22 three-digit SIC version 5 manufacturing sectors in the South African economy for which data is available. The list of sectors included in the panel is that specified in Table 1. This provides a 22 × 28 panel with a total of 616 observations.<sup>24</sup> Variables for the manufacturing sector include the output, capital stock, and labor force variables and their associated growth rates. For data on TFP growth in South African manufacturing, we rely on Fedderke (2002).

To control for the market conditions firms face, and other determinants of productivity, we also employ:<sup>25</sup>

- The skills mix of the labor force in each manufacturing sector. The ratio is of high and medium skill levels to unskilled labor. We denote the variable as SKRAT. Since TFP decompositions in South Africa do not control for changing skills composition of the labor force, it is vital to control for the skills ratio in any determination of TFP, in order to correct for the resultant upward bias in the TFP measure.<sup>26</sup>

- The net export ratio of each manufacturing sector,<sup>27</sup> incorporated in the hypothesis in the literature that export competitiveness may require strong innovative capacity. We denote the variable as NX.

- R&D expenditure by manufacturing sector is compiled from published survey data on R&D expenditure. Data is collected for private sector R&D expenditure, public sector R&D expenditure, and expenditure by tertiary educational institutions earmarked for each of the 28 manufacturing sectors.<sup>28</sup> All expenditure is real.

- Two measures of industry concentration, given by the Gini index and Rosenbluth index are computed for each industry in each year over the sample period. Data is obtained from Fedderke and Szalontai (2005). We denote the variables GINI and ROSEN respectively.

- The total number of patents registered in South Africa, in order to serve as a proxy for the quality of intellectual property rights.<sup>29</sup> We denote the variable as PATENT.

- An index of property rights in South Africa, as a second proxy for the quality of the property rights environment. The hypothesis is that the general quality of property rights may impact on the quality of intellectual property rights.<sup>30</sup> We denote the variable as PROPERTY.

From Perkins, Fedderke and Luiz (2005) we obtain measures of infrastructure capital stock and investment as follows:

- Economic infrastructure:

- Gross fixed capital formation in infrastructure, denoted GFCF (1995 prices).

- Fixed capital stock of infrastructure, denoted FCS (1995 prices).

> 24 In general, South Africa reports data on 28 3-digit manufacturing sectors. Some of these had to be excluded from the analysis for reasons of data availability. Television, radio & communications equipment and Professional & scientific equipment did not have data on R&D expenditure, while Tobacco, Plastic products, Television, radio & communications equipment and Other transport equipment lacked data on labour force skills levels. Petroleum products lacked consistent information on industry concentration.

> 25 A fuller discussion of these data and their quality see Fedderke (2005).

> 26 See the more detailed discussion of this point in Fedderke (2002).

> 27 Computed as X+XIM<sup>whereXdenotesexports,andIMimports.</sup>

> 28 The surveys are the Resources for R&D surveys undertaken by the Office of the Scientific Adviser to the Prime Minister/President and the Council for Scientific and Industrial Research (CSIR). Full details of this data available from authors on request.

> 29 For details on the construction of this variable see Fedderke, de Kadt and Luiz (2001).

> 30 For details on the construction of this variable see Fedderke, de Kadt and Luiz (2001).

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