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2: author: Laliotis,Ioannis
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4: author: Testaverde,Mauro
5: author: Tzimas,Giannis
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docna: 0: docna: Reacting Quickly and Protecting Jobs : The
            Short-Term Impacts of the COVID-19 Lockdown on the Greek
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docty: Policy Research Working Paper
subtopic: Rural Labor Markets,Employment and Unemployment,Labor Markets,Climate Change Mitigation and Green House Gases,Social Development & Poverty
teratopic: Rural Development,Social Protections and Labor,Environment,Poverty Reduction
abstracts: cdata!: This paper uses administrative, survey,
            and online vacancy data to analyze the short-term labor
            market impacts of the COVID-19 lockdown in Greece. The
            analysis finds that flows into unemployment have not
            increased; instead, separations were lower than would have
            been expected given trends in recent years. At the same
            time, employment was about 12 percent lower at the end of
            June than it would have been without the pandemic. The
            interrupted time-series and difference-in-differences
            estimates indicate that this was due to a dramatic slowdown
            in hiring during months when job creation typically peaks in
            normal years, mostly in tourism. Although the reasons for
            these patterns are not formally tested, the analysis
            suggests that the measures introduced to mitigate the
            effects of the crisis in Greece played an important role.
            These measures prohibited layoffs in industries affected by
            the crisis and tied the major form of income support to the
            maintenance of employment relationships.
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            Short-Term Impacts of the COVID-19 Lockdown on the Greek
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display_title: Reacting Quickly and Protecting Jobs
            : The Short-Term Impacts of the COVID-19 Lockdown on the
            Greek Labor Market
listing_relative_url: /research/2020/08/32316987/reacting-quickly-protecting-jobs-short-term-impacts-covid-19-lockdown-greek-labor-market
topicv3: Labor Markets m1326488 855,Social Insurance m1326950 751,Climate Change Mitigation m1327079 579,Jobs m1503931 2670,Unemployment m1504051 2747
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model_extractions: Policy Research Working Paper 9356

# Reacting Quickly and Protecting Jobs

The Short-Term Impacts of the COVID-19 Lockdown on the Greek Labor Market _Gordon Betcherman Nicholas Giannakopoulos Ioannis Laliotis Ioanna Pantelaiou Mauro Testaverde Giannis Tzimas_ Social Protection and Jobs Global Practice August 2020
Policy Research Working Paper 9356

# **Abstract**

This paper uses administrative, survey, and online vacancy data to analyze the short-term labor market impacts of the COVID-19 lockdown in Greece. The analysis finds that flows into unemployment have not increased; instead, separations were lower than would have been expected given trends in recent years. At the same time, employment was about 12 percent lower at the end of June than it would have been without the pandemic. The interrupted time-series and difference-in-differences estimates indicate that this was due to a dramatic slowdown in hiring during months when job creation typically peaks in normal years, mostly in tourism. Although the reasons for these patterns are not formally tested, the analysis suggests that the measures introduced to mitigate the effects of the crisis in Greece played an important role. These measures prohibited layoffs in industries affected by the crisis and tied the major form of income support to the maintenance of employment relationships.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mtestaverde@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Reacting Quickly and Protecting Jobs: The Short-Term Impacts of the COVID-19 Lockdown on the Greek Labor Market**<sup>1</sup>

Gordon Betcherman School of International Development and Global Studies, University of Ottawa Nicholas Giannakopoulos Department of Economics, University of Patras Ioannis Laliotis Department of Economics, City, University London Ioanna Pantelaiou Department of International and European Economic Studies, Athens University of Economics and Business and World Bank Mauro Testaverde World Bank Giannis Tzimas Department of Electrical and Computer Engineering, University of Peloponnese

**Keywords:** Greece, COVID-19 pandemic, labor market impacts **JEL codes:** J21, J60, J68

> 1 Corresponding author: Mauro Testaverde, <u>mtestaverde@worldbank.org. The authors would like to</u> thank Athina Raftopoulou for insights and feedback while developing this paper. The authors would also like to acknowledge the contribution of Spyros Ntouroukis in collecting and organizing online job posting data.
# **1. Introduction**

The COVID-19 pandemic has had dramatic consequences for economies and labor markets around the world. The pandemic has been unique in triggering both supply and demand shocks. To contain the spread of the virus, governments introduced various confinement and lockdown measures that shut down businesses and removed workers from their jobs. At the same time, there have been substantial declines in demand despite large government transfers to firms, workers, and households in many countries. According to the World Bank’s June forecast, global GDP is expected to fall by 5% in 2020, with the decrease projected at 7% in the advanced economies (World Bank, 2020). The ILO estimates that the decrease in labor demand in the second quarter of 2020 compared to the last quarter of 2019 will be equivalent to the loss of about 400 million jobs worldwide (ILO, 2020c). The OECD expects that overall unemployment in member countries will be in double digits for the remainder of 2020, more than twice the 5.3% unemployment rate in the first quarter of the year (OECD, 2020).

While a dramatic reduction in labor demand has been a consequence of the pandemic everywhere, there has been considerable variation in how different labor markets have adjusted to the shock. Take the examples of two G-7 countries that have been hard-hit by the virus. In the United States, the unemployment rate more than tripled between February and May, with 14 million workers added to the unemployment rolls in this period. Italy, on the other hand, experienced a decline in unemployment, with almost 400,000 fewer unemployed workers in May compared to February. There, the adjustment to declining labor demand has occurred through labor force withdrawal. There are also important differences emerging between countries in terms of the types of jobs and workers most affected by the crisis.

In this paper, we analyze how the labor market in Greece has been affected during the early months of the pandemic and lockdown. Greece is a particularly interesting case, both because of its situation prior to the arrival of COVID-19 and because of how the pandemic has been handled. Greece entered the current crisis just as it seemed to be finally emerging from the protracted and deep recession the country had endured since the financial crisis began more than a decade earlier. After declining every year from 2008 to 2013, Greece’s real GDP has experienced modest growth since 2014, with an annual increase of about 2% in 2018 and 2019. At the same time, labor market conditions, while still difficult, were improving. After peaking at over 27% in 2013, the unemployment rate had slowly but consistently declined to 14.3% in March of this year, the lowest monthly rate in a decade.

To this point, Greece has been quite successful in holding the pandemic in check. While many neighboring countries suffered rapid escalations in cases and deaths, and severe burdens on health care systems, for the most part Greece has been able to avoid difficulties of such magnitude. As of mid-July, Greece had fewer than 4,000 confirmed cases which, on a per capita basis, is roughly one-twentieth of the rate in Spain and one-tenth of Italy’s rate. Fewer than 200 deaths have been attributed to the virus, about one-thirtieth of the rate in Italy and Spain. These numbers reflect the swift response of the government after the first case was confirmed on February 26. After that, and within a relatively narrow time window, closures shut down public events, schools, workplaces, travel, and public gatherings, and finally a general stay-at-home 2
order was implemented on March 23 (eventually lifted on May 4). The government was also very active in introducing a series of measures to help employers and workers weather the economic consequences of the lockdown. These included various forms of tax and rent relief for businesses, unemployment benefit extensions, financial support and social insurance coverage for employees whose contracts have been suspended, financial support for the self-employed, and prohibitions on dismissals for businesses shut down by state order.

Nonetheless, as elsewhere, the pandemic and lockdown are having major economic impacts in Greece. The Hellenic Statistical Authority (ELSTAT) reports that on a seasonally adjusted basis, GDP declined by 1.6% in the first quarter compared to the fourth quarter of 2019.<sup>2</sup> In its Summer Forecast, the European Commission predicts that economic activity will decrease by 9% in 2020.<sup>3</sup> In this situation, a significant deterioration in labor market conditions would be expected.

The main empirical contribution we make in this paper is to describe in some detail how the Greek labor market has evolved in the first few months of the pandemic. Relying on a range of sources including administrative data, survey data, and data from online job posting sites, we document the drop in employment following the imposition of the lockdown and the subsequent flat employment trend through the first months that followed. Two things are particularly noteworthy about the patterns we have observed. First, while Greece did not experience the major declines in employment that some other OECD countries did in March, April, and May, these are normally months when employment growth is substantial in the heavily seasonal Greek economy. Second, employment in these months in 2020 differed from the story in previous years not because the lockdown fueled large numbers of separations but rather it choked off new hiring in what should have been expansionary months. In fact, compared to recent years, separations were comparable or even lower. We attribute this largely to the regulations and wage subsidies introduced by the government that were designed to minimize job loss.

Indeed, one of the messages from our analysis is that policy choices help to explain how the labor market has responded differently to the pandemic shock in different OECD countries. A particularly relevant distinction is between countries, like Greece, that have primarily linked their support to the maintenance of the employment relationship through dismissal restrictions, wage subsidies, and short-term compensation and those countries that have largely let layoffs occur and supported workers through unemployment benefits and cash transfers. Highlighting this link between policy choices and labor market outcomes is a second contribution of this paper.

The remainder of the paper is organized into the following sections. In section 2, we review the early evidence on how the COVID-19 pandemic has affected labor markets in OECD countries. We note how a roughly similar shock has translated into different outcomes across countries and highlight the role of government mitigation policies in shaping those outcomes. Section 3 turns to the pandemic in Greece. It describes the measures introduced by the government to control the spread and to compensate

> 2 The relevant ELSTAT press release can be found here.

> 3 The Summer 2020 Interim forecast is <u>here.</u>

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firms and workers, and it uses mobility data to document the evolution of the lockdown. In section 4, we describe the different data sources and methods used for our labor market analysis. That analysis is presented in section 5. It tracks employment and unemployment trends during the lockdown, with an emphasis on how the overall picture has been shaped by the dynamics of hirings and separations. Finally, in section 6, conclusions are presented as well as some key research questions moving forward to understand the impact of the pandemic on the Greek labor market.

# **2. Literature Review**

## **2.1 Challenges related to data and measurement**

The COVID-19 pandemic has affected entire economies and labor markets at a very fast pace, requiring policy makers to have access to up-to-date information to design suitable and timely policy responses. However, the disruptions caused by the pandemic and the speed with which the crisis has unfolded have shown that the methods and sources normally used to track labor market outcomes may have significant shortcomings in this context. For example, even gold standard surveys designed to provide up-to-date information, such as the Current Population Survey (CPS) in the United States, may not be sufficient to keep up with the fast spread of the virus and its disruptive impacts. Administrative data released with shorter time lags, as in the case of UI claims in the United States,<sup>4</sup> have also shown to have shortcomings that limit their effectiveness in timely and comprehensively informing policy responses during the pandemic (Cajner et al, 2020).

Furthermore, social distancing and other transmission prevention measures have affected several data-related activities around the world with potential impacts on data quality and reliability. ILO (2020b) shows that data collection, supervision, cleaning and analysis have been affected in several countries. Adjustments in survey instruments, data collection methods and weighting schemes have become necessary to address issues related to low response rates and non-random patterns in nonresponses. Even with these adjustments, response rates have dropped in several cases. The United States Bureau of Labor Statistics documents that the response rate for the 2020 May Establishment Survey was 69%, compared to a 75% average between March 2019 and February 2020. The corresponding figure for the household survey in May 2020 was 67%, compared to the 82% average over the 12 months ending in February 2020 (BLS, 2020). Furthermore, focusing on the nature on non-responses in the March and April 2020 rounds of the CPS, Montenovo et al. (2020) show that the drops in responses in these two months were not random.

The specific disruptions emerging from the crisis have also implied that standard labor market definitions may not be sufficient to fully capture labor market dynamics under the pandemic. For example, given the different forms of mobility restrictions and social distancing measures currently in place, variations in unemployment may be misleading. In fact, in the COVID-19 era, slow increases in unemployment may coexist with significant job losses. This is because non-employed people, despite being interested in working, might not be actively looking for a job as a result of restrictions on economic activities or the perceived risk of contracting the disease at work. As such, going beyond the analysis of employment, unemployment and labor force

> 4 Official estimates on UI claims are released 12 days after the end of the week they refer to.

4
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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As a result of this, unemployment was not greatly affected in these countries. The implications of reductions in hours worked were significantly different in Canada and the United States. In Canada, almost half of the reduction in hours worked was due to people losing their jobs. In the United States, two-thirds of the decline in hours worked was due to people losing their jobs. Among those who lost their jobs, relatively more people became inactive in Canada, while the majority became unemployed in the United States. Furthermore, these disruptions did not equally affect all workers and segments of the economy. Estimates suggest that women, migrants, young people, informal workers, and specific vulnerable sectors and occupations were particularly hit by the crisis (ILOa, 2020; OECD, 2020).

The policies introduced to attenuate the disruptions caused by the pandemic have likely played a role in the way labor markets have responded in different countries. Gentilini et al. (2020) show that since the beginning of the outbreak, 200 countries implemented more than 1,000 social protection and employment measures to address the impacts of the crisis. While most of these interventions were cash transfer programs, several countries also introduced policies specifically focused on attenuating the labor market impacts of the crisis: 64 countries provided unemployment benefits, 53 social security subsidies, 69 wage subsidies, 24 labor market regulation adjustments and 10 shorter work time benefits.

Some clear patterns have emerged after these initial months of policies’ implementation. A first group of countries has focused on policies and programs aimed at preserving existing employment relationships, often implemented through the provision of subsidies to reduce labor and other costs for employers and/or the introduction of measures to limit dismissals. New Zealand, Germany, Denmark, France, and Switzerland are all countries in which take-up rates in job retention schemes have been high. A second group of countries has focused on mitigating the impacts of the crisis on workers by expanding unemployment insurance systems. In the United States, Israel, Norway, Canada and Ireland, the unemployment insurance system has played an important role in response to the crisis. While projections, administrative data and surveys suggest that increases in unemployment have been minimal for the first group of countries, unemployment has increased significantly for the second group (Rothwell, 2020; OECD, 2020).

A large body of research in the last months has focused on better understanding the impacts of COVID on labor markets in specific countries, with particular attention to identifying groups severely affected by the crisis and jobs at risk. An increasing number of papers has also focused on the impacts of the pandemic on small firms, on the role of policies in shaping labor market dynamics, and on potential shock-induced changes in labor market behaviors such as job search.

Studies focused on the United States suggest that significant job losses were recorded in March and April, with some initial signs of recovery in May, which however seem to have slowed down by the end of June. Evidence from both standard surveys (Béland et al., 2020a; Cowan, 2020) and private sector data (Coibion et al., 2020; Cajner et al. 2020) points to unprecedented drops in employment, increases in unemployment and declines in labor force participation. Between February and April 2020, it was estimated that at least 20 million people lost their jobs (Coibion et al., 2020; Cajner et al. 2020). These patterns have been accompanied by significant declines in job vacancies posted by firms (Kahn et al., 2020; Campello et al., 2020). Estimates based 6
on real-time population surveys document strong increases in employment and declines in unemployment during May and most of June. However, the current figures are still well below their pre-COVID levels (Bick and Blanding, 2020).

These findings are confirmed by studies focused on small businesses. Kurman et al. (2020) find that until mid-April employment in small businesses in the services sector dropped by 60%, equivalent to the loss of 18.2 million jobs. However, from mid-April to June more than half of the closed businesses reopened, resulting in 9.1 million additional jobs, mainly taken by previously furloughed workers. Bartik et. al (2020) find similar patterns and point to significant heterogeneity across sectors, with retail, arts and entertainment, personal services, food services, and hospitality reporting the largest declines. Using CPS data, Fairlie (2020) confirms that economic activities by small businesses significantly declined in April and only partially recovered in May.

Overall, the research so far seems to unanimously show that the workers hit the hardest by the crisis are women, young, low-educated (Béland et al., 2020a; Bick and Blanding, 2020; Cho and Winters, 2020; Cowan, 2020; Montenovo et al., 2020) or with an ethnic minority or migration background (Béland et al., 2020a; Borjas and Cassidy, 2020; Cho and Winters, 2020; Cowan, 2020; Fairlie et al., 2020; Montenovo et al., 2020). Nevertheless, some evidence suggests that during the initial stages of the crisis men might have been disproportionately affected (Béland et al., 2020a) and that some older workers might have chosen to go on early retirement (Coibion et al., 2020; Cowan, 2020).

These studies also explore whether social distancing measures have disproportionally impacted specific categories of workers. Findings from this research show that jobs that cannot be performed from home are at higher risk, while, jobs in workplaces classified as essential face lower risks (Béland et al., 2020a; Cajner et al., 2020; Montenovo et al., 2020). As an estimated 93% of workers around the world live in countries with some forms of workplace restrictions (ILOa, 2020), a large body of research across the world has focused on identifying vulnerable occupations, with a particular focus on jobs that cannot be performed from home and in sectors that have been severely affected by the shutdown (Diengel and Neiman, 2020; Garrote-Sanchez et al., 2020; Hatayama et al., 2020; Hicks et al., 2020; Mongey et al. 2020; Pouliakas and Branka, 2020; Saltiel, 2020). ILO (2020a) shows that while only 7.9% of workers around the world worked from home before the crisis, almost 18% are in jobs or have access to the infrastructure that could allow them to work from home in the future. This research also shows that working from home is more feasible in high-income countries (23%) than in low-income countries (13%). Focusing on Greece, Pouliakas (2020) shows that more than one-third of jobs in the Greek labor market could be performed from home.

A number of studies have tried to identify the channels driving the observed employment impacts. Using data from the United States’ Current Employment Statistics (CES), Brinca et al. (2020) try to disentangle the aggregate COVID shock in its demand and supply components. They observe that in April 2020 employment in the private sector was significantly lower than its historical average and estimated that more than 65% of this impact was due to labor supply shocks, i.e. inability of workers to perform their jobs. Kong and Prinz (2020) conclude that restaurant and bar limitations and non-essential business closures were the only transmission prevention measures that in the United States led to an increase in UI claims. Barrero et al. (2020) 7
using forward-looking firm level data, find that the COVID-19 induced shocks lead to 3 new hires for every 10 layoffs. They also project that the total number of entire working days performed from home will triple after the end of the pandemic and that between 32% and 42% of all layoffs will be permanent.

Findings of research focused on the Canadian labor market are in line with the results in the United States. Using the Canadian Labor Force Survey up to April 2020, Béland et al. (2020b) document substantial increases in unemployment (approximately 5 percentage points) and drops in labor force participation (3.7 percentage points), hours worked (1.5 percentage points) and wages (0.4 percentage points). These impacts were less severe for essential workers or workers who can work remotely, while they were more pronounced for younger and less educated workers. Differently from the results for the United States, they do not find evidence of differential effects by gender or of disproportionate impacts of the crisis on labor market outcomes for migrants.

Adams-Prassl et al. (2020) compare the impacts of COVID-19 on jobs, earnings and hour worked in three countries that have introduced different policies in response to the pandemic, i.e. the United Kingdom, United States and Germany. They find substantial differences across and within countries. Job losses in the United States and the United Kingdom were substantially higher (18% and 15%) than in Germany (5%), a country with a well-established short-time work scheme. They point out that the United Kingdom also introduced a similar scheme, which however does not allow furloughed workers to do any work for their employers, thereby potentially discouraging firms from applying. Not surprisingly, the study also finds that furloughing was more prevalent in the United Kingdom (43%) than in the United States (31%), a country that strongly relied on the expansion of unemployment benefits to respond to the crisis. The study also finds that in all countries, people who can work from home are less likely to lose their jobs. This is also the case for people with permanent contracts, fixed hours and in salaried jobs. In the United States and the United Kingdom, less educated workers and women were found to be more likely to lose their jobs during the pandemic. This is not the case in Germany. Based on a survey covering China, Japan, Korea, the United States, the United Kingdom, and Italy, Belot et al (2020) also find that young people are severely affected by the crisis in these countries.

Focusing on Denmark, another country that introduced significant measures to encourage job retention, Bennedsen et al. (2020) provide additional evidence on the strong impact of these policies in helping firms keep their workers. Estimates presented in this study suggest that the policies introduced by the Danish government contributed to a reduction in layoffs by 81,000 jobs and increase in furloughs by 285,000. Employment subsidies seem to have a stronger correlation with job retention, while the correlation is weaker for cost subsidies and the evidence for tax subsidies is mixed. The authors conclude that labor subsidies meet their objective of preserving employer-employee relationships, while the impact of the other policies is less clear.

Sweden is another example of a country that has leveraged on strong job retention interventions to respond to the crisis. Hensvick et al. (2020) study the impact of the COVID-19 crisis on job search using real-time data from the job board of the Swedish Public Employment Service. They find that between March and May employers posted 8
40% fewer vacancies. The drops were significant in sectors such as hotels and restaurants, and entertainment, as well as in occupations that are more difficult to perform from home. They also find that users reduced job-search intensity and seemed to have re-directed their searches to occupations that are more likely to be performed from home and more resilient to the crisis.

Alstadsæter et al. (2020) study the impacts of COVID-19 on layoffs in Norway, a country that has strongly relied on unemployment insurance benefits to mitigate the impacts of the crisis. Their analysis based on UI claims data shows that almost all layoffs up to April 19 were temporary. Even if accounting for only 10% of the total number of layoffs, permanent layoffs generated a 1.5 percentage point increase in unemployment, a significant month-to-month variation for Norway. They also show that layoffs affect populations that are already financially vulnerable (low-income, low-educated, immigrants) and are more common in jobs that require physical proximity, especially in the initial phases of the crisis. Similarly, they find that in the early stages of the crisis, the impacts were mostly felt by women and young workers, but as time passed men and older workers were also significantly impacted.

The Korean experience also provides interesting insights as the government mainly relied on testing and tracing and less on lockdowns to contain the spread of the virus. Aum et al. (2020) find that a one per thousand increase in infections leads to an almost 3% decrease in local employment. They compare these effects to those in the United Kingdom and the United States, where lockdowns were introduced, and note that employment losses were almost double in these countries. Shedding light on the channels driving these results, the authors find that employment losses were mainly due to a slowdown in hiring by firms and to transition of workers out of the labor market rather than to unemployment. The authors note that at the time of publication, the Korean government had not implemented any public furlough scheme. The paper also shows that employment losses were mainly experienced in small businesses (less than 30 employees), and that the workers with the highest probability to lose their jobs were less educated, young, employed in low-wage occupations, with temporary contracts, or self-employed. Men were also more affected than women. With the exception of the gender results, these results are similar to those found by other studies for the United States and the United Kingdom.

# **3. COVID-19 in Greece: Evolution and measures taken**

## **3.1 The spread of the virus and the lockdown measures**

The first case of COVID-19 in Greece was confirmed on February 26. Figure 1 shows the trend in new cases from that date. Compared to many other countries in Europe, where cases and fatalities exploded quickly, the pandemic progressed slowly in Greece. The government has been credited with reacting quickly to the pandemic, introducing various restrictions even when cases and fatalities were quite low. For example, on March 10, before most of Europe, schools and universities nationwide were ordered closed, when there were just 89 confirmed cases and no deaths. The first virus-related death in Greece was recorded on March 12. The outbreak peaked in early April when new cases were approaching 100 per day. By April 21, there were 2,401 confirmed cases; 150 new cases, all asymptomatic, were related to one refugee facility located in Northern Greece. As of early July, Greece had around 3,500 confirmed cases and slightly fewer than 200 deaths from the virus.

9
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# **3.2 Economic impact and mitigation measures**

As expected, restrictions imposed by the government as well as the demand shock that affected most sectors resulted in a major economic slowdown with significant consequences for businesses and workers. GDP for the 1st quarter of 2020 decreased by 1.6% in comparison with the 4th quarter of 2019, while in comparison with the 1st quarter of 2019, the decline was 0.9%.<sup>6</sup> Various projections from national and international agencies estimate that GDP will shrink between 5.7% and 10% in 2020. A key factor will be declining exports, and especially tourism and shipping.<sup>7</sup> To keep the economy afloat during the pandemic, the Greek government introduced a range of measures to support affected businesses and their employees. The government mobilized an immediate aid package amounting to 6.8 billion euros (or 3.5% of GDP) for March and April and legislated an additional package of 24 billion euros in May in order to stimulate the restart of the economy in the aftermath of the crisis.

The first legislative act to support businesses (March 11) was intended to provide firms with liquidity through the extension of tax and social contribution compliance deadlines, discounts on certified tax liabilities in case they were paid in due time, and suspension of debt payments. About 800,000 firms that had been financially affected in terms of a decline in their turnover or had ceased operation by state order were eligible for these and other benefits described below, on the condition of no layoffs.<sup>8</sup> On March 18, the Ministries of Finance and Labor announced and then legislated a new package of measures. The key component was the provision of an 800-euro stipend (covering the period from March 15 to April 30, eventually extended through May) to workers whose contracts had been suspended because of the suspension of operations of their enterprise. In addition, for these workers, the government covered all social insurance contributions and all tax payments were suspended for a period of four months. The same measure was applied to freelancers, self-employed, and individual business owners with up to 20 employees.<sup>10</sup> Enterprises whose operations had been mandatorily suspended and affected employees were asked to pay just 60% of their rents for March to May.

Overall, by early May, approximately 1.2 million employees and 550,000 selfemployed and freelancers had benefited from this scheme. A one-off stipend of 600 euros in the form of a special training program was provided to specific professionals (economists/accountants, engineers, lawyers, doctors, teachers, and researchers) in April.<sup>9</sup> These occupations became eligible for the €800 financial support as of May. The budget allocation for the stipends for employees, freelancers/self-

> 6 <mark>See</mark> Table 2 <u>here.</u>

> 7 <mark>See the relevant Centre of Planning and Economic Research report</mark> <u><mark>here.</mark></u>

> 8 <mark>The list of eligible businesses was defined by virtue of Ministerial Decision by the Ministry of Finance (MoF), included business activities (sectors) per Code of Business Activity and was updated regularly during the health crisis. The most recent version of the list is provided on the MoF website.</mark>

> 9 The eligibility criteria for the freelancers, self-employed and individual business owners who were entitled to such financial support were explained in Ministerial Decision 39162 ΕΞ 2020/ GG B’ 1457/16.04.2020.

14
employed/individual businesses, and professionals is €2.36 billion, with an additional €1.36 billion for the social insurance payments.<sup>10</sup> Additional funds were allocated to benefits for unemployed workers. On March 20, a measure was introduced to extend payments of the regular unemployment benefit, the long-term unemployment benefit, and the unemployment allowance for the selfemployed by 2 months for those whose entitlement ended on March 31. The measure was then extended to cover those whose entitlement ended at the end of April and at the end of May. In addition, a lump sum stipend of €400 was introduced for 155,000 long-term unemployed individuals, registered with the public employment agency (Hellenic Manpower Employment Organization (OAED)) from April 1, 2019 who were maintaining their status until April 16, 2020 and were not receiving any other benefit from the state. The budget allocation for these measures related to unemployment benefits is about €300 million.

In terms of numbers affected and financial commitment, the government’s mitigation measures have emphasized the preservation of employment in enterprises where operations were suspended. A key condition of the benefits provided to affected businesses was that they were obliged to maintain the same headcount. In fact, layoffs in designated industries were temporarily prohibited from March 18 until the restriction was lifted on June 16.

# **4. Data and methods**

This section briefly presents the data sources, relevant indicators, and the methods that are used to assess labor market adjustments in Greece due to the COVID-19 pandemic and the relevant mitigation policies.

## **4.1. Labor market indicators**

We report monthly estimates from the Labor Force Survey (from ELSTAT) on the labor force participation rate, employment to population ratio, and unemployment rate for the periods January-April 2019 and January-April 2020 to see differences in trends before and during the lockdown.<sup>11</sup> We also use the LFS data to identify how other aspects of the Greek labor market have been affected by the COVID-19 crisis. In addition, to assess the degree of labor market slackness, we calculate the extended labor force indicator which is simply the active labor force plus the “potential additional labor force” (PALF), which takes into account persons seeking work but not immediately available and persons available for work but not seeking work.

> 10 <mark>Information regarding fiscal responses to the economic fallout from the coronavirus was provided by the Bruegel data sets; see</mark> <u><mark>here.</mark></u>

> 11 It should be noted that the pandemic and mitigation measures affected the LFS data collection process, to some extent. From mid-March 2020 onwards, the LFS data collection switched from a blended style of personal and telephone interviews to solely telephone interviews. This decreased the response rate compared to previous months, especially in urban areas. The relevant ELSTAT press release is here.

15
# **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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begins after week 9 in 2020, corresponding to the first COVID-19 case, while, for separations, the exposure period is after week 12 when layoffs were restricted by the government.

We use a difference-in-differences (DiD) approach, with the identifying assumption being that control and treated weeks move on parallel trends before the exposure period. This is tested visually as well as by including leads of the treatment for a sufficient number of weeks before (Autor, 2003; Cookson and Laliotis, 2017). The model is the following: where m is the number of hires (or separations) in week t (t=1,...,21) for group i (i=0,1), Treat is a binary indicator equal to 1 if the week is observed in 2020 and 0 if observed before (average of weeks 2018-2019), Post is a dummy switched on during the exposure period for both groups (week 9 or 12) and e is an error term. In this quasiexperimental setup, the coefficient of interest is the one associated with the interaction of treatment and exposure period indicators, i.e., α3. As the lockdown was lifted on May 4, we restrict this analysis for weeks 1-21 so our DiD estimates are not affected by increased labor market activity due to relaxing restrictions. Under the pre-exposure parallel trends assumption, the estimated DiD coefficients will indicate the short-term labor market impacts of COVID-19. As in (1), the model is estimated using negative binomial regressions.

# **4.4. Online vacancies**

Daily data on the number of vacancies posted online are extracted from the two most popular job search portals in Greece. In order to achieve a wide coverage of the market, we use Alexa’s ranking, which is web traffic data-based metric.<sup>14</sup> An automated data acquisition mechanism was set up to scrape and store daily information on job postings from all selected portals. Extracted data were pre-processed (e.g., string cleaning, language detection, avoid multiple entries per job ad and harmonization of company name and sectoral affiliation) before being used for the analysis. Advanced machine learning techniques were employed for deduplication. After deduplication, a total of 17,812 job vacancies were collected. Most job vacancies cover occupations such as sales and purchasing agents and brokers, administrative and specialized secretaries, administration professionals, transport and storage laborers, and information and communications technology professionals. A combination of Natural Language Processing and Name Entity Extraction/Recognition methods was adopted to extract the core information from the job postings. Examples of job posting fields extracted through this process include job title, job description, job category, location, job type, contract type, experience, qualification, employer, employer type, firm location, firm size. The extracted data cover the period from January 2020 through June 2020.

> 14 The the two highest-ranked job portals according to Alexa (www.alexa.com) are kariera.gr and jobfind.gr. Alexa’s traffic data take into account websites’ unique visitors and page views. A recent assessment conducted by Cedefop also lists kariera.gr and jobfind.gr among the top private job portals in Greece based on different sources, including a study conducted by ELSTAT in 2017 and based on number of advertisements, number of monthly visitors and Alexa ranking (Cedefop, 2018).

17
## **4.5. Job search and finding employment**

One additional question we investigate is whether the lockdown and economic slowdown may have reduced job search activity. To answer this question, we use individual-level data from the quarterly LFS to estimate the probability that those not working in a specific quarter were actively searching for a job during that quarter. The estimation sample consists of jobless individuals observed in the first quarters of 20172020 who lost their jobs over the previous two years. We estimate the following: Prob(Ui=1) = a + Y<sup>2020</sup> ×[b<sup>q</sup> Q<sup>q</sup> i + γΧi]+ εi (3) where U indicates the i-th non-employed jobseeker, Y<sup>2020</sup> is a dummy indicator which takes the value of 1 for the first quarter of 2020 and 0 for first quarters of earlier years, Q is a vector of quarter dummies (q=1,...,8) and X is a vector of observable individual characteristics, i.e. gender, age, country of birth, education, region and sector of economic activity in the individual’s last job, as well as indicators for the reason they stopped working (i.e. laid-off, contract termination, and other reasons).

In addition, we estimate the probability of finding employment during the first quarter in each of 2017, 2018, 2019, and 2020 for those were out of employment prior to that quarter.

Prob(Ei=1) = α + Y<sup>2020</sup> ×[β<sup>t</sup> T<sup>t</sup> i + δZi]+ ηi (4) where E indicates the i-th individual entered into employment, Y<sup>2020</sup> is a dummy indicator which takes the value of 1 for the first quarter of 2020 and 0 for first quarters of earlier years, T is a vector of year dummies (t=2018, 2019, 2020) and X is a vector of individual characteristics, i.e. gender, age, country of birth, education, and region. Both models in (3) and (4) use a probit link function.

# **5. Analysis of impacts on the labor market**

## **5.1. Labor market indicators**

Table 1 shows non-seasonally-adjusted monthly estimates of the main labor market indicators from the LFS since the onset of the pandemic. The February figures show the improvement in the Greek labor market during the period prior to the pandemic.<sup>15</sup> Most notably, the number of workers unemployed in February 2020 was 16.9% lower than in 2019, corresponding to a nearly three percentage point drop in the unemployment rate (from 19.8% to 17%). On the eve of the pandemic, unemployment had been falling compared to the previous year and there had been a modest increase in employment.

The data for March and April present the evolution of labor market conditions while the lockdown was in place. Normally these are months when seasonal factors lead to job creation and an improvement in labor indicators in Greece. In 2020, employment numbers rose very slightly in March and April, less than they had in 2019. The unemployment figures are interesting. Between February and March 2020, the number of unemployed actually decreased by 13.8%, larger than the decrease in 2019.

> 15 Disaggregated trends in the main labor market indicators for 2018Q1, 2019Q1, and 2020Q1 are in Appendix Table A.1.

18
However, as the lockdown continued in April, unemployment numbers rose by 9.8%. Yet unemployment in April 2020 was still 14.3% lower than it had been a year earlier.

**<u>Table 1. Main labor market indicators, February, March, and April 2019 and 2020</u>**

||<br>February|<br>March|<br>April|<br>% change|<br>(monthly)|
|---|---|---|---|---|---|
||[1]|[2]|[3]|[2] vs [1]|[3] vs [2]|
|||2019<br>||||
|[4] Employed<br>[5] Unemployed<br>[6] Inactive|3758.9<br>928.0<br>3261.8|3846.3<br>844.0<br>3254.4|3884.3<br>852.6<br>3203.9|2.3<br>-9.1<br>-0.2|1.0<br>1.0<br>-1.6|
|||2020||||
|[7] Employed<br>[8] Unemployed<br>|3779.2<br>771.6|3813.0<br>665.4|3839.3<br>730.3|0.9<br>-13.8|0.7<br>9.8|
|[9] Inactive|3353.1|3423.1<br>% change (ann|3329.4<br>ual)|2.1|-2.7|
|[7] vs [4]|0.5|-0.9|-1.2|||
|[8] vs [5]|-16.9|-21.2|-14.3|||
|[9] vs [6]|2.8|5.2|3.9|||

Source: Labor Force Survey (EL.STAT.) Notes: Seasonally unadjusted estimates for persons 15-74 years old.

In part, the falling unemployment rates reflect higher inactivity, as jobless workers were less likely to search for work. Table 1 confirms that more workers were inactive in February 2020 than one year earlier. The pandemic accentuated this trend: in March inactivity increased by 2.1%. However, labor force participation statistics need to be carefully interpreted while the lockdown is in place since some people who are counted as inactive might still have some attachment to the labor force. This is evident when we consider the “potential additional labor force” (PALF), which includes those “seeking work but who are not immediately available” and those “available for work and wanting to work but not currently seeking work” (Hornstein et al., 2014). According to LFS data, the size of the PALF increased by 40% in 2020Q1 compared to 2019Q1, and by 72% compared to 2019Q4.<sup>16</sup> This suggests that lockdown measures increased the underutilization of labor, with growing numbers awaiting recall, unable to look for jobs because of the lockdown, or discouraged by the lack of new job openings. As economic activity resumes, these marginally attached workers may be more likely to (re)join the labor force.

# **5.2 Unemployment claims**

Figure 4 shows the monthly number of recipients of unemployment insurance benefits. The top panel shows overall beneficiaries, demonstrating a pattern which reflects the seasonal character of the Greek economy. The bottom panel shows that new claims for unemployment insurance benefits increased slightly in February and March 2020, and then tripled in April 2020. However, in May, initial claims moved back to a level and trend similar to that of earlier years.

> 16 See ELSTAT Table 10 <u>here.</u>

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**Figure 4. Unemployment insurance benefit recipients and new monthly claimants**

<!-- Start of picture text --> 300,000<br>2017 2018 2019 2020<br>280,000<br>260,000<br>240,000<br>220,000<br>200,000<br>180,000<br>160,000<br>140,000<br>120,000<br>100,000<br>80,000<br>60,000<br>40,000<br>20,000<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>120,000<br>2017 2018 2019 2020<br>110,000<br>100,000<br>90,000<br>80,000<br>70,000<br>60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>Job seekers paid UIB<br>Initial Claims for Unemployment Benefits<br><!-- End of picture text --> Source: OAED Monthly Reports. Authors’ calculations.

# **5.3 Labor market flows**

The employment registry, ERGANI, provides a unique data source since employment levels and flows in and out of employment can be tracked on a daily basis. Figure 5 presents the day-by-day employment for 2020, from the start of the year to the end of June. The first COVID case and the introduction of the workplace restrictions are marked with vertical lines on the chart so that the employment trend can be observed with reference to these key dates. In order to provide perspective on the 2020 numbers, a counterfactual trend line is included which estimates what the 2020 20
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On the other hand, as the bottom panel of Figure 6 shows, there is no evidence that separations have increased because of the pandemic and lockdown. In fact, the actual number of separations is below what would have been expected if the 2018-19 trends had continued in 2020. This is true for layoffs, quits, and contract terminations (Appendix Figure A.1). Certainly, in the case of layoffs, this can be explained by the government measures to protect jobs by prohibiting layoffs in affected industries and by tying income support to the maintenance of employment relationships. The reduction in quits is not surprising since one would expect fewer workers to leave their jobs in a deteriorating labor market. It should be noticed that there is initial evidence of a small uptick in the number of separations at the end of the period, which may reflect the easing of the layoff restrictions.

Using monthly data on net labor market flows, we observe that the crisis has affected sectors and occupations differently. Table 2 presents the difference between 2020 and 2019 in the size of net flows (new hires minus separations) for January, February, March, and April by sector of economic activity. The results suggest that the accommodation and food sector was particularly affected by the crisis. The negative impacts were especially severe in March and April, the months in which tourism would normally be gearing up for the summer season. In March, accommodation and food services accounted for 52% of the 2020 net job decreases, relative to 2019, while by April, this share was 84%.

**<u>Table 2. Comparison of net job flows between 2019 and 2020 by sector</u>**

||Diff<br>|erence: Month<br>|2020–Mont<br>|h 2019<br>|
|---|---|---|---|---|
|NACE Rev. 2 sector of economic activity:|January|February|March|April|
|Agriculture, forestry etc.<br>|402|8|-413|-127|
|Mining and quarrying|22|-75|-164|-58|
|Manufacturing|1151|83|-5014|-482|
|Electricity, gas, steam etc.|-419|829|-42|-248|
|Water supply; sewerage etc.|-187|-188|-53|-18|
|Construction|548|-383|-1472|292|
|Wholesale and retail trade|1009|592|-7310|-8119|
|Transportation and storage|500|371|-6556|-3572|
|Accommodation and food service|3865|3415|-43120|-84491|
|Information and communication|-80|-665|-1965|447|
|Financial and insurance activities|-485|-188|-415|126|
|Real estate activities|-17|-34|-405|-769|
|Professional, scientific and technical|-634|-1303|-3000|-389|
|Administrative and support service|-688|-515|-4896|-3435|
|Public administration, defense etc.|-941|-2612|235|-260|
|Education|299|-350|-1798|214|
|Human health and social work|179|-846|-943|2038|
|Arts, entertainment and recreation|573|-728|-4684|309|
|Other service,households and extra|-119|-295|-1623|-2114|
|Total additionaljobs|4978|-2884|-83638|-100656|

Source: ERGANI and National Institute of Labor and Human Resources (NILHR). Authors’ calculations.

Table 3 presents a similar analysis by occupation. During March and April, the most affected group was workers employed in services and shop and market sales workers.

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This group accounted for two-thirds of the total drop in net job flows in April. A large number of workers in this occupation in Greece are employed in the tourism sector.

We now turn to our more formal analysis of the impact of the pandemic on labor market flows, first using interrupted time series and then difference-in-difference estimates. Regarding the former, we regress the log of total new hires (or separations) on a day-of-week, month, year fixed effects and a linear time trend, and then plot the mean residual by week and year. The results are shown in Figure 7; vertical lines are set at the weeks when the pandemic started and when restrictions on layoffs were implemented. The results confirm what we observed through the descriptive data analysis. There is a pronounced decline in new hires after the pandemic appeared and this decline started in those weeks during which new hires peaked in the pre-pandemic years. At the same time, separations were lower compared to the respective weeks of pre-pandemic “normal” years.

**<u>Table 3. Comparison of net job flows between 2019 and 2020 by occupation</u>**

|<br>|<br>Diffe<br>|<br>rence: Month<br>|<br>2020–Mont<br>|<br>h 2019<br>|
|---|---|---|---|---|
|NACE Rev. 2 sector of economic activity:|January|February|March|April|
|Legislators, senior officials and managers|-10|43|-641|-498|
|Professionals|418|-2890|-3951|990|
|Technicians and associate professionals|1970|-2018|-8894|-1362|
|Clerks|-951|-2508|-11418|-14781|
|Service workers and shop and market sales<br>workers|1333|3456|-35567|-66066|
|Skilled agricultural and fishery workers|43|-89|-417|-388|
|Craft and related trades workers|-156|491|-3285|-1452|
|Plant and machine operators and assemblers|511|-11|-7577|-3452|
|Elementaryoccupations|1860|552|-13527|-16683|
|Total additionaljobs|5018|-2974|-85277|-103692|

Source: ERGANI and National Institute of Labor and Human Resources (NILHR). Authors’ calculations.

As the data show signs of over-dispersion, Equation (1) is also estimated using negative binomial regression. Under this specification, _b1_ indicates the mean daily change on each outcome after the onset of the pandemic and _b2_ denotes the effect of layoff restrictions. Estimation results are summarized in Table 4. The interrupted time series regression estimates confirm the graphical evidence. In 2020, new hires decrease by a significant 1.12%, on average, each day during the pandemic crisis. The effect is more sizeable for part-timers (1.38%) and those in shift work (2.09%), although full-timers are the biggest group in the labor market. Moreover, it seems that the labor market responded with a slight delay, in terms of full-time hires, after the onset of the pandemic, although coefficient estimates are everywhere negative. On the other hand, separations are significantly decreased relative to pre-pandemic days. This is true especially for firings which, as already noted, were restricted during the crisis.

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**Table 4. Pandemic onset and layoff restriction effects on hires and separations:** **<u>Interrupted time series estimates.</u>**

||<br>Days since<br>pandemic|Days since<br>layoff<br>restrictions|Days since<br>pandemic|Days since<br>layoff<br>restrictions|
|---|---|---|---|---|
|Dependent variable:<br>|[1]|[2]|[3]|[4]|
|Total new hires<br>|-.0112***<br>(.0029)|-.0058**<br>(.0023)|.0002<br>(.0020)|-.0047**<br>(.0019)|
|Full-time new hires|-.0071***<br>|-.0055***<br>|.0011<br>|-.0049***<br>|
||(.0027)|(.0020)|(.0019)|(.0019)|
|Part-time new hires|-.0138***<br>|-.0056**<br>|-.0007<br>|-.0044**<br>|
||(.0031)|(.0025)|(.0021)|(.0021)|
|Shift-work new hires|-.0209***|-.0079***|-.0011|-.0049*|
||(.0038)|(.0031)|(.0028)|(.0028)|
|Total separations|-.0001<br>|-.0070***<br>|.0050***<br>|-.0099***<br>|
||(.0030)|(.0022)|(.0017)|(.0016)|
|Quits|.0020<br>|-.0087***<br>|.0060***<br>|-.0103***<br>|
||(.0028)|(.0020)|(.0016)|(.0015)|
|Firings|.0041<br>|-.0120***<br>|.0075***<br>|-.0137***<br>|
||(.0031)|(.0020)<br>|(.0016)|(.0014)<br>|
|Contract terminations|-.0031|-.0028|.0024|-.0077***|
||(.0037)|(.0031)|(.0028)|(.0025)|
|Day of week fixed effects|Yes|Yes|Yes|Yes|
|Month fixed effects|No|No|Yes|Yes|
|Year fixed effects|No|No|Yes|Yes|
|Daily time trend|Yes|Yes|Yes|Yes|
|Period covered|01 Jan 2020 –<br>30 Jun 2020|01 Jan 2020 –<br>30 Jun 2020|01 Jan 2018 –<br>30 Jun 2020|01 Jan 2018 –<br>30 Jun 2020|
|Observations|182|182|912|912|

Source: ERGANI. Authors’ calculations. Notes: Negative binomial regression estimates. Robust standard errors in parentheses. *** at 1%, ** at 5% and * at 1%.

Turning to the DiD estimates, this approach requires an identifying assumption that control and treated weeks move on parallel trends before the exposure period. To assess this, we have graphed weekly trends for total employment, hires, and separations for both time periods (see Appendix Figure A.2). In each case, the figures show that the trends move in parallel before the outbreak.

Table 5 presents the DiD results for cumulative employment, total new hires and total separations. Results for the last two variables are also presented by type of hire and separation. In all cases, the estimated DiD parameters are sizeable and highly significant confirming that the labor market impact of the pandemic has been quite severe, at least in the short-run. Cumulative employment and new hires (overall and by job type) in 2020 fell substantially after week 9, relative to the control group. For separations, the exposure period is set at week 12 when the government intervened to restrict layoffs. The associated DiD coefficients, overall and by separation type, are also sizeable and significant.

In addition to the graphical evidence confirming that employment, hires, and separations trended similarly before the pandemic onset and the layoff restrictions (Appendix Figure A.2), we also report results of an additional test. In this test outcomes are regressed on the set of controls already controlled for in Table 5, plus leads of the interaction term (Equation (2)) that range from one to five weeks before 26
the actual treatment takes place. The size and significance of those estimates will indicate how the series trended before the treatment period (Table 6).

**Table 5. Pandemic and layoff restriction effects on labor market: Difference-in-** **<u>differences results.</u>**

||DiD<br>coefficient|Treatment group<br>coefficient|Treatment period<br>coefficient<sup>1</sup>|
|---|---|---|---|
|Dependent variable:|[1]|[2]|[3]|
|Cumulative employment|-.0602*** (.0092)|.0964*** (.0034)|.0127 (.0128)|
|Total new hires|-1.196*** (.1718)|.1138* (.0624)|.5483** (.2312)|
|Full-time hires|-1.097*** (.1594)|.1171 (.0724)|.6174*** (.2218)|
|Part-time hires<br>|-1.264*** (.2009)|.1326** (.0651)|.4919* (.2524)|
|Shift work hires<br>|-1.558*** (.2978)|.0432 (.1037)|.5835 (.3558)|
|Total separations|-1.330*** (.1650)|.1539 (.1113)|.1702 (.1590)|
|Firings|-1.407*** (.2076)|.1711 (.1101)|.1748 (.2052)|
|Quits|-1.236*** (.1576)|.1507* (.0812)|.1066 (.1462)|
|Contract terminations|-1.423***(.2167)|.1481(.1702)|.2550 (.2176)|

Source: ERGANI. Authors’ calculations.

Notes: Negative binomial regression estimates. Robust standard errors in parentheses. All models include a constant and a weekly linear trend. Sample size covers weeks 1-21 (lockdown lifting) and the effective observations are 42 in all models (21 weeks; 2 groups).<sup>1</sup> Treatment period for cumulative employment and hires is week 9 onwards (pandemic onset). Treatment period for separations runs from week 12 onwards (layoff restrictions). *** at 1%, ** at 5% and * at 1%.

**<u>Table 6. Testing for parallel trends before the treatment period.</u>**

||-5 weeks<br>lead|-4 weeks<br>lead|-3 weeks<br>lead|-2<br>weeks<br>lead|-1 weeks<br>lead|0 weeks<br>lead|
|---|---|---|---|---|---|---|
|Dependent variable:<br>|[1]|[2]|[3]|[4]|[5]|[6]|
|Cumulative employment|-.008*<br>|-.002<br>|-.005***<br>|-.002<br>|-.001<br>|-.051***<br>|
||(.004)|(.001)|(.001)|(.001)|(.001)|(.008)|
|Total new hires|-.050<br>|.372***<br>|-.222***<br>|.076***<br>|-.001<br>|-1.28***<br>|
|Full-time hires|(.076)<br>-.120**<br>(.052)|(.021)<br>.486***<br>(.018)|(.021)<br>-.345***<br>(.018)|(.021)<br>.065***<br>(.018)|(.021)<br>-.086***<br>(.018)|(.157)<br>-1.05***<br>(.149)|
|Part-time hires|-.039|.289***|-.128***|.075***|.060**|-1.421***|
||(.106)|(.023)|(.023)|(.023)|(.024)|(.192)|
|Shift work hires|.199*<br>|.232***<br>|-.048<br>|.131***<br>|.082**<br>|-1.878***<br>|
||(.110)|(.034)|(.033)|(.034)|(.033)|(.288)|
|Total separations|-.148<br>|.020<br>|.197***<br>|.025<br>|.015<br>|-1.437***<br>|
||(.126)|(.017)|(.017)|(.017)|(.017)|(.149)|
|Firings|-.141|-.010|.324***|.162***|.411***|-2.050***|
||(.108)|(.020)|(.020)|(.020)|(.020)|(.179)|
|Quits|-.244***<br>(.090)|-.032**<br>(.015)|.215***<br>(.015)|.190***<br>(.015)|.094***<br>(.015)|-1.472***<br>(.154)|
|Contract terminations|-.050|.075***|.143***|-.209***|-.422***|-.983***|
||(.196)|(.023)|(.023)|(.023)|(.023)|(.174)|

Source: ERGANI. Authors’ calculations.

Notes: Negative binomial regression estimates. Robust standard errors in parentheses. All models include a constant and a weekly linear trend. Sample size covers weeks 1-21 (lockdown lifting) and the effective observations are 42 in all models (21 weeks; 2 groups).<sup>1</sup> Treatment period for cumulative employment and hires is week 9 onwards (pandemic onset). Treatment period for separations runs from week 12 onwards (layoff restrictions). *** at 1%, ** at 5% and * at 1%.

For cumulative employment, the results confirm the parallel trend hypothesis for treated and control groups before the pandemic. The estimated coefficients are not significant and remarkably low; the effect comes when specifying a zero weeks lead 27
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Equation (2) and to assess the probability that those not employed are actively seeking work. The associated marginal effects are shown in Table 7.

**-** **<u>Table 7. Job seeking during the COVID 19 pandemic</u>**

||<br>2019-2020<br> <br>|<br>2017-20<br>|20<br>|
|---|---|---|---|
||[1]<br>[2]|[3]|[4]|
|Reasons for stop working||||
|Laid-off<br>Contract termination|-.128***<br>(.034)<br>-.112***<br>(.035)<br>-.143***<br>-.144***|-.156***<br>(.031)<br>-.163***|-.139***<br>(.031)<br>-.158***|
|Other|(.023)<br>(.023)<br>-.047<br>-.046|(.022)<br>.018|(.022)<br>.022|
||(.045)<br>(.045)|(.043)|(.043)|
|Quarters since stop working||||
|0 (current quarter)|-<br>-.383***|-|-.467***|
||(.079)||(.069)|
|1|-<br>-.135***|-|-.130***|
||(.031)||(.031)|
|2|-<br>-.022|-|-.065*|
||(.043)||(.039)|
|3|-<br>-.222***|-|-.210***|
||(.062)||(.060)|
|4|-<br>-.070|-|-.134**|
||(.065)||(.056)|
|5|-<br>-.148***|-|-.164***|
||(.056)||(.054)|
|6|-<br>-.049|-|-.027|
||(.057)||(.056)|
|7|-<br>-.054|-|.028|
||(.056)||(.055)|
|Observations|3416|7637||

Source: EL.STAT, Quarterly Labor Force Survey-LFS (2017 – 2020, 1<sup>st</sup> quarter).

Notes: Reported estimates are average marginal effects drawn from a probit model (interaction effects model) and correspond to the post 2020Q1 period (additive effect). Based on the ILO definition of unemployment, the dependent variable takes the value of 1 if the individual is considered to be unemployed and 0 otherwise. Sample includes all individuals (aged 25-54) who have stopped working during the last 8 quarters (2 years). All models include controls for gender, age, country of birth, education, region and sector of economic activity of the last job. In parentheses, white heteroskedasticity corrected standard errors are reported. All estimates are weighted using the sampling weights provided by the EL.STAT. *** at 1%, ** at 5% and * at 1%.

We observe (column 2) that, for those who had been laid-off, the probability of searching for work during the first quarter of 2020 was 11.2 percentage points lower than in the first quarter of 2019. For those who were not working because their last employment contract had been terminated, the drop in the probability of searching employment was even stronger, i.e. 14.4 percentage points lower. The largest drop was experienced by those who had lost their job within the current quarter (38.3 percentage points). This suggests that the slackness of the labor market in the first quarter of 2020 mostly affected the newly jobless individuals. These results are confirmed even when additional years of first quarters are added in the model (column 4).

We now turn to the estimation results of the probability of finding employment during the 1<sup>st</sup> quarter of 2020. Table 8 presents the estimated marginal effects of the probit model described in equation (3). We observe (column 2) that the probability of finding employment in the first quarter of 2020 was 4.6 percentage points lower than during the first quarter of 2019. When additional years are added (columns 3 and 4), we 29
observe that the estimated marginal effect for the first quarter of 2020 compared to the corresponding quarter of 2017 is negative although only marginally significant. These results suggest that the slackness of the labor market in the first quarter of 2020 may well have contributed to slowing down the employment prospects of jobless individuals.

**<u>Table 8. Employment entry during the onset of COVID pandemic</u>**

|||2019|-2020|2017-|2020|
|---|---|---|---|---|---|
|Year start work||[1]|[2]|[3]|[4]|
||2020|-.045***<br>(.010)|-.046***<br>(.010)|-.017*<br>(.009)|-.016<br>(.009)|
||2019|-|-|.028***|.029***|
|||||(.010)|(.010)|
||2018|-|-|.001|.001|
|||||(.009)|(.009)|
|Fixed effects||-|Yes|-|Yes|
|Observations||3|802|79|42|

Source: Hellenic Statistical Authority (EL.STAT.), Quarterly Labor Force Survey-LFS (2017 – 2020, 1<sup>st</sup> quarter).

Notes: Reported estimates are average marginal effects drawn from a probit model (the first year serves as the reference category, i.e., 2019 and 2017 in columns 1-2 and 3-4, respectively). Based on the ILO definition of employment, the dependent variable takes the value of 1 if the individual has start working in the current employer during the last 3 months and 0 otherwise. Sample includes all individuals (aged 15-54) who have start working any month during the last 8 quarters (2 years). In all models fixed effects include controls for gender, age, country of birth, education, region. In parentheses, white heteroskedasticity corrected standard errors are reported. All estimates are weighted using the sampling weights provided by the EL.STAT. *** at 1%, ** at 5% and * at 1%.

# **6. Conclusions**

In many respects, Greece has been an interesting case for studying the COVID-19 pandemic and its impacts on employment. The virus itself has been controlled well when compared to other countries in Europe and elsewhere in the OECD. To a significant degree, this was due to a stringent lockdown quickly imposed by the government after the first cases were confirmed in late February. Even without widespread contagion, though, the pandemic has had an important economic impact, with GDP expected to decrease as much as 10% in 2020.

The timing of the pandemic and lockdown is also an important part of the Greek story, in two ways. First, COVID-19 arrived at a point when the economy seemed to be finally on a sustainable growth path after the economic crisis that had persisted for the past decade. Second, the lockdown covered a period when the heavily seasonal Greek economy, quite reliant on tourism, would normally be gearing up and creating large numbers of jobs – something that could not happen in the spring of 2020. This is important to keep in mind in order to fully understand how the labor market has been affected by the COVID-19 crisis. A final characteristic of the Greek experience was the government’s decision to mitigate the economic consequences of the crisis by introducing regulatory and income support measures to maintain employment relationships. This has had an important effect on how the lockdown and the reduced labor demand have played out in the labor market.

We use administrative, survey, and online vacancy data to analyze how employment in Greece was affected during the first few months of the COVID-19 pandemic and 30
lockdown. Our main findings are the following: First, in the early months of the lockdown, labor force participation and unemployment fell, while there was very little change in employment levels. Second, job search activity declined, both because of continued attachment to employers who had suspended operations and because of almost no hiring activity. Third, by the end of June, we estimate that (registered) employment was 11.9% less than it should have been, based on trends from the previous two years. Fourth, this lost employment was entirely due to the sharp decline in hiring activity in the first few months of the crisis. This is evident from both the administrative and online vacancy data. As noted above, the early months spanned a period when seasonal activities, especially related to tourism, would normally be expanding and this needs to be taken into consideration in assessing the impact of the crisis. Most of the “missing” jobs thus far in 2020 have been in accommodation and food, which reflects the pandemic’s effect on tourism. Fifth, and somewhat unexpectedly, separations to the end of May were _lower_ than would be predicted based on the trends from recent years. This almost certainly was due to the government’s measures to protect existing employment relationships. This was done through a prohibition of layoffs in industries affected by the crisis and by tying the major form of income support to the maintenance of jobs.

To sum up, the analysis points to the important role that policy has played in determining how the Greek labor market has adjusted to the pandemic and lockdown. The measures put in place by the government to mitigate the effects of the crisis on employers and workers emphasized job protection. The decreased labor demand, then, translated into a downturn in hiring rather than increases in separations that would lead to higher unemployment. In this respect, Greece has been similar to some other European countries that have adopted measures to avoid layoffs. This stands in contrast to some other countries, like the United States and Canada, where unemployment rose quickly as policies emphasized income support more than job protection. Of course, it is still far too early to assess the efficacy of the different approaches. However, at least in the short run, the policy stance adopted by Greece and others to maintain employment relationships where possible seems to have had positive attributes.

We are still, of course, in the early days when it comes to understanding how COVID19 is affecting labor markets in Greece and elsewhere. The analysis in this paper largely covers just the lockdown period and does not include analysis of what is happening to employment as Greece emerges from the lockdown and implements a “new normal,” which may or may not include further lockdowns. So, there is an important research agenda going forward.

In the next stage of our research, we plan to assess three topics. First, an in-depth examination of how the impacts of the pandemic and lockdown were distributed across different types of workers, different occupations and industries, and different parts of the country. Second, an updated analysis of the labor market and employment relations adjustments as Greece emerges from the lockdown and as the mitigation measures are phased out. Third, the medium-term impacts of the mitigation strategies based on maintaining employment relationships rather than income support for workers who have lost their jobs. This last issue is particularly relevant for the ongoing debate in labor market policy about protecting jobs vs. protecting workers.

31