# **4.2. Unemployment claims**

As an indicator of the evolution of unemployment, we present data from OAED, the public employment agency, on the number of unemployment benefit recipients and the new claims for benefits covering the period from January 2017 to May 2020.

# **4.3. Labor market flows**

Administered by the Ministry of Labor and Social Affairs (MoLSA), ERGANI is the national employment registry in Greece and covers all registered employers who contribute to the Social Security System. The ERGANI monthly reports provide daily information on labor market flows in the private sector, and we use the reports from January 2018 through June 2020. More specifically, the data we analyze covers new hires, overall and by type (full-time, part-time and shift work), and separations (layoffs, quits and contract terminations). On a monthly frequency, labor market flows are disaggregated by gender, age and region. In addition, we disaggregate these flows by occupation (2-digit) and sector of economic activity (2-digit) for the periods JanuaryApril 2019, and January-April 2020.<sup>12</sup> Using ERGANI daily data, the change in daily flows since the onset of the pandemic and the government restriction on layoffs can be analyzed through a simple regression framework. More specifically, we adopt a single group interrupted time series analysis, in order to compare how outcomes change between the pre-pandemic period and two post-pandemic sub-periods, i.e., one since the onset of the pandemic and one since the government intervention to protect jobs: yt = a + b1(ct×St) + b2(ct×Rt) + Wt<sup>d</sup> + ct + ut where y is the daily number of hires (or separations), S is dummy for the period after the onset of the pandemic (26 February 2020), R is a dummy switched on after the implementation of layoff restrictions (18 March 2020), c a linear daily time trend, and W is a vector of day-of-week fixed effects (i.e. d=1,...,7); u is an error term. Model (1) is estimated with negative binomial regressions and for two different sample sizes: (a) one for 2020 only, and (b) one covering the total period (2018-2020) for which ERGANI daily data are available. In the latter case, models additionally control for month and year fixed effects. Under the assumption that pre-pandemic labor market flows would have prevailed in the absence of the pandemic and the government responses to it, this method offer an approach for identifying COVID-19-related impacts on daily labor market activity.

However, changing trends before and after the pandemic onset and the related government interventions could be driven by unobserved factors. To account for such unobservables, we follow Powdthavee et al. (2019) and Metcalfe et al. (2011) in constructing a “control group” of observations based on trends from earlier years unaffected by COVID-19. More specifically, we compare the size of weekly labor market flows during weeks after Greece was exposed to the virus (“treated” group) with weekly flows for previous years (“control”) group.<sup>13</sup> The exposure period for hires

> 12 Due to data limitations in the ERGANI monthly reports, we extracted information on monthly labor market flows (January-April 2019 and January-April 2020) disaggregated by occupation (2-digit) and sector of economic activity (2-digit) from the National Institute of Labor and Human Resources (NILHR) website.

> 13 Averaging over weeks for years before 2020 also smooths out any seasonal effects.

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