data to obtain comparable measures of revenues, costs and profits, and the breakdown of costs into material inputs, labor costs and fixed costs. We also use the information on firms’ economic sector to assign firms to one of three groups—high, medium and low impact—which we assume face a 100%, 50% and 20% drop in demand respectively during the lockdown or period of reduced activity. The categorization of sectors into impact groups follows the taxonomy by Vavra (2020). We present the effects of the shock as percentage deviations in outcomes (e.g. profits) compared to the baseline year (the latest available data), typically 2018 or 2019. All replication codes are available here.<sup>3</sup> The first part of our analysis simulates the impact of the COVID-19-triggered revenue shock on firms. We start by examining firms’ profitability. The average formal firm reports an annual profit margin of 10% and therefore can only absorb shocks to its revenue of a similar magnitude over the year. In a scenario in which firms do not adjust their costs, only 27% remain profitable following a three-month lockdown. Once we allow for firms to adjust material inputs, 44% remain profitable. The largest firms tend to fare a bit better, which slightly mitigates the aggregate profit loss.

In a second step, we estimate the impact of the lockdown on formal employment, as measured by payroll losses. As re-contracting formal labor is costly (and wages are rigid), we assume that firms cut their payroll only when it is necessary to avoid making losses. Thus, firms which can absorb the demand shock without becoming unprofitable do not lay off workers. Under these assumptions, we find that the average firm cuts its annual payroll by 6.7% (26.8% of quarterly payroll). In aggregate, 5.8% of the annual payroll (23.2% of the lockdown quarter payroll) is lost.

Third, we use the panel dimension of the data to predict plausible exit rates. We measure the pre-COVID exit rates among loss-making and profit making firms as an average across years, and find these rates to be remarkably stable across countries and over time. We then apply these rates to the post-COVID profit distribution, allowing for the revenue shock and for firms’ material and payroll adjustments. The results suggest that the share of firms exiting would on average double, with large variation across countries. Aside from the exit rates, the firm-level results are relatively homogeneous across the countries we study. However, firms in poorer countries might fare better than we predict, as they could join the informal sector instead of shutting down completely, and because their true profitability might be higher than observed in the data due to more misreporting.

The second part of our analysis focuses on the implications of our firm-level results for governments’ policies and public finances. First, we estimate to what extent wage subsidies—a widely discussed policy tool, common in many European countries (Giupponi and Landais, 2020)—can mitigate formal employment losses. We find that, on average, even a 90% wage subsidy can only prevent 30% of the predicted payroll cuts. To understand this result, note that in our simulations, wage subsidies affect formal employment only

> 3Replication codes remain live and will continue to be updated with extensions based on feedback from governments and researchers. World Bank Policy Notes that summarize the results for each of the 10 countries will be published on the authors’ websites in August.

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