conflicts; infrastructure; regulations; adjustments; investors; use of help from (public) institutions; employees; and apprentices.

In Côte d’Ivoire, data were collected for two successive years, covering 234 and 230 manufacturing enterprises in 1995 and 1996, respectively. A moving panel structure was applied, which implies that some of the firms surveyed in 1995 also were surveyed in 1996. Likewise, some workers surveyed in 1995 were also included in the sample in 1996. Problems of sample attrition and missing values for some key variables haunt the surveys and have seriously reduced the sample size, both in terms of number of observations and variables, requiring that we pool the two samples. By doing so, we implicitly assume that the relatively few firms (26) and workers (95) that are counted twice are no source of bias; that is, firms and workers present in both years do not, in any systematic fashion, deviate from firms and workers not present in both years.<sup>10</sup> The wages of the pooled sample are deflated, using 1995 as the base year. The samples do not contain information on regional locality, presumably because the vast majority of firms are located in Abidjan. Hence, no regional or spatial deflation is feasible. To the extent that some firms are from outside Abidjan, results may lead to a downward bias in the salaries of workers employed in these firms.

Furthermore, women represent only 10 percent of the observations. This may mirror the true picture; but the sample is deemed too small for meaningful analysis of women, and these observations are, therefore, excluded from the analysis. Hence, both sample and any policy recommendations are limited to male workers employed in the manufacturing sector. The number of observations is 891 males that work in 128 different manufacturing enterprises.<sup>11</sup> Despite the abovementioned problems, the data set remains highly interesting, since it is among the first data collected on manufacturing enterprises in Côte d’Ivoire. For the selected group consisting of male workers employed in the manufacturing sector, we are able to perform meaningful analysis despite the limitations discussed above.

> 10 In the regression analysis, we include a dummy that equals 1 if the year is 1996 and zero otherwise.  This dummy almost always turns out to be significant.  Our interpretation is that, while the samples may differ, none can, a priori, be said to be more representative of the “true” population.  Pooling the samples can perhaps average out any nonrepresentativeness, resulting in a pooled sample that may even be more representative.

> 11 Ideally, one would take into account the potential (self)-selection taking place here, and estimate, e.g., a selection model in the vein of Heckman (1979) and others.  However, it is increasingly recognized that this method requires assumptions that are not likely to hold.  For instance, assumptions on what is determining the selection mechanism has to be made, and the results in the wage-equation are often very sensitive to these assumptions.  Therefore, we choose not to apply any selection method.  Given that we only study males, the issue of self-selection may be less of a problem as compared to biases, had the analysis included females, where selection tends to be most prevalent.

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