We estimate the margins for the variable of interest (e.g., size, sector, or country), for which we compare results across waves, keeping the distribution of size, sector, and country constant across waves to ensure that the results are not driven by differences in composition. To control for variations in the sample size by countries, estimations are weighted using the inverse of the number of observations in each country, that is, each country has the same weight in our summary statistics.<sup>7</sup> Our baseline estimation method uses pooled cross-section analysis including the full sample of firms in both waves. All results we presented are estimated conditional averages from regressions, unless specified. For robustness, we also replicate the analysis using a panel data approach, restricting the sample to firms responding to both waves of the survey. Overall, results are consistent across these different approaches.<sup>8</sup> To further understand the potential bias arising due to firm attrition, we implemented three alternative empirical strategies as a robustness check.<sup>9</sup> Using the subset of panel firms, we implemented the following robustness tests for the outcomes pertaining to the probability of being open, change in sales, probability of falling into arrears, and probability of accessing public support. _First_ , we re-weight the sample using the inverse of the probability of responding to the follow-up survey based on firm characteristics and performance during wave 1 to adjust for non-response (Wooldridge, 2002). _Second_ , we use the estimated probability of responding to the follow-up survey as a control in the regressions. _Third_ , we use a bounds approach where the outcomes are determined by a set of (extreme) assumptions on firm responses (Kling et al., 2007; Blattman et al., 2020). More specifically, we conjecture two scenarios: (i) estimate an imputed value of the outcome variable based on firm characteristics and past performance; and (ii) assume that all firms that did not answered the survey closed (i.e., impute a decrease in sales in 100%).

# **4. Six Stylized Facts on the Impact of COVID-19 on Impact and Recovery of Firms**

## **4.1 Stylized Fact 1: Months after the pandemic, some aspects of business performance show signs of recovery, albeit partial, thereby suggesting a possible ’U’ shaped pattern.**

The COVID-19 pandemic had a large and persistent impact on the operation and performance of firms. The first wave of the BPS and WBES COVID-19 showed that, on average, firm sales fall by 49%; almost 2 in 5 firms had already fallen or were expecting to fall into arrears in the following 6 month-period; 57% of the firms had reduced wages or hours worked of their employees, while 19% fired workers (Apedo-Amah et al., 2020).

The second wave of the survey data shows a positive trend of business recovery from this shock. We note the following key points on recovery: _First_ , more firms are now operational, in spite of modest changes in mobility.<sup>10</sup> Overall 93% of the surveyed firms were operational in the second

> 7In some countries, sampling weights are available in order to produce nationally representative results at the country level, but for cross-country comparison purposes, we do not include these weights. 8These additional results are available upon request.

> 9These methods are described in McKenzie (2021).

> 10See Appendix Figure C1. This could be explained by a decrease in mandatory closure measures that were less likely to be implemented in successive waves of the pandemic and replaced by other measures to restrict mobility and social

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