electricity services, we assign a value for _S0_ in eight missing industrial sectors following intuitive criteria.<sup>15</sup> Table 7 presents some summary statistics on these two subsamples of firms.

One standard worry with firm surveys is the potential non-response bias, as some firms may not respond to specific questions. Overall, non-response is more frequent among small and service firms, and this is also the case when looking at missing data on generator ownership. This is a standard observation in firm surveys, the main reasons including lack of time and/or information by the respondant—a situation more likely to occur in small firms. Because of the way generator ownership is distributed among firm size categories, this may affect our estimates, even if these non-responses are not driven by strategic considerations at the firm-level. While we have no systematic way to address this problem, results not shown here to save space indicate that most of our conclusions below are robust to excluding small firms and all service activities respectively.<sup>16</sup> The results from estimating equation (8) are shown in Table 8. All specifications include country, industry and year fixed effects, and standard errors are clustered at the countryindustry level. We introduce in columns 1 and 2 the number of power outages and its square. Column 1 reports the results with only fixed effects, while column 2 introduces firm-level controls (age of firm, location, whether it exports or not, whether it has foreign capital or not, and size). To the extent that more power outages are likely to lead to more generator ownership, we expect the coefficient for the number of outages to be positive, and this is indeed what we obtain. The square value of the number of outages should be negative to match the concavity stressed in the theoretical model, and indeed we find that its coefficients are negative and significant in both columns. The threshold values indicated below mean that

> 15 A ―core‖ set of sectors is systematically included in all country surveys so that the missing ones represent relatively few firms overall. (See Dethier, Hirn and Straub 2010, for a detailed description of the surveys). We consider Sports Goods, Other manufacturing, and Mining and quarrying as industrial sectors that do not rely heavily on electricity (the cost share of electricity in these sectors is less than 3.5 percent in the full sample of countries) and assign to these sectors, as well as to Accounting and finance and Advertising and marketing a value of _S0=0_ .  Symmetrically, we assign a value _S0=1_ for firms operating in IT services, Hotels and restaurant, and telecommunications (average cost share of electricity above 8.6 percent in the full sample for the first two).

> 16 A more general issue is the fact that surveys cannot provide information on firms that were not born, because of credit constraints or unreliable public power supply.  Dethier, Hirn and Straub (2010) address this ―camels and hippos‖ self-selection issue in details, stressing in particular that econometric models like the one in this paper only provide information about the effect of constraints on the sample of existing firms, and that the analysis of entry would require different models. Note however, as pointed out there, that self-selection is hardly ever likely to be complete, so some informative variation should remain in the data.