that other firm support measures saved jobs in the timeframes considered. In Georgia, similarly, we found no evidence that any policy support measures induced firms to retain more workers.<sup>4</sup> Although these measures could have protected jobs to some extent, by supporting aggregate demand, limiting scarring effects and thereby setting the stage for the recovery and subsequent job growth, we were not able to confirm such impacts in the time frame studied at the firm level. At the same time, the binary (0-1) variables capturing policy support and the sample size may not provide sufficient power to capture these effects. If policies did fail to affect job losses, this could be because their targeting or reach was inadequate. Stylized facts reported by Cirera et al. (2021) suggest that policy support measures in developing countries have suffered from limited reach, mismatch between policies provided and those most sought, and mis-targeting of support. Finally, we found no evidence that firms with a higher share of employees working from home were able to retain more permanent workers. Although a number of firms adopted or expanded their use of digital / online tools, we found no evidence that these business adaptations saved significant numbers of PFPS jobs. Our results suggest that although investments in digital technology can be instrumental for certain sectors, firms, occupations, and firm functions, they cannot fully substitute for face-to-face interaction or physical presence in all sectors, and they may not compensate adequately for a lack of demand.

The methodology produces a distribution of job losses by demographic group that is similar across the two countries. For both, the methodology predicts a greater percentage of lost jobs for men; for those under 25 (and in the case of Georgia, over 45); for those with less education and with lower pre-COVID wage levels.

These predictions are not always borne out in the observed data, however. Overall, we find that the model predicts the level of further job losses fairly well. It predicts the distributional impacts by gender, nationality, and educational attainment, but it fails to predict the observed differences by age group and wage level. This is likely in part due to the lack of more detailed and specific data at the firm level on employment and employment losses by occupation. Results also align with observed high-frequency phone data from Georgia by age group and educational attainment, but the phone survey is not sufficiently comparable to reach conclusions on the methodology’s accuracy for that country. In addition, sector-level predictions are difficult to verify given definitional mismatches in the data, but the rates of job loss generally rank similarly to those observed. Overall, because our projections rely only on sector-, firm-, and broad occupational- class level shocks, we attribute the divergence between observed and projected outcomes at least in part to unobservable behavioral differences on the part of employers and workers that are correlated with gender, age, and unobserved skills or productivity, in addition to firm data limitations.

The rest of the paper is organized as follows. The next section summarizes the relevant literature on the jobs impacts of COVID-19. Section 3 presents the context in Jordan and Georgia, describes the data we use, and presents our methodology. Section 4 presents our econometric results for both countries. Section 5 presents the methodology for projecting job losses onto the labor force, projection results, and a comparison of projected future job losses with survey-measured future ones. It also presents PFPS job losses in the context

> 4 Although financial difficulties were correlated with permanent closures, there was no clear way to address possible simultaneity between these two outcomes.

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