THE DETERMINANTS OF EXPORT SUPPLY IN THE SOUTH AFRICAN MANUFACTURING SECTOR: AN INDUSTRY LEVEL PANEL DATA ANALYSIS

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1 The Asian-African Determinants Journal of Export of Economics Supply in and the Econometrics, South African Vol. Manufacturing 11, No. 1, 2011: Sector: THE DETERMINANTS OF EXPORT SUPPLY IN THE SOUTH AFRICAN MANUFACTURING SECTOR: AN INDUSTRY LEVEL PANEL DATA ANALYSIS Muhammed Hajat and Uma Kollamparambil * ABSTRACT This study analyzes the cost determinants of the South African manufacturing sector, with specific focus on unit labor costs and productivity. The study reveals that the decrease in labor costs, and increase in labor productivity has led to an increase in manufacturing exports from 1982 to The analysis was done using panel data techniques such as pooled OLS, Fixed Effects and Dynamic Fixed Effects. Of these techniques it was concluded that the Fixed Effects estimates were best suited for the long run analysis and Dynamic Fixed Effects for short run analysis. The study finds that unit labor costs have a significant negative effect on export supply, an effect that is stronger in the long run than in the short. Furthermore, it was observed that labor productivity and wages each have a significant positive and negative effect on export supply respectively. Another significant finding of this study is that the real effective exchange rate has a strongly negative impact on export supply in both the short, and the long, run. Keywords: manufacturing exports, labour productivity, cost competitiveness, dynamic fixed effects 1. INTRODUCTION Export-led growth has had a significant influence on policy formation in post-apartheid South Africa. So far this policy focus has yielded mixed results in that, although exports of higher technology goods, such as automobiles, have increased, South African comparative advantage, in the Ricardian sense, remains grounded in resource-based sectors. The implications for South Africa are to either find ways of reducing relative costs of production or to restructure exports into faster growing sectors. The proposed study deals with the international competitiveness of South African manufacturing, focusing on cost competitiveness measured by unit labor costs. This issue is explored by analyzing the impact of factors such as unit labor costs and labor productivity on the exports of South African manufacturing industries. Fig. 1 indicates that the trend of falling unit labor costs and rising labor productivity, allied with favorable exchange rate levels have all resulted in an increase in manufacturing exports. These observed trends have also received support from tariff liberalization. Given the existence of such trends, this study is aimed at studying how South African manufacturing export supply behaves with regards to unit labor costs and labor productivity over the short, and long, run. * Senior Lecturer, School of Economic and Business Sciences, University of Witwatersrand, Johannesburg, umakrishnan.kollamparambil@wits.ac.za

2 28 Muhammed Hajat and Uma Kollamparambil Figure 1: Trends in South African Unit Labour Costs, Productivity and Manufactured Exports: Source: Quantec Database Note: All series are calculated using indices with 2005 being the base year. This study is especially relevant given the current trade debates in South Africa with trade unions like COSATU calling for the devaluation of the rand to increase the price competitiveness of South African exports. 2. LITERATURE REVIEW Labor cost comparisons have been used to analyze South Africa s cost competitiveness because they allow the implementation of Ricardian theory, (Golub, 2000; Edwards & Schoer, 2001; Edwards & Golub, 2004) hence their empirical appeal and relevance to the debates within the South African economy. Edwards & Schoer (2001) note that the drawbacks of such a theoretical framework lies in the simplifying assumptions made in that labor is the only factor of production and full productive specialization occurs, however they also argue that due to the mobility of capital and globalization of production labor costs are important determinants of export productive competitiveness. MacDougall (1951) and Golub (1994) provide evidence of the ability of Ricardian trade theory to accurately predict the way trade flows between countries behave. Also, Turner & Golub (1997) identified relative unit labor costs as the best indicator of productive competitiveness. These studies highlight the importance and correctness of approaching the issue of competitiveness through Ricardian trade theory. Some of the findings of labor cost analyses are of interest to this particular study. Golub (2000) found that South African labor costs were relatively competitive to those of industrialized countries in the 1990s but not so compared to other developing countries. Another measure of competitiveness is that of international cost and price competitiveness which measures the comparative prices or costs across countries in a common currency (Golub, 2000). Since this study focuses on the determinants of South African manufacturing exports in isolation from other countries, pure labor cost analysis is sufficient and has been supplemented with the

3 The Determinants of Export Supply in the South African Manufacturing Sector: 29 inclusion of the real effective exchange rate to proxy for the effect of global economic conditions on those exports. Edwards & Golub (2004) focus on the changing cost competitiveness of South African labor and the effect this has had on South African export performance, at the industry level. They extended the analysis originally conducted by Golub (2000) by including total factor productivity in their productivity calculations. They were thus able to assess how the efficiency of all factor inputs impacted on South Africa s export performance. Also since labor productivity depends on capital input, they argue that labor productivity alone can be a misleading indicator of efficiency according to the Ricardian model. Some of the conclusions drawn on the basis of Edwards and Golub s (2004) research find that South African exports are highly responsive to changes in competitiveness in the form of labor costs and productivity. Interestingly, while they observe that the improvements in South Africa s competitiveness during the 1990 s was largely due to the depreciation of the Rand, they argue that this process of improvement cannot be sustained indefinitely without having inflationary consequences. Other conclusions made from the study include the ability of public policy to galvanize export growth through investing in education to increase the productivity of laborers (Fedderke, 2002). They also suggest that tariff liberalization be continued and where necessary increased to allow for greater investments into newer technologies. Edwards & Golub (2004) cite work done by Jonsson & Subramanian (2000) to support their suggestion since this particular study showed a positive relationship between improved TFP growth and trade liberalization. Edwards & Golub s (2004) final, and possibly most crucial conclusion, in light of the debates within South Africa, is that competitive gains from an increase in productivity would dissipate if real wages increased substantially. This particular study is, in some ways, a re-application of the one conducted by Edwards & Golub (2004). More specifically, while both share the same theoretical background, hypotheses and empirical approach, this study focuses solely on South African exports where the previous work also looked at relative competitiveness of South African exports. Also, this study could be seen as an extension, or an updating, of the work done previously insofar as it looks at an updated timeframe, whilst also employing a different data source. In essence, the comparability of results between the two bodies of work is restricted to the way in which labor costs, productivity, of both labor and total factor, affect the amount of exports of South Africa s 28 manufacturing industries. It would be grossly negligent to exclude studies conducted at the firm level (Melitz 2008; Rankin et al, 2006 ; Edwards et al, 2008 and Rankin & Schoer, 2010) since these studies offer some detailed insights into the issue of export performance within the various industries of a country. Nevertheless, given the aggregate nature of this study we have restricted our literature review to studies based on industry level data. 3. HYPOTHESES AND THEORETICAL FRAMEWORK According to Ricardian Trade Theory a country will have a comparative advantage in the production of goods where it can produce those goods relatively cheaper, or relatively

4 30 Muhammed Hajat and Uma Kollamparambil more efficiently, than other countries and will thus have an incentive to export these goods globally. Where: The export condition is therefore: a ij W i < a nj W n e a ij is the unit labor requirement in the domestic country in the jth industry s production i.e. L ij / Q ij where Q is value-added and L is labor employment. W i is the domestic wage rate per worker in terms of the domestic currency e is the exchange rate measured in domestic currency units per 1 unit of foreign currency a nj is the unit labor requirement of the nth partner country in the jth industry W i is the ith country s wage rate per worker in terms of that country s currency Based on this condition, the relative unit labor cost of the domestic country is: W i / W n e Which should be less than the relative labor requirements, a nj / a ij, for the domestic country s production in the jth industry to be cost competitive. This formulation implies that potential trade flows depend on relative labor efficiency (the inverse of relative labor requirements) as well as relative wage rates and the exchange rate. Incidentally, by the definition of the unit labor requirement: a ij = L ij / Q ij It is clear that this framework can be adapted to analyze labor productivity (q ij ), which is defined as output per worker: q ij = Q ij / L ij If one were to incorporate labor productivity into the above export condition, it would look as follows: Which leads to: W i / q ij < W n e / q nj W i / W n e<q ij / q nj Let c i denote the competitiveness of the ith country, then: or in terms of productivity: c i = (a nj / a ij ). (W i / W n e), c i = (q ij / q nj ). (W i / W n e) Therefore, lower unit labor costs, or higher labor productivity, relative to other countries as well as a depreciation of the domestic currency positively affects a country s competitiveness internationally.

5 The Determinants of Export Supply in the South African Manufacturing Sector: 31 The Ricardian framework, above, is particularly appealing for this analysis because it s central focus is on the relative unit labor costs and, similarly, relative labor productivity, and it is these costs that form the central point of contention in South Africa. Furthermore since capital is mobile and production is globally fragmented the price of non-tradables such as labor is of greater importance compared to output costs. This argument has been supported by Turner & Van t Dack (1993) and Turner & Golub s (1997) surveys of the literature on competitiveness. Compared to other trade theories, the Ricardian framework has advantages in that it allows for technological differences between countries, which is unlike the Heckscher-Ohlin Samuelson model (which is based on factor endowments), for instance. This feature of the theory is useful because of large, persistent gaps between labor and total factor productivity that have been observed by Harrigan (1999) among others. Ricardian trade does however imply that countries will specialize in industries where they hold comparative advantages, a feature which is not evident practically. This weakness can be addressed by incorporating product differentiation into the model. Another assumption of the model is that labor is the only non-tradable factor of production; violation of this assumption weakens the grounding of relative unit labor costs as a measure of competitiveness. (Edwards & Golub, 2004). 4. DATA This study will use data on the South African manufacturing sector for the 28-year period across 28 industries according to the 3-digit SIC classification. The data used in this study is gathered from The South African Reserve Bank and the Quantec statistical database, namely, the South African standardized industry analysis. Data is collected for the following: Industry Wages and Salaries in the Manufacturing sector Industry level employment data Industry level unit labor costs Industry level labor productivity Industry level Manufacturing export values Industry level output changes (annual percentage change) Industry level labor changes (annual percentage change) Industry level capital changes (annual percentage change) Real effective exchange rate ( ) Industry level capital-labor ratios All variables except exports, imports and labor are expressed at constant 2000 prices. Exports and imports are expressed in terms of current basic prices, due to lack of availability of data series at 2000 prices. Unit labor costs and labor productivity are expressed as relative to, the chosen base, 2005 levels.

6 32 Muhammed Hajat and Uma Kollamparambil 4.1. Variable Definitions Ulc: Unit labor cost measures the average cost of producing one unit of output. Unit labor cost is equal to wage rate or earnings per worker (w) times the number of workers (N) divided by the output produced by the workers (Q): Unit labor cost = (w*n) / Q. w*n is a measure of the cost of labor. Lprod: Labor productivity is the most widely used productivity concept. Labor productivity is the ratio between output (Q) and the labor input (LI) used to produce that output: Labor productivity = Q / LI = output per unit of labor input. Labor productivity can be expressed as output per worker (by dividing total output by total number of workers employed) or as output per hour (by dividing total output by the total number of hours worked). Wage: Remuneration per employee is a measure of the amount of money that each employee receives in the economy without considering distribution issues. Remuneration per employee (w) is equal to the total compensation of employees (C) divided by the number of employees (N): w = C / N. Exp: Exports of goods and services is equal to the sum of all merchandise exports (free on board), net gold exports and all service receipts from the rest of the world. Imp: Imports of goods and services is equal to the sum of all merchandise imports (cif) and all service payments to the rest of the world. Labor: Employment figures indicate the number of paid employees and include casual, seasonal and informal workers. Employment consists of three main categories, namely highly skilled, skilled, semi-and unskilled and informal labor. Grosscapinvest: Gross domestic fixed investment refers to additions to the country s capital stock (i.e. the purchase of capital goods), without making provision for depreciation and the spending originated within the country s borders. Gross domestic fixed investment consists of buildings and construction works, transport equipment, machinery and other equipment and transfer costs. Fixedcapconsum: Consumption of fixed capital refers to the decrease in the value of capital goods or the cost of replacing such goods due to obsolescence and wear and tear. Consumption of fixed capital consists of buildings and construction works, transport equipment, machinery and other equipment and transfer costs. Consumption of fixed capital is also equivalent to depreciation of fixed capital. K/L:The data collected for gross fixed capital investment and consumption of fixed capital were used in the construction of the capital-labor ratio for each industry as was the case in Edwards and Golub (2003). The ratio was calculated as follows: (K / L) it = (grosscapinvest it fixedcapconsum it ) / labor it

7 The Determinants of Export Supply in the South African Manufacturing Sector: 33 TFP:In order to incorporate total factor productivity into the model, it was decided that the annual percentage change in output should be regressed on the annual percentage change in both capital and labor. The regression was run using the least squares dummy variable technique for panel data, excluding the constant. The choice of technique was made on the basis that total factor productivity is clearly time invariant. The use of dummy variables for both industry and time help to better capture the movements of TFP. The errors obtained from this regression would then, by definition, represent total factor productivity, which is nothing other than the increase in output not explained by the increase in either capital or labor. REER: The real effective exchange rate was obtained from the South African Reserve Bank and follows the bank s calculation. It is a weighted index of bilateral exchange rates between South Africa and it s trade partners weighted according to their relative importance in South Africa s bilateral trade. It is used in the analysis as a proxy for the demand for South African exports, particularly an increase in the REER can be interpreted as an decrease in demand, with a decrease interpreted analogously. 5. RESEARCH METHODOLOGY This paper utilizes econometric analysis, specifically panel data analysis, to assess the structure of South African manufacturing trade at the industry level by estimating the export supply function. Specifically, this paper uses an adapted version of the model formulated in, both, Edwards & Golub (2004) as well as Golub & Hseih (2000) since its focus is not on comparatively estimating competitiveness but rather analyzing the specific competitive nature of South African manufacturing and its determinants. This adaptation of previous empirical work is due to data accessibility issues with regards to other countries. Edwards & Golub (2004) reviewed the various specifications used empirically to assess the cross-sectional relationship between relative unit labor costs and the structure of trade. Their review included MacDougall (1951), Stern (1962) and Balassa s (1963) studies on bilateral trade between the UK and the US noting the differences in these analyses as being primarily the use of total trade for both countries, for the former two, and trade to third markets for the latter. Based on their review, Edwards & Golub (2004) chose to follow the model specification of Golub & Hseih (2000) who use bilateral trade flows as the dependent variable and relative unit labor costs as well as relative productivity as the explanatory variables. The methodology used to estimate the proposed export supply function utilizes the ordinary least squares regression technique and the panel will be estimated using within effects regressions as well as the least square dummy variable regression analysis and dynamic fixed effects to attempt to best capture the industry, and time, specific relationships that may be evident (see appendix 1 for technical notes). The choice of fixed effects over random effects is based on the focus of capturing the time variant characteristics of the proposed relationship. Implicit in the choice of the OLS methodology is the assumption of normality for error terms, the non-stochastic nature of the explanatory variables, homoscedasticity and no autocorrelation, all of which will be checked using various diagnostic testing methods as prescribed by previous empirical papers as well as Gujarati (2003).

8 34 Muhammed Hajat and Uma Kollamparambil When taking into account the panel structure of the dataset, heterogeneity enhances the explanatory power of the model, and is in that instance embraced. OLS however is not suitable for running the dynamic panel model because the explanatory variables are no longer strictly exogenous, as is required, and explanatory variables are correlated with the error terms causing estimators to be biased and inconsistent. Due to these weaknesses, the dynamic models proposed in this study use the generalized method of moments technique developed by Arellano and Bond (1991). Furthermore, the results obtain from each of pooled OLS, within-effects, least squares dummy variable and dynamic fixed effects regression will be compared to identify which technique is best suited to capture the relationships between the proposed explanatory variables and export supply in the South African manufacturing sector. The determinants of export supply proposed, and analyzed, in this study are unit labor costs, labor productivity, the real effective exchange rate, capital-labor ratios of each industry and total factor productivity. The dominance of labor related determinants of export supply is borne from the argument made by Turner and Golub (1997), among others, that labor is the most important non-tradable input of production. This view is further supported by Fagerberg (1988) where relative unit labor costs are used to measure international competitiveness. Proposed model specifications: 1. Log(X it ) = a 1 + b 1 log(ulc it ) + b 2 (K/L) it + u it1 2. Log(X it ) = a 2 + b 3 log(lprod it ) + b 4 log(wage it ) + b 5 log(tfp it ) + u it2 3. Log(X it ) = a 3 + b 6 log(lprod it ) + b 7 log(wage it ) + b 8 log(tfp it ) + b 9 log(reer it ) + u it3 4. Log(X it ) = a 4 + b 10 X i,t-1 + b 11 log(ulc it ) + b 12 (K/L) it + u it4 5. Log(X it ) = a 5 + b 13 X i,t-1 + b 14 log(lprod it ) + b 15 log(wage it ) + b 16 log(tfp it ) + u it5 6. Log(X it ) = a 6 + b 17 X i,t-1 + b 18 log(lprod it ) + b 19 log(wage it ) + b 20 log(tfp it ) + b 21 log(reer it ) + u it6 Where X i represents industry i s exports measured in millions of rands. ULC i is unit labor costs in each industry, LProd i is the labor productivity of each industry and Wage i is the cost of one unit of labor in each industry. Equation 1, 2 and 3 statically analyze how ULC, LProd, Wage, TFP and REER affect the supply of exports within each industry. Equations 4, 5 and 6 include lagged exports as a dependent variable since they concentrate on the dynamic of impact ULC, LProd, Wage, TFP and REER have on export behavior in each industry. These equations are just the dynamic versions of the first three specifications. All variables in the model are natural logs so that estimation can be interpreted in terms of percentage changes, and coefficients of the dependent variables can be interpreted as elasticities, removing any unit measurement biases that may creep in. The expected signs for each of the abovementioned explanatory variables are as follows: X i, t-1 Ulc Lprod Wage TFP REER K/L Sign >0 <0 >0 <0 >0 <0 >0 The effect of unit labor costs is expected to be negative since as these costs increase, they represent a decrease in the cost competitiveness of that specific industry which is likely to

9 The Determinants of Export Supply in the South African Manufacturing Sector: 35 reduce the amount of exports supplied by that industry. Labor productivity is expected to have a positive effect on exports since an increase in productivity allows for an increase in output for a given amount of labor input. Wages are expected to negatively impact on export supply for the same reasons as unit labor costs. An increase in total factor productivity, like labor productivity, increases the amount of output produced for a given amount of inputs, which would positively affect the supply of exports. The effect of the real effective exchange rate is expected to be negative since an increase represents and appreciation which means that the Rand would be more expensive. This would negatively impact on exports since they would become relatively more expensive than competing goods. The expected sign of the capitallabor ratio is positive since it reflects an increasing capital intensity, which positively affects exports. The expected sign of lagged exports is expected to be positive, that is because we expect that since entering into export markets depends of firm size and efficiency, firms who export continue to do so across periods. The inclusion of the lagged value of exports in the dynamic panel analysis has been taken from the study done by Edwards & Golub (2004), which incorporated it as an explanatory variable in their model. A possible validation for its inclusion is that it allows us to evaluate both long- and short-run elasticities of export supply (Edwards & Golub, 2004). It should be noted that this study is focused solely on South Africa and not it s competitiveness vis-ˆ-vis other countries and this is precisely why, although the above model specification is adopted from Edwards & Golub (2004), it has been adapted to include, instead of relative values, only South African values of the independent variables, and it also allows for the original model to be extended to include the real effective exchange rate. The Results obtained from the above-mentioned estimations were tested for the effects of structural breaks in the data due to policy shifts (e.g. the placing of trade sanctions on South Africa over the period , the introduction of GEAR in 1996, change in monetary policy framework to inflation targeting in 2000, the Asian currency crisis in 1997 and 1998, the recession over the period ), industry trade protection developments, industry specific trade volatility (e.g. oil price shocks), outliers in the dataset, and the contribution of total factor productivity (a combination of labor and capital productivity) to the structure of trade within each industry. These tests will be conducted by the inclusion of time-dummy variables into each specification to ascertain whether any of these periods have a significant impact on exports. 6. RESULTS Initially the model was run using the pooled OLS regression technique. This technique leads to unbiased but inefficient estimates (Gujarati, 2003) due to the panel nature of the dataset. It is, nonetheless, a useful starting point for panel data analyses since it provides us with a vague indication of the model s behavior. The use of OLS estimation technique on panel data set leads more often than not to the violation of assumptions behind the OLS technique. Having noted the above weaknesses, a more important empirical question can be posed, to assess the performance of this technique. Do the estimates acquired from pooled OLS comply with the theoretical expectations of the behavior of the model s variables? For the first specification (model 1), unit labor costs have a significant, negative effect on exports, as expected, and the

10 36 Muhammed Hajat and Uma Kollamparambil capital-labor ratio positively affects exports, also as expected. The results from the second specification (model 2) show that while labor productivity and wages affect exports as expected, positively and negatively respectively, total factor productivity does not exhibit the expected effect, since theoretically it should be positive. Model 3 only maintains two theoretically expected effects, that of labor productivity and the real effective exchange rate, whilst the other estimates are against such expectations. Next, the static model specifications, model 1, 2 and 3 above, were estimated using the, more appropriate techniques of fixed effects, specifically those of within-effects and least square dummy variables approaches. Random effects estimation is not used because, as per Gujarati (2003), that technique is more appropriate for analyzing time invariant characteristics of a model, and the focus of this study is solely on time variant characteristics of export supply as specified by the above model. The results of the within effects regression for model 1 accord with the theoretical expectations, the co-efficient of unit labor costs is negative and that of the capital-labor ratio is positive, and the results are all statistically significant. Model 2 s results show that labor productivity has a strong, positive effect on export supply and wages a strong, negative effect. The effect of total factor productivity conflicts with the expected effect, negative rather than positive, its co-efficient is statistically insignificant whereas those of labor productivity and wages are significant. Model 3 s results show that, as expected, labor productivity positively affects export supply while the real effective exchange rate and wages have a negative effect on export supply. Moreover, these results are all statistically significant. Total factor productivity was again statistically insignificant. Drawing upon previous studies (Edwards & Golub, 2004; Aysan & Hacihasanoglu, 2007) Models 1, 2 and 3 were run using the dynamic panel data technique (Read: models 4,5 and 6). Specifically, this study uses the technique developed by Arellano and Bond (1991) to ensure that estimates are unbiased. This technique requires the use of Generalized Method of Moments (GMM) since it violates the assumptions of, strict exogeneity of regressors and no autocorrelation, Ordinary Least Squares. Of the two types of Arellano-Bond estimators, onestep and two-step GMM, it was decided that the one-step GMM estimator was to be used. The reason for this choice being that, although two-step GMM estimators are better than the onestep alternative in the presence of heteroscedasticity, their standard errors for small samples tend to be too small (Aysan & Hacihasanoglu, 2007). The dynamic models included in this study can be interpreted as the short run analysis of the long run static models. This gives us an insight into how export behavior progresses over time. It is assumed that all of the explanatory variables included in each model specification are predetermined, since the error components at each time point have feedback effects on the following period s value (Aysan & Hacihasanoglu, 2007). The Sargan test was used to test the validity of the chosen instruments in the model. This test operates by checking that instruments are uncorrelated with the errors of the first differenced equation. Based on the results of the Sargan test, the null hypothesis of valid over-identifying restrictions failed to be rejected. In each instance, the models were tested for 1 st and 2 nd order autocorrelation of residuals. It was observed that only the first specification, Model 4, had residuals whose average autocovariance

11 The Determinants of Export Supply in the South African Manufacturing Sector: 37 of order 1 was nonzero. This is acceptable since the validity of GMM estimates is based on no 2 nd order autocorrelation (Arellano & Bond, 1991). The results confirm that no 2 nd order autocorrelation is evident in any of the specifications. The results of the dynamic fixed effects regressions are as follows: Model 4 accords with expectations since unit labor costs are shown to significantly, negatively impact on exports. Also, both, the capital-labor ratio and lagged exports have a positive, significant coefficient as expected. The results of specification 5 show that only labor productivity and lagged exports are significant, both exhibiting their respective expected coefficient signs. Model specification 6 showed that labor productivity, wages, the real effective exchange and lagged exports were all significant, all having the expected effects. Interestingly, total factor productivity was insignificant in both dynamic, and static, models. (See appendix 2 for graphical analysis of predictive power of all six models) Table 1 (Specification 1 & 4) POLS FE DPD lnulc *** *** *** (0.142) (0.127) (0.017) kl 0.409*** 0.281*** 0.042*** (0.06) (0.089) (0.004) lnx.l *** (0.004) Constant *** *** 0.783*** (0.695) (0.633) (0.103) BP/CW chi 2 (1)= 0.04 LR chi 2 (27) = Heteroscedasticity test Prob>chi 2 = Prob>chi 2 = 0.00 AIC/SIC N/A Sargan Test N/A N/A chi 2 (625)= Prob>chi 2 = st order autocorrelation F(1,27) = z = Prob>f = Pr>z = nd order autocorrelation N/A N/A z = Pr>z = Wald Test N/A N/A chi 2 (3) = *** (1%) **(5%) significance level Table 2 (Specification 2 & 5) POLS FE DPD lnlprod *** 0.529** (0.244) (0.339) (0.243) lnwage *** (0.166) (0.321) (0.201) contd. table 2

12 38 Muhammed Hajat and Uma Kollamparambil lntfp (0.087) (0.053) (0.015) lnx.l *** (0.086) Constant 3.123** *** (1.565) (2.609) (1.653) BP/CW/LR chi2(1) = LR Heteroscedasticity test Prob>chi2 = chi2(27) = Prob>chi2 = 0.00 AIC/SIC N/A Sargan Test N/A N/A chi2(163) = Prob>chi2 = st order autocorrelation F(1,26) = F(1,26) = z = Prob > f = Prob>f = Pr>z = nd order autocorrelation N/A N/A z = Pr>z = Wald Test N/A N/A chi2(4) = *** (1%) **(5%) significance level Table 3 (specification 3 & 6) POLS FE DPD lnlprod *** ** (0.244) (0.211) (0.228) lnwage *** *** (0.143) (0.184) (0.148) lntfp (0.079) (0.028) (0.025) lnreer *** *** *** (0.904) (0.492) (0.248) lnx.l *** (0.127) Constant *** *** 8.168*** (4.647) (2.569) (1.963) BP/CW chi2(1) = 0.00 LR Heteroscedasticity test Prob>chi2 = chi2(27) = Prob>chi2 = 0.00 AIC/SIC N/A Sargan Test N/A N/A chi2(115) = Prob>chi2 = st order autocorrelation F(1,21) = F(1,21) = z = Prob> f = Prob > f = Pr>z = nd order autocorrelation N/A N/A z = Pr > z = Wald Test N/A N/A chi2(5) = *** (1%) **(5%) significance level POLS FE DPD

13 The Determinants of Export Supply in the South African Manufacturing Sector: 39 All of the above estimations were rerun with the inclusion of time dummy variables in order to analyze the importance of time-specific events relative to the base year of In particular, these variables were included to assess the impact of various policy shifts and global economic conditions. The periods under consideration were, namely, when global trade sanctions were placed on the apartheid regime, which is the period where GEAR was introduced to attempt to boost South African international trade, the time span of the Asian currency crisis, 2001 because it was then that South African monetary authorities adopted the inflation targeting policy framework, 2005 which accounts for the large oil price shocks experienced, and representing the latest global economic recession. In the first period under consideration, , the estimates show that whilst not all of these years are statistically significant, the effect of trade sanctions were to adversely impact on exports in those years that were statistically significant. Assessing the impact of policy shifts such as GEAR ( ), the results again accord with intuition, in that the significant years exhibit a positive impact of such shifts on exports. This period overlapped the Asian currency crisis of but since South African manufacturers export mainly to Europe and America and compete with Asian economies, the effect of GEAR is supported. The effect of adopting inflation targeting in 2001 comes out as positive, this can be explained as an increase in the price competitiveness of South African manufactures. The effect of the large oil price shocks in 2005 were shown not to have any negative effects on manufacturing exports. The final period under consideration, , the latest financial crisis also had no negative impact on exports relative to CONCLUSIONS This study was focused on analyzing the cost determinants of the South African manufacturing sector, with a specific focus on unit labor costs and productivity. At the outset it was observed that unit labor costs have decreased, and labor productivity increased, for the period under analysis. The overall effect of these trends being an increase in manufacturing exports from 1982 to The analysis was done using panel data techniques such as pooled OLS, Fixed Effects and Dynamic Fixed Effects. Of these techniques it was concluded that the Fixed Effects estimates were best suited for the long run analysis and Dynamic Fixed Effects for that of the short run. The study finds that unit labor costs have a significant negative effect on export supply, an effect that is larger in the long run than in the short. Furthermore, it was observed that labor productivity and wages each have a significant positive and negative effect on export supply respectively. The most peculiar finding of this study is that total factor productivity was an insignificant determinant of export supply. A possible explanation for this could be that the growth of total factor productivity has been too slow relative to other export determinants over the period in question. Another significant finding of this study is that the real effective exchange rate has a strongly negative impact on export supply in both the short, and the long, run. The implications of this study are that in order to boost manufacturing export performance from a supply point of view, the state should focus on increasing labor productivity whilst

14 40 Muhammed Hajat and Uma Kollamparambil ensuring that wages remain in check. The latter prescription implies that a strong inflationtargeting framework should be maintained. This will ensure inflationary pressures do not cause wages to rise by more than the rise in productivity. The former prescription enforces the need for the government to make improvements to education and training of individuals from the ground up, starting with basic education. Another area of focus should be the growth of total factor productivity. This is linked to improving the local investment climate to entice foreign investors to invest in local companies using newer technologies. Also, a continuation of tariff liberalization, or an intensification of current liberalization would also positively affect total factor productivity (Jonsson & Subraminian, 2000). Finally, given the effect of the real effective exchange rate, the government should seek to ensure that the Rand should remain competitive in real terms. This is because any strong appreciation of the Rand would have severe, negative consequences for manufacturing exporters. References Arellano, M. & Bond, S., (1991), Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, The Review of Economic Studies, Vol. 58, No. 2, pp Aysan, A. F., & Hacihasanoglu, Y. S., (2007), Investigation on the Determinants of Turkish Expot-Boom in 2000s, MRPA paper no. 543, posted 07 November. Balassa, B., (1963), An Empirical Demonstration of Classical Comparative Cost Theory Review of Economics and Statistics, 4: Edwards, L. & Golub, S., (2004), South Africa s International Cost Competitiveness and Exports in Manufacturing World Development Journal, Vol. 32, No. 8, pp , Elsevier Ltd. Edwards, L., Rankin, N.A., Schoer, V., (2008), South African Exporting Firms: What Do We Know and What Should We Know, Journal of Development Perspectives, 4 (1): Edwards, L. & Schoer, V. (2002), Measures of Competitiveness: A Dynamic Approach to South Africa s Trade Performance in the 1990s, South African Journal of Economics, 70: Fedderke, J. W., (2002), The Structure of Growth in the South African Economy: Factor Accumulation and Total Factor Productivity Growth , South African Journal of Economics, Vol. 70, No. 4, pp Golub, S., (1994), Comparative advantage, exchange rates, and sectoral trade balances of major industrial countries, IMF staff papers, Vol. 41, June Golub, S., (2000), South Africa s International Cost Competitiveness, Trade and Industrial Policy Strategies Working Paper No. 14, South Africa. Golub, S. & Hsieh, C., (2000), Classical Ricardian Theory of Comparative Advantage Revisited, Review of International Economics, Vol. 8, No. 2, pp Gujarati, D., (2003), Basic Econometrics 4th Edition (International Ed.), McGraw-Hill. Harrigan, J., (1999), Estimation of Cross-country Differences in Industry Production Functions, Journal of International Economics, Vol. 47, pp Jonsson, G. & Subramanian, A., (2000), Dynamic Gains from Trade: Evidence from South Africa, International Monetary Fund Working Paper no. 00/45.

15 The Determinants of Export Supply in the South African Manufacturing Sector: 41 MacDougall, G., (1951), British and American Exports: A Study Suggested by the Theory of Comparative Costs: Part 1, The Economic Journal, Vol. 61: Melitz, M. J., (2008), International Trade and Heterogeneous Firms, The New Palgrave Dictionary of Economics, Second Edition, Palgrave Macmillan. Rankin, N. A. & Shoer, V., (2010), Export Destination, Product Quality and Wages in a Middle Income Country. The Case of South Africa, Presented at Fifth IZA/World Bank Conference on Employment and Development, Cape Town. Rankin, N. A., Soderbom, M. & Teal, F., (2006), Exporting from Manufacturing Firms in Sub-Saharan Africa, Journal of African Economies, 15. Stern, R., (1962), British and American Productivity and Comparative Costs in International Trade. Oxford Economic Papers, 14, 3: Turner, A. & Golub, S., (1997), Towards a System of Unit Labor Cost-Based Competitiveness Indicators for Advanced, Developing and Transition Countries Staff Studies for the World Economic Outlook, IMF. Turner, P. & Van t Dack, J., (1993), Measuring International Price and Cost Competitiveness, Bank for International Settlements Economic Papers, No. 39. APPENDIX 1: Technical Notes on Dynamic Panel Data We are interested in estimating the parameters of models of the form y it = y it 1 + x it _ + u i + e it for i = {1,..., N} and t = {1,...,T} using datasets with large N and fixed T. By construction, y it 1 is correlated with the unobserved individual-level effect u i Removing u i by the within-effects transformation (removing the panel-level means) produces an inconsistent estimator with T fixed. In order to address this correlation we must first difference both sides and look for instrumental-variables (IV) and generalized method-of-moments (GMM) estimators. First differencing the model equation yields: y it = y it 1 + x it_ + e it The u i (time-invariant errors) are eliminated, but the y it 1 in y it 1 is a function of the e it 1, which is also in e it. So y it 1 is correlated with e it by construction. We must, therefore construct estimators using lagged levels of y it as instruments for y it 1. For instance, if e it is IID over i and t (errors are serially uncorrelated), y it 2 would be a valid instrument for y it 1. Arellano and Bond (1991) showed how to construct estimators based on moment equations constructed from further lagged levels of y it and the first-differenced errors. We are creating moment conditions using lagged levels of the dependent variable with first differences of the errors e it. First-differences of strictly exogenous covariates are also used to create moment conditions. If the regressors are strictly exogenous, e it cannot affect x is for any s or t. If, however, the regressors are predetermined, E[x is e it ] = 0 for s t but allow E[x is e it ] 0 for s > t, e it may affect x is for s > t. When the variables are predetermined, it means that we cannot include the whole vector of differences of observed x it into the instrument matrix. We just include the levels of x it for those time periods that are assumed to be unrelated to e it.

16 42 Muhammed Hajat and Uma Kollamparambil APPENDIX 2: Illustration of Model s Predictive Accuracy

17 The Determinants of Export Supply in the South African Manufacturing Sector: 43

18