-related performance. The revealed factor intensity for each product j for a given factor type is summarized by the following equation: = ∑ where fi is the factor endowment level for a given country i and the weights (w) are given by: / = ∑( / ) The methodology uses a slightly modified version of revealed comparative advantage (RCA) that was proposed by Hausman, Hwang and Rodrik (2007) in generating their measure of revealed technology content (PRODY). This modified RCA serves as the weight in averaging the factor abundance across countries producing a good. Whereas the denominator in Balassa’s index is the share of good j in world trade, in this variation the denominator is the sum of export shares across countries of good j. This ensures the weights add up to one and eliminates a problem of large RCA values when values are very close to zero. Nevertheless, Shiritori, Tumurchudur and Cadot (2010) mention two main limitations of use the Balassa’s index or its variations: countries and commodities are double-counted and, secondly, they are based on gross exports instead of net exports (exports minus imports). As the study points out, the double-counting actually makes little difference, and therefore correcting for it is not worth the trouble. Meanwhile, we discuss the second item in the next section. 7 The SACU region includes Botswana, Lesotho, Namibia, South Africa and Swaziland. 2 Traditional theory of factor-content of trade is based on relative factor endowments, as expressed in the Heckscher-Ohlin theory of trade: countries export goods intensive in the factor with which they are relatively well-endowed. The discussion of factor endowments traditionally has been isolated to human and physical capital. Recent studies in development economics make a strong case for treating institutions as not only essential for development but also a factor input into the production process on par with physical and human capital. An economy in which firms can trust in enforcement of contracts and rule of law would be one in which economic actors can coordinate their actions. Applying the analysis to the specific case of Eurasian economies, Lederman, Pathikonda and Rojas (2012) generated these figures and included institutional capital as a factor endowment. In this study, we extend this model to additional “factors” as there may be a slightly broader set of capabilities that determine involvement in task-based trade. III. Framework and Data This is a data-intensive exercise that requires indicators to represent underlying capabilities, disaggregated international trade data, and finally, a classification of which products are likely to be trade within GVCs. We take up each of these in turn. A. Identifying the factors and indicators that matter for GVCs As noted previously, the global value chain literature is yet to give a clear picture of the drivers of GVC participation and competitiveness. A number of studies have looked at the determinants of production fragmentation in general8and of supply chain trade between countries9. Other studies have tested the importance of specific drivers of GVCs, including trade policy10, transport11, trade logistics12, and time zones13. Finally, several firm-level studies have assessed the determinants of offshoring strategies, identifying the importance of factors like productivity and both skills and capital intensity at the firm level14. So while these studies, along with the policy literature on GVCs, give us a sense of what factors are likely to be important in determining GVC dynamics, the question of which specific drivers matter most and when they matter most for country-level participation in GVCs remains open. One of the reasons for this is that data that identifies clearly what is and is not “GVC trade” is still problematic, despite the significant progress that has been made in recent years. Another reason is that there are significant links across many of these drivers, making endogeneity problematic. We therefore focus on a set of capabilities that are most common in the theoretical, policy, and empirical literature on GVC trade. These are summarized in Table 1, along with the specific variables chosen and their data sources. It is worth noting that because this analysis is carried out by looking at global at the country and sector level, some factors that may have a big impact on GVC trade and 8 See, for example, Hillberry (2011) 9 See, for example, Rahman and Zhao (2013) 10 Orefice and Rocha (2014) 11 Hummels and Schaur (2014) 12 Saslavsky and Shepherd (2012) 13 Dettmer (2014) 14 See, for example, Corcos et al (2013), Defever and Toubal (2013), and Jabbour (2012) 3 investment at a bilateral level – such as preferential trade agreements, bilateral investment treaties, common language, and time zone – are not included. Selection of capabilities to include in the analysis, and most importantly the specific variables chosen to represent them, were in part dependent on country coverage (see Annex Table A1 and A2). In each case, the most recent data point was used in the case of each dataset. Our final dataset covers 102 countries, which together represent 81% of world trade in 2012 (Annex Table A3). We divided our capabilities into three categories:  Fixed: capabilities that cannot be changed by a country.  Long-term policy variables: capabilities that can be changed gradually over a relatively long time horizon.  Short-term policy variables: capabilities that can be changed directly through a policy shift or negotiations. Fixed capabilities include proximity to markets and natural capital. In terms of proximity to markets, we utilized a GDP-weighted distance measure provided by CEPII. This was then indexed and reversed so that it becomes a measure of closeness. The level of natural capital in dollar terms was provided by the World Bank Wealth of Nations dataset, with a custom update provided for Botswana. This measure includes subsoil assets, Long-term policy variables include human capital, physical capital and institutional capital. Human capital is measured as average years of school of the population ages 15 and up. This is the most comprehensive set of human capital data available, as international PISA test scores are not available with such country coverage. Physical capital stock per capita is based on gross fixed capital formation estimates from the World Bank World Development Indicators, where initial capital is set by the rate of depreciation, growth rate and population rate, and accumulation by the perpetual inventory method. We elect to divide physical capital by the population and keep natural capital in aggregate terms because the former can serve as a proxy for productivity while the total value of an extractable resource is what may more likely influence investors’ decisions rather than how it compares to the size of the population. The level of institutions is given by the World Governance Indicators’ rule-of-law rating. This is an incomplete measure of institutional strength, as the WGI itself also covers voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality and control of corruption. In practice, however, these measures are highly correlated and we choose the measure that may best relate to the strength of the business environment. Short-term policy variables include logistics/connectivity, wage competitiveness, market access and access to inputs. Logistics capabilities are given the Logistics Performance Index (LPI), a widely known and cited international rating system by the World Bank. There is some overlap between this measure and proximity to markets. Wages are measured by the World Bank Doing Business minimum legal wage. Although unit labor costs were preferred as a measure of productivity-adjusted wages, there is no database with sufficient coverage of countries to be suitable for the dataset. Finally, market access and access to inputs are taken from the estimates generated by Kee, Nicita and Olarreaga (2008, 2009), who 4 generate a single measure that encapsulates tariff barriers and ad-valorem equivalent of non-tariff barriers for countries and their trading partners. Table 1: Capabilities and the Indicators Used to Represent Them Category Capability Indicator Units Source Fixed Proximity to markets GDP-weighted Kilometers* CEPII distance Natural capital Total value of Current $US World Bank Wealth of natural capital billion Nations Changeable Human capital Average years of Years Barro and Lee (2010) schooling (>15 years old population) Physical capital Capital stock per 2005 $US World Development person thousands Indicators (WDI) and World Bank staff calculations Institutional capital Rule of Law Rating from - Kauffman, Kraay and 2.5 to 2.5 Mastruzzi (2010) Policy Logistics/connectivity Logistics Rating from 1 World Bank Variables Performance Index to 5 Wage Minimum wage for a $US* World Bank Doing competitiveness 19-year old worker Business Annex or an apprentice (Employing Workers) Market access Overall Trade Uniform tariff Kee, Nicita and Restrictiveness Index equivalent of Olarreaga (2008, 2009) (of Trading Partners) partner country tariff and non-tariff barriers* Access to inputs Overall Trade Uniform tariff Kee, Nicita and Restrictiveness Index equivalent of Olarreaga (2008, 2009) country tariff and non-tariff barriers* *These variables were inverted such that all indicators have the same direction; a higher figure signifies a greater capability. B. Underlying product-level trade data The analysis used disaggregated trade data from 2012 at the six-digit HS 1998 classification. The data are tabulated using importer records, which are considered to be more accurate than the corresponding exporter records. Shiritori, Tumurchudur and Cadot (2010) took the additional step of accounting for agricultural distortions. We also remove country-product pairs with nonzero nominal rates of assistance, the rationale being that export and production subsidies would otherwise distort the revealed factor intensities of products. 5 Our methodology does not adjust for the use of imported inputs for the production of export goods. Using value-added data15 or net exports would not be consistent with the goals of this analysis, which is aimed at assessing participation in GVC networks. Value-added data would be more appropriate to use in explaining the extent to which the country was involved in global value chains. To use an illustration, during the 2000s, the small South Caucasus country of Armenia was involved in cutting and polishing imported raw diamonds and sending them onward to Belgium and Israel. This activity ultimately declined. From our perspective we would be interested in knowing that Armenia was first involved and then lost that business. Even in the height of Armenia’s diamond trade, the activity from a value-added perspective was quite minimal given the high value of the raw diamonds, but participation in this production network was robust and we would want to take proper account of it. Furthermore, if we were to net out imports from exports in the same product line, this would not account for robust intra-industry trade taking place among the largest exporters. Additionally, the databases available are not available at the level of disaggregation (~5000 products) that we take advantage of by using trade data. Finally, as described in a recent survey paper by Amador and Cabral (2014), the advantage of using this method as opposed to the value-added method from input-output tables is the high country coverage and comparability between countries. C. Classification of individual “GVC products” The designation of GVC products is taken from lists generated by Athukorala (2010)16 and Sturgeon and Memedov (2011). Athukorala uses a list of parts and components in the UN Broad Economic Classification (BEC) Registry and the product list of the WTO Information Technology Agreement with the harmonized system (HS) of trade classification at the 6-digit level. The author then relied on firm- level surveys from Thailand and Malaysia to fill gaps in the list. Therefore, the list has a bias toward the products that made up the network activities in Southeast Asia at that time. The list has been used a number of times to measure GVC trade, as in a recent paper by Wignaraga, Kruger and Tuazon (2013). Separately, Sturgeon and Memedov (2011) compiled a list of GVC-related products, with broader sectoral coverage. They begin with modifying the BEC registry, combining capital and consumption goods into a single “final goods” category. They isolate “true” (differentiated, customized, product- 15 Value-added data has been collected at a sectoral level in such databases as the World Input-Output Database (WIOD), UNCTAD’s EORA database, and the Trade in Value Added (TiVA) database. A recent study (African Development Bank, 2014) took the approach of calculating GVC participation using the EORA database defined as the sum of backward and forward integration as a share of gross exports. (Backward integration is the share of foreign value added in a country’s exports, and forward integration is the share of a country’s value -added exports that are embedded in the exports of other countries.) 16 Athukorala, Prema-Chanda (2010). “Production Networks and Trade Patterns in East Asia: Regionalization or Globalization” ADB Working Paper Series on Regional Economic Integration, No. 56. This was compared to another set of products coded by Sturgeon and Memedovic (2011). Athukorala coded a greater number of network products, although mainly focused on parts and components. There was only minor overlap between the two coding systems. 6 specific) intermediates out of the BEC classification. This list has greater industry coverage of clothing and textiles than Athukorala’s list. To get the largest measure of GVC products, we combine the two lists. The original lists by Athukorala in HS 1996 nomenclature and Sturgeon and Memedov in SITC Revision 3 nomenclature were converted to HS1988/92 nomenclature using a concordance table. In total, the Athukorala provide 500 codes and the Sturgeon and Memedov provide 860 codes at the HS 1998/92 six-digit classification. Duplicate codes were removed to yield a total of 1237 product codes (see Annex Table 5 for a breakdown of products by sector). There are shortcomings in utilizing these lists. Most importantly, they do not take into account the expansion of network activities after they were composed. Agri-food cross-border value chains have become increasingly important, for example, and provide opportunities for developing countries, but we do not take these sectors into account. The list of products is isolated to manufacturing sectors. Additionally, by looking at products that are most likely to be traded within production networks globally, we ignore any production sharing that may occur based on the unique capabilities of a certain country or set of countries. Based on this coding scheme, Table 2 shows the value of GVC exports for SACU and comparator regions. SACU countries produce less than 3 percent of the value that ASEAN exports and about 4 percent of what Eastern Europe17 exports. South Africa accounts for about 92% of SACU’s GVC exports. Table 2: Value of GVC Exports, 2012, by Region Region Value (in $000s) ASEAN 492,000,000 Eastern Europe 319,000,000 Mercosur 31,200,000 South Africa 11,200,000 BLNS 985,222 EAC 589,240 This remainder of this paper is structured as follows. First, we investigate what capabilities matters for participation in GVCs. Then, we outline how SACU countries fare on these capabilities. Finally, we take up the issue of how SACU countries have performed with the endowments they have and which sectors may be most consistent with existing capabilities. IV. Results A. What capabilities matter for GVC trade? 17 In this paper, we refer to Eastern Europe as EU11 countries plus Turkey. 7 The previous section gave a preview of the capabilities assessed in this study and the indicators used to represent them. We can then assess the extent to which these capabilities vary with one another. The two fixed endowments are not correlated with each other: natural capital and proximity to markets have largely no relationship. However, many of the capabilities considered changeable endowments are correlated to one another (Table A5). Physical capital and institutions have a correlation coefficient above 0.8, meaning that these largely occur together across countries. As for the policy variables, wage competitiveness and logistics are highly negatively correlated with each other, but the other variables have little or no linear relationship. We run a test of statistical significance between GVC products and non-GVC products on each capability. Table 2 shows that all of these capabilities except wage competitiveness are generally important in the export of products known to be traded within production networks (“GVC products”). This is illustrated by the greater intensity of GVC products compared to non-GVC products for each indicator type, except low wages. Countries with low wages are not more likely to be involved in global value chains. Table 3 shows the differences between final and intermediate products. Intermediate products have a greater intensity of capabilities, except natural capital, wage competitiveness and access to inputs. This aligns with our understanding of the evolution of production networks in Eastern Europe, for example, where only after years of assembly of imported car components did countries begin to produce and then export car parts. Table 2: Revealed Capability Intensity of GVC Products versus Non-GVC Products Category Capability GVC Products Non-GVC Products Fixed Proximity to markets 0.0085*** 0.003 Natural capital 0.0061*** 0.0024 Changeable Human capital 0.012*** 0.0035 Physical capital 0.0079*** 0.0042 Institutional capital 0.0113*** 0.0033 Policy Logistics/connectivity 0.0157*** 0.0046 Variables Wage competitiveness -0.0052 -0.0036* Market access 0.0048 0.0037 Access to inputs 0.0044** 0.0006 Table 3: Revealed Capability Intensity of Final versus Intermediate Products Category Capability Final Intermediate Products Products Fixed Proximity to markets 0.049 0.372*** Natural capital 0.206 0.244 Changeable Human capital 0.087 0.189*** Physical capital -0.066 0.227*** Institutional capital -0.02 0.24*** 8 Policy Logistics/connectivity 0.141 0.484*** Variables Wage competitiveness 0.076*** -0.13 Market access -0.08 0.099*** Access to inputs 0.238*** 0.194 *** Indicates statistically significant difference at p <0.01. ** Indicates statistically significant difference at p<0.05. * Indicates statistically significant difference at p<0.1. B. How do SACU countries rate on these capabilities? SACU countries vary on these capabilities. On the level of human capital as measured by the average years of schooling, Botswana and South Africa score high, while Namibia lags behind. South Africa’s total natural capital far outpaces other countries, with Botswana in a distant second. In terms of physical capital per capita, Botswana and South Africa have a substantial lead over the other countries. Having had success managing revenues from resource extraction, Botswana’s institutions are rated ahead of the other countries, including South Africa, with Lesotho and Swaziland lagging behind the others in this area. Geographically, of course, all countries are in a similar situation. South Africa is a much more sophisticated logistical hub, however. South Africa, being a more industrialized country, has a higher minimum wage, which should be seen in the context of its higher productivity. In terms of market access and access to inputs, all countries are in a favorable position, except for Swaziland on market access. Table 4: Current Endowment Levels, SACU Count Natur Acces ry al Wage s to Human Capit Physical Institutio Proximity Logisti Competitiven Market Input Capital al Capital ns to Markets cs ess Access s Years $ $US per Rating GDP- Rating Minimum Index Inde of billio capita 2.5 to weighted 1 to 5 wage in $US (0 to 1) x (0 schooli n 2.5 distance for a 19-year to 1) ng (age index (0 old worker >15) to 1- or an closest) apprentice BWA 9.6 30 19.70 0.66 0.42 2.49 105.0 0.89 0.99 LSO 6.6 2.5 3.98 -0.30 0.38 2.37 104.1 0.90 0.96 NAM 6.0 14 9.90 0.23 0.44 2.66 -- 0.91 0.99 SWZ 7.6 13 4.61 -0.50 0.40 -- 107.5 0.68 0.98 ZAF 8.6 790 13.61 0.10 0.35 3.43 646.4 0.94 0.95 To put the above figures in context, Figure 1a and 1b show the standardized capability levels and compared to two regions of major importance in global value chains, ASEAN and Eastern Europe. South Africa is separated from the rest of SACU given its size and level of industrialization. Because the figures are standardized, capabilities can be compared to one another and read in terms of standard deviations 9 from the average country. Once standardized, we show the difference between the standardized capability level of the SACU countries and that of comparator regions to create a “capability gap”. South Africa has several advantages relative to ASEAN and Eastern Europe: logistics, natural capital, market access and access to inputs. The figures illustrate the large distance from markets for South Africa, particularly relative to Eastern Europe. South Africa also lags behind the two other regions in terms of physical capital per capita and wage competitiveness. South Africa has better level of human capital and institutions than ASEAN on average, but lower levels of these two capabilities than Eastern Europe on average. Figure 1a-b: Capability Gap, South Africa versus Comparators v. ASEAN Logistics 0.65 Institutions 0.33 Proximity to Markets -0.78 Physical Capital -0.20 Natural Capital 0.23 Market Access 0.67 Access to Inputs 1.25 Human Capital 0.26 Wage Competitiveness -0.83 -3.00 -2.00 -1.00 0.00 1.00 2.00 v. Eastern Europe Logistics 0.18 Institutions -0.48 Proximity to Markets -2.61 Physical Capital -0.43 Natural Capital 0.47 Market Access 0.38 Access to Inputs 0.13 Human Capital -0.74 Wage Competitiveness -0.38 -3.00 -2.00 -1.00 0.00 1.00 2.00 10 We repeat the same exercise with the average of the Botswana, Namibia, Lesotho and Swaziland. These countries fare worse than South Africa but they are not without some advantages relative to comparators. These countries are more competitive than South Africa on wages. BLNS countries also have better access to inputs relative to both comparators and more highly rated institutions than ASEAN countries on average. On the other hand, BLNS countries have, on average, lower levels of logistics capabilities, market access and natural capital than comparators, all of which are strengths for South Africa. Figure 2a-b: Capability Gap, BLNS versus ASEAN and Eastern Europe v. ASEAN Logistics -1.02 Institutions 0.25 Proximity to Markets -0.53 Physical Capital -0.29 Natural Capital -0.33 Market Access -0.49 Access to Inputs 1.60 Human Capital -0.16 Wage Competitiveness 0.08 -3.00 -2.00 -1.00 0.00 1.00 2.00 v. Eastern Europe Logistics -1.49 Institutions -0.55 Proximity to Markets -2.37 Physical Capital -0.52 Natural Capital -0.09 Market Access -0.79 Access to Inputs 0.48 Human Capital -1.16 Wage Competitiveness 0.53 -3.00 -2.00 -1.00 0.00 1.00 2.00 C. How has SACU performed in GVC trade given its mix of capabilities? 11 The transformation from endowments to exports is not automatic. For SACU countries, we compare the intensity on each capability throughout a country’s export basket against the underlying abundance of that capability. Each of the five countries’ nine capabilities is represented by points on Figure 3. The standardized level of each capability is measured along the x-axis, and the extent to which each of these nine capabilities is represented in the export basket (“average revealed capability intensity”) is measured by the y-axis. For a given trade flow, the average revealed capability intensity is a weighted average derived by multiplying the revealed intensity of a product in a capability by that product’s share in a given trade flow and summing across the entire basket. This is analogous to the EXPY calculation of Hausmann, Hwang and Rodrik (2007). We can see an upward trend, showing that as a country has a greater endowment of a capability, that capability is more likely to be represented in its export basket. This is not a surprising result, but the figure also shows dispersion around the trend line and several instances where an endowment is not “expressed”. Many of the instances where endowment is not “expressed” are related to Botswana. The overwhelming preponderance of diamond exports may be producing this result. Figure 3: Endowments and Average Revealed Capability Intensity 1 0.8 Average Revealed Capability Intensity 0.6 0.4 0.2 0 -2 -1.5 -1 -0.5 0 0.5 1 1.5 -0.2 -0.4 -0.6 -0.8 -1 Endowment Level, Standardized Are SACU countries out-performing or under-performing their capabilities? Table 5 compares each SACU country’s ranking on a particular capability with the country’s ranking on its averaged revealed capability intensity. As shown previously, we generally expect that the abundance of a capability shows up in exports intensive in that capability. The deviation from the global rank in abundance and the global rank in export intensity can be exploited to see how countries are faring in turning capabilities into exports. We show the difference between the ranking of each SACU country’s capability level against the rank of 12 the average revealed capability intensity of exports, with a positive number denoting that a country is outperforming its capabilities and a negative number indicating that a country is under-performing. The results present a mixed picture across endowments, but with some useful takeaways across countries. Focusing on the most important capabilities (as shown in Table 2), Swaziland is doing far better than its endowment levels would suggest. South Africa, Lesotho and Namibia are doing roughly on par with their endowment levels, while Botswana is lagging given its endowment levels. Table 5: Difference between Endowment Level Ranking and Export Intensity Ranking, SACU Countries South Botswana Lesotho Namibia Swaziland Africa Logistics -33 -9 -13 +74 -6 Proximity to Markets -32 -15 -9 +32 +44 Institutions +4 -6 -7 +53 +15 Natural Capital -65 -20 -32 +52 -37 Physical Capital -50 -23 -1 +54 +19 Market Access 24 -29 -22 +44 -13 Access to Inputs +3 +17 -3 -51 -8 Human Capital -5 +13 +22 +25 +21 Wage Competitiveness +27 +34 -11 -64 0 D. Which GVC sectors should SACU countries target based on their current capabilities? What sectors could they participate in with greater capabilities? To understand the current situation and prospects for SACU countries more deeply, we now disaggregate GVC products by industry and products. We sub-divide the entire product universe into 17 sectors, of which GVC products span 12 of these. Table 6 shows the sectoral breakdown and the value of global trade by sector in 2012. The two-digit codes refer to the first two digits of the HS chapter. GVC trade is dominated by the machinery/electronics and transportation sectors. The sectors with the greatest trade value are not necessarily those for which GVC intensity is greatest. We define “GVC intensity” as the share that GVC trade comprises in world trade within that sector. Table 6: Value and Intensity of GVC Trade, by Sector GVC Trade Total Trade Intensity: (US$ million) (US$ million) GVC/Total 84-85 Mach/Elec 3,340,000 4,270,000 78% 86-89 Transportation 1,090,000 1,500,000 73% 61-63 Clothing 357,000 405,000 88% 90-97 Miscellaneous 355,000 942,000 38% 50-60 Textiles 130,000 228,000 57% 64-67 Footwear 116,000 125,000 93% 72-83 Metals 52,000 1,210,000 4% 39-40 Plastic / Rubber 46,600 756,000 6% 13 68-71 Stone / Glass 13,500 621,000 2% 28-38 Chemicals 12,800 1,560,000 1% 41-43 Hides, Skins 7,810 103,000 8% 44-49 Wood 3,760 387,000 1% 01-05 Animal - 291,000 0% 06-15 Vegetable - 452,000 0% 16-24 Foodstuffs - 506,000 0% 25-27 Minerals - 3,600,000 0% 98-99 Special - 518,000 0% Each GVC sector may also be broken down between final and intermediate products. Intermediates have been shown to take up an increasingly large portion of global trade in recent years (citation needed). Table 7 shows the breakdown between final products and intermediate products within each GVC sector. Many sectors are aligned with only one GVC position (final or intermediate). Clothing (final) is downstream of textiles (intermediate), for example. For other sectors such as transportation, there is a more equal split between final products and intermediates in world trade: 25 percent of transportation products are final products and 75 percent are intermediate products. Table 7: Relative Position in GVC, by Sector Total Number of % Intermediate Sector18 GVC Products % Final Products Products 39-40 Plastic / Rubber 12 0 100 41-43 Hides, Skins 5 80 20 50-60 Textiles 320 0 100 61-63 Clothing 233 99 1 64-67 Footwear* 42 69 31 68-71 Stone / Glass 6 0 100 72-83 Metals 33 0 100 84-85 Mach/Elec* 360 14 86 86-89 Transportation* 56 25 75 90-97 Miscellaneous* 167 54 46 *These sectors have a substantial number of both final and intermediate GVC products and are therefore broken up in later tables. Any GVC product – as well as any other product – requires a particular mix of capabilities to be produced. GVC participation is to a great extent the result of the available mix of capabilities in the economy. The series of radar charts presented in Figure 6 help illustrate the difference between SACU’s “endowments” and the capabilities required by GVC products. Each of the nine capabilities is a separate direction on the spider chart. The units of the axes are standard deviations. The two lines presented signify:  Endowments: SACU’s existing capabilities (blue) 18 Wood and chemicals sectors were removed from this list because they are only represented by one product. 14  Requirements: Intensity of GVC products within sector in a given capability (red) The figures show the distance between SACU’s capabilities and the capabilities required for participation in the largest sectors of GVC trade. In the subsequent section of the paper, we quantify the total distance between what is available and what is required as a “similarity index.” Taking machinery/electronics, for example, SACU’s capabilities are substantially below what is required to produce in this sector except in two areas: access to inputs and wage competitiveness. On the other hand, existing capabilities seem to be more aligned with clothing and textiles. Figure 6: SACU’s Capabilities Versus Industry Requirements (Selected Sectors) 84-85 Machinery/Electronics 86-89 Transportation Endowment Requirement Endowment Requirement Human Capital Human Capital 1 1 Rule of Law Physical Capital Rule of Law Physical Capital 0 0 Proximity to -1 Wage Proximity to -1 Wage Markets Competitiven… Markets Competitiven… -2 -2 Access to Access to Connectivity Connectivity Inputs Inputs Natural Capital Market Access Natural Capital Market Access 61-63 Clothing 50-60 Textiles Endowment Requirement Endowment Requirement Human Capital Human Capital 1 1 Physical Rule of Law Physical Capital Rule of Law 0 Capital 0 Proximity to -1 Wage Proximity to -1 Wage Markets Competitiven… Markets Competitiven… -2 -2 Access to Access to Connectivity Connectivity Inputs Inputs Natural Capital Market Access Natural Capital Market Access The following tables show the total gap between SACU’s current capabilities and the capabilities required to export within a GVC sector. We rank the sectors on this “similarity index.” In this index, the lower the score is, the greater the similarity between existing and required capabilities. This similarity index is weighted by the importance of a given capability for a sector, so that if a capability like physical capital is more important for a producing within a sector, the gap between the physical capital that is available and what is required takes on greater importance. 15 We show the sectors that are within one standard deviation and two standard deviations. Neither should be taken as an absolute threshold for participation. None of the scores are below zero because an "excess" capability in one area does not cancel out a deficit in another area. The reason that not all sectors are in play is the role of fixed endowments. We include both final and intermediates, and where applicable, we divide final products from intermediates. Four sectors (footwear, machinery/electronics, transportation and miscellaneous manufactured articles) were broken up in this way. The products within a sector are weighted according to their share of the product’s trade value. We show several scenarios as listed below. In the simulations two through five, if a country’s capabilities are lower than the listed threshold, they were replaced by a higher capability level. If they are higher initially, they remain the same. 1. Current capabilities19 2. Only short-term policy variables20 raised to the median 3. Both short- and long-term policy variables21 raised to the median 4. Both short- and long-term policy variables raised to the 60th percentile 5. Both short- and long-term policy variables raised to the 75th percentile The results show that clothing and leather goods are the GVC exports most closely aligned with current capabilities. From the starting point, South Africa and Botswana have capabilities closer to some of the biggest GVC sectors by value such as machinery/electronics and transportation, while the other three countries are relatively further away. This is denoted by the similarity scores below 3 for all sectors for these two countries. When short-term policy variables are placed at the median, more sectors are in play for Swaziland and Lesotho. It does not change the picture much for South Africa. Bringing long-term policy variables as well to the median lowers the distance to the sectors requiring greatest capabilities. Capabilities at the 75th percentile bring an even greater number of sectors under one standard deviation, including machinery/electronics, wood and footwear. It is important to note that interaction between the capabilities is not fully accounted for here. For example, if physical capital per capita is raised to the 90th percentile, then wage competitiveness, by the measurement we are using, would decrease, not increase. Because of that, we do not simulate any changes in a country’s wage competitiveness, but instead leave it where it is. The results illustrate an important message about feasible participation. Textiles, for example, appear on the export horizon for several SACU countries, but then the sector never seems appears within one 19 Two measurements are missing in the original databases and these gaps were filled in the following way. Namibia’s wage competitiveness is assumed to be no better than Swaziland, and Swaziland’s logistics are assumed to be the same as Lesotho’s. 20 Short-term policy variables are logistics, market access and access to inputs. We elect to leave wage competitiveness where it is. 21 Long-term policy variables are human capital, physical capital and institutional capital. 16 standard deviation even at the 75th percentile of capabilities because SACU’s fixed endowments are not favorable for this sector. For nearly all countries, this story applies to footwear as well. The results also provide a surprising story about final versus intermediate GVC exports. While intermediates generally tend to have a greater intensity in many capabilities (referring back to Table 3), within sectors divided between final and intermediates, intermediate exports of machinery/electronics appear ahead of final products. This does not align with the conventional understanding of production networks in which intermediates are seen as more sophisticated than final assembly. Certain intermediates in this sector may indeed be in play before final products. Table 8: Similarity Index, Current Capabilities Sector BWA LSO NAM SWZ ZAF 41-43 Hides, Skins 0.60 0.99 0.85 2.71 0.45 61-63 Clothing 0.71 1.23 0.84 2.29 0.74 64-67 Footwear- Final 1.81 2.22 1.79 3.65 1.33 64-67 Footwear - Intermediate 1.73 2.46 2.06 3.63 1.43 84-85 Mach/Elec - Intermediate 2.05 3.38 2.73 4.51 1.58 50-60 Textiles 2.08 2.58 1.97 3.28 1.70 90-97 Miscellaneous - Intermediate 2.35 3.73 3.05 4.85 1.84 39-40 Plastic / Rubber 2.21 3.56 2.90 4.63 1.91 84-85 Mach/Elec - Final 2.65 3.81 3.15 4.95 1.97 86-89 Transportation - Intermediate 2.58 3.98 3.28 5.10 2.07 68-71 Stone / Glass 2.61 4.04 3.35 5.13 2.11 86-89 Transportation - Final 2.78 4.27 3.55 5.39 2.25 72-83 Metals 2.73 4.20 3.48 5.33 2.26 90-97 Miscellaneous - Final 2.92 4.43 3.73 5.52 2.41 Table 9: Similarity Index, Short-Term Policy Variables Raised to Median Sector BWA LSO NAM SWZ ZAF 41-43 Hides, Skins 0.38 0.68 0.77 0.62 0.45 61-63 Clothing 0.52 0.92 0.83 0.70 0.74 64-67 Footwear- Final 1.50 1.85 1.70 1.75 1.33 64-67 Footwear - Intermediate 1.44 2.10 1.97 1.91 1.43 84-85 Mach/Elec - Intermediate 1.71 2.97 2.63 2.81 1.58 50-60 Textiles 1.77 2.14 1.86 2.08 1.70 90-97 Miscellaneous - Intermediate 1.99 3.29 2.95 3.14 1.84 39-40 Plastic / Rubber 1.88 3.15 2.80 3.00 1.91 84-85 Mach/Elec - Final 2.27 3.36 3.05 3.21 1.97 86-89 Transportation - Intermediate 2.20 3.53 3.18 3.38 2.07 68-71 Stone / Glass 2.26 3.61 3.25 3.46 2.11 86-89 Transportation - Final 2.38 3.80 3.44 3.65 2.25 17 72-83 Metals 2.34 3.75 3.38 3.60 2.26 90-97 Miscellaneous - Final 2.57 4.00 3.63 3.85 2.41 Table 10: Similarity Index, All Policy Variables Raised to Median Sector BWA LSO NAM SWZ ZAF 41-43 Hides, Skins 0.38 0.52 0.46 0.50 0.45 61-63 Clothing 0.52 0.61 0.48 0.57 0.74 64-67 Footwear- Final 1.50 1.69 1.50 1.64 1.33 64-67 Footwear - Intermediate 1.44 1.66 1.45 1.61 1.43 84-85 Mach/Elec - Intermediate 1.71 2.47 2.05 2.43 1.57 50-60 Textiles 1.77 2.03 1.76 1.97 1.70 90-97 Miscellaneous - Intermediate 1.99 2.80 2.38 2.76 1.84 39-40 Plastic / Rubber 1.88 2.67 2.24 2.63 1.90 84-85 Mach/Elec - Final 2.27 2.90 2.52 2.86 1.96 86-89 Transportation - Intermediate 2.20 3.04 2.62 3.00 2.06 68-71 Stone / Glass 2.26 3.12 2.69 3.08 2.10 86-89 Transportation - Final 2.38 3.30 2.87 3.26 2.25 72-83 Metals 2.34 3.26 2.83 3.22 2.25 90-97 Miscellaneous - Final 2.57 3.50 3.07 3.47 2.41 Table 11: Similarity Index, All Policy Variables Raised to 60th Percentile Sector BWA LSO NAM SWZ ZAF 41-43 Hides, Skins 0.29 0.33 0.27 0.31 0.40 61-63 Clothing 0.52 0.61 0.48 0.57 0.74 64-67 Footwear- Final 1.32 1.44 1.29 1.39 1.31 84-85 Mach/Elec - Intermediate 1.51 1.84 1.60 1.80 1.34 64-67 Footwear - Intermediate 1.26 1.39 1.22 1.34 1.39 90-97 Miscellaneous - Intermediate 1.79 2.17 1.92 2.13 1.61 39-40 Plastic / Rubber 1.69 2.05 1.80 2.01 1.68 50-60 Textiles 1.56 1.72 1.51 1.65 1.68 84-85 Mach/Elec - Final 2.08 2.33 2.10 2.28 1.75 86-89 Transportation - Intermediate 2.00 2.43 2.17 2.38 1.84 68-71 Stone / Glass 2.06 2.49 2.24 2.44 1.87 86-89 Transportation - Final 2.18 2.67 2.42 2.62 2.02 72-83 Metals 2.15 2.63 2.38 2.59 2.02 90-97 Miscellaneous - Final 2.37 2.87 2.61 2.82 2.17 Table 12: Similarity Index, All Policy Variables Raised to 75th Percentile 18 Sector BWA LSO NAM SWZ ZAF 41-43 Hides, Skins 0.29 0.32 0.26 0.31 0.39 61-63 Clothing 0.52 0.61 0.48 0.57 0.74 84-85 Mach/Elec - Intermediate 0.97 1.06 0.93 1.02 0.91 90-97 Miscellaneous - Intermediate 1.17 1.26 1.12 1.21 1.07 39-40 Plastic / Rubber 1.11 1.20 1.05 1.15 1.20 86-89 Transportation - Intermediate 1.36 1.45 1.31 1.40 1.24 64-67 Footwear- Final 1.23 1.33 1.18 1.28 1.25 86-89 Transportation - Final 1.44 1.53 1.39 1.49 1.27 68-71 Stone / Glass 1.42 1.51 1.37 1.46 1.27 64-67 Footwear - Intermediate 1.15 1.26 1.09 1.21 1.32 72-83 Metals 1.48 1.58 1.44 1.53 1.34 90-97 Miscellaneous - Final 1.61 1.70 1.56 1.65 1.39 84-85 Mach/Elec - Final 1.54 1.63 1.50 1.59 1.42 50-60 Textiles 1.43 1.58 1.36 1.50 1.57 V. Concluding Remarks  GVC participation is based on a variety of factors, but logistics, proximity to markets and institutions seem to be most important.  SACU’s low participation in GVCs can be traced back to the level of capabilities these countries have. Nearly all SACU countries are either performing roughly at the expected level.  SACU countries can remove or counteract deficiencies that have thus far left them out of production networks.  Increasing capabilities can lead to increasing participation in manufacturing sectors. Initially this relates to participation in sectors like clothing and footwear and over the long-term in sectors like machinery/electronics. 19 References African Development Bank (2014), African Economic Outlook. Amador, J. and S. Cabral (2014), “Global Value Chains: Surveying Drivers, Measures and Impacts” Banco de Portugal, No. 3. Athukorala (2010), “Production Networks and Trade Patterns in East Asia: Regionalization or Globalization?” ADB Working Paper Series on Regional Economic Integration, No. 56. Hausman, Hwang and Rodrik (2007), “What You Export Matters” Lederman, D., V. Pathikonda and L. Rojas (2012), “Do Eurasia’s Endowments Constrain Product and Market Diversification? Preliminary Findings”, mimeo. Shiritori, Tumurchudur and Cadot (2010), “Revealed Factor Intensity Indices at the Product Level”, UNCTAD Policy Issues in International Trade and Commodities Study Series, No. 44. Sturgeon, T. and O. Memedovic (2010), “Mapping Global Value Chains: Intermediate Goods Trade and Structural Change in the World Economy”, UNIDO Development Policy and Strategic Research Branch Working Paper, No. 5. Wignaraja, G., J. Kruger and A.M. Tuazon (2013), Production Networks, Profits, and Innovative Activity: Evidence from Malaysia and Thailand, Asian Development Bank Institute Working Paper Series, No. 406. 20 ANNEX Table A1. Indicators Used to Represent GVC-Related Endowments Concept Indicator (and unit) Source Year # Countries Remarks *Asterisk indicates low coverage. 1 Proximity to markets GDP-weighted CEPII 2011 224 distance (inverted) 2 Natural capital Total value of natural World Bank Wealth of 2010 205 Updated with revised capital in US$ Nations Botswana figure 3 Human capital Average years of Barro and Lee (2010) 2010 146 schooling (>15 years old population) 4 Physical capital Capital stock per WDI 2010 138 person 5 Institutions Rule of Law Worldwide Governance 2013 212 Indicators (Kauffman, Kraay and Mastruzzi) 6 Logistics/Connectivity Logistics World Bank 2014 160 Used “international LPI” Performance Index 7 Wages Minimum wage for a World Bank Doing Business 2014 153 19-year old worker Annex (Employing or an apprentice Workers) 8 Market Access Overall Trade World Bank Overall Trade 2009 167 Rationale: accounts for Restrictiveness Index Restrictiveness Indices preferential access, including (of Trading Partners) GSP and AGOA that drive some FDI in GVCs. 9 Access to Inputs Overall Trade World Bank Overall Trade 2009 105* Restrictiveness Index Restrictiveness Indices (OTRI) 21 Table A2. Other Indicators Considered Concept Indicator Source Year # Countries *Asterisk indicates low coverage. Services Restrictiveness Overall services trade World Bank Services Trade Restrictions 2012 104* restrictiveness Database Connectivity Liner Shipping Connectivity UNCTAD based on data from 2013 155 Index Containerisation International Online, via WDI Domestic Market Size Domestic market size = (GDP+ World Economic Forum GCR 2014 144 Import Value) –Export Value Institutions Perception of intellectual World Economic Forum GCR 2014 144 property protection (1.02) Infrastructure Number of Electrical Outages Enterprise Surveys 2009-14 130 Per Month (latest figure) Infrastructure Perception of quality of overall World Economic Forum GCR 2014 144 infrastructure (2.01) Wages/Productivity Unit labor costs UNIDO INDSTAT2 2009 70* Table A3: Capability and Country Coverage, Options Considered Value of trade (in US$ % of world trade Number of countries Billion) represented a) First 8 106 14,100 83% b) First 8 + OTRI 87 13,800 81% c) First 8 + OTRI + STRI 79 13,700 81% 22 Table A4. Endowment Levels, by Country Country Natural Wage Human Capital Physical Institution Proximity to Competitivenes Access to Capital Capital s Markets Logistics s Market Access Inputs Years of $ billion $US per Rating 2.5 GDP-weighted Rating 1 Minimum Index (0 to 1) Index (0 schoolin capita to 2.5 distance index (0 to 5 wage in $US to 1) g (age to 1-closest) for a 19-year >15) old worker or an apprentice SACU Countries Botswana 9.6 30 19.70 0.66 0.42 2.49 105.0 0.89 0.99 Lesotho 6.6 2.5 3.98 -0.30 0.38 2.37 104.1 0.90 0.96 Namibia 6.0 14 9.90 0.23 0.44 2.66 -- 0.91 0.99 Swaziland 7.6 13 4.61 -0.50 0.40 -- 107.5 0.68 0.98 South 8.6 790 13.61 0.10 0.35 3.43 646.4 0.94 0.95 Africa Comparators Argentina 9.3 820 16.50 -0.58 0.23 2.99 635.1 0.86 0.91 Chile 10.2 830 19.36 1.29 0.22 3.26 -- 0.83 0.93 Colombia 7.7 520 10.25 -0.33 0.55 2.64 309.9 0.94 0.79 Ethiopia -- 150 -- -0.76 0.67 2.59 -- 0.83 0.88 Cambodia 6.0 60 0.85 -1.09 0.55 2.74 43.0 0.77 -- Turkey 7.0 510 17.55 0.10 0.92 3.5 167.5 0.90 0.93 Thailand 7.5 730 12.01 -0.20 0.58 3.43 248.5 0.89 0.90 23 Table A5: Number of GVC and Non-GVC Product Codes (HS1988/92), by Sector GVC Non-GVC Total 01-05 Animal 0 193 193 06-15 Vegetable 0 312 312 16-24 Foodstuffs 0 178 178 25-27 Minerals 0 147 147 28-38 Chemicals 1 735 736 39-40 Plastic / Rubber 12 177 189 41-43 Hides, Skins 5 58 63 44-49 Wood 1 218 219 50-60 Textiles 320 194 514 61-63 Clothing 233 58 291 64-67 Footwear 42 13 55 68-71 Stone / Glass 6 181 187 72-83 Metals 33 510 543 84-85 Mach/Elec 360 397 757 86-89 Transportation 56 76 132 90-97 Miscellaneous 167 213 380 98-99 Special 0 1 1 TOTAL 1236 3661 4897 24 Table A6: Correlation between Capabilities Fixed capabilities: Proximity to Markets Natural Capital Proximity to Markets 1.0000 Natural Capital 0.0174 1.0000 Long-term policy variables: Human Capital Physical Capital Institutions Human Capital 1.0000 Physical Capital 0.5946 1.0000 Institutions 0.6529 0.8246 1.0000 Short-term policy variables: Logistics/ Wage Market Access Access to Inputs Connectivity Competitiveness Logistics/ Connectivity 1.0000 Wage Competitiveness -0.7133 1.0000 Market Access 0.2419 -0.1666 1.0000 Access to Inputs 0.2448 -0.2304 -0.0192 1.0000 25