find that these results are driven by the impact of policies implemented in upper-middle-income and high-income countries<sup>25</sup> which is where the implementation of various forms of wage subsidy policies was more frequent.

Assessing rigorously the impact of policies is complex based on our cross-sectional data and the fact that a firm has access to policy support cannot be considered exogenous. Given our available information, it is not easy to identify an instrument that would work for different types of policies. For this reason, we present here some descriptive evidence based on simple OLS regressions but try to compare firms that are as comparable as possible. Specifically, we compare firms that applied and received policy support with those that also applied but did not receive it. Additionally, we always control for country, sector, size and time fixed effects, to make sure that we are narrowing our comparison to firms that are as similar as possible. We take these results as indicative evidence about the possible effect of policies while realizing we are not identifying precisely their causal impact as our results may still be affected by a selection bias.

We focus on four firm-level outcomes of interest, some are outcomes that we expect could be directly affected by the policies (i.e. likelihood of laying off workers, expected future sales growth, likelihood of falling into arrears), while the remaining one could be at the same time affected by policy but also operate as a mechanism that in turn influences future firm-level outcomes (i.e. probability of investing in digital technology and solutions). The results are presented in Table 3 where we separate our analysis for different groups of policies (each policy is separately analyzed in different columns). We observe that certain groups of policies appear to be more effective than others. Specifically, monetary transfers and access to credit which may be relaxing short-term credit-constraints and liquidity problems are correlated with higher future expected sales growth, as well as with higher probability of investing in digital solutions. Wage subsidies, in line with our prior and its stated objectives, is negatively correlated with the probability of firms laying off workers, while it does not seem to significantly influence future sales or likelihood of falling into arrears in the coming months. Tax support<sup>26</sup> only appears to be positively correlated with future expected sales growth, while it does not appear to influence future likelihood of falling into arrears, and accordingly does not appear to be successfully correlated with reducing the likelihood that liquidity constraints turns into solvency problems. Finally, payments deferral<sup>27</sup> seems to be the least effective of all the policies with some marginal effect and positive correlation with the likelihood of expanding the use of digital platforms.<sup>28</sup>

# **10. Conclusion**

Governments around the world responded to the deep economic impact of COVID-19 by rolling out more than 1,600 policy initiatives to support small and medium-sized businesses. While some learning from previous crises has proven useful in the initial response phase, policy makers and

> find that this elasticity is significantly smaller (less negative) than the elasticity of firms that do not benefit from policy support at the 5 percent confidence interval.

> 25These results are shown in Figure A3 in the online appendix.

> 26Tax support includes fiscal exemptions, reductions as well as tax deferrals.

> 27This only refers to deferral of rent, mortgage, or utilities.

> 28The interested reader will find a more detailed and granular breakdown of individual policies in Table A1 in the supplementary material in the Online Appendix.

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