as well as data about specific tasks carried out at work. For Georgia, we use the 2019 Labor Force Survey (LFS) by the National Statistics Office of Georgia (GeoStat). The LFS also provides information on employment for different socio-demographic groups defined by gender, age, education level, sector, occupations and earnings. Our final sample for all datasets includes “employed” individuals in the private sector.<sup>5</sup> In addition, we use the STEP (Skills Towards Employability and Productivity) survey for Georgia to understand workers’ amenability to working from home.

To construct variables as proxies for labor demand and supply shocks, our paper also draws from three additional sources of data. First, we use the list of essential industries developed by the Italian government in the absence of published country-specific lists. As Italy was one of the countries affected earliest, the government had made a significant effort to determine essential industries. Their list uses NACE industrial classification codes, which can be mapped to the ISIC industry classification used in the WBES. We also use occupation-level data from the Occupational Information Network (O*NET) to estimate the infection risks of workers. O*NET has information on work activities data for 775 occupations on the level of 4-digit NAICS (North American Industry Classification System). We follow WEF<sup>6</sup> and calculate scores on the extent to which workers in given occupation face infection risks based on their responses to three questions regarding exposure to disease and infection, contact with others and physical proximity to others . Finally, we proxy changes in global demand by sector using the data from UN Comtrade and FlightRadar24. For the detailed information about the construction of variable used, please refer to Appendix 1 in the <u>longer version of this</u> paper.

To complement our findings and assess the crisis impact on workers who are outside our model (i.e., not formally employed in the private sector), we utilize real-time surveys on labor markets. For Jordan, the Economic Research Forum (ERF) conducted the COVID-19 MENA Monitor Household Survey (CMMHH), a nationally representative panel survey conducted among mobile phone users aged 18-64. The baseline wave of this dataset was collected in February 2021. This is a reliable and detailed survey that is more similar in scope and design to the LFS than other surveys conducted during the pandemic, such as the World Bank’s high-frequency phone survey. For Georgia, the World Bank conducted a COVID-19 (Georgia) High-Frequency Phone Survey (GHFPS) that cover a national random sample of mobile phone users aged 18-64. Both surveys aim to collect data on the socioeconomic impacts of COVID-19 on households and individuals, including information on job and income loss.

3.2 Pre-COVID Country Context Both Jordan and Georgia had experienced relatively stable growth over the years prior to 2020. Jordan’s real GDP growth averaged 2.4 percent between 2012 and 2020 and GDP per capita reached USD 4,405.5 in 2019.

> 5 Given our purpose to predict job losses in 2020, we used LFS 2019. With a full year of repeated cross-sectional data, it contains sufficient observations, and the impacts of COVID-19 are already reflected in the 2020 LFS. Given one purpose of our paper is to develop a method for predicting jobs lost in absence of post-COVID household level data, using the 2020 dataset from Georgia would undermine this objective.

> 6 <u>https://www.weforum.org/agenda/2020/04/occupations-highest-covid19-risk/. The three factors are equally weighted.</u>

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