To obtain measures of the investment climate quality we use the data on perceived constraints to the productivity and sales growth by the existing enterprises. We construct dummy variables that are equal to one if a particular constraint (we consider electricity, water supply, technology, financial services, labor issues, telecommunications & postal services, and regulations & policies) is rated as a "a major problem", and zero otherwise. Similar in spirit to the suggestion by Escribano and Guasch (2005), we then compute averages of these variables across all firms in the community, thus generating variables indicating the proportions of firms that perceive a given constraint “a major problem” in the community. Consequently, our investment climate variables are constant across firms and households in each community, which ensures, for example, that two small garment enterprises located next to each other face the same investment climate. Also, as noted by Escribano and Guasch (2005), smoothing the data in this way should have the additional advantage of mitigating endogeneity bias.

As can be seen in Table 14, column (2), most of the coefficients on the investment climate variables are individually insignificant, and some have the ‘wrong’ sign. The only individually significant coefficient is the proportion of firms considering regulations and policies as a major problem, which is inversely related to the likelihood of having a nonfarm enterprise. However, looking across to column 3, where we model the likelihood of having started a firm at some point during the last three years, the results are weaker. On balance, it would seem clear that subjective investment constraints are not strongly correlated with participation or entry, conditional on the other explanatory variables.

Now consider the effects of shocks on participation. The survey contains information on whether the following events have occurred over the last four years: death of household member; illness of household member; loss of job of household member; food shortage; drought; flood; crop damage; and price shock. For each household, we construct shock variables by counting the number of years over the last four years in which each of these events has occurred. That is, each shock variable takes the value 0, 1, 2, 3 or 4. We want to investigate if households respond to shocks (at least in part) by participating, or not participating, in the nonfarm enterprise sector. A priori, this does not seem a far-fetched idea: entry and exit costs are low, and enterprise decisions could be closely linked to household events (see e.g. Mead & Liedholm, 1997). The results, shown in Table 14, however, are weak: the only coefficient on the shock variables that is significantly different from zero is that on price shocks. The sign of the marginal effect is positive, hence there is some evidence a price shock increases the likelihood of running a firm, perhaps because this is a way of generating some extra cash. It should be noted, however, that we find no such effect when modeling entry (column 4), and so the safest conclusion to draw here is probably to say there is no strong evidence that shocks matter for enterprise participation.

Finally we consider the role played by credit constraints. One common argument in the microeconomic literature on poverty traps is that combinations of high entry costs and poor access to

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