variables for each manufacturing subsector and each time period are introduced. The analysis of a policy change using the D-in-D methodology assumes that the treatment and control group show similar trends prior to the tax reform. In figure 6 of the previous section we have observed comparable trends, before 1996, in the evolution of exports across the South African manufacturing sectors of the sample.

34. We use a panel database of 9 manufacturing subsectors (automotive, chemicals, machinery other than motor, textiles, leather, rubber, cork and wood, paper, iron and steel, and other manufacturing), sourced from the UNIDO ISIC database and spanning 10 years, from 1994 to 2004<sup>18</sup> . The dependent variable in the analysis is the natural logarithm of total South-African manufacturing exports (US$ nominal, deflated by the South African Producer Price Index (PPI)), obtained from UNComtrade. The choice of the South African PPI as deflator is done in order to capture the macroeconomic dynamics of the South African economy and the national price into the dependent variable. Alternative deflators, namely the US PPI, were tried and did not alter the results. The main constraint in conducting this study has been the lack of complete sectoral data.

35. The starting set up gives the following specification: (1) 36. Where i (i=1....9) denotes the sector and t (t=1994...2004) the year. is the automotive (treatment sector) dummy variable taking the value 1 for automotive and 0 otherwise. The coefficient captures possible differences between the automotive sector and the other manufacturing sectors prior to the policy change. is the time dummy variable taking the value 1 in the post treatment period (> 1995) and 0 in the pre-treatment period (≤1995). Consequently, captures aggregate factors that would cause changes in manufacturing exports after 1995 even in the absence of a policy change. is an indicator variable coded 1 if the observation is in the treatment group (automotive sector) and in the second time period (>95), 0 otherwise. is thus the D-in-D estimator, capturing the response of total manufacturing exports to the change in export tax incentives related to the MIDP in South Africa.

37. The vector contains other control variables that vary over time and that typically affect a country‟s export supply. The choice of these variables was based on results of studies on the determinants of South Africa‟s export performance that highlight the importance of supply rather than demand factors (for instance Edwards and Alves, 2006). The following variables are identified as main factors enhancing the profitability of export supply in South Africa (also in line with Tsikata, 1999; and Golub and Ceglowski, 2001): relative prices, inflation, industry concentration, infrastructure, variable production costs, tariff rates and skilled labor.

38. Based on this research and data availability, we have considered the following control factors in our study: (i) as a proxy of relative prices, we use the manufacturing terms of trade index (having also considered the general Terms of Trade), which proves to be

> 18 Due to missing observations for the sectoral variables in the latter years, we had to limit the study to 2004.

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