with only 79 observations. We did not group Lesotho with the pool of SADC countries because the survey of this country is from 2003 and the information on the IC and C variables is quite different. Burundi presents similar problems—the information on the PF for this country is for a single year (2005), and the number of observations is only 101. Although Burundi belongs to the EAC—along with Kenya, Uganda, and Tanzania—we did not pool Burundi with any of these countries because the information on the PF and the IC comes from different years and with different information on the IC and C variables. Cape Verde is another country with information for a single year (2006) and with only 47 observations available for regression analysis. Because of its obvious difference from the rest of the ECOWAS countries—different per capita income and its condition as an insular state, as well as other geographical considerations—we did not include Cape Verde in this pool. As a result, no regression analyses were conducted for Cape Verde, Lesotho, and Burundi.

By running the regressions country by country we can use as many infrastructure and other IC and C variables as are available. This allows us to gain heterogeneity estimating the impact of infrastructure on productivity. In addition, we can use more variables as proxies for firm-level, unobservable fixed effects, and we do not have to constrain ourselves to the subset of IC variables common to all the countries.

# _Cleaning the data_

The IC databases are, in some respects, troublesome. From table B.1 (see the Appendix) it is clear that out of the total number of establishments surveyed there are a considerable number of observations with at least one PF variable missing, and/or with outlier observations in the PF variables.<sup>9</sup> This problem becomes more acute for some countries—such as Algeria, Senegal, Eritrea, Tanzania, and Mauritius—where more than half the observations are missing for the regression analysis (see the upper panel of table B.1), which results in the sample representativity being lost. To reduce the effects of this sample selection bias, we apply a preliminary data-cleaning process that allows us to retrieve a considerable number of establishments for the analysis; it is based on a robust simple version of the EM-algorithm of Dempster et al. (1977) (for more details see Little and Rubin, 1987; Escribano et al., 2008). First, we exclude those plants with missing values in all the PF variables—sales, materials, capital stock, and labor cost. We convert outlier observations of PF variables into missing observations, then proceed as follows: (a) we replace the missing values by the corresponding (cells) industry-regionsize median of the variables keeping from 15 to 20 observations in each cell; (b) if we do not have enough observations in some cells we replace them with the corresponding industry-size medians; (c) if we still do not have enough observations in those cells we replace them with the region-size medians; and (d) if still necessary, in the last step we compute the medians only by size and/or by industry to replace those missing values. Table B.1 shows that the number of available observations in all the countries considerably increases with the application of this data-cleaning process. Tables B.2.1 and B.2.2 (see the Appendix) show the distribution of the observations (by country and year, and by

> 9 By _outlier_ s we mean those observations with ratios of materials to sales and/or labor cost to sales greater than 1.

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