participation trends becomes important to fully understand labor market dynamics during the crisis (ILO, 2020b; Abraham, 2020; Hamermesh, 2020).

Several efforts have been made over the last months to address these challenges. Some researchers have complemented administrative and survey data with online vacancy data that record information in real-time and are available with short time lags (Kahn et al, 2020; Campello et al., 2020; Hensvick et al.; 2020). Kong and Prinz (2020) and Goldsmith-Pinkham and Sojourner (2020) use Google search data to predict UI claims in the United States with the objective to reduce the time lag with which this information becomes available. Other authors have leveraged private sector data, specifically payroll data (Cajner et al., 2020), data from a time and scheduling software (Kurman et al., 2020) or data from daily purchases (Coibion et al., 2020). By combining these data with information from traditional data sources or augmenting these data with newly collected COVID-related information, they have been able to provide timely and detailed insights on the labor market impacts of the crisis.

Several other researchers have used newly collected data based on surveys specifically implemented to better understand the impacts of the crisis. In the United States, Bick and Blandin (2020) fielded a survey that follows a similar structure to the CPS but that generates more timely estimates. Brynjolfsson et al. (2020) used Google Consumer Surveys to collect two waves of survey data in April and May 2020. Bartik et al. (2020) focused on firms and collected data from approximately 5,800 businesses using an online survey. Online surveys were also used in the United Kingdom (Gardiner and Slaughter, 2020) and in Belgium (Baert at al. 2020) to collect data from workers, and in Denmark (Bennedsen et al., 2020) to collect data from firms. Finally, some researchers implemented multi-country online surveys. Adams-Prassl et al. (2020) covered Germany, the United States and the United Kingdom, while Belot et al. focused on China, Japan, the Republic of Korea, the United States, the United Kingdom, and Italy.

# **2.2 Evidence on employment impacts and the role of policies**

Combining data from surveys, administrative and real-time sources, the ILO estimates large drops in employment due to the pandemic. To address the data and methodological challenges emerging from the crisis, the ILO developed what they refer to as a “nowcasting” model, which provides real-time statistical prediction based on a multiplicity of traditional and non-traditional data sources (ILOa, 2020). Based on this model, the ILO estimates that between April and June 2020, Europe alone experienced a decline in hours worked equivalent to 37 million full-time jobs compared to the last quarter of 2019. Projections for the second half of 2020 show that even in the most optimistic scenario, hours worked would still be far from pre-COVID levels. The OECD projects unemployment in OECD countries to be at 11.5% in mid2020, twice the level at the end of 2019. The projections for the rest of the year still show unemployment rates well above the pre-outbreak levels, with the most optimistic scenario suggesting levels comparable those recorded during the peak of the Global Financial Crisis (OECD, 2020).

These significant drops in hours worked are the result of different labor market adjustments in various countries. ILO (2020c) shows that working hour losses were not due to significant job losses in the United Kingdom and in Korea, as the vast majority of workers were still able to keep their jobs even if working fewer or no hours.

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