of what we know about job losses in other segments of the labor market. Section 6 concludes with a discussion of the methodology, policy implications and directions for future research.

# **2. Related Literature**

Our work contributes to the rapidly expanding literature aiming to understand the impacts of COVID-19 on labor markets. Early in the crisis, most of the empirical studies focused on high-income countries given that real time data is more available for them. Many studies highlighted the heterogeneous effects of the pandemic on different segments of workers and traced these in part to the differential ability to work remotely (Adams-Prassl et al., 2020), but also to issues such as infection risk, labor market attachment or tenure, and policy support measures (Lee et al., 2021; Chetty et al., 2020; Montenovo et al., 2020; Dang, Huynh & Nguyen, 2020). Prassl et al. (2020), for example, find that job losses differed substantially even across rich countries. They were much lower in the initial phase of the crisis in Germany, at 5 percent, than in the U.S. (18 %) and the U.K (15 %). They also find that the pandemic has exacerbated existing inequalities across workers within countries. Chetty et al. (2020) use real-time data from private companies for the United States and show that low-wage workers are more likely to experience job losses that lasted several months compared to high-wage workers. Lee et al. (2021) also find that the negative impact on employment was larger for women, minorities, the less educated, and young people in the United States, and Albanesi & Kim (2021) find similar gendered effects on the US labor <mark>market. In addition, del Rio-</mark> Chanona et al. (2020) estimate supply and demand shocks in the U.S. in the earlier stage of the pandemic using pre- and postCOVID datasets. In Australia, the impact on unemployment and reduced working hours was in the low single digits. However, workers with up to high-school education experienced larger reductions in their labor force participation and working hours than others. Moreover, immigrants and individuals with shorter job tenure or occupations unsuitable for remote work were hit the hardest in terms of unemployment (Guvet et al., 2020). Garrote Sanchez et al. (2020) use information on essential sectors in Italy and the U.S. and the recent 2018 European Labour Force Survey (EU LFS) to identify workers in non-essential industries. They find that jobs most at risk account for 30 percent of all EU employment, and a larger share of jobs in economically disadvantaged regions tend to be vulnerable ones.

After lockdown orders had eased, some studies consider the potential shocks to labor supply arising from workers’ reluctance to work due to infection risk on the job. For example, Aum et al. (2020) show that fear of infection contributed to reductions in employment in the Republic of Korea, where the government used intensive testing and contact tracing rather than enforcement of lockdown measures. They find that workers in high-contact industries can be affected the hardest and experienced the greatest reductions in local employment.

Empirical studies on developing countries were much fewer given the data limitations. Some authors focused on anticipating both the magnitude of impacts and how they would likely be distributed by taking lessons from the developed world. Some posited that certain job characteristics raised the risk of job loss. For example, Zheng et al. (2020) and Adams-Prassl et al. (2020) estimated the degree to which people can work from home during the pandemic to mitigate workplace closures or capacity limits. Other studies relied on occupation-level data from O*NET (Dingel & Neiman, 2020; Avdiu & Nayyar, 2020) or workers’ task content information from pre-COVID labor force and skills surveys (Hatayama et al., 2020; Gottlieb et al., 2020; 5