including generalized residuals from first stage regressions to correct for simultaneity and/or selection bias. Selection bias is a concern if non-response to key survey questions for our analysis is not random. We include generalized residuals in later stages, including the inverse Mills ratio from a selection equation, whenever they are statistically significant.<sup>15</sup> We must adopt some identifying assumptions (or exclusion restrictions) to estimate proximate causal relationships. These are as follows: predetermined (pre-COVID) variables such whether the respondent to the COVID follow up survey had changed since the 2019 WBES , the firm’s size category (captured by dummy variables), and experience of the firm’s top manager (for missing observations/selection); the level of foreign ownership (for digital adaptation); foreign ownership and preCOVID usage of banks for working capital or a line of credit (for subsequent liquidity problems and for the receipt of government support)<sup>16</sup> ; the share of the firm’s workers with a university degree, the ICT-related task content of jobs in the sector, and firm-size (for the share of employees working from home), and exogenous global demand shocks for the change in the export share of sales.<sup>17</sup> There may be unobserved attributes, such as firm ability or opportunity, which would cause a violation of the assumptions that these variables are not correlated with unobservables in the later stage regressions. Given this possibility, we tested whether the excluded variables were significant in the final stages, and they were not.<sup>18</sup> The regression model is therefore written as follows: + τ Where is a vector of potentially endogenous variables: self-reported financial difficulties (JJii = α + βQQii + ρωωii + ∀XXii ii + εεii ), the use of new or expanded electronic platforms ( ), and the share of work from home ( ); is a vector of exogenous ii ωω FF ii AA EEIIssh XX 15 We elect a standard econometric approach to arrive at the best linear unbiased estimates of coefficients on explanatory variables. We estimate multiple versions of the same equation and use a T-test of the significance of an included regressor is a test of whether it should be included in the model. We found the coefficient estimates to be generally stable. In general, we prefer a parsimonious model. This increases potential applicability to other contexts and improves efficiency of the estimations. For some tables, we report insignificant regressors of interest, nonetheless. Generalized residuals that are not significant show that the correction is not a significant determinant of the outcome variable and should be excluded. Cross-validation is another method for model selection and provides the best linear unbiased predictor, but our main focus in this phase is to estimate and test the significance of various contributors to job loss. In practice, cross-validation would be computationally complex and impractical when there are multiple stages in the general causal model.

16 In practice, we find that unobservable determinants of the propensity to receive policy support were not significant in later stages of the estimation.

17 We cannot model firm entry and exit given data limitations. The sampling frames were not refreshed with each round of the CFUWBES. Moreover, we cannot model firm closures as of round 1 within the same model structure as that used for firms in operation, because variables are not captured for those that have shut down. For example, some of those firms may have received wage subsidies or had supply chains disrupted after round 1, but this is not observed in round 2. Because we attempt to control for selection bias, whether due to non response or firm exit, we still in principle have unbiased impact estimates. Moreover, it does not appear that the inability to model firm exit and entry biases our aggregate results, given that in practice our forward-projected estimates without this aspect modeled match the CMMHH data well (see subsequent sections).

18 First stage regression results are provided in Appendix 2 of the long version of the paper. In Jordan, we found that missing values for key variables were more likely if the respondent was not the same, the firm was not medium-sized, and the top manager had fewer years of experience. In Georgia, having mixed or foreign ownership was associated with lower rates of missing values.

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