predict the average value of outcome _Yi_ over the full sample but conditioning on both size and whether the business is led by a man or a woman.<sup>12</sup> We show these estimates in the following sections and report whether the gender difference is statistically significant at the 95% confidence level, but the full set of results from ordinary least squares and the Probit estimations is available in the appendix.<sup>13</sup>

# **5. The shock**

This section chronicles the impact of COVID-19 across five important dimensions of firm-level outcomes: business closures, disruptions in supply channels, sales revenues, financial risks, and the owners’ expectations about the future. For each outcome, we first report unconditional gender gaps, followed by a more detailed discussion of conditional gender gaps, including industry- and firm-size specific results.

## **5.1 Temporary business closures**

At the onset of the COVID-19 shock (spring of 2020) an estimated 70% of businesses were temporarily closed but 6 weeks after the peak (proxied using Google mobility trends around transit stations) this fraction averaged 25%; it then declined to 10% around week 15 (Apedo-Amah et al., 2020). The data suggests that women-led businesses resumed operations at a slower pace relative to men-led businesses (Table 2). The unconditional average predicted likelihood of operating regularly 6 weeks or more after the peak was 85.9% for men-led businesses and 84.5% for businesses led by a woman, a statistically significant difference of 1.4 percentage points (pp). This gender gap declines but remains statistically significant if we control for firm size and sector (for a conditional difference of -1.1 pp), which shows that some of the difference between men- and women-led businesses in the probability to remain closed six weeks after the peak crisis reflects gender differences in firm size and the fact that male and female entrepreneurs tend to operate in different industries. This finding is qualitatively in line with the evidence from other works in the literature. Based on data for 488 small and growing businesses in emerging markets, ANDE (2020b), for example, reports that women-led businesses were significantly more likely to shut down due to COVID-19 than men-led businesses. The Future of Business Survey (FBS), a data source for small- and medium sized enterprises with a Facebook Business page, reports that businesses led by women were seven pp more likely to be closed at the time of the survey compared to businesses led by men (Facebook et al., 2020).

Further disaggregation of our results by firm size using the conditional estimates shows that the gender gap in temporary business closure is significant among micro-enterprises (-2.6 pp) and medium and large firms (-1.6 pp). In terms of sectors, we see that the gender gap is largest, and to the disadvantage of women, in the hospitality sector (-5.3 pp) followed by other services (-4.7 pp)–two sectors in which female entrepreneurs are disproportionately engaged. Conversely, there is a gender gap to the disadvantage of men among manufacturing enterprises (2.6 pp). Moreover, female entrepreneurs’ disadvantage in the likelihood that their businesses remain closed is driven by countries that experienced severe shocks and large declines in mobility (third or fourth quartile, as per Google data). Among countries with less severe shocks, gender differences in the probability of businesses to remain closed are either not significant (first quartile) or to the

> 12We run our computations using STATA. We use the command margins to compute the gender gaps.

> 13Our preferred specification to disentangle price from composition effects is interacting our controls with gender dummies instead of using Oaxaca-Blinder decompositions because most of our controls are fixed effects, and in these instances the results from Oaxaca-Blinder decomopsitions are difficult to interpret and highly sensitive to the choice of omitted categories (Fortin et al., 2011).

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