losses in each country by workers we designate as “fixed input” (non-production) employees and “variable input” (production) employees using the country-specific pooled (2013 and 2019) WBES data. We estimate a set of sector-specific polynomials relating the number of production to non-production workers for each aggregated sector (See Appendix 2 in the longer version of this paper for details) and then compute the ratio of losses by each group within the appropriate ranges for each sector.<sup>35</sup> Using these estimated polynomials, we find that the estimated ratio between production and non-production workers varies substantially over the range of employment levels and by industry. Utilizing these empirical relationships therefore in principle makes our prediction more accurate than one that implicitly assumes that losses occur in equal measure across tasks and levels of total employment. In practice, without this adjustment to occupational category, we find that we are less able to replicate the demographic distribution of realized job losses, especially by level of education and wage quintile.<sup>36</sup> After assigning the resulting mean percentage job losses to PFPS workers represented in the baseline labor force survey by sector and occupational level, we compute, using LMPS survey weights, the percentage of PFPS workers projected to lose their job. In addition to the total rate of job loss, we compute the approximate percentage of PFPS job losses by sector of employment (relative to baseline), by gender, age, educational attainment, wage level and nationality.<sup>37</sup> 5.2 Validity Checks Having thus estimated PFPS job losses, we next assess the predictive accuracy of the magnitude and allocation by demographic groups of our modeled estimates. To do so, we use two bases for comparison. One is wave 2 of the CFUWBES. The second is labor force survey data collected at around the same time as this wave. In the first instance, we predict job losses using the model estimated using CFUWBES1 (Section 4) using the data on the influence variables from wave 2 to predict cumulative job losses for wave 2. We then compare these to the results we obtain when, instead of the model, we utilize a direct computation of average sectorfirm-size job losses from CFUWBES2.<sup>38</sup> If the wave 1 coefficients were no longer valid by wave 2, one would see an important divergence in the results from these two approaches.<sup>39</sup> We do not see such a divergence. Nonetheless, this provides only an imperfect check on the ability of our estimated model to predict near-term future job losses in wave 2. This is because the job losses directly tabulated using CFUWBES2 themselves may not be a perfect measure of the “truth.” Certain sectors may be under-represented in the survey, and it may 35 We use the highest order polynomial for which the highest order term is statistically significant. 36 We compared estimates with and without this adjustment only for Jordan.

37 For Jordan, we apply the net job losses from CFUWBES1 to the employment structure in 2016. We attempted to reweight observations to match the composition by age, gender, and industry in the MENA Monitor Survey, which has a smaller sample and does not include all nationalities. We found that this does not change our results appreciably, so we prefer the simpler approach of using the sampling weights in the LMPS.

38 This approach can provide a short cut for assigning job losses to workers for situations in which such data are available and the objective is not to understand the drivers of job loss and risk or predict out of sample job losses.

39 This could be due to the well-known Lucas critique. In particular, dynamic effects may alter causal coefficients over time.

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