outages). The coefficient of interest (Beta 1) is negative and significantly different from zero at the one percent level. That is, firms have lower sales where electricity connections are more difficult to obtain. Sales are also higher where labor input is higher. Once usage of bank financing is included (column 2), these results remain significant. The same is true when the trade variables are included (column 3). This specification also shows that sales are higher among firms that import at least parts of their production inputs. Finally, column 4 shows the specification with all the covariates included, plus the interaction terms between industry fixed effects and the electricity variable. These interactions identify which industries are more affected by the electricity connection process. When the interaction terms are included, it stands out the vast majority of the interaction terms are negative in particular for industries such as manufacturing refined petroleum, manufacture of tanning and dressing of leather and manufacturing of motor vehicles, indicating that these industries are more likely affected by an inefficient electricity connection process. These results are presented in Table 16b. We include only the industries that have enough observations for the estimation of coefficients to be possible.

The Enterprise Survey database covers many cities within a country while the Getting Electricity database covers only the major business city in the country. In countries where the ease of obtaining an electricity connection varies considerably across cities, including all the firms covered in each country in Enterprise Surveys can lead to imprecise estimates of the relationship between firm performance and the electricity connection process. We therefore try to improve the fit of our model by restricting the sample of firms to only those located in the major business city of the country. The results for the restricted sample are presented in Table 17a. The same coefficients are significant as in the full sample specifications but the coefficient of our new Getting Electricity variable become more negative and the overall R-squared increases.

# _Industry Level Specifications Results_

In a next step we collapse the firm data at the industry level as shown in every other column of Tables 16a and 17a. The industry level specifications use the same sets of covariates as in the firm level regressions. Compared to the firm level regressions, the same variables remain significant but the coefficients for past sales decrease slightly and those for labor input and the import dummy increase. The coefficient of our new Getting Electricity variable turns positive in all specification of the full sample. But once we restrict the sample to the Doing Business city only, the coefficient become negative again once enough controls are added to the regression (columns 2, 3 and 4). Moving up one position in the ranking from a more complex electricity connection process to a less complex one is associated with an increase of 0.2 in the log of firm sales and a 0.1 increase in the log of industry sales. Once the interaction terms are included in the restricted sample, as in the firm level regressions, there remain industries such as manufacture of tanning and dressing of leather and manufacturing of motor vehicles that also show a negative relation between the ranking in the ease of electricity connection and industry sales. These results are presented in Table 17b.

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