269 SAJEMS NS Vol 2 (1999) No 2 Productivity and Export Growth in the South African Manufacturing Sector 1 Larette van Rensburg and Willem Naude School of Economics. Money & Banking, Potchefstroom University for CHE ABSTRACT This paper empirically investigates the direction of causality between export and productivity growth in 22 South African manufacturing industries. Data spanning the period 1972 to 1993 are used. Standard Granger-causality tests are complemented by cointegration analyses and the estimation of a theoretically derived regression model. Bearing in mind methodology difficulties caused by inadequate data, little evidence of a statistically signiiicant relationship between export growth and productivity growth is found. Only in case of the chemicals and wood processing sectors were statistically significant evidence found that export expansion could cause an increase in productivity. Domestic demand expansion was found to be a possibly more significant determinant of productivity increases. JEL F 43 INTRODUCTION It is often claimed that there exist a positive association between export growth and economic growth (e.g. Kavoussi, 1984). In the literature the view is that export growth will cause economic growth (Edwards, 1992). The case for this particular causal direction can be explained as follows. First, an increase in exports may allow an increase in imported capital goods, which raises the growth rate of capital formation and thus stimulates productivity of labour and capital, leading to output growth (Islam, 1998: 416). Second, export-oriented industries, particularly in manufacturing, are argued to be more susceptible to productivity improvements (due to exposure to competition and foreign technology transfers) and these lead to more investment, scale-benefits and more rapid output growth (Levine & Reneh, 1992: 943). Productivity growth is thus seen as central in the causality chain from export growth to output growth. Kavoussi (1984: 241) states that a reason for the positive association between export and economic growth is due to "a favourable impact of exports on total factor productivity". R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 2 (1999) No 2 270 Cross-country studies generally support the existence of a positive association between export and output growth. However, individual country studies based on time-series data tend to produce ambiguous results2 (Fajana, 1979; Goncalves & Richtering, 1987). Recently Chand (1998: 15) even goes as far as to maintain that "the link between trade, trade policy and economic growth remains an open question". Problematic in these studies is the issue of whether the associations found may be interpreted as a sign of causation between export growth and economic growth, since a significant correlation between variables does not necessarily imply that the one causes the other (lung & Marshall, 1985; Hsiao, 1987; Chow, 1987). In South Africa, the direction of causality between export growth and economic growth may be important due to the declared intention of the new government to pursue an export-led growth path (GEAR, 1996). A key strategy to achieve high export growth in South Africa is the emphasis in trade and industrial policy on opening up the economy through trade liberalisation (Coetzee, et aI, 1997). Various incentive programmes have been introduced to stimulate exports - especially of manufactured goods (see the appendix). If the direction of causality between export and output growth in South Africa is indeed from export to output, then the above policy emphasis may be justified. However, if causality runs in the opposite direction, other measures which could raise economic growth may be more appropriately emphasised over the short and medium term. Examples of these could include labour market policies and restructuring, government expenditure and taxation policies, and selective import protection (see also Bell & Cattaneo, 1998). It is the purpose of this paper to investigate the direction of causality between export growth and output growth. Given the fact that South Africa's trade liberalisation programme is relatively recent, being implemented in accordance with the Uruguay Round of the GAIT from January 1995, it may be to soon to pick up statistically significant effects of higher export growth on output growth from official data. The theoretical justification for the direction of causality from export growth to output growth implies a time-lag since higher export growth must first result in higher total factor productivity as a precondition for sustained higher output growth). Therefore, to be able to obtain preliminary indications as to the direction of causality between export growth and output growth, this paper will focus on the relationship between exports and productivity. Due to the prominence of the manufacturing sector in the tradable sectors of the economy, the relative stability of output prices, and the significant removal of trade protection measures from this sector, manufacturing data on the 25 main manufacturing sectors will be used. The use of data from the manutacturing sector is appropriate given Kavoussi's (1984: 247) finding that the impact of R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 271 SAJEMS NS Vol 2 (1999) No 2 exports from the manufacturing sector on total factor productivity is greater than that of primary exports. He states that "among middle-income countries (such as South Africa), gains from export expansion are significant only in those economies that shift to exports of manufactured products". 2 EXPORTS AND PRODUCTIVITY The proposal that exports may raise productivity (i.e. that causality runs predominantly from exports to productivity) is a departure from the standard, neo-classical trade theory. In the neo-classical trade theory - specifically the comparative cost theory associated at first with David Ricardo4, labour productivity is treated as exogenous. In the comparative cost theory, each country will have a comparative advantage and will export those commodities for which its relative output per worker exceeds its relative money wage in that particular industry (i.e. where it has a particular productivity advantage). In this theory, the association between productivity and exports is clear: only those goods in which a country has more productive labour, will exports be successful (Harberler, 1937). In reality, productivity is affected by a host offactors, such as R&D, innovation, accumulation of physical and human capital, infrastructure, economies of scale, market structure, demographic change, and the introduction of new technology (Yadavalli, 1998b: 8). More recently the economic literature have begun arguing that most of these factors are influenced by a country's trade regime and degree of openness to international trade. The recent view in economic theory, inspired by the endogenous or "new" growth literature, is that higher exports may raise productivity (Kunst and Marin, 1989: 699-700). Endogenous growth theory departs significantly from the assumptions of the classical and neo-classical growth theories with respect to growth and trade, specifically with respect to the endogeneity of productivity. Productivity growth is associated with increasing returns to scale, learning-by- doing (LBO) and "spill-over" effects of export expansion (Grossman & Helpman, 1990; Lucas, 1988; 1990; Romer, 1986; 1990). Regarding the positive impact of exports on productivity, the following related arguments can be put forward in justification (Bhagwati, 1978; Kavoussi, 1984): First, exports may facilitate the allocation (or re-allocation) of investment in the most efficient and competitive sectors of an economy. Increasing specialisation in export-oriented sectors may stimulate productivity in those sectors (Emery, 1967: 471; Piazollo & Wurth, 1995). R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 2 (1999) No 2 272 Second, higher export growth may enable manufacturing firms to achieve economies of scale because they now have access to international markets - not only domestic markets - and can thus make larger scale operations profitable (Grossman & Helpman, 1901: 243). Third, increased international competitiveness due to exporting onto global markets may increase the pressure on export-oriented industries to keep costs relative to outputs low (Teitel & Thoumi, 1986: 466). The latter may create incentives for the introduction of technological change which improves productivity (Geroski, 1989: 577). Fourth, the growth of exports may be seen to have a positive influence on productivity of the whole economy of a country via externalities of exports on other sectors (Bhagwati, 1978: 213). Fifth, foreign contributions to the local stock of knowledge increase with the number of commercial transactions between domestic and foreign agents (Grossman & Helpman, 1990: 168). Six, an increase in exports may permit a relaxation in a country's balance of payments constraint, which facilitates domestic growth through improved availability of essential imports (Teitel & Thoumi, 1986: 469). Within recent "endogenous" growth theories the view has however also been put forward that the direction of causality may not be from exports to productivity, but rather the other way around. Piazollo and Wurth (1992: 272) describe these as technology theories of trade and/or "new" infant industry arguments. Krugman (1984) and Rodrik (1988) for instance, states that import protection may cause productivity gains by allowing domestic sales to expand. In this regard Krugman (1984) shows that under a oligopolistic market structure with Cournot behaviour import protection can lead to productivity gains which stimulate export expansion. According to this argument import protection may lead to productivity gains by allowing domestic sales to expand. Import protection may thus under certain market conditions act as an export promotion policy. Abeysinghe and Yeok (1998) provide empirical evidence that import protection and the related or implied policy of exchange rate overvaluation has stimulated productivity and export growth in Singapore. Teitel and Thoumi (1986) found empirical evidence that import protection may have stimulated productivity in certain industries in Brazil and Argentina. More particularly, they claim that the direction of causality runs from import protection to industrial "maturity" to higher productivity to higher exports.s They also perform back-of-the-envelope R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 273 SAJEMS NS Vol 2 (1999) No 2 calculations to show that average annual growth rates of 4.14% in total factor productivity (TFP) would be needed if infant industries in developing countries are to eliminate a 50% cost disadvantage relative to developed countries industries over ten years (Teitel and Thoumi, 1986: 465). More recently theories of intra-industry trade have emphasised a bi-directional causality between exports and productivity (Piazollo & Wurth, J992: 273). In these theories greater trade (exports) changes a country's market structure towards fewer firms, more concentrated declining cost industries and a larger proportion of resources in declining cost industries (Helpman & Krugman, 1985; 1989). 3 PRODUCTIVITY AND EXPORTS IN SOUTH AFRICA'S MANUFACTURING SECTOR 3.1 Summary Statisti.:s Tables 1 and 2 below summarise the salient features of productivity and export growth in South Africa's manufacturing sector. In Table I, the situation between 1972 and 1990 is summarised. Table 2 summarises the situation between 1990 and 1996. The year 1990 has been selected as a cut-off date since the subsequent period represents South Africa's reintegration into the international community. Table 1 Output, Produdivity and Exports in ManuCaduring, 1972 - 1990, (Average Annual Growth %) L Sedor ~rages Tobacco Textiles Clothing Leather Products Footwear Wood and wood products i Furniture Paper and Paper products Printing and publishing Chemical products Rubber products Output 2.6% 7.2% 4.81% -0.31% 3.96% 2.84% 4.05% 1.87% 2.99% 4.6% 1.45% 3.85% 6.11% MFP -0.52% 2.33% 3.11% -I.l 0010 2.06% 1.24% 1.54% 0.53% 1.63% 0.48% -1.31% -2.84% 5.48% Export! Output(%) 11.21% .26% 3.58% 12.8% 10.28% 15.99% 3.49% 7.95% 2.59% 8.87% 1.40% 9.15% 3.99% Exports -1.13% 8.53% 14.15% 2.49% 3.07% 6.27% 5.84% 11.45% 15.07% 4.77% -1.07% 4.84% 4.78% R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 2 (1999) No 2 274 Table 1 continued Sedor Output MFP Export/Out- Exports put (%) Plastic products 8.65% 3.33% 1.18% 6.33% Glass and glass products 4.85% 0.43% 5.98% 6.98% Other non-metallic 0.64% -0.97% 6.18% 1.83% mineral Iron and steel basic 1.58% -0.71% 19.85% 9.11% industries Non-ferrous metal basics 055% 1.33% 27.41% 8.72% Metal products -0.28% -1.83% 4.65% 0.77% Machinery 2.8% -1.1% 9.97% -0.58% Electrical machinery 3.89% -0.23% 3.69% 5.27% Motor vehicles and parts 2.87% 0.38% 2.51% 7.55% (Based on data obtained from the IOC's Sectoral ManufactUring Data Senes) Table 1 shows that rubber products, tobacco production, and plastic production showed the largest increases in multi factor productivity (MFP) growth over the period 1972-1990, in each case of above 3% per annum. Some sectors, e.g. chemicals, metal products and textiles experience negative growth rates in MFP growth over the period. When the average export growth rates are considered, Table 1 shows that the fastest average export growth rates were experienced in the furniture (15%), tobacco (14%), wood and wood products (11%) and iron and basic steel industries (9%). The latter sector was also the sector with the highest proportion of exports in total sales (27%) over the period. In the case of tobacco and furniture there would seem to be a strong positive association between export growth and productivity; however, the iron and basic steel industry achieved high export growth despite a negative growth in MFP. Based on Table 1, the Spearman rank-order correlation coefficient between export growth and MFP growth was calculated. Its value of 0.60 would suggest that there is not a very strong statistical association between manufacturing industries that experienced rapid export growth and industries that experienced rapid productivity improvements. Table 2 below show that between 1990 and 1995, the highest MFP growth was experienced by the textile and clothing industries, and the highest export growth by the clothing industry (2.9%), paper and paper products (11%), plastic products (8%), and non-ferrous basic metal products (4.7%). R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 275 SAJEMS NS Vol 2 (1999) No 2 Table :z Output, Productivity and Exports in Manufacturing, 1990- 1995, (Average Annual Growth 0/.) Sector Output MFP Export/Out. Exports put (o/e) (1990-93) Food -0.93% -2.50010 6.49% -1.28% Beverages -0.61% -6.19% 4.52% 1.76% Tobacco products -2.71% 0.95% 5.11% -12.90% Textiles -1.55% 2.90% 20.33% -4.35% Clothing 2.35% 2.50% 7.39% 2.94% Leather products 1.23% -2.41% 22.35% 3.21% Footwear -3.32% -1.09% 3.36% -3.20% Wood and wood products -0.17% -1.25% 11.24% -3.14% Furniture -0.39% -1.94% 7.30% 2.77% Paper and paper products -2.12% -1.28% 11.l7% 11.68% Printing and publishing -0.39% -3.29% .85% -14.9% Chemical products 1.73% 1.53% 10.16% -3.13% Rubber products 0.10% -3.25% 4.93% 0.15% Plastic products 2.67% -3.99% 1.82% I 8.45% Glass and glass products -1.09% -2.26% 6.16% -7.59% Other non - metallic -2.08% -0.48% 7.95% 1.13% ! minerals Iron and steel basic -1.08% 1.72% 40.70% -0.75% industries ! Non - ferrous metal basic 3.64% -9.93% 40.99% 4.71% Metal products -0.46% -0.73% 6.91% 2.29% Machinery -0.29% 1.68% 10.44% 3.11% Electrical machinery -0.52% -4.99% 6.58% 6.79% Motor vehicles and motor 0.55% -3.72% 8.93% 22.22% vehicle parts Table 2 indicate that iron and basic steel, non-ferrous metal basic industries in particular increased their export share significantly - however in both these sectors productivity improvements was negligible. This could support the recent findings of Iscan (1998: 123) based on a panel data study for Mexico, that trade liberalisation has a short-term beneficial impact on productivity growth rates (as firms that did not export previously now become more competitive and reap bene tits from exports), but no significant long-term effect on productivity ~rowth rates. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 2 (1999) No 2 276 Using the data in Table 2, the Spearman rank-order correlation coefficient between export growth and productivity growth between 1990 and 1993 was calculated. Over this period the correlation between export and productivity growth declined to 0.43, suggesting the association between productivity and exports had weakened since 1990. 3.2 Existing Empirical Findings for South Africa Existing studies into the relationship between trade policy and productivity, specifically exports and productivity, is largely lacking in South Africa. The only existing publicly available studies are those by Kohler and Holden (1992) and Piazollo and Wurth (1992; 1995). These studies differ from the present study in that they are more concerned about the determinants of productivity and the relationship between trade liberalisation and output growth than with the direction of causality between exports and productivity. Thus, although not explicitly testing for causality, Kohler and Holden (1992: 24) estimates a regression equation between South African manufacturing's relative export share and relative labour productivity. They find that "labour productivity appears to have been an insignificant determinant of export performance in South Africa's manufacturing industries". Piazollo and Wurth (1992: 290) do not find strong evidence in favour of export- led growth in South Africa. Using standard Granger-causality tests on South African manufacturing between 1972 and 1990, they find that "in all but one of the manufacturing sectors economies of scale due to an increased domestic demand seems to be the main determinant for the productivity growth" (Piazollo & Wurth, 1992: 290). Furthermore, they found that exports contributed positively to productivity growth in only 6 industries, namely • Beverages • Footwear • Wood and wood products • Other plastic products • Other chemical products • Glass and glass products Piazollo and Wurth (1995) test for cointegration between total factor productivity (TFP) and production and TFP and exports using Johansen's (1988) maximum-likelihood method. They establish that in only eight cases are there indications of significant cointegration between the variables. This would suggest, generally, that exports and productivity are not cointegrated in South R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 277 SAJEMS NS Vol 2 (1999) No 2 African manufacturing and that therefore there exists no stable long-run equilibrium relationship between exports and productivity as claimed in theory. Moreover, the results would imply that additional variables ought to be included in the cointegrating equation until a cointegration relationship be found - which would suggest that additional factors determines productivity growth and exports growth. Piazollo and Wurth (1995: 188) finds in this regard that "productivity development in South African manufacturing industries appears to be mainly due to domestic scale economies, as for 17 industries total production exerts a significantly positive influence on total factor productivity". 4 DATA AND METHODOLOGY 4. t DeS(!ription of Data To test for the direction of causality between productivity and export growth in the manufacturing sector in South Africa, 22 main manufacturing sectors was chosen. The "other" sectors were omitted. These sectors are appropriate representatives of export-oriented industries in South Africa. Their various shares of exports in total output averaged between 0.8% in case of printing and publishing and 40% in case of iron and basic steel industries between 1990 and 1995 as can be seen from Table 2 above. Annual data was obtained from the Industrial Development Corporation's (lDC) sectoral data series, spanning the period 1972 to 1993, and from the National Productivity Institute (NPI) spanning the period 1960 to 1996. The lack of a longer timeframe remains a significant weakness of the present study. The proxy for total factor productivity used in this paper is the IDC's and NPI's "multi factor productivity" variable. This has been obtained by dividing the production index of a sector by a weighed average of the labour and capital input indices. The latter are calculated by dividing the value-added index for a sector by its employment and fixed capita] index. The modelling framework used in this paper utilises firstly the concept of causality proposed by Granger (1969). Secondly, for those sectors where there is an indication that export growth affects productivity growth, an extended regression equation was estimated, following Okuda (1997). 4.2 Granger Causality According to Granger's (l969) concept of causality, a variable, say total factor productivity in this case (Zt), is said to be "Granger-caused" by a variable, say R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 2 (1999) No 2 278 exports (~), if the information in past and present ~ helps to improve the forecasts of the Zt-variable (Judge et ai, 1988: 768). Formally, Z, will be Granger-caused by ~ iffor some period t the following obtain: (I) Where 0 2 = the conditional mean square error (MSE) of the optimal forecast of Z,(\) given the information in Q,; Here Q,\{Xsls!>t} on the right hand side of(1) denotes all information in Q( that is not in {Xsls!>t}. It is standard to assume that (Z .. ~) is generated by a stationary, normally distributed bivariate VAR with lagp process that can be represented as (2.1 and 2.2) X, will not Granger-cause Z( if and only if (3) and Zt will not Granger-cause X, if and only if a?1 I = a 21 2 = a 21 3 = ... == a 21 = 0 -. .. ." (4) Thus, in estimation of (2) the null hypotheses are of no Granger causality. These null hypotheses will be tested using a standard F-test to test the validity or otherwise of the imposition of the restrictions specified in (3) and (4). A bivariate system such as this where Z, is caused by X, and Xt is caused by Z, is called a feedback system. The usual way to determine the appropriate lag length (p) in (2) is to use Akaike's Information Criterion (AlC) - see Akaike (1981). Following Piazollo and Wurth (1992: 300) however, the maximum lag length chosen here is 4 in order not to loose too many degrees of freedom. 4.3 Cointegration Analysis Standard Granger-causality tests as described above are subject to a number of shortcomings (Judge et al,). If there would exist a stable long-run equilibrium R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 279 SAJEMS NS Vol 2 (1999) No 2 relationship between variables so that they could be said to be cointegrated (see Engle and Granger, 1987) then an error-correction specification exists. In such an error-correction model, temporal causality can arise from two sources - non- zero coefficients of lagged change variables (which corresponds to standard Granger-causality tests) or through the error-correction terms. The standard Granger-causality tests typically omit to take these latter effects into consideration (see Miller & Russek, 1990; Kalulumia & Yourougou, 1998). Granger (1986) and Engle & Granger (1987) established that a vector of time series, all of which are stationarl only after differencing, may have linear combinations which are stationary without differencing. In such case, those variables are cointegrated. The concept of cointegration provides justification for the use of error correction models (ECM's). In this paper cointegration analysis was carried out by using Engle & Granger's two-stage modelling approach, whereby cointegration between variables is first achieved and the residuals from the cointegrating regression are used as a (stationary) levels term in the ECM. 4.4 Extended Regressions One of the weaknesses of Granger-causality tests as described in 4.2 above is the lack of many relevant variables. For the sectors where there are an indication that export growth are significantly associated with productivity growth, the following equations was estimated, following Kouda (1997: 371). 11 In TFP= a o +a,llIn KL, +a2 1l In EXPROD, +a 31lInPENE, + a 4 1l1nPROD, +e, Where TFP total factor productivity (7) KL =:: the factor intensity of a subsector, defined as the amount of capital per worker EXPROD =:: the export ratio, defined as the ratio of exports to total production PENE =:: the import penetration ratio, defined as imports/(production+imports- exports) PROD = the total real production of a subsector The above specification is intended to take into consideration such industrialisation-related variables as factor intensity, involvement in exports, and import Iiberalisation. Specifically, KL is included to capture the effect of capital R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 2 (1999) No 2 280 intensification associated with the expected industrialisation of the South African economy. The variable PENE would measure the productivity impact of import liberaIisation. All estimations were done using either Microfit (version 3) and/or PC-GIVE 8.0 student version. 5 RESULTS 5.1 Unit Root Tests Betore estimating the equations presented in section 4 above, the various variables were subjected to tests for stationarity'. If in the above Y, is generated by the following first-order autoregressive (AR) process Z, = pZ'_1 + ul , then YI will only be stationary if Ipl < 1. If p = I then YI will be non-stationary. A variable is stationary if it does not have a unit root. For instance, if the first- order AR process generating YI is re-written as (l-pL)Z, = Ub where L is the lag operator then the characteristic equation is (l-pL) = O. In this case there is only one root (L lip) so that stationarity requires that Ipl < 1 (Harris, 1995: 18) It is problematic if non-stationary data is used in regression analysis, as it can lead to "spurious regressions" which occurs as inferences based on standard t- and F- tests are invalid in case of non-stationary data (see BanaIjee, et al. 1993: 93-4). If a variable is non-stationary in its levels but stationary after first-dift'erencing, it is said to contain one unit root in its levels and can be described as integrated of order one, denoted ~I(l), Correspondingly a stationary variable is denoted as being -1(0), More generally, a process is said to be integrated of order d (-I (d» if it needs to be differenced d-times to be stationary (Engle & Granger, 1987:252). Before econometrically estimating the relationship between productivity and export growth as described in section 4 above, the various productivity and export variables used for the 23 main manufacturing sectors were tested for stationarity. For this purpose use was made of the Augmented Dickey-Fuller (ADF) unit-root tests (see Dickey & Fuller, 1981; Naude, 1992). This test is a test based on the t-statistic on ~ in equation (8) : n L1Y, a + pt + fJY,-1 + LYiL1Y,.i + u, (8) i=1 Where n denotes the lag length. The null hypothesis is Ho : ~=O, i.e. of unit root. Rejection of the null implies that YI is -1(0), and failure to reject implies that YI is non-stationary, i.e. -1(1). Critical values for the ADF (n) tests are given in MacKinnon (1991) and supplied with the PC-GIVE 8.0 software. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 281 SAJEMS NS Vol 2 (1999) No 2 The results of an ADF(4) test on multifactor productivity and exports in South Africa's manufacturing sector is summarised in Table 3 below. Table 3 Augmented Dickey-Fuller Tests for Stationarity of Multifactor Productivity in Soutb African Manufacturing Sector Produe- Export- Cbaagein Change in tivity levels levels productivity exports Food 0.179 -U88 -2.356 -2.476 Beverages -2.028 0.281 -0.916 -2.049 Tobacco Products -1.676 -1.190 -1.959 -1.934 Textiles -1.303 -2.428 -1.653 -2.084 Clothing -0.329 -2.414 -2.990 -1.665 Leather Products -1.899 -1.830 -1.869 -1.826 Footwear -2.208 -1.577 -I. 775 -1.971 Wood and Wood -0.800 -2.824 -1.965 -3.471 * Products Furniture -2.646 0.667 -2.6118 -2.329 Paper and Paper -2.376 0.219 -3.012 -4.28\ ** Products Printing and 0.417 -2.349 -3.031 1.601 Publishing Chemical Products -4.036** -1.295 -1.840 -2.515 Rubber Products -1.816 -0.025 -1.481 -2.263 Plastic Products -2.118 0.213 -2.524 -2.079 Glass and Glass -1.014 -0.964 -3.927** -2.569 Products Other non-metallic -0.813 -2.227 -2.697 -1.666 mineral Iron and steel basic -1.517 -0.305 -3.325* -2.866 industries Non-ferrous metal -1.516 -2.343 -2.488 -1.942 basic Metal Products -0.659 -1.606 -2.283 -2.088 Machinery -0.558 -1.118 -2.488 -2.065 Electrical machinery -0.149 0.020 -3.265* -1.887 Motor vehicles and -1.339 -1.313 -2.068 -1.398 motor vehicle parts (an asterisk mdicates sIgmficance at a 1% level of confidence and two astensks at a 5% level of confidence) R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 2 (1999) No 2 282 Table 3 indicates that the ADF test fail to reject the null of a unit root in both the levels and first differences of the variables for all the sectors. The failure to reject the null in first differences is problematic8, but may be due to the inability of the test statistic to perform well given the short number of observations used (n 21). Following Naude, van Heerden and van der Merwe (1997) it is assumed here that levels variables of manufacturing sub-sectors are -I(l r Subsequent results are dependent on this assumption and further research may be required to clarify this issue. It should be noted that tests on aggregate manufacturing data spanning the period 1948 - 1995 finds the levels variable to be-l(l). 5.2 Co-integration Tests Table 4 contain the results of the co-integration tests (using the Engle-Granger two-step methodology) between productivity and exports for the 22 manufacturing sectors. Table 4 Cointegration Tests for Exports and Productivity Sector ADF(4) for Cointegrated cointegration P,X (yes/no) :1 •• Food 0.14744 No ! Beverages - \.8683 No Tobacco Products -2.0932 No I Textiles -0.5407 No • Clothing 1.2665 No I. Leather Products -1.6335 No Footwear -2.1939 No . Wood and Wood Products 0.1382 No I Furniture -2.2216 No Paper and Paper Products -2.4365 No • Printing and Publishing -0.8302 No Chemical Products -2.4144 No Rubber Products -1.2583 No Plastic Products -1.9928 No Glass and Glass Products -1.0174 No Other non-metallic mineral Products -0.2069 No II Iron and steel basic industries -1.7576 No R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . I 283 SAJEMS NS Vol 2 (1999) No 2 Table 4 continued Sector ADF(4) for Cointegrated cointegration p,x (yes/no) Non-ferrous metal basic industries -2.3609 No Metal Products 0.7401 No Machinery -0.5834 No Electrical machinery -2.1891 No Motor vehicles and motor vehicle parts -2.5105 No Table 4 show that no evidence of cointegration can be found between productivity and exports in South Africa's manufacturing sector. This may suggest that there exists no long-run equilibrium relationship between productivity and exports in South Africa as proposed in theory. As discussed in section 4.3 this would require the standard Granger-causality tests to be pertormed using first differenced variables. The results from such tests are reported in the following section. 5.3 Granger-causality Tests The results from the Granger-causality test are summarised in Table 5 below with the F-test statistics from applying the relevant zero restrictions on lagged values of the respective variables as explained in section 4 above. Table 5 Granger Causality Test for exports and Productivity Growth in South African Manufacturing, 1972-1993. Direction of causality Sector Exports to Productivity to Productivity Exports i Food F(4,9) - 1.491 F( 4,9) - 0.582 Beverages F(4,9)= 0.116 F(4,9) = 0.365 Tobacco F(4,9) - 1.133 F(4,9) - 2.982** Textiles F(4,8) 0.186 F(4,8) 0.514 Clothing F(4,9) 0.30 F( 4,9) = 0.186 Leather and leather products F(4,9) - 2.145 F( 4,9) = 0.246 Footwear F(4,9) - 0.914 F(4,9) - 0.118 Wood and wood products F( 4,9) = 8.107* F(4,9) = 1.619 Furniture F(4,9)= 1.391 F( 4,9) = 0.496 Paper and paper products F(4,9) = 0.720 F(4,9) 2.467 Printing and publishing F(4,8) = 2.318 F( 4,8) - 0.067 R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 2 (1999) No 2 284 Table 5 continued Sector Exports to Productivity to Productivity Exports Chemicals F(4,8) = 8.045* F(4,8) == 2.893** Rubber products F(4,9) 0.591 F( 4,9) == 0,434 Plastic products F( 4,9) = 0.527 F(4,9) == 0.205 Glass and glass products F(4,9) - 1.730 F( 4,9) = 0,485 Other non-metal products F(4,9) 1.580 F( 4,9) = 1.70 I Iron and steel basic industries F(4,9) 1.659 F( 4,9) == 2.307 Non-ferrous metal basic industries F(4,9) = 0.823 F(4,9) == 2.008 Metal products F(4,9) 2,406 F(4,9) = 5.781 * r~f"Y F(4,9) 0.022 F(4,9) = 2.898** trical machinery F(4,9) = 0.093 F(4,9) - 1.901 Motor vehicles and motor vehicle F(4,9) 0.227 F(3,9) = 0.521 parts (Note : an astensk mdlcates slgmficance at a 5% confidence level and two asterisks at a 10% level of confidence). Table 5 shows that there exists significant causality from productivity to exports in only four manufacturing industries, namely machinery, metal products, chemicals and tobacco. In case of chemicals, there is evidence of bi-directional causality, although the evidence seems to favour a stronger causality from expol1s to productivity in the case of chemicals. Table 5 indicates that there are only two manutacturing sectors where causality seems to run from exports to productivity, namely wood products and chemicals. It can be noted that Piazollo and Wurth (1992) also found that exports contributed to productivity growth in these sectors. Generally, the results in Table 5 support the findings in Table 4, namely that little significant relationship seems to exist between productivity in exports in South Africa's manufacturing industry. To further analyse the sectors where a significant relationship seem to exist, a regression equation (equation 7) as described in section 4.4 was estimated. 5.4 Regression Model Results Equation (7) was estimated for the following sectors: • Chemicals • Wood & wood products • Tobacco R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 285 SAJEMS NS Vol 2 (1999) No 2 • Machinery • Metal products The results are summarised in Table 6 below. Table 6 Regression Results (t-ratio in brackets) Seetor