either a manual or an electric sewing machine or both. 86% of both firms with and without workers have at least one manual sewing machine, making a shift to this less productive technology possible for most firms in the event of a power outage. About 75% of one-person firms and 88% of firms with workers have either an electric sewing machine or some other type of auxiliary electric equipment, such as an electric iron or embroidery machine, undergirding the potential productivity importance of power outages for electrified firms in this industry.

All firms in the analysis sample and most firms in the industry utilize the labor of the firm owner, who is typically a skilled garment maker who has completed apprenticeship training in the trade. About half of the firms in the sample also employ non-owner labor. To shed light on the composition of the workforce in the Ghanaian garment making industry, the analysis turns to the baseline dataset from **?** , which includes worker-level information from 752 garment making firms in Ghana.<sup>6</sup> In that sample, 630 firms employ 197 workers who have already completed an apprenticeship, 72 family workers, and 1,908 apprentices, for a workforce composition that is 91% apprentices and family workers. Although there is skill heterogeneity among apprentices (and family workers) driven by progression in the apprenticeship (among other things), for the purposes of this paper family workers and apprentices are conceptualized as unskilled labor and what are colloquially referred to as “paid workers” (who have completed an apprenticeship) as skilled labor.

Firm owners and skilled workers are generally able to utilize any of the three production technologies, and often also possess specialized skills in fashion design, embroidery, and the use of other specialized machines. Capital utilization by apprentices varies according to the capital available in their training firm and their progression in the apprenticeship, and they generally learn each production technology in order of ascending productivity. In the **?** data, over 95% of apprentices can effectively use at least one type of sewing machine six months into their training. The pay structure for skilled and unskilled workers differs dramatically in this context. Apprentices are paid low weekly or monthly wages that are tied to the overall revenues of the firm, while skilled workers are much better paid piece rate employees. In the **?** baseline dataset, though skilled workers constitute only 9% of the workforce, they earn 61% of all the wages reported in the data.

Many features of the garment making industry mirror other small-scale manufacturing trades in Ghana. Apprenticeships are widespread in skilled construction, cosmetology, auto repair, cobblery, and artisan crafts. Variation in reliance on manual and electric equipment is also common in these trades. Perhaps not surprisingly, the profit distribution of the garment making firms in the sample studied in this paper is statistically indistinguishable from the distribution of incomes among all self-employed Ghanaians in the Ghana Living Standards Survey ( **?** ).

> 6This dataset includes other industries as well; here the study draws on descriptive statistics from the garment making portion of the sample and from the 2013 firm baseline survey before any experimental intervention.

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