in response to outages. These firm owners increase their labor supply on days with lights immediately following a blackout day and decrease their labor supply on the second of two consecutive blackout days.

For each blackout day in a workweek, firms with workers increase the probability of staffing a worker by 3%, increase the number of worker days staffed by 4%, and increase total worker hours by 4%, small but meaningful changes in an environment with nearly two blackouts in each five day workweek (Table 5). Worker labor inputs are increased dynamically on blackout days, on the second of two consecutive blackout days, and on days with lights following blackout days, across the board working more relative to paired days with lights (Table 4).

While the analysis finds no evidence of input changes (beyond firm owner labor supply) for one-person firms, as proxied by expenses, firms with workers exhibit significant drops in weekly wages paid (Table 6).<sup>17</sup> Each additional blackout day is associated with a reduction of 1.29 GhC in weekly wages paid. This figure amounts to a dramatic 20% reduction. The final labor input finding comes from Table 5, which shows that despite increases in worker hours and decreases in total wages paid, the data fail to reject no effect of outages on within-firm worker hourly wages.

# **5.3 Interpretation**

This study has shown that one-person firms suffer larger profit and revenue losses than firms with workers, suggesting that labor input adjustments in microenterprises ameliorate the negative effects of unreliable electricity. The prior section then outlines three comparative statics on worker labor inputs, which can be used as a basis to examine _how_ labor input adjustments in microenterprises ameliorate the negative effects of outages: worker hours increase in response to blackouts, weekly wages paid by firms decrease in response to blackouts, and the data fail to reject that blackouts have no effect on (average firm-level) worker hourly wages. The context discussion in Section 2 suggests that the key to rationalizing this set of findings may be found in exploring the skill composition of these labor input adjustments.

Ideal data for identifying differential effects on the labor inputs of skilled and unskilled workers would individually classify workers by skill level and wages by individual worker. Worker-level data of this type would allow for direct tests of both the skill composition of labor input adjustments, and the effects of blackouts on the total and hourly earnings of workers by skill level. Unfortunately, the firm-level panel data enumerates workers 1 through n, but does not differentiate them by skill level. A related data limitation is that the wage data in the firm-level panel is lumped together as all wages paid by the firm in a given week and thus cannot be attributed to individual workers.

Given these data limitations, the study uses information on the composition of the workforce in the garment making industry in Ghana to draw speculative conclusions about the mechanisms behind the

> 17Note that inventory costs in Ghanaian garment-making firms tend to be relatively small. As with other skilled small businesses in Ghana, garment making customers typically supply the raw materials (fabric), leaving little in the way of materials inputs from the firm’s perspective.

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