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South Africa: Selected Issues

Author(s):
International Monetary Fund
Published Date:
March 2000
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V. South Africa’s Pattern of Trade

A. Introduction and Summary 87

156. In the past ten years, South Africa’s international trade has undergone substantial structural change, including the end of the trade embargo, the elimination of nontariff barriers, a large reduction in tariffs, and the end of the generalized export subsidies. With the removal of these trade distortions, it would seem useful to examine the pattern of South Africa’s trade today. On another front, one of the interesting phenomena in South Africa has been the high capital intensity of production in the context of very high unemployment rates. Is this capital intensity also reflected in South Africa’s pattern of trade, that is, are South Africa’s net exports relatively capital intensive?

157. The analysis finds that the majority of South Africa’s trade is with the so-called high- income countries, most importantly the European Union; this trade tends to be characterized by large exports of natural resource commodities and large imports of sophisticated manufactured goods. We find that South Africa tends to be a net exporter of capital-intensive goods to high- and middle-income countries, in apparent contradiction of the Heckscher-Ohlin-Samuelson theorem. Moreover, it appears as if this capital intensity increased during the 1989-97 period.

158. Subsection B describes some of the stylized facts concerning South Africa’s trade; with whom South Africa carries out the majority of trade; what goods make up South Africa’s trade; and what the relative factor intensities are of the goods that South Africa trades with its various partners. Subsection C outlines the theoretical considerations and the empirical approaches relevant to testing the Heckscher-Ohlin-Samuelson (HOS) theorem; Subsection D reviews other recent studies on South Africa’s trade; and Subsection E presents the results of two approaches to testing the validity of the HOS theorem in South Africa: the standard factor content approach, in the tradition of Leontief (1954) and modified by Learner (1980), and the commodity composition approach. Subsection F concludes.

B. South Africa’s Pattern of Trade

159. Three main themes are apparent when looking at South Africa’s pattern of trade: the high concentration of trade with relatively high-income countries; the very diverse net export patterns with respect to different commodity groups; and the positive correlation between capital intensity and net exports in the commodity pattern of trade.88

The country and commodity composition of trade

160. South Africa has a relatively open economy, with trade accounting for 32 percent of GDP in 1997. From a geographical standpoint, South Africa’s trade is relatively concentrated with the European Community (EU), with which it conducts 39 percent of its trade (38 percent of exports and 41 percent of imports); East Asia and Pacific, with which it conducts 24 percent of its trade (27 percent of exports and 22 percent of imports); North America (15 percent of trade, 12 percent of exports, and 18 percent of.imports); and sub- Saharan Africa (11 percent of trade, 14 percent of exports, and 7 percent of imports).

161. When trading partners are grouped according to their income level, South Africa’s trade appears even more concentrated, with 79 percent of total trade conducted with high- income countries, compared with just 9 percent and 12 percent conducted with middle- and low-income countries, respectively (Figure 12). Moreover, this concentration is present even when South Africa’s trade is disaggregated into natural resources, basic manufactured goods, and sophisticated manufactured goods.89 As Tables 15 and 16 show, for all categories of goods, the overwhelming majority of trade is carried out with the high-income countries. However, during the course of the 1990s, trade with the high-income countries declined in relative terms while it increased with the middle-income countries (in the case of natural resources) and the low-income countries (in the case of sophisticated manufactured goods).

Figure 12.South Africa: Distribution of Trade by Partner Country,1989 and 1997

Sources: WEFA South Africa, and IMF staff estimates

Table 15.South Africa: Trade partners, by Commodity Group and Trading Partner Group, 1989
TradingShare of Trade in:Net Exports as a Percent of Trade
PartnerTotalNaturalManufactured goodsTotalNaturalManufactured goods
TradeResourcesBasicSophisticatedTradeResourcesBasicSophisticated
High income89.292.883.593.17.571.9-3.9-84.0
Middle Income4.02.56.71.614.945.112.9-53.9
Low income6.84.79.75.338.6-72.975.188.3
Total100.0100.0100.0100.09.964.44.9-74.3
Sources: WEFA South Africa; IMF staff estimates
Sources: WEFA South Africa; IMF staff estimates
Table 16.South Africa: Trade partners, by Commodity Group and Trading Partner Group, 1997
TradingShare of Trade in:Net Exports as a Percent of Trade
PartnerTotalNaturalManufactured goodsTotalNaturalManufactured goods
TradeResourcesBasicSophisticatedTradeResourcesBasicSophisticated
High income78.382.373.680.2-1.466.2-4.2-73.9
Middle Income9.312.47.88.0-7.96.312.3-57.9
Low income12.45.218.711.827.0-22.035.533.0
Total100.0100.0100.0100.01.554.14.5-60.0
Sources: WEFA South Africa; IMF staff estimates
Sources: WEFA South Africa; IMF staff estimates

162. On an net export basis, South Africa, in 1997, had close to a zero balance in its overall merchandise trade account, comprising small absolute deficits with the high- and middle- income countries and a moderately large surplus with the low-income countries. As a proportion of total bilateral trade, the imbalance with the high-income countries is negligible (a deficit of under 2 percent), while that with middle- and low-income countries (a deficit of 8 percent mid a surplus of 27 percent, respectively) is much higher; in the latter case, the imbalance is a matter of some contention between South Africa and its African partners (see Table 16).

163. The near-zero balance on overall net exports masks the existence of large imbalances in trade across commodities (see Tables 15 and 16). South African trade is characterized by large surpluses in natural resources, similarly large deficits in sophisticated manufactured goods, and near balance in basic manufactured goods. This aggregate pattern essentially reflects the separate commodity trade patterns with its high- and middle-income partners.90 In contrast, trade with the low-income countries is characterized by large South African surpluses in both basic and sophisticated manufactured goods, which are only partially offset by a deficit in natural resource commodities.91

Factor intensity of South Africa’s production and trade

164. On the basis of a cursory inspection of the input-output coefficients (Table 17 and Figure 13), one can make some general observations about the relative factor intensities of the three broad sectors. First, the production of basic manufactured goods tends to be more capital intensive than that of sophisticated manufactured goods; the production of the latter, which tend to be net imports for South Africa, are, on average, more skilled labor intensive. Second, the natural resource sector does not exhibit any homogeneity in terms of the factor intensities of its subsectors: agriculture is the most capital-intensive sector of all three-digit categories, the legacy of past policies designed to create a large, mechanized farming sector, while gold mining is the most unskilled labor intensive subsector of all the three-digit categories. Third, capital-labor ratios declined across-the-board during the 1989-97 period, while the ratio of skilled to unskilled labor generally increased.

Figure 13.South Africa: Factor Input Ratio,1989 and 1997

Sources: WEFA South Africa, and IMF staff estimates

Note : See Table 17 for definition of items on the horizontal axes

Table 17.South Africa: Factor Intensities,1997
High-skilledSemi,TotalCapitalCapital-Skilled-
andunskilled,LaborLaborUnskilled
skilledand informalratioratio
AG Agriculture, forestry, & fishing0.150.180.330.672.020.80
COCoal& mining Ore mining0.270.220.49:0.511.041.22
GGold & uranium Ore mining0.200.410.810.390.630.50
0Other mining0.220.250.470.531.130.88
FFood0.270.190.460.541.161.42
BVBeverages0.270.150.410.591.431.82
TOTobacco0.270.150.420.581.391.80
TXTextiles0.260.310.570.430.750.82
A PWearing apparel0.260.390.650.350.540.67
LELeather & leather products0.240.230.470.531.131.08
FWFootwear0.250.290.540.480.860.88
WWood & wood products0290.240.500.470.901.19
PPaper & paper products0.280.160.440.561.251.77
PRPrinting, publishing, & recorded media0.420.140.560.440.793.04
CKCoke & refined petroleum products0.270.190.460.551.201.44
CBasic chemicals0.310.150.480.541.182.05
OCOther chemicals & manmade fibres0.330.150.480.521.082.20
RRubber products0.300.220.520.480.921.36
PLPlastic products0.300.210.520.490.941.44
GLGlass & glass products0.260.230.490.511.031.11
NMNonmetalic minerals0.260.230.490.511.051.14
FEBasic iron & steel0.300.220.520.480.911.36
NFBasic nonferrous metals0.230.150.390.611.581.53
MPMetal products, excluding machinery0.320.250.570.430.761.29
MAMachinery and equipment0.380.210.590.410.701.76
EM. Electrical machinery0.360.270.630.370.601.76
TV. Television, radio, & communication equipment0.330.270.800.400.661.24
SCProfessional & scientific equipment0.290.240.530.47.0.88:1.18
MVMeter vehicles, parts, & accessories0.340.190.530.470.881.80
OTOther transport equipment0.400.240.640.360.551.66
FUFurniture0.310.280.590.410.691.09
0Other industries0.270.200.470.531.121.38
Sources: WEFA South Africa; IMF staff estimatesNote: the upper shaded region represents the natural resources goods; the lower shaded region represents the sophisticated manufactured goods; and the unshaded region represents the basic manufactured goods.
Sources: WEFA South Africa; IMF staff estimatesNote: the upper shaded region represents the natural resources goods; the lower shaded region represents the sophisticated manufactured goods; and the unshaded region represents the basic manufactured goods.

165. In this context, it is useful to look at various rank correlations between net exports and relative factor intensities (Figure 14). For overall trade, there is a positive correlation between net exports and the capital-labor ratio. This result holds when examining trade with each of the high-, middle-, and low-income countries. At least with respect to the high- income countries, this result is somewhat counterintuitive and will be investigated more formally in the following sections. Another result, which is more intuitively appealing, is the correlation between net exports and the ratio of skilled to unskilled labor, which is negative for trade with high-income countries and positive for trade with low-income countries.

Figure 14.South Africa: Rank correlation between Net Exports and Factor Ratios,1997

Sources: WEFA South Africa, and IMF staff estimates

C. Explaining the Pattern of Trade: Theory and Empirical Approaches to Testing

166. In the basic two-sector, two-factor, two-good Hecksher-Ohlin-Samuelson (HOS) model, a country exports those goods whose production uses intensively the factor in which the country is relatively well endowed and imports those goods whose production uses intensively the factor that is relatively scarce in the country. Because this proposition does not generalize easily with many goods and factors (Deardorff, 1984)92 empirical testing of this proposition takes two forms.

Factor content approach

167. The first approach is an extension of the “factor content” version of the HOS theorem. This says that countries will be net exporters of their abundant factors and net importers of their scarce factors. The proof of this proposition can be stated as

where A is the m x n matrix of technology coefficients whose typical element, akj represents the quantity of the kth factor used per unit of production of good jT is the n x 1 vector of net exports; Ei the endowment of factors of country i; Ew is the world’s endowment vector of factors, which is summed over all i’s; and Bi is a scalar.

168. If Qi is the vector of outputs of country i, factor market equilibrium requires AQi=Et. Summing over all countries yields

Identical and homothetic tastes imply that the consumption vectors Ci of each country are proportional to each other and to world output (Qw):Ci=QwBi.

Country i𠈙s trade, Ti is given by Ti=QiCi,

and the factors embodied in trade are

If there are two factors of production, capital (K) and labor (L), the two equations derived from (3) above are

where KT and LT are capital and labor embodied in net exports. It is then natural to define capital and labor abundance for a particular country relative to the world’s endowments; that is, a country is relatively capital abundant if

169. In one of the first tests of the HOS proposition, Leontief (1954) drew attention to the “paradox” whereby the United States appeared to be relatively labor abundant because of his empirical demonstration that the capital-labor ratio embodied in U.S. exports was smaller than that in U.S. imports. While many subsequent studies have tried to reconcile the observed U.S. pattern of trade with the theory, Learner (1980) has shown that Leontief s test was not the appropriate one, especially in the case of unbalanced trade. For example, if a country has a large trade surplus, it is possible for it to be a net exporter of factor services with which it is relatively poorly endowed. Indeed, in the U.S. data studies of Leontief, the United States was a net exporter of both capital and labor services, in part because it had a large trade surplus.

170. Learner (1980) shows that a valid test of the factor content proposition in the presence of unbalanced trade is to compare factor ratios in trade versus those in consumption. Specifically, if a country is a net exporter of both capital and labor services, it is relatively capital abundant if the capital intensity of net exports exceeds the capital intensity in consumption (i.e., KT/LT>Kc/Lc).93

Commodity composition approach

171. An alternative route to testing the HOS theorem is to conduct a regression analysis of the commodity composition of trade, with the regression equation taking the form where Tj represents net trade of commodity j, the 0’s are the gross factor input requirements (factor intensities), and P’s the associated coefficients.94Equation (5) itself can be theoretically justified based on an underlying trade model that relates a country’s autarky price to factor intensities and factor abundance defined relative to the world. The justification is less than perfect because, whereas the underlying trade relationship requires putting measures of abundance and factor intensities on the right-hand side of the equation, the regression only uses measures of factor intensities.

172. Deardorff (1984) points out a number of important features that should be bome in mind in estimating equation (5). First, according to the theory, the independent variables must be factor shares and not relative physical ratios, although many studies have resorted to the latter. Second, the factor shares must be the total factor shares, that is, those derived from the gross input-output coefficients and not those from the direct input-output coefficient, because gross factor intensities determine autarky prices.95 Third, the dependent variable should be net exports; gross exports may behave very differently from net exports, reflecting the phenomenon of intra-industry trade, about which the standard HOS theory has very little to say. Fourth, the dependent variable must be scaled, preferably by a measure of the size of the world market. However, this has rarely been done in practice: many studies have not scaled at all or have scaled by final output or by gross trade (exports plus imports). Fifth, because of the likely relation between the variance of the error term and the industry size, heteroscedasticity could significantly affect the estimation of equation (5) and thus should be corrected for. Finally, it would be preferable to test the HOS theorem by applying it to bilateral trade and not to aggregate trade. This is especially important for countries, like South Africa, that have less extreme relative endowments of labor and capital.

173. Two versions of the theory set out in equation (5) are usually tested with correspondingly different estimation techniques. One version, using regression analysis, tests for the sign on the coefficient of the various factor shares on the right-hand side; in the other version, the dependent variable is binary rather than continuous, because the theory of comparative advantage can explain only the direction, not the quantity, of trade flows. Thus, a number of studies have used probit and logit analysis to test the probability that the sign of the dependent variable is related to the explanatory variables.

174. Of the two versions, the factor content version is better grounded in theory. The commodity composition approach is theoretically deficient because, although the HOS theorem is a relationship among three variables—factor abundance, factor intensity, and trade—the empirical testing involves only intensity and trade. However, Bowen and Sveikauskas (1992) show, on the basis of extensive multicountry and multicommodity analyses, that this deficiency is not severe. Thus the commodity composition approach remains a useful way to test the HOS theory.

D. Comparison with Other Work

175. Two recent papers, Tsikata (1998) and ILO (1999), have also examined South Africa’s pattern of trade. However, their analyses suffer from the following shortcomings. Tsikata (1998) examines the pattern of exports rather than that of net exports, raising questions as to how her results should be interpreted. Second, Tsikata classifies products as skilled labor, unskilled labor, resource, or capital intensive on the basis of a priori criteria drawn from experience around the world. This classification could fail to capture an important feature of South Africa’s production structure, namely, that certain sectors could actually be capital intensive (because of various distortions) even though they might be classified as labor intensive in other countries. Third, Tsikata only looks at aggregate exports, that is, exports with all partners, rather than trade with different trading partners, which, theory suggests, is a more appropriate approach, especially if there are significant variations in the pattern of trade across trading partners (as appears to be the case for South Africa). Fourth, Tsikata uses physical factor intensities rather than those in terms of factor shares, which Deardorff (1984) suggests is the more appropriate one. Tsikata does not undertake any formal econometric analysis of the pattern of trade.

176. The examination of the patterns of South African trade in the recent paper by the ILO (1999) consists essentially of a categorization of product categories according to (physical) capital-labor ratios and natural resource intensity 96 and the correlation of these to the trade performance of the sector. The determination of whether a sector is export oriented or import substituting is based on an index of revealed comparative advantage. In addition to the arbitrary nature of the definitions and classifications, the ILO paper suffers from the shortcomings noted above in relation to Tsikata (1998). The ILO paper does, however, extend the analysis in one important direction by incorporating South Africa’s endowment of natural resources and raising the possibility that South Africa’s apparent abundance of capital might be related to mi assumed inherent capital intensity of resource-based sectors. In the following subsection, we control explicitly for South Africa’s endowments of natural resources in order to test the hypothesis of capital intensity of trade and production.

E. Results

Factor content approach

177. Tables 18 and 19 contain the results of the analysis based on measuring the factor content of South Africa’s trade in manufactured goods for 1989 and 1997. Although exports are overwhelmingly more capital intensive than imports (with a capital-labor ratio of 0.18 for exports versus a ratio of 0.11 for imports in 1997), South Africa is a net importer of both capital and labor services (note the negative entries in the “Net trade” columns under “Total Trade” in Table 19). However, the more appropriate comparison, that is, between the factor intensity of net trade (0.03) and that of consumption (0.11), shows that, being a net importer of both capital and labor services, South Africa is relatively capital abundant and exhibits a capital-intensive pattern of trade.

Table 18.South Africa: Services Embodied in Actual Trade and Consumptions, 1989
FactorUnitsTotal TradeHigh-IncomeMedium-IncomeLow-Income
ExportsImportsNet tradeConsumptionNet tradeConsumptionNet tradeConsumptionNet tradeConsumption
CapitalBillions of rand3846-8481-13.2486.1-0.67473.66.2466.7
LaborThousands of man-years560923-3639442-3039383-1299209948986
Capital-labor ratio0.070.050.020.050.040.050.010.050.070.05
Skilled laborThousands of man-years238418-1804,070-1514,041-593,94940.53,849
Unskilled laborThousands of man-years322505-1835,373-1525,341-695,259545,136
Skilled-unskilled tabor ratio0.740.830.980.760.990.760.860.750.750.75
Capital/labor abundanceCapitalCapitalCapitalCapital
Skilled/unskilled abundanceUnskilledUnskilledUnskilled
Note:Give factors a and b, where at bt ac and bc are the amounths of a and b embodied in net trade and consumption, Learner (1980) shows thta the country is relatively more endowed in a if and only of the following three conditions hold:(i) at>0, bt<0,(ii)at>0, bt>0, at/bt>ac/bcor(iii)at<0, b,t<0, at/bt<ac/bc
Note:Give factors a and b, where at bt ac and bc are the amounths of a and b embodied in net trade and consumption, Learner (1980) shows thta the country is relatively more endowed in a if and only of the following three conditions hold:(i) at>0, bt<0,(ii)at>0, bt>0, at/bt>ac/bcor(iii)at<0, b,t<0, at/bt<ac/bc
Table 19.South Africa: Services Embodied in Actual Trade and Consumptions, 1997
FactorUnitsTotal TradeHigh-IncomeMedium-IncomeLow-Income
ExportsImportsNet tradeConsumptionNet tradeConsumptionNet tradeConsumptionNet tradeConsumption
CapitalBillions of rand113110-19419-49549549413487
LaborThousands of man-years6131,043-5583,879-5894,761-2104,382594,114
Capital-labor ratio0.180.110.030.110.080.12-0.020.110.220.12
Skilled laborThousands of man-years276480-2641,723-2832,135-951,948291,824
Unskilled laborThousands of man-years337563-2942,156-3.062,625-1152,434302,289
Skilled-unskilled labor ratio0.820.850.900.800.920.810.830.800.970.80
Capital/labor abundanceCapitalCapitalCapitalCapital
Skilled/unskilled abundanceUnskilledUnskilledUnskilledSkilled
Note:Given factors a and b, e al, bl, ac, and bc are the amounts of a and b embodied in net trade and consumption, Leamer (1980) shows that the country is relatively more endowed in a if and only if one of the following three conditions hold.(i) al> 0l bl < 0,(ii) al> 0l bl > 0, a1/bl > a1/bl or(iii) al< 0l bl < 0, a1/bl < a1/bl

178. South Africa is also a net importer of skilled and unskilled labor services, and thus the correct test condition for skilled labor versus unskilled labor intensity requires a comparison between net trade and consumption in those services (i.e., 0.90 and 0.80, respectively, in 1997). Following Learner, these results show that South Africa is more endowed with unskilled labor because the skilled-unskilled labor ratio is greater for net trade than for consumption.

179. With respect to the disaggregated trade data, South Africa is revealed through trade to be capital rich in comparison with each of its trading partners.97 It is not surprising that South Africa’s net exports to low-income countries is relatively capital intensive, given its level of income and the development of the manufacturing sector. However, it is somewhat surprising that South Africa’s net exports to richer countries is relatively capital intensive. The disaggregated data also demonstrate that South Africa’s net exports to its high- and medium- income trading partners is more unskilled labor intensive, while its net exports to low-income partners is more skilled labor intensive, which would be the expected result.

Cross-commodity regression approach

180. This analysis also focuses on net exports of the various manufacturing goods sectors. Table 20 presents the regression results that use a simple ordinary least squares (OLS) estimation procedure, while Tables21 and 22 contain the results of the logit analysis. For the OLS regressions the dependent variable is net exports scaled by total trade, the scaling is necessary to keep the explanatory variables from picking up the effect of size. As the theory is agnostic about the quantitative magnitude of the parameters, only their sign (along with the statistical significance) is reported. All r-statistics are computed based on White’s heteroscedasticity-consistent standard errors.

Table 20.South Africa: OLS Regression Results on the pattern of Trade,1997
Independent VariableDependent Variable; Net Exports/Total Trade
(Total Factor Intensities)All partnersHigh-income partnersMedium-income partnersLow-income partners
Capital-labor ratioPositivePositivePositivePositive
(4.08)***(3.37)***(4.97)***(.23)
Skilled-unskilled labor ratioNegativeNegativePositivePositive
(2.02)**(2.72)**(.49)(1.18)
Resource intensityNegativeNegativePositivePositive
(.41)(.97)(.42)(1.77)*
Import intensityNegativeNegativeNegativeNegative
(1.38)(1.28)(.78)(.73)
Test for heteroscedasticity 1/0.0030.6830.6924.78**
Adjusted R20.4240.3380.5330.22
Note: "Positive" and "Negative" denote the sign of the coefficient t-statistic in parenthesis. t-statistic are computed vased on White’s heteroscedasticity-consistent standard errors. Astreisks denote singnificance at 1(three astreiks),5(two), and 10(one) percent, respectively.Given factors a and b, where al, bl, ac, and bc are the amounts of a and b embodied in net trade and consumption, Leamer (1980) shows that the country is relatively more endowed in a if and only if one of the following three conditions hold.

Teh test for heteroscedasticity is an F-test(Reset). Astreik suggest the presence of heteroscedasticity

Note: "Positive" and "Negative" denote the sign of the coefficient t-statistic in parenthesis. t-statistic are computed vased on White’s heteroscedasticity-consistent standard errors. Astreisks denote singnificance at 1(three astreiks),5(two), and 10(one) percent, respectively.Given factors a and b, where al, bl, ac, and bc are the amounts of a and b embodied in net trade and consumption, Leamer (1980) shows that the country is relatively more endowed in a if and only if one of the following three conditions hold.

Teh test for heteroscedasticity is an F-test(Reset). Astreik suggest the presence of heteroscedasticity

Table 21.South Africa: Results of Logit Regression Analysis, 1997
Dependent variable
TotalTotalTotalHighHighHighMiddleMiddleMiddleLowLowLow
IncomeIncomeIncomeIncomeIncomeIncomeIncomeIncomeIncome
Constant-1.94 *-0.86-1.68-2.00 **-0.72-0.60-1.86 *-1.04 *-0.93-0.94-1.15-0.48
Capital-labor ratio2.66 ***2.47 **2.77 ***2.44**1.92*1.07-0.21-0.67
Capital-unskilled labor ratio2.672.76***1.79*-0.29
Skilled-unskilled labor ratio-1.64*-2.44**-1.68*-1.75*-2.52**-2.29**-0.60-1.18-0.331.271.011.50
Intermediate imported inputs-0.16-0.16-0.38-0.06
Resourceinputs 1/0.430.511.66*0.92
L-R statistic9.91***10.24***11.81**11.66***11.76***11.98**6.79**6.83**10.68**4.224.336.28
R20.280.290.320.320.320.330.180.180.280.130.140.20
Note: number denote t-statistic. *** represents significance at 1 percent, ** at 5 percent, and * at 10 percent. Dependent variable is a binary variable. If net exports > 0, dependent variable=1;0 otherwise

Direct intermediate inputs of the agriculture and mining sectors

Note: number denote t-statistic. *** represents significance at 1 percent, ** at 5 percent, and * at 10 percent. Dependent variable is a binary variable. If net exports > 0, dependent variable=1;0 otherwise

Direct intermediate inputs of the agriculture and mining sectors

Table 22.South Africa: Results of Logit Regression Analysis with pooled 1989 and 1997 Data
Dependent variable
TotalTotalTotalHighHighHighMiddleMiddleMiddleLowLowLow
IncomeIncomeIncomeIncomeIncomeIncomeIncomeIncomeIncome
Constant-1.94 *-1.87 *-1.94 *-2.00 **-1.97 **-1.32-1.86 *-2.17 **-2.23 **-0.940.300.34
Dummy1/1.270.84-0.072.14 **
Capital-Labor ratio2.66 ***3.76 ***3.62 ***2.77 ***3.45 ***3.35 **1.92 *2.85 ***2.49 **-0.21-0.250.17
dum_id2/-0.52-1.27-1.15-2.30-0.51-1.79 *-0.230.82
Skilled-unskilled labor ratio-1.64 *-2.69 ***-2.21 **-1.75 *-2.32 **-2.87 **-0.60-0.530.131.270.540.49
dum_su3/-1.22-0.320.36-1.57
L-R statistic24.29***22.27***21.00***18.57***17.71***14.2111.49 **11.34***8.48 **6.771.370.40
R20.330.300.280.260.250.200.160.160.120.120.020.01
Note: number denote t-statistic. *** represents significance at 1 percent, ** at 5 percent, and * at 10 percent. Dependent variable is a binary variable. If net exports > 0, dependent variable=1; 0 oh\ther wise

Dummy 1 in 1989; 0 in1997

Dum_kl =Dummy * captial labor ration

Dum_su =Dummy * unskilled labor ration

Note: number denote t-statistic. *** represents significance at 1 percent, ** at 5 percent, and * at 10 percent. Dependent variable is a binary variable. If net exports > 0, dependent variable=1; 0 oh\ther wise

Dummy 1 in 1989; 0 in1997

Dum_kl =Dummy * captial labor ration

Dum_su =Dummy * unskilled labor ration

181. For the logit analysis, regressions were done using 1997 data only (Table 21.), and also pooling the 1989 and 1997 data (Table 22). Two specifications were used to study the determinants of overall trade in manufactured goods, as well as of trade with the high-, middle-, and low-income countries separately. The first specification used the capital-labor ratio and the skilled-unskilled labor ratio as the two explanatory variables, while the second specification used the capital-unskilled labor ratio and the skilled-unskilled labor ratio as the two explanatory variables. In the analysis using the pooled data, only the results from the first specification are reported.

182. The OLS and logit regressions using the 1997 data produced similar results. First, consistent with the calculation of the rank correlation coefficients in Subsection B, the coefficient on the capital-labor ratio is positive and significant in relation to net exports with high- and middle-income partners, but not with low-income partners. This outcome indicates that, the higher the capital-labor ratio in the production of a commodity, the greater the probability that South Africa will be a net exporter of that commodity. Furthermore, in the case of trade with high-income partners, this is true even after controlling for resource intensity.98 In other words, South Africa’s trade is not capital intensive because it is also concurrently resource intensive, as the ILO study tended to suggest. Second, the coefficient on the skilled-unskilled labor ratio is negative and significant for trade with high-income partners, indicating that, the higher the skilled-unskilled labor ratio in the production of a given commodity, the lower the probability that South Africa will be a net exporter of that product. That coefficient, however, is insignificant for trade with medium- and low-income partners.

183 The logit analysis of the pooled 1989 and 1997 data upholds the results obtained from the analysis of the 1997 data alone. In addition, because of the negative and significant value of the variable formed by interacting a time dummy variable with the capital-labor ratio coefficient (dum_ kl), it appears as if the capital intensity of net exports to high-and medium- income countries increased over the 1989-97 period.99

184. Thus, South Africa’s net exports to high-income countries are capital intensive and tend to be more unskilled labor intensive. Net exports to middle-income countries are also capital intensive, but the distinction between skilled and unskilled labor does not appear to be important. For net exports to low-income countries, the regression analysis suggests that relative factor intensities are not significant in explaining the prevailing pattern of trade.

F. Conclusion

185. South Africa, which would be expected to be relatively labor abundant in its trade with high-income countries, is actually revealed through trade to be relatively capital abundant. Despite being relatively well endowed with labor in quantity terms, the actual trading pattern suggests that, in priceterms, labor is relatively expensive. This situation draws attention to aspects of the labor market that may need to be addressed.

186. These counterintuitive results may have an alternative explanation. Specifically, the standard HOS theorem assumes that countries have identical production technologies and employ the same production techniques. If this is not the case, then differences in production technologies and techniques, as well as different factor endowments, will determine trading patterns. Apartheid-era economic policies affecting the price of capital and labor may still be exerting an important influence on the choice of South Africa’s capital-intensive production techniques. However, lingering effects of apartheid-era policies are unlikely to explain the increased capital intensity of net exports over the 1989-97 period.

    BowenHarryLeoSveikauskas1992“Judging Factor Abundance,”Quarterly Journal of EconomicsVol.107 (May) pp. 599-620.

    DeardorffAlan1984“Testing Trade Theories and Predicting Trade Flows,”in Handbook of International EconomicsVol.1ed. byRonald W.Jones andPeter B.Kenen (Amsterdam: North-Holland).

    LearnerEdward1980“The Leontief Paradox, Reconsidered,”Journal of Political EconomyVol.88: (No.3) pp. 495-503.

    LeontiefWassily1954“Domestic Production and Foreign Trade: The American Capital Position Re-examined,”Econ. InternazionaleVol.7 (No.1) pp. 3-32.Reprinted in Readings in International Economicsed. byRichardCavesandHarryJohnson (Homewood, Illinois: R.D. Irwin for the American Economic Association, 1968).

    TsikataYvonne1998“Liberalization and Trade Performance in South Africa” (unpublished; Washington, D.C.: World Bank).

    International Labor Organization1999Studies in the Social Dimension of Globalization: South Africa(Geneva: International Labor Organization).

Prepared by Trevor Alleyne and Arvind Subramanian

All the analysis in this study is based on a detailed input-output table (supplied by WEFA of South Africa) with 45 sectors (defined at ISIC 3-digit level) and four factors of production(capital plus three types of labor). The study focuses on the 32 nonservice, or commodity sectors, of which 28 are manufacturing and four are natural resources. For the purposes of this study, the labor factors were aggregated into skilled labor and unskilled labor. Trade data were available for 12 groups of partner countries, which were combined into 3 categories: The EU (including the rest of western Europe), North America (including Mexico) and East Asia and Pacific constitute the high-income countries; sub-Saharan Africa and South Asia make up the low-income country group; and the remaining countries constitute the middle-income countries.

See the footnote to Table 17 for a definition of the commodity groups. In 1997, trade in natural resources constituted 33 percent of total trade, while trade in basic manufactured goods and sophisticated manufactured goods constituted 38 percent and 29 percent, respectively.

In the case of natural resource trade with the middle-income countries, South Africa’s surplus is relatively small because of offsetting petroleum imports.

The significant reduction in the magnitudes of ratios since 1989 may simply reflect a large increase in the value of total trade with the low-income countries.

For example, how can goods be ranked by factor intensities when there are more than two factors?

Of course, if the country is a net exporter of capital but a net importer of labor, i.e., KT> 0 and Lr < 0, the country is also considered to be capital abundant

Because the factor shares sum to unity, the regression equation has no constant term.

It is clear—at least for nontraded inputs—that the factors used in producing them should be accounted for in assessing the potential for trade in a good that uses these inputs. This is so because the costs of these factors will be passed through to the goods.

Product categories are classified as capital intensive, intermediate, or labor intensive depending on whether the capital-labor ratio (rand/employment) is greater than 15, between 5 and 15, or less than 5, respectively. Similarly, a sector is classified as resource intensive if it uses a minimum of 20 percent of inputs from natural resource sectors.

South Africa is a net exporter of factor services to low-income countries but a net importer from high-income countries. The appropriate test condition to determine relative factor abundance is therefore different, but in both cases the conclusion is similar.

For trade with middle-income countries, the results are more ambiguous. The logit analysis suggests that, when resource intensity is accounted for, the capital-labor ratio loses its significance as an explanatory variable. However, the OLS regression indicates that the capital-labor ratio is significant even after controlling for resource intensity

The dummy variable was set equal to 1 in 1989 and 0 in 1997.

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