# **1. Introduction**

It has been clear from the start of the COVID-19 pandemic that it would adversely impact workers worldwide. Potentially massive job losses were foreseen as resulting from closures and other public health measures, and governments responded with policies to mitigate income and job losses and shore up welfare. However, given the interplay of shocks to labor demand and supply as the crisis unfolded, targeting and calibrating benefits to those impacted or to preserve jobs that would be viable after the crisis has proven challenging (Fujita & Moscarini, 2017). The limited availability of data on jobs outcomes, especially in many low- and middleincome countries, has only added to the difficulty of designing cost-effective mitigation measures.

A spate of papers appeared early in the pandemic characterizing the likely risk factors for job loss (Mongey et al., 2020; Avdiu and Gaurav, 2020). Without data to the contrary, risk indices were based on such factors as the ability to work from home or exceptions to lockdowns for essential activities, which appeared particularly germane to the early phase of the crisis and, arguably, to developed economies. Once data became available, a literature emerged documenting the scale and distribution of realized job losses, including by gender, educational attainment, sector of employment, and income level, especially in developed countries but not excluding developing ones (Palacios-Lopez et al., 2021, Kugler et al, 2021). Some papers emphasized the supply side (e.g., Dingel and Neiman, 2020), and others considered infection-risk-induced demand shocks in rich countries (e.g., Aum et al., 2020). Later in the crisis, data and stylized facts began to accrue on how firms in developing countries have been impacted, how they have adjusted to the crisis, how impactful policy responses have been, and how their workers have been affected (Apedo-Amah et al, 2021). Yet empirical evidence on the importance of various channels of impact on job loss in developing economies, and therefore the risk of further job losses, has remained little understood.

This paper’s main contributions to the literature on COVID-19 labor market impacts are twofold. First, it adds to the evidence base on the factors most important to (formal, private sector) jobs losses as well as on how well these factors alone can explain the demographic patterns of job losses observed. Second, it provides a general methodological approach for predicting such impacts in other near-term periods and similar country contexts. To develop the evidence base, we utilize firm, labor force, and other data to identify the main determinants of job loss due to the pandemic. This includes an estimation of the empirical importance of the ability to work from home, infection risk, digital technologies, and other factors featured in the early literature, as well as a variety of key demand-side factors. This empirical estimation comprises the first major step of our methodology. In our second step, we use the empirical model from the first step to project job losses onto the pre-COVID labor force by sector, firm-size, and broad occupational category. This allows us to tally projected loss rates in the aggregate, as well as by demographic group. To further test our methodology and provide evidence on the extent to which economywide, sector, and firm-level drivers of job loss can explain the ultimate demographic trends in the data, we compare these predictions to data from a pandemicperiod labor force survey.

Finally, to enhance our methodological contribution, we provide a comparative perspective of the evidence from two upper middle-income economies — Jordan and Georgia, where the requisite data were available to link demand side shocks to workers’ attributes and occupational task content. Our country choices are also especially relevant, given that by some metrics, middle income country labor markets have been the most 2