COVID-19 and (gender) inequality in income: The impact of discretionary policy measures in Austria

Christl, Michael; De Poli, Silvia; Kucsera, Dénes; Lorenz, Hanno Article COVID-19 and (gender) inequality in income: The impact of discretionary policy measures in Austria Swiss Journal of Economics and Statistics Provided in Cooperation with: Swiss Society of Economics and Statistics, Zurich Suggested Citation: Christl, Michael; De Poli, Silvia; Kucsera, Dénes; Lorenz, Hanno (2022) : COVID-19 and (gender) inequality in income: The impact of discretionary policy measures in Austria, Swiss Journal of Economics and Statistics, ISSN 2235-6282, Springer, Heidelberg, Vol. 158, Iss. 1, pp. 1-17, https://doi.org/10.1186/s41937-022-00084-6 This Version is available at: https://hdl.handle.net/10419/259779 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 https://doi.org/10.1186/s41937-022-00084-6 ORIGINAL ARTICLE COVID-19 and(gender) inequality inincome: theimpact ofdiscretionary policy measures inAustria Michael Christl1* , Silvia De Poli1, Dénes Kucsera2 and Hanno Lorenz2 Abstract This paper analyzes the impact of the COVID-19 crisis on household income in Austria, using detailed administrative labor market data, in combination with micro-simulation techniques that enable specific labor market transitions to be modeled. We find that discretionary fiscal policy measures in Austria are key to counteracting the inequalityand poverty-enhancing effect of COVID-19. Additionally, we find that females tend to experience a greater loss in terms of market income. The Austrian tax–benefit system, however, reduces this gender differences. Disposable income has dropped by around 1% for both males and females. By comparison, males profit mainly from short-time work scheme, while females profit especially from other discretionary policy measures, such as the one-off payment for children. Keywords: COVID-19, EUROMOD, Micro-simulation, STW, Automatic stabilizers JEL Classification: D31, E24, H24 © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. 1 Introduction The COVID-19 crisis has had a severe impact on economies all around the world, with a corresponding effect on incomes; certain economies have been affected more severely than others. The impact of the crisis not only depends on the regional development of the pandemic, but also on the country-specific exposure to certain sectors that are more likely to be influenced. Countries more exposed to tourism and to global value chains, are more dependent upon international development, while more closed economies might be less affected by the crisis. The Austrian economy is both, strongly dependent on tourism and closely linked to global value chains. Thus, the economic downturn in 2020 was greater than in other central European countries, with a drop in real GDP of 6.6%. The COVID-19 crisis has had a significant impact on the job market in 2020, thus affecting incomes on a similar scale. Various lockdowns led to severe restrictions within companies operating in different sectors. As a result, the increase in unemployment was considerably greater than trends observed in past decades and crises. In 2020, unemployment levels in Austria rose to their highest since 1946, the unemployment rate (as defined by national authorities) reached 12% during the COVID-19 crisis in 2020 compared to 7.4% in 2019. Additionally, at the peak of the crisis in April 2020, almost 30% of those in employment were transferred to short-time work (STW), meaning that their working hours were reduced substantially. In addition to the generous STW scheme (with a net replacement rate of up to 90%) which has already proven its worth in stabilizing the effect on income during the financial crisis of 2008/2009, the Austrian government implemented several discretionary policy measures to cushion the significant loss of income among households. Two one-off payments for the unemployed, as well as a special payment for families (depending on the number Open Access Swiss Journal of Economics and Statistics *Correspondence: [email protected] 1 Fiscal Policy Analysis (B2 Unit), European Commission (JRC), Seville, Spain Full list of author information is available at the end of the article Page 2 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 of children) were introduced as an additional measure to protect households’ incomes and stabilize consumption. All these developments have had a severe impact on household incomes. Given that standard survey data to analyze the impact on household income, which is a very significant socio-economic factor, are usually not available, a detailed simulation of the impact on a micro-level is highly important, not only from an academic point of view, but also from the perspective of policy-making. Our work contributes to the literature on several levels, which is very fast-growing in relation to the impact of COVID-19. Firstly, we simulate wage compensation schemes on a micro-level, using a novel methodology of labor market transitions, based on micro-simulation techniques.1 This allows us to transit individuals to both unemployment and short-time working schemes in standard models. Secondly, we add to the discussion regarding the cushioning effect of discretionary policy measures. Our paper investigates how well policy responses are able to cushion income losses in Austria, and how inequality and poverty have been affected by the COVID-19 crisis. Our focus lies on household disposable income. We will mainly focus on direct labor market interventions, as income generated from employment is the most important element. Hence, thirdly, our detailed administrative data, relating to the number of people in short-time work schemes and the scale of these schemes, allow us to model the duration and the reduction of working hours, to facilitate a more precise assessment of the impact of the COVID19 crisis on the Austrian labor market. With an additional distinction between male and female employees in STW and unemployment, we assess whether the pandemic has resulted in additional gender differences. We find that discretionary policy measures in Austria are key to mitigating the loss in income resulting from the COVID-19 crisis, especially with regard to poorer households. As a result, these measures are also crucial in fighting the inequality-enhancing effect of the COVID19 crisis and in substantially offsetting the increase in poverty caused by the crisis. We also show that these discretionary policy measures help both males and females. However, males seem to profit more significantly from STW schemes, while females from other discretionary policy measures. Our paper contributes to the literature in two ways. First, contrary to Almeida etal. (2021b) or Christl etal. (2021a) we are able to distinguish between different policy instruments. We differentiate between automatic stabilizers and discretionary policy measures. Within the discretionary policy measures, we focus on the impact of each single component: the STW schemes, the one-off payments for unemployed, as well as the one-off payment for families. Second, we introduce a counterfactual scenario that simulates a hypothetical scenario where STW schemes and other discretionary policy measures do not exist. We estimates the loss in working hours in the Austrian economy due to COVID-19 and move the corresponding amount of people to unemployment or social assistance. This allows us to get an intuition of the impact of the COVID-19 pandemic on household income in the absence of discretionary policy measures. Additionally, we are also able to analyze the effectiveness of those policy measures in stabilizing household income. This paper is structured as follows: Sect.2 briefly introduces the related literature, Sect. 3 describes the discretionary policy measures introduced, by the Austrian government to mitigate the income loss of households during the COVID-19 crisis. In Sect.4, both the methodology and the underlying administrative data used are explained in detail. Section5 presents the results, while Sect.6 concludes. 2 Literaturereview The economic literature on the impact of COVID-19 is growing rapidly: many articles investigate the consequences of the virus on inequality in different countries. Clark etal. (2020) studied how inequality was affected during the COVID-19 crisis. Using survey panel data, they investigated income inequality in France, Germany, Italy, Spain and Sweden. They found that the pandemic effect in 2020 could be divided into two periods. With the exception of Germany, relative income inequality increased in the countries investigated during the first period (January to May); however, during the second period (by September), as a result of the effect of various policy interventions, the initial increase in income inequality had been reversed. Almeida et al. (2021a) estimated the impact of the COVID-19 pandemic on household income within all EU Member States and the EU. They found that in 2020, disposable household income in the EU fell by around 9.3%. However, discretionary fiscal policy measures played a significant cushioning role, reducing the extent of income loss. They found that the average equivalent disposable income dropped by just 4.3%. They also estimated the significant effect of the tax–benefit systems in mitigating the impact of the pandemic on poverty and inequality; however, they also identified substantial differences across countries. Similarly, Cantó et al. (2021) evaluate government policy responses in April 2020 in Belgium, Italy, Spain 1 See also Cantó etal. (2021), Brewer and Tasseva (2020), Christl etal. (2021a) or Christl etal. (2021b). Page 3 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 and the UK. Additionally, Christl etal. (2021a) estimate the impact of COVID-19 related policy measures in a cross-country setup for all EU Member States, showing that policy measures were cushioning substantially the income loss and the inequality increasing feature of the COVID-19 pandemic. Besides the differences in terms of automatic stabilization of the tax–benefit system to mitigate the effect on household income, caused by the COVID-19 pandemic, it was noted that COVID-19-related policy responses differ substantially across countries, leading to rather different impacts on the income protection mechanism.2 Several studies have analyzed the effect of STW during the crisis. STW prevents many shortand long-term effects on the labor market and consequently, on household or individual income. Stevens (1997) and Davis and von Wachter (2011) argue that loss of earnings resulting from job losses are considerably more persistent and severe, when these occur during a recession. Additionally, workers forgo returns to experience, which in turn, affects their employment prospects in the future [see, e.g., Jarosch (2015)]. Specifically in relation to the COVID-19 crisis, Christl etal. (2021b) investigated the impact of STW schemes on German household income, using a micro-level approach, combined with labor market transition techniques to simulate the effect of COVID-19 on the German labor market. The impact of the pandemic was found to be significantly regressive, with a detrimental impact on the poorest households, which was almost entirely offset by automatic stabilizers and discretionary policy measures. The STW schemes and especially the one-off payment for children were found to be an effective policy in terms of mitigating any income loss, particularly among the poorest families in Germany. The effects of the COVID-19 crisis on men and women were found to be different from other economic downturns, as “standard” recessions mainly affected the economic sectors where men primarily work, whereas women work tend to work in non-cyclical sectors, such as health care or education. In the recent financial crisis in 2008, the job losses sustained by men were much higher than was the case for women. Since the current crisis has not affected not only industry, but also service occupations (accommodation and food service activities), the effect on job losses between genders has been more balanced. Nevertheless, school and daycare closures increased the need for childcare, which in turn, had a greater effect on mothers, rather than fathers. Adams-Prassl et al. (2020) show that the first few months of the pandemic had a negative impact on laborforce participation and hours worked. These effects were found to be higher for less-educated workers and women, which exacerbated pre-existing inequalities. Using a simulation model, Alon etal. (2020) show that the impact of COVID-19 is likely to further increase gender inequality, by placing a disproportionate burden on women with additional childcare duties. Since the gender distribution of the labor force is different among economic sectors, additional gender differences arise due to the feasibility of working from home. Although widely studied, there is no clear evidence of the effect of working from home. While Bloom etal. (2015) and Arntz etal. (2019) find that working from home is likely to reduce (or at least not increase) wage differences between male and female workers, Goldin (2014) and Bertrand (2018) highlight results which prove the converse. In the current COVID-19 crisis, Bonacini etal. (2020) find that working from home is easier for older, better-educated and higher-paid male workers, thereby increasing labor-income inequality. Palomino and Sebastian (2020) also estimate irregular wage losses and increasing wage inequality between males and females in four hypothetical scenarios regarding stringent policy responses (two months of lockdown, two months of lockdown plus six months of partial functioning of closed activities at 80%, 70% and 60%) across 29 European countries. However, they indicate that STW schemes are likely to compensate for the negative effect of the pandemic on increasing gender differences. 3 COVID‑19‑related policy measures inAustria In this section the main discretionary policies, introduced to fight the negative impact of the COVID-19 crisis on household income, are briefly described. All policies have been modeled in detail in the microsimulation model, EUROMOD, in order to estimate the impact of those policies on an individual and a household level. 3.1 COVID‑19 STW In order to counteract the negative economic impact of COVID-19, the Austrian government introduced a new STW scheme in March 2020, the ‘COVID-19-Kurzarbeit’ [see, e.g., RIS (2020b)]. The program allows for a temporary reduction of normal working hours and remuneration. During the STW phase, employees enjoy job security and the employed person cannot be dismissed for an additional month after the period of short-time work comes to an end. In the case of terminations for personal reasons, the employer is obligated to employ a new employee. Three phases of the program were introduced in 2020. During the first phase, COVID-19 STW was introduced 2 Similarly, Brewer and Tasseva (2020) conducted a study on the UK only, measuring the effects of policy responses from early 2020 Page 4 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 retroactively for three months, starting on March 1, 2020. During this phase it was permitted to reduce normal weekly working hours by at least 10% (up to a maximum of 90%) averaged across the STW period. Within the scheme, flexible working time reduction also allowed for a temporary reduction of up to 100% during certain weeks, provided that the average working time over the whole period of STW was at least 10%. Employees were guaranteed up to 90% (up to 1700 EUR gross monthly salary), 85% (between 1700 EUR and 2685 EUR gross monthly salary) or 80% (above 2685 EUR gross monthly salary) of their former net income, regardless of the extent of work reduction. No STW compensation was paid for any portion of salary with a gross monthly income in excess of 5370 EUR. The policy was extended for a further three-month period under the same conditions in the second phase. Should STW be requested for a further three months, employees would have to use three weeks of their vacation from the current vacation year (if they had accrued the sufficient number of days). In the third phase of COVID-19 STW, new admissible minimum and maximum working hours were introduced. The previous minimum threshold was increased from 10% to 30%. In special cases, minimum working hours below this limit could be agreed (especially during the second lockdown period which began in November). The maximum working hours were adjusted to no more than 80%. Under new rules, it was mandatory for employees to attend additional training events during quiet periods, resulting from STW. The additional training had to be agreed with the Public Employment Service Austria and could commence at any time; it could be interrupted in accordance with the employer’s labor requirements and taken up again within a period of 18 months. 3.2 One‑off payment fortheunemployed In addition to short-time work, another important discretionary policy measure was introduced, to mitigate the consequences of the pandemic: a one-off payment for the unemployed. Two separate one-time payments were introduced for eligible unemployed persons in 2020 [see, e.g., RIS (2020a)]. The first one-off payment was introduced during the period between May and August. Any person, who was registered as unemployed for at least 60 days during this period, received a one-off payment of 450 EUR. Similarly, the second one-off payment provided 450 EUR in addition to the unemployment benefit, if the period of unemployment lasted for at least 45 days between September and November. This allowance was gradually reduced according to the duration of unemployment: 300 EUR for a period of unemployment lasting between 30 and 45 days and 150 EUR for a duration of unemployment of between 15 and 30 days. Both payments were also introduced for recipients of unemployment assistance. 3.3 Special payment forfamilies Families with children received a special COVID-19-related one-off payment in September 2020. Every household with children received an additional payment of 360 EUR with their family allowance for every child living in the household [see, e.g., RIS (2020c)]. 4 Methodology anddata 4.1 Methodology To assess the impact of a severe crisis, such as the COVID-19 crisis, detailed information on household income is required. Due to the lack of up-to-date survey data, several different methods are used to forecast the impact of profound effects on the labor market at a micro-level. In the literature, two approaches are typically discussed [see, e.g., Gasior and Rastrigina (2017)]: re-weighting and modeling labor market transitions. Re-weighting of the underlying micro-data can be used to adjust the micro-data to up-to-date macro-data. This approach has the advantage of accounting not only for changes in the labor market, but also for changes in the labor market structure. So far, several papers, such as Almeida etal. (2021a) and Li etal. (2020), have taken advantage of this modeling approach to estimate the impact of the COVID-19 crisis on household income, as well as its related indicators, such as the Gini index (income inequality) and poverty. However, as argued by, e.g., Gasior and Rastrigina (2017) or Cantó etal. (2021), this approach has certain shortcomings. Firstly, the new pool of unemployed is assumed to have similar characteristics to that observed in the data, an assumption that can be disproved during the COVID-19 crisis, since its effects was driven by several lockdown measures and certain sectors were more severely impacted than others. Secondly, as regards the re-weighting approach, a detailed simulation of compensation schemes (such as STW schemes) cannot be directly taken into account. Thus, the potential heterogeneity across the income distribution of such schemes also cannot be accounted for. Therefore, other papers, such as Christl etal. (2021a), Christl etal. (2021b), Cantó etal. (2021), Brewer and Tasseva (2020) and Figari and Fiorio (2020) have simulated adjustments to the underlying micro-data, using microsimulation techniques to model labor market transitions. The basic idea is to model transitions from employment to both unemployment and other compensation schemes (such as STW schemes). Given specific individual information, both the hypothetical unemployment benefit and Page 5 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 wage compensation can be simulated and individual benefits can be estimated. This approach enables all microdata to be updated, using all available information. In this paper, we follow exactly this approach. We use detailed data of the EU Statistics on Income and Living Conditions (EU-SILC) in combination with EUROMOD3 to simulate the whole tax–benefit system of Austria. The version used is based on the policy year of 2020, combined with input data from EU-SILC 2018. Market income variables and non-simulated benefits are uprated to 2020, using specific uprating factors.4 Labor market changes related to COVID-19 are simulated, using up to date detailed administrative data on the number of persons becoming unemployed and moving to STW schemes. This information allows us to replicate labor market changes by moving individuals from one state to another. We then adjust the labor market characteristics and income of each individual, which changes the latter’s labor market status on micro-level. Additionally, we simulate the variables needed for the simulation of unemployment benefits (such as previous work history, previous wages, duration) and STW schemes (such as hour reduction, previous wages, duration). These adjustments are performed using the Labor Market Adjustment (LMA) add-on, which is a EUROMOD tool that can be used to simulate labor market transitions to employment, unemployment and monetary compensation schemes. The detailed description of the add-on can be found in the technical annex of Christl etal. (2021a).5 Using EUROMOD, we can then recalculate the whole tax–benefit system, taking into account the new labor market status of individuals that have been observed as a result of the impact of COVID-19. To identify those that transit to wage compensation schemes and unemployment, we use detailed information from the Public Employment Service Austria (AMS). These administrative data not only facilitate a detailed view of specific sectors, often argued to be a main driver of the unequal impact of the COVID-19 pandemic, but also an analysis of gender. Detailed information on the data will be discussed in Sect.4.2.1. 4.1.1 Definition ofsimulation scenarios Following Christl et al. (2021b), we base our analysis on the comparison of three different scenarios: firstly, a baseline scenario that is the 2020 policy scenario, not including the effect of COVID-19 and not including the impact of the pandemic on the labor market. Secondly, we consider a COVID-19 scenario that not only includes the simulation of related discretionary policy measures, but also the COVID-19-related adjustment of the labor market (transitions to both unemployment and STW schemes according to external, administrative information). Thirdly, we create a counterfactual scenario, in which we assume the COVID-19-related labor market shock; however, we assume the absence of the COVID19-related discretionary policy measures. This allows us to estimate the impact of discretionary policy measures in mitigating the effect on household income during the COVID-19 crisis. Let f be the tax–benefit function that depends on the tax–benefit structure (the specific policy rules in place), P as well as on the status of the labor market LM. We assume that the policy rules P can either constitute the standard rules that were in place before COVID-19, the so-called automatic stabilizers, PAS , or can include the discretionary policy measures PCOVID . The labor market condition, LM, can either be a scenario without COVID-19-related changes affecting the labor market ( LMNoTrans ) or with COVID-19-related labor market transitions ( LMTrans ). Therefore, we can define our three scenarios as follows: • Baseline scenario: f(P AS 2020 , LM NoTrans 2020 ) . • COVID-19 scenario: f(P Covid 2020 , LM Trans 2020 ) • Counterfactual scenario: f(P AS 2020 , LM Trans 2020 ) Please note that in the counterfactual scenario, we assume that instead of entering into compensation schemes, people would only have access to the traditional automatic stabilization mechanisms, such as unemployment benefits. In this scenario, we assume the same loss in terms of hours worked, as in the COVID-19 scenario. However, the impact affects less people, since individuals that become unemployed reduce their working hours to zero, while under STW schemes, individuals can reduce their working hours to a certain level (retrieved from external data). To estimate the direct COVID-19 effects PECovid in 2020, we consider the changes between the first two scenarios, focusing on both changes in the labor market and policy changes (responses): (1) PE Covid X=X  f  PAS 2020, LMNoTrans 2020  −X  f  PCovid 2020 , LMTrans 2020  3 For more information on EUROMOD, see Sutherland and Figari (2013). 4 For more details on the uprating factors, see the https:// eurom odweb. jrc. ec. europa. eu/ using eurom od/ count ryrepor tsAustrian EUROMOD Country Report. 5 See also the Summary Note For EUROMOD: Labour Market Adjustment Add-on (https://euromod-web.jrc.ec.europa.eu/sites/default/files/2021-02/ LMAAdd-On%20Documentation.pdf). Page 6 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 The function X can either constitute a certain income concept (disposable income or market income), but also indicators such as the AROP or the Gini coefficient. We then define the policy effects of the traditional automatic stabilizers (in the absence of discretionary policy measures) related to a function, X, as the difference between the first and the third scenario. Comparing the two policy effects ( PEAS X and PECovid X ) allows us to gain an insight into the impact of STW and other discretionary policy measures. 4.1.2 Automatic stabilization coefficient In crisis times, automatic stabilizers as well as discretionary policy measures play a central role in cushioning household income. To assess the income stabilizing effect of the Austrian tax–benefit system, as well as any of its individual components, we follow the approach of Dolls etal. (2012) that was also employed by Christl etal. (2021a) in a cross-country set up and by Christl etal. (2021b) for Germany and defines the Income Stabilizing Coefficient (ISC) as: where  Y D i is the disposable income change of an individual i and  Y M i is the change in the market income of the individual i. An ISC =0.8 would imply that 80% of the effect on market income is absorbed by the tax–benefit system. Following this approach, we can further decompose the effect of several tax–benefit instruments, such as taxes, social security contributions and benefits, which are typically called automatic stabilizers. Additionally, and of special interest, is an analysis of the impact of discretionary policy measures (such as short-time work and other measures, e.g., the aforementioned one-off payments for the unemployed) on the automatic stabilization mechanism of the tax–benefit system. We, therefore, define discretionary policy measures DPMi as the sum of the benefit of STW STWi , the two one-off payments for the unemployed BUNOOP i , as well as the one-off payment for children BCHOOP i : We then further decompose the ISC: (2) PE AS X=X  f  PAS 2020, LMNoTrans 2020  −X  f  (PAS 2020, LMTrans 2020  (3) ISC =1− iY D i i YM i = iY M i−iY D i i YM i (4) DPMi =STW i +BUN OOP i +BCH OOP i (5) ISC =iY M i−iY D i i YM i =iTi+SICi−BENi−DPM i i YM i where, Ti are taxes, SICi social insurance contributions, BENi benefits, DPMi are all the discretionary policy measures paid or received by an individual i. Following this notation, we are able to decompose the income stabilization to the specific tax–benefit instruments. 4.2 Data 4.2.1 Administrative data forunemployment andSTW In order to evaluate the effect of a transition to STW or to unemployment, the labor market status of all individuals are adjusted, using monthly data from the Public Employment Service Austria (AMS). The simulation of the STW is based on the data available from March until December 2020, and takes into account information relating to the number of people in STW, the normal working hours and the reduction of working hours as a result of STW across sectors and gender. As an immediate impact of the COVID19 crisis, unemployment increased. Figure1 highlights that the first few months of the crisis, in particular, were critical: in April almost 600,000 people were registered as unemployed and almost twice as many were on STW. Therefore, more than 1.5 million people or almost 40 percent of the labor force, were either unemployed or in STW. Hence, STW has succeeded in limiting the impact of the COVID19 crisis on the labor market and on unemployment. Due to the lockdown and official closures, as well as the unequally distributed home-office possibilities, certain sectors have been more significantly affected that others. To capture this effect in the simulation of labor market transitions, we include detailed information in our estimates relating to the use of STW by sector. Fig. 1 Labor force in the first year of the COVID-19 crisis. Note: Data available on 2/2021. Source: Own calculation, Public Employment Service Austria (AMS). Page 7 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 Figure2a highlights that from March until the end of the year 2020, more than half of the labor force within the sector, “accommodation and food service activities”, were either unemployed or in STW schemes. Focusing on the date, the effect of the first lockdown was greatest in this sector. At the end of April, more than 90% of people in this sector were either unemployed or in STW. The utilization of short-time work differed substantially across sectors. More than every fourth employee worked to a limited extent in the areas of “accommodation and food service activities” and “arts, entertainment and recreation”, but STW was also used to a great extent in the sectors with the highest numbers of employees, namely the “wholesale and retail trade”, “repair of motor vehicles” and “manufacturing”. Our model will take these detailed sectorial differences into account. Detailed administrative data allow us to calculate the share of the reduction in working hours, as highlighted in Fig.2band in Table3 in the Appendix. This shows that the reduction in working hours peaked during the months of lockdown. During the period from March to December, the average reduction in working hours was equal to 53%. Nevertheless, the pattern in the reduction of working hours indicates huge differences across sectors. The reduction in working hours was highest in the sectors “arts, entertainment and recreation” and “accommodation and food service activities”, recorded in excess of 66%, while the sectors “mining and quarrying” and “water supply, sewerage, waste management” reported the lowest figures at less than 40%. Due to general gender differences in employment by sector and the fact that certain sectors have been more Fig. 2 The impact of the COVID-19 crisis on the labor market by sector in 2020. Note: Data available on 2/2021. Average for the period March to December 2020. Source: Own calculation, Public Employment Service Austria (AMS). Page 8 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 significantly affected than others, our paper will also shed light on the gender differences in unemployment and STW, and the consequences on income. The gender difference in unemployment rate is highlighted in Fig.3. We can see that before the COVID-19 crisis hit the Austrian labor market, the unemployment rate was slightly higher for males than for females. This, however, changed when first lockdown measures were introduced and general unemployment increased. While in February the unemployment rate of females was about 8% and the one for males about 10%, in April the rate increased to about 13% for males and 14% for females. On the other hand, when looking at STW, administrative data reveal that the relative share of employees in STW (as a fraction of the number of employees) was higher for in the case of male employees at the beginning of the COVID-19 crisis. Nevertheless, since October female employees are on average slightly more likely to be in STW, compared to their male colleagues, as highlighted in Fig.4a. During the last year, these two effects therefore, almost cancel one another out. On the other hand, Fig.4b shows that there are gender differences in relation to the average reduction in working hours in STW. The reduction was greater in the case of female employees each month since the COVID-19 STW was introduced. 4.2.2 Estimating theduration ofSTW There is no information in the administrative data for how long individuals stayed in STW schemes. We therefore set up a model based on survival probabilities to obtain estimates for the duration of STW scheme. As shown in Fig.4a, we have detailed information on the number of individuals in STW in each month. This information is available by gender and each sector of activity. The STW started in March, and we assume that the total number of people entering in STW over the year is reached in the month with the highest share of people (April 2020). During the following months, we assume that some people managed to go back to work and no new persons entered into the scheme. Due to the second wave of COVID-19, there is a slight increase in the number of persons in STW in November and December. We assume that people entering in the schemes in November and December are employees who were already in STW in previous months. To estimate the duration in STW, we sort the months in a descending order, based on the number of persons in STW in each month. This allow us to estimate the probability to go back to work in each month. Using these probabilities, we estimate the share of people staying from 1 up to 10 months, by sector of activity and gender.6 Figure 5 shows the duration in STW of people that moved to short-time work by gender and sector. We see that related to the duration, there are no big differences Fig. 3 Unemployment rate by gender in 2020 Source: Own calculation, Public Employment Service Austria (AMS). Fig. 4 Differences in STW schemes by gender in 2020. Note: Data available in 2/2021. STW available in the year 2020 only, from March till December. Source: Own calculation, Public Employment Service Austria (AMS). 6 10-month duration is the maximum duration in 2020, since STW schemes started in March 2020. Page 15 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 Furceri etal. (2021), the observed cushioning effect of policy measures might not be long-lasting. As they have shown, the rise in inequality following after major epidemics over the last two decades (SARS, H1N1, MERS, Ebola and Zika) has been higher in episodes of greater austerity. Policy makers should keep that in mind, when the supportive measures related to COVID-19 are running out, and debt reduction will become a primary goal of policy makers. Appendix See Figs.10 and 11, Table3. Fig. 10 Net Replacement Rates by gender (Standard tax benefit model). Source: Own calculation, using EUROMOD I3.0+ Fig. 11 ISC in Austria by gender, counterfactual scenario. Note: Percentage change in equivalized original and disposable income compared to the baseline scenario by income deciles. Income deciles are based on the baseline scenario distribution of equivalized disposable income. Source: Own calculations, based on EUROMOD I3.0+ Page 16 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 Table 3 Share of working hours in short-time work on normal working hours of an employee in short-time work. Source: Own calculation, Public Employment Service Austria (AMS). Data available on 2/2021 Sector 03/2020 04/2020 05/2020 06/2020 07/2020 08/2020 09/2020 10/2020 11/2020 12/2020 Agriculture, forestry and fishing 61.2% 64.4% 53.2% 45.9% 49.6% 48.9% 49.1% 45.1% 62.4% 62.4% Mining and quarrying 29.2% 30.2% 21.9% 20.0% 30.5% 26.5% 23.0% 21.2% 24.8% 24.8% Manufacturing 41.9% 43.4% 35.5% 29.2% 25.9% 25.8% 23.7% 25.9% 31.5% 31.5% Electricity, gas, steam and air conditioning supply 44.7% 45.0% 28.7% 18.7% 51.1% 51.5% 46.5% 47.7% 66.8% 66.8% Water supply, sewerage, waste management 30.3% 29.5% 22.2% 20.0% 26.6% 22.8% 33.2% 37.3% 62.7% 62.7% Construction 40.7% 36.7% 22.1% 21.9% 31.9% 29.8% 30.5% 34.3% 39.6% 39.6% Wholesale and retail trade, repair of motor vehicles 55.1% 57.0% 35.3% 30.4% 32.9% 29.8% 30.7% 38.5% 51.0% 51.0% Transportation and storage 49.6% 52.1% 46.8% 39.3% 40.8% 32.6% 33.7% 40.8% 51.4% 51.4% Accommodation and food service activities 68.7% 85.6% 64.9% 45.5% 43.2% 37.0% 40.9% 44.7% 73.6% 73.6% Information and communication 51.8% 56.1% 51.7% 45.1% 43.3% 38.4% 37.2% 46.2% 54.2% 54.2% Financial and insurance activities 53.0% 64.2% 56.4% 50.7% 52.5% 51.7% 53.5% 54.4% 58.2% 58.2% Real estate activities 57.8% 66.7% 56.9% 51.4% 50.4% 44.3% 44.7% 50.5% 60.5% 60.5% Professional, scientific and technical activities 52.9% 54.2% 47.3% 41.4% 42.7% 38.3% 37.5% 41.9% 46.3% 46.3% Administrative and support service activities 47.0% 55.7% 45.2% 40.9% 43.4% 42.0% 42.9% 49.6% 57.1% 57.1% Public administration and defence 84.4% 75.8% 55.2% 37.5% 54.5% 47.1% 49.9% n.a. 71.2% 71.2% Education 62.6% 65.9% 51.8% 36.0% 40.2% 33..0% 30.6% 51.3% 60.8% 60.8% Human health and social work activities 59.1% 65.4% 47.9% 32.0% 36.4% 31.0% 31.9% 45.3% 47.0% 47.0% Arts, entertainment and recreation 65.9% 74.7% 72.0% 50.5% 47.2% 41.6% 46.2% 51.4% 64.8% 64.8% Other service activities 62.5% 76.1% 42.8% 34.4% 33.4% 29.5% 30.8% 32.0% 62.1% 62.1% Activities of hh as employers, and others 61.6% 86.3% 77.8% 70.2% 78.5% 76.9% 87.0% 66.7% 69.5% 69.5% Page 17 of 17 Christletal. Swiss Journal of Economics and Statistics (2022) 158:4 Abbreviations AMS: Public employment service Austria; STW: Short-time work; LMA: Labor market adjustment; EU-SILC: EU statistics on income and living conditions; ISC: Income stabilizing coefficient; CF: Counterfactual scenario; MI: Market income; CI: Confidence interval. Acknowledgements We are indebted to the many people who have contributed to the development of EUROMOD and the LMA add-on. We are especially thankful to TH, CL, AP and AT as well as to SB. The authors are grateful to the participants of the IAB Lunch Time Seminar Series “Corona – Leveller or Amplifier of Social and Economic Inequality?” for helpful comments. The content of this article does not reflect the official opinion of the European Union. Responsibility for the information and views expressed in the article lies entirely with the authors. Author Contribution Each author has contributed equally to the conception and design of the work as well as to the acquisition, analysis and interpretation of data. All authors read and approved the final manuscript. Funding The authors received no financial support for the research, authorship, and/or publication of this article. Availability of data and materials The data that support the findings of this study are available from ESTAT and AMS (Public Employment Service Austria) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of ESTAT. Declarations Competing interests The authors declare that they have no competing interests. Author details 1 Fiscal Policy Analysis (B2 Unit), European Commission (JRC), Seville, Spain. 2 Agenda Austria, Vienna, Austria. Received: 19 October 2021 Accepted: 19 January 2022 References Adams-Prassl, A., Boneva, T., Golin, M., & Rauh, C. (2020). Inequality in the impact of the coronavirus shock: Evidence from real time surveys. Journal of Public Economics, 189. Almeida, V., Barrios, S., Christl, M., De Poli, S., Tumino, A., & Van Der Wielen, W. (2021a). The impact of covid-19 on households income in the EU. Journal of Economic Inequality (forthcoming). Almeida, V., Barrios, S., Christl, M., De Poli, S., Tumino, A., & van der Wielen, W. (2021b). The impact of covid-19 on households income in the EU. The Journal of Economic Inequality, pp. 1–19. Alon, T., Doepke, M., Olmstead-Rumsey, J., & Tertilt, M. (2020). The impact of covid-19 on gender equality. NBER Working Paper No 26947. Arntz, M., Sarra, B.Y., & Berlingieri, F. (2019). Working from home: Heterogeneous effects on hours worked and wages. ZEW - Centre for European Economic Research Discussion Paper No 19-015. Bertrand, M. (2018). Coase lecture: The glass ceiling. Economica, 85(338), 205–231. Bloom, N., Liang, J., Robertsand, J., & Ying, Z. (2015). Does working from home work? Evidence from a Chinese experiment. The Quarterly Journal of Economics, 130(1), 165–218. Bonacini, L., Gallo, G., & Scicchitano, S. (2020). Working from home and income inequality: risks of a ‘new normal’ with covid-19. Journal of Population Economics, 34, 303–360. Brewer, M., & Tasseva, I. (2020). Did the UK policy response to covid-19 protect household incomes? EUROMOD Working, Paper., No EM12/20. Cantó, O., Figari, F., Fiorio, C., Kuypers, S., Marchal, S., de la Cruz, M. R., Tasseva, I. V., & Verbist, G. (2021). Welfare resilience at the onset of the covid-19 pandemic in a selection of European countries: Impact on public finance and household incomes. Christl, M., De Poli, S., Figari, F., Hufkens, T., Papini, A., & Tumino, A. (2021a). The cushioning effect of fiscal policy in the EU during the covid-19 pandemic. JRC Working Papers on Taxation and Structural Reforms No 02/2021. Christl, M., De Poli, S., Hufkens, T., Peichl, A., & Ricci, M. (2021b). The role of shorttime work and discretionary policy measures in mitigating the effects of the covid-19 crisis in Germany. CESifo Working Paper No 9072/2021. Clark, AE., D’Ambrosio, C., & Lepinteur, A. (2020). The fall in income inequality during covid-19 in five European countries. Tech. rep. Clinton, K., Kumhof, M., Laxton, D., & Mursula, S. (2011). Deficit reduction: Shortterm pain for long-term gain. European Economic Review, 55(1), 118–139. Davis, S. J., & von Wachter, T. (2011). Recessions and the costs of job loss. Brookings Papers on Economic Activity, 2, 1–72. Dolls, M., Fuest, C., & Peichl, A. (2012). Automatic stabilizers and economic crisis: US vs. Europe. Journal of Public Economics, 96(3-4):279–294. Figari, F., & Fiorio, C. V. (2020). Welfare resilience in the immediate aftermath of the covid-19 outbreak in Italy. Tech. rep., EUROMOD Working Paper. Furceri, D., Loungani, P., Ostry, J. D., & Pizzuto, P. (2021). The rise in inequality after pandemics: Can fiscal support play a mitigating role? Industrial and Corporate Change, 30(2), 445–457. Galí, J., López-Salido, J. D., & Vallés, J. (2007). Understanding the effects of government spending on consumption. Journal of the European Economic Association, 5(1), 227–270. Gasior, K., & Rastrigina, O. (2017). Nowcasting: Timely indicators for monitoring risk of poverty in 2014–2016. Tech. rep., EUROMOD Working Paper. Goldin, C. (2014). A grand gender convergence: Its last chapter. American Economic Review, 104(4), 1091–1119. Jarosch, G. (2015). Searching for job security and the consequences of job loss. Unpublished, Manuscript., Princeton University. Li, J., Vidyattama, Y., La, H.A., Miranti, R., & Sologon, D.M. (2020). The impact of covid-19 and policy responses on Australian income distribution and poverty. arXiv preprint arXiv: 20090 4037 Palomino, R. J. J. C., & Sebastian, R. (2020). Wage inequality and poverty effects of lockdown and social distancing in Europe. Covid Economics, 25, 186–229. RIS. (2020a). Gesamte rechtsvorschrift für arbeitslosenversicherungsgesetz 1977. Reschtsinformationssystem des Bundes, Arbeitslosenversicherungsgesetz 1977, Artikel IV, §66. RIS. (2020b). Gesamte rechtsvorschrift für arbeitsmarktservicegesetz. Reschtsinformationssystem des Bundes, Arbeitsmarktservicegesetz, Abschnitt 3, §37b. RIS. (2020c). Gesamte rechtsvorschrift für familienlastenausgleichsgesetz 1967. Reschtsinformationssystem des Bundes, Familienlastenausgleichsgesetz 1967, Abschnitt I, §8. Stevens, A. H. (1997). Persistent effects of job displacement: The importance of multiple job losses. Journal of Labor Economics, 15, 165–188. Sutherland, H., & Figari, F. (2013). Euromod: The European union tax-benefit microsimulation model. International Journal of Microsimulation, 6(1), 4–26. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.