labor market losses. Overall, the share of the working age population who reported being employed in April-June 2020 was, on average, 31 percent lower than prepandemic levels. However, as we note in the previous section, the absolute magnitudes of these initial job losses, inclusively in the earlier studies, should be interpreted with caution, as the magnitude of any recall bias associated with the framing of the prepandemic employment question in the HFPS is unknown.<sup>15</sup> Given that many countries did not have a survey in all three periods, it is possible that dynamics of certain indicators across the three time periods may be a function of the composition of the sample within each time window. To address this concern, we also consider employment estimates for a sample restricted to countries with data points in all three periods, such that the sample composition remains constant across the three time periods. We attempt this whenever possible and include results for a “country panel” in Annex 6, however, data coverage varies significantly depending on the harmonized indicator. Overall, employment trends for a panel of 31 countries are similar to the full sample (Table A6.1).

Updated estimates reaffirm that the initial impacts, as well as the pace of the subsequent gains, varied considerably across country groups. MICs experienced the largest employment losses in the initial stage of the pandemic, followed by an increase in employment levels in the second half of 2020 and through 2021. Among a small sample of HICs, the initial magnitude of job losses was much smaller, but has been followed by a much slower pace of recovery. In LICs, losses of employment were subdued, compared to those reported in MICs. There could be several contributing factors behind lower employment losses in LICs. First, the extent of restrictions to mobility and economic activity, as measured by the OxCGRT stringency index, was somewhat lower on average in LICs. In addition, LICs have a much higher share of agricultural employment and a higher share of population residing in rural areas, which have not been affected as much by mobility restrictions as those living in urban areas. Finally, the HFPS data do not directly capture changes at the intensive margin, such as changes in hours worked which have been shown to have been affected significantly (ILO

> 15One possible estimate of recall bias within the HFPS is to compare a respondent’s recalled employment status for the previous wave and their employment status reported in that wave (in countries asking both questions). Another estimate of recall bias is possible by comparing the HFPS estimate based on recall with prepandemic LFS data. This is likely to be an upper bound estimate because several other sources of error are expected to add to any recall bias when making this comparison. Using these methods, we estimate prepandemic employment levels in HFPS include recall bias less than zero, on average, in the first case (24 countries), and up to 6 percentage points using the upper bound LFS method (26 countries). Upper bound recall bias estimates remain smaller than employment losses in 2021 estimated from HFPS for 16 of the 21 countries with both data sources, suggesting evidence of unrecovered employment is quite robust (Figure A4.4).

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