_L. Fox and A.M. Oviedo_ _Institutions and Labor Market Outcomes in Africa_ growth rates across the different age and size classes, except between the excluded ones and the rest.<sup>27</sup> The legal system coefficient is not significant, and remains so when we include the other firm characteristics. Annual employment growth seems to be closely associated to ownership status, capital (measured by owning a power generator), and to a lesser extent, to exporter status and technology use (measured by the use of e-mail). The first result –sole proprietor’s firms grow faster- is likely associated with the fact that single-owner firms are also smaller, and younger. The other two results on the other hand, reflect the fact that more productive firms also grow faster, and they are more likely to be exporters and to use technology more intensively.

INSERT TABLE 13 When we use long-term growth as the dependent variable, results are quite different, and reveal further elements of employment dynamics in manufacturing. First, note that the differences in growth across size and age classes are much more marked for long term growth. Micro firms still grow faster, but there are also large differences in growth between small, medium, and large firms. For age, results are the opposite with older firms growing more, which is obvious given that older firms are likely to be more productive and hence to exhibit more sustained growth over long periods.

In addition, the legal origin dummy has now a negative and significant coefficient, which may suggest that in civil law countries firms may tend to grow more slowly over long periods. However, its magnitude falls by half once we include other firm characteristics in the regression. In particular, we find that exporters, firms with more capital, firms that use technology more intensively, and firms that invest in human capital (measured by training provision), are likely to grow more over long periods. This is also true, albeit with a smaller effect, for firms with access to credit. Interestingly, the coefficient of ownership is now negative, suggesting that singleowner firms might have higher short term growth, but in the long term those that survive, and grow more are multiple-owner firms.

Altogether, the explanatory power of our specifications is quite low, ranging from 0.06 to 0.14, which indicates that there are many unobserved factors that influence employment growth, and that are not captured by our variables. In any case, the results from these simple regressions suggest that the regulatory environment is not a good predictor of the job creation performance of the manufacturing sector in the short run, although we do find that in the long run more regulated countries (in our case countries with a civil law tradition) tend to have firms that grow less over their lifetime.

Our next exercise consists of estimating a similar equation to (1) but instead of using a single country dummy to capture all country-level variables that could affect net job creation, we use a set of country-level controls that include GDP growth (over the last year if the dependent variable is short term employment growth, and growth trend over 1996-2005 if the dependent variable is long run growth); and two DB measures, the overall regulation rank, and the labor regulation rank. Each rank is converted into a number that is between 0 and 1 (where 1 is the most regulated), as follows:

> 27 Very old firms also grow at a rate significantly smaller than that of young firms.

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