Unemployment's long shadow: The persistent impact on social exclusion

Pohlan, Laura Article Unemployment's long shadow: The persistent impact on social exclusion Journal for Labour Market Research Provided in Cooperation with: Institute for Employment Research (IAB) Suggested Citation: Pohlan, Laura (2024) : Unemployment's long shadow: The persistent impact on social exclusion, Journal for Labour Market Research, ISSN 2510-5027, Springer, Heidelberg, Vol. 58, Iss. 1, pp. 1-16, https://doi.org/10.1186/s12651-024-00369-8 This Version is available at: https://hdl.handle.net/10419/308524 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/ Pohlan J Labour Market Res (2024) 58:12 https://doi.org/10.1186/s12651-024-00369-8 ORIGINAL ARTICLE Open Access © The Author(s) 2024. 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/. Journal for Labour Market Research Unemployment’s long shadow: thepersistent impact onsocial exclusion Laura Pohlan1,2,3,4* Abstract This paper studies the long-term consequences of unemployment on different dimensions of social exclusion. Based on longitudinal linked survey and administrative data from Germany and an event study analysis combined with inverse propensity score weighting, I document that becoming unemployed has lasting adverse effects on both individuals’ material well-being and their subjective perception of social status and integration, persisting even after four years. An examination of effect heterogeneity underscores that the enduring effects of job loss are more pronounced for individuals confronted with challenging labor market conditions, those with a history of repeated unemployment, and individuals with lower levels of educational attainment. Keywords Unemployment, Social exclusion, Panel data, Event study, Inverse probability weighting JEL Classification I31, J64 1 Introduction Job loss often results in a significant reduction in the economic and social well-being of individuals and their families, with adverse impacts on opportunities for social inclusion. Numerous studies have established that unemployment is associated with both physical and mental health issues (see, e.g., Cygan-Rehm etal. 2017; Stauder 2019) and that unemployment diminishes overall life satisfaction (Suppa 2021a, for example, provides an overview). Furthermore, losing employment increases the risk of social exclusion and marginalization, as documented by studies focusing on specific dimensions of social inclusion such as material standards of living, social relationships, social participation and the sense of belonging to society (see, e.g., Gundert and Hohendanner 2014; Dieckhoff and Gash 2015; Christoph and Lietzmann 2016; Kunze and Suppa 2017). The existing research literature points to a negative association between unemployment and various aspects of social integration. However, the literature suffers from two blind spots. Firstly, these studies focus on single dimensions only. Secondly, it is often not possible to definitively establish whether the observed social exclusion among the unemployed can be attributed solely to unemployment. Pohlan (2019) examines the effects of job loss on multiple dimensions of social exclusion. Based on German linked survey and administrative data and a combined inverse propensity score weighting and difference-in-differences approach, the author finds that unemployment has particularly detrimental effects on the subjective perception of social integration, access to economic resources, and an individual’s well-being. Becoming unemployed also impedes the satisfaction of psychosocial needs typically associated with employment, such as social status and enhanced self-efficacy. However, Pohlan (2019) only considers the effects for a maximum duration of one year in unemployment. In this paper, I shed light on the long-term impact of job loss on *Correspondence: Laura Pohlan Laura.P[email protected] 1 Institute for Employment Research (IAB), Regensburger Strasse 104, D-90478 Nuremberg, Germany 2 IZA – Institute of Labor Economics, Bonn, Germany 3 ZEW – Leibniz Centre for European Economic Research, Mannheim, Germany 4 Labor and Socio-Economic Research Center (LASER), Nuremberg, Germany 12 Page 2 of 16 L.Pohlan different dimensions of social exclusion, as well as the heterogeneity of these effects dependent on labor market conditions and individual resources.1 On the one hand, the material and social effects of unemployment may intensify the longer the period of unemployment lasts, as stress factors such as financial pressure, unsuccessful job applications, and psychosocial deficits might accumulate over extended periods of joblessness (Paul and Moser 2009).2 On the other hand, it could also be the case that unemployed individuals get used to their situation, leading to a reduction in the magnitude of these effects over time. However, the empirical literature on subjective well-being suggests little to no habituation to unemployment (see, e.g., Clark etal. 2008). Ultimately, some people will also find a job again over time, which could prevent and counteract the process of social exclusion. The well-being literature generally shows that reemployment improves subjective well-being. However, the extent of improvement remains ambiguous, as several large-scale longitudinal studies have found evidence of long-term scarring effects of unemployment, i.e. negative effects that persist even after returning to work (see, e.g., Clark etal. 2001; Lucas etal. 2004; Young 2012; Mousteri etal. 2018; Eberl etal. 2023).3 How effectively reemployment can heal these scars should depend, not least, on the quality of working and employment conditions (Gundert and Hohendanner 2014). In particular, long periods of unemployment can result in unstable employment relationships as well as substantial and persistent wage declines in the future (e.g., Arulampalam 2001; Böheim and Taylor 2002; Gangl 2006; Jost 2022). Moreover, unemployment may not only leave financial scars but also non-pecuniary scars, as the loss of a job can lead to increased feelings of future uncertainty as well as lower job security, work quality and job satisfaction (e.g., Dieckhoff 2011; Young 2012; Brand 2015; Hetschko etal. 2019). The contribution of this paper to the literature is threefold. Firstly, this paper provides a careful analysis of the long-term impact of unemployment on several dimensions of social exclusion based on the most recent version of the PASS-ADIAB, which links 15 waves of panel survey data with administrative data from the German Federal Employment Agency. This allows me to draw conclusions about which aspects of social exclusion - access to material resources, social engagement, and a person’s subjective perception of social integration and social status - are most affected by unemployment and which recover more quickly than others. Secondly, I delve into mechanisms by examining the heterogeneity of these effects, dependent on future employment status, general labor market conditions, and individual resources. Thirdly, this paper methodologically extends previous studies by applying a combination of inverse propensity score weighting and event study analysis, increasing the reliability of the results by making workers who become unemployed comparable to workers who remain employed across many dimensions, including individual labor market histories. In this way, I get as close as possible to determining the causal long-term effect of unemployment on social exclusion without relying on exogenous variation. In this paper, I track individuals for up to four years after becoming unemployed and show that unemployment has long-lasting effects on different dimensions of social exclusion. In terms of material resources, the findings reveal a substantial decline in disposable household income, which amounts to 20 percent after one year and decreases to 13 percent after four years. This tighter financial situation is further manifested in the deterioration of living standards among individuals who experienced job loss. They suffer from an enduring deprivation of basic goods and activities. These economic constraints, in turn, also impact the opportunities available to individuals for participating in social and cultural activities. As a consequence, individuals tend to forgo relatively costly activities such as dining out at restaurants, attending cinema or theater events. Interestingly, there are no observable changes in their memberships in social organizations like clubs over the course of the observation period. These findings are consistent with Kunze and Suppa (2017), who document strong and lasting effects of unemployment on the attendance of cultural events, cinema, and pop concerts, but no effects on active participation in sports or voluntary work. The authors even find a positive long-term effect on helping friends and neighbours. Rözer et al. (2020) also show that some dimensions of social networks, such as contact with relatives or neighbors, may actually improve with longer periods of unemployment while support from acquaintances, including colleagues, systematically declines as a result of shortand long-term unemployment. Regarding subjective assessments of social status and social integration, I find that experiencing unemployment 1 I consider transitions from employment to unemployment regardless of the reason. Therefore, the term “job loss” refers to both voluntary and involuntary termination of employment and is used interchangeably with “job termination” and “becoming unemployed”. In a sensitivity analysis, however, I focus exclusively on involuntary job termination. 2 In Germany, unemployed individuals typically receive insurance-based unemployment benefits equivalent to 60% (67% for claimants with children) of their previous net salary for a period of 12 months. After the expiration of insurance-based benefits, needy individuals receive means-tested benefits, which can lead to a considerable reduction in payments. 3 Nonetheless, a few studies in this field are questioning the scarring hypothesis (such as Rauf 2021 or Zhou etal. 2019). Page 3 of 16 12 Unemployment’s long shadow: thepersistent impact onsocial exclusion significantly reduces individuals’ perceived social status and their sense of belonging to society. While the impact on social status and social integration diminishes over time, these effects persist even four years after becoming unemployed. Notably, during this four-year period, a substantial proportion of individuals finds a job again. However, despite an increasing probability of reemployment over time, the results indicate that unemployment leaves lasting scars. When examining effect heterogeneity by categorizing individuals based on their prospects for reemployment and their resources to cope with unemployment, I show that the long-lasting impacts of job loss are amplified for individuals who face challenging labor market conditions, have more experience with unemployment periods, and have lower levels of educational attainment. In summary, this paper underscores the importance of adopting a nuanced perspective on social exclusion, one that takes into account the multifaceted nature of the experience and its enduring consequences. The rest of the paper is structured as follows. Section2 presents theoretical considerations regarding the concept of social exclusion and the consequences of unemployment. Section3 describes the data and the measurement of the outcome variables. Section4 presents the empirical identification strategy. Section5 describes the sample, and shows model diagnostics. Section6 presents the results of the empirical analysis. Section7 concludes. 2 Theoretical considerations The concepts of social inclusion and its counterpart, social exclusion, are not uniformly defined (Room 1995; Kronauer 1998; Bartelheimer 2004; European Commission 2010; Bartelheimer et al. 2020). Definitions employed in welfare research and within political contexts may display some notable distinctions, yet they also share certain core elements. Grounded in the notion of opportunity realization, the measurement of integration opportunities is based on an individual’s capacity to pursue a personally desired and socially accepted way of life (Bartelheimer 2004). Realized social inclusion depends on both an individual’s capability to act and an individual’s actual actions (Sen 1985), necessitating individuals, within the context of personal and societal constraints, to actively shape their lives in a self-determined manner. Social exclusion is a complex, multidimensional process that unfolds across various dimensions of society, including education, employment, healthcare, social services, political institutions, and interpersonal relationships. Within this framework, interrelated forms of participation, encompassing material, social, cultural, and legal-institutional dimensions can be discerned. This paper predominantly concentrates on material and social integration, which manifest particularly in access to financial resources, standards of living, and engagement in social interactions with others. In modern working societies, the chance of social integration is to a large extent determined by integration into working life (Promberger 2008). Gainful employment not only grants access to material resources, a vital prerequisite for participation in social and cultural life, but it also fulfills fundamental psychosocial needs (Jahoda 1981). These include maintaining a regular daily routine, social contacts beyond one’s immediate family, engaging in meaningful activities aligned with collective objectives, and obtaining socially recognized status. Through its multifaceted functions, gainful employment significantly shapes individuals’ perceptions of their ability to manage their lives. Given that job loss results in the forfeiture of functions that facilitate participation and may not always be adequately compensated in other life domains, unemployment is regarded as a major risk factor for social exclusion. Job loss and the resulting loss of earned income directly affect a household’s available financial resources. However, this loss can be mitigated by alternative sources of income, such as unemployment benefits or social security benefits. In addition, other household members might compensate the income loss by taking up a job or increasing working hours, or savings are touched so that becoming unemployed does not immediately translate into a reduction in the standard of living or an increase in deprivation. The extent and manner in which the standard of living is affected depends on various factors, including the degree of financial constraints and the duration of unemployment. Integration into social relationships and organizations, often fostered by gainful employment, holds a pivotal role in facilitating social participation. Work environments frequently serve as the breeding ground for social connections and networks, which may extend to personal acquaintances and friendships. Additionally, those in employment are generally better positioned than the unemployed to sustain social relationships and engage in clubs or organizations. This advantage stems from a combination of having more financial resources for social activities and the positive impact that gainful employment can have on self-esteem and self-efficacy expectations (Tisch and Wolff 2015). Furthermore, in working societies, individual’s employment status largely defines their social status (Jahoda 1981; Paul and Batinic 2010). Gainful employment represents a widely recognized social norm for people of working age. In the absence of socially acknowledged alternative roles, such as child-rearing or caregiving responsibilities, economic inactivity is 12 Page 4 of 16 L.Pohlan generally considered less prestigious than employment. Consequently, the violation of the norm can be perceived as a personal setback by the unemployed themselves and by those around them, potentially leading to psychological distress and feelings of social exclusion. This shows that perceived social status encompasses more than just objective aspects such as earnings and occupational prestige. For example, adherence to social norms, contribution to collective goals and the use of individual skills can also play an influential role. Thus, the long-term consequences of job loss on social exclusion largely hinge on the prospects of reemployment. Long-term unemployment, in particular, can exacerbate shortcomings in social participation alongside material constraints, as a sustained decrease in living standards also inhibits engagement in social and cultural activities. This situation can further deteriorate due to the negative impact on self-esteem and effects on mental and physical health. This paper provides a comprehensive examination of the long-term consequences of unemployment on social exclusion, employing a range of relevant indicators. I examine how job loss affects individuals’ financial resources and living standards, as well as their social participation. In addition to objective metrics, this study also explores how individuals self-evaluate their societal integration and social status after becoming unemployed. 3 Data andmeasurement ofoutcomes 3.1 Data source andsample selection The PASS-ADIAB links the Panel Labor Market and Social Security (PASS) survey with administrative personal information from the Integrated Employment Biographies (IEB) of the German Federal Employment Agency.4 In the PASS survey, which has been running since 2006, individuals living in Germany and their households are repeatedly surveyed at annual intervals about their economic and social living conditions, whereby households receiving welfare benefits are oversampled (Trappmann et al. 2019). Poverty, benefit receipt, and transitions into and out of unemployment are the main focus of the survey. In addition to objective indicators used to delineate living conditions, the PASS survey also incorporates information on how respondents subjectively evaluate their material and social circumstances, as well as their overall well-being. The PASS survey is particularly suited for the present analysis for several reasons. Firstly, the PASS data incorporates information on access to material resources, social engagement and a person’s subjective perception of social integration and social status, which serve as my outcome variables. Secondly, by consisting of equal parts drawing from long-term unemployed individuals and a stratified sample of the German population, the PASS is ideally suited to investigate the impact of unemployment on social exclusion, as I observe many transitions in and out of unemployment. Thirdly, the PASS can be linked to the administrative data of the German Federal Employment Agency. Thereby, the data are enriched with daily information on individuals’ employment histories, particularly with regard to the beginning and end of employment and unemployment episodes.5 I use the administrative information to measure the employment state at the time of the interview and as determinants of the probability of becoming unemployed in the propensity score estimation. While survey information on wages and employment states might suffer from misreporting and selectivity issues (see, e.g., Pedace and Bates 2000), the administrative data source delivers detailed and complete high-quality data on individual employment histories, as well as previous job characteristics, including wages and firm-level characteristics. The PASS-ADIAB 7520 allows the analysis of 15 consecutive panel waves covering the period from 2007 to 2021. This study focuses on individuals who participated in the PASS survey for at least two consecutive waves and whose survey data can be successfully linked to administrative IEB data. I only consider individuals who were 18 to 64 years old at the first observation point ( t=0 ), were employed with non-zero wages according to the IEB and did not receive unemployment benefits at that time, and either transitioned to unemployment (treatment group) or remained employed (control group) by the second observation point ( t=1 ). Treated individuals are excluded from the control group. The final estimation sample consists of 1,060 individuals in the treatment group and 29,375 individuals in the control group. 3.2 Measurement ofoutcome variables In the following, I describe how the outcome variables household income, deprivation, social engagement, social status and social integration are measured. The PASS questions underlying the outcome variables are presented in Appendix A.1, a description of their construction in Appendix Table A.1 and summary statistics in Appendix Table A.2. I use two variables to measure access to material resources enabling a basic standard of living and social participation. First, I use the equivalent household 4 This paper is based on the PASS-ADIAB version 7520 v1 (DOI: 10.5164/ IAB.PASS-ADIAB7520.de.en.v1) and can be accessed via the Research Data Centre (FDZ) of the IAB. 5 On average, over 80% of the respondents agreed to merging the two data sets in each wave (Berg etal. 2022). Page 5 of 16 12 Unemployment’s long shadow: thepersistent impact onsocial exclusion income, which represents a need-weighted per capita monthly income. Second, I use (weighted) deprivation indexes. The typical operationalization of material deprivation implies that a person is only considered deprived when they cannot achieve a particular ‘doing’ or ‘being’ because they do not have the financial resources (Guio 2009). The PASS-deprivation scale was developed by the Institute for Employment Research referring to previous surveys (e.g., the EU-SILC, the British Household Panel, and the “Niedrigeinkommens-Panel”) and empirical research (e.g., Andreß and Lipsmeier 1995; Nolan and Whelan 1996). Specifically, the households are asked to indicate whether they possess a set of basic goods considered essential for an appropriate standard of living. For instance, these items include, among others, having an apartment with at least as many rooms as there are individuals living there, sufficient winter clothing, a computer with internet access or a car. Moreover, household members are asked about their participation in activities that fulfill basic needs, such as buying new clothing, paying bills or saving a fixed amount of money. The questions also cover involvement in social activities like inviting friends over for dinner at home or going to the cinema once in a while. I split the index into the two dimensions, deprivation of goods and deprivation of activities, as in particular in the short-run, becoming unemployed might primarily increase nonparticipation in activities. The two deprivation indexes are based on a list of 11 goods and 12 activities, respectively.6 Survey participants also indicated whether their households lacked these goods or abstained from certain activities due to financial constraints or for other reasons. In order to construct the deprivation indexes accurately, I only consider items that are missing for financial reasons. This approach ensures that deliberate choices, such as a household opting not to own a car or a television, are not misinterpreted as indicative of a diminished standard of living. Finally, items are weighted on the basis of the proportion of the population that considers a particular item to be indispensable. I quantify social participation by using information on the activities individuals engage in as part of organizations or associations. The PASS survey includes a question on whether the respondent actively participates in various groups, including unions, political parties, church communities, clubs, such as music, sport or culture clubs, or other organizations. Based on their responses, I construct a variable that ranges from 0 to 5, indicating how many activities the individual is involved in. Unfortunately, the PASS-ADIAB lacks precise data on an individual’s social network. There is only information on the number of close friends (including family members) individuals have, which is unlikely to be affected by periods of unemployment (see Pohlan 2019 and Krug and Prechsl 2020 as well as the results presented in Figure B.4 in the Appendix). For this reason, this information is not considered further. To measure social status, I use a question from the PASS survey where respondents are asked to rank themselves on a scale of 1 to 10. A ranking of 1 implies belonging to the bottom of society, while a ranking of 10 indicates being positioned at the top. A very similar question is included in the ISSP (International Social Survey Programme), a cross-sectional household survey of European countries, and has been analyzed in studies focusing on cross-country comparisons (see, e.g., Lindemann and Saar 2014; Poppitz 2016 and Saar etal. 2017). To quantify social integration, I rely on the subjective perception of social belonging, which spans from 1 to 10. This scale represents a spectrum from feeling excluded (1) to experiencing a strong sense of belonging and integration within society (10). This measure of the overall subjective evaluation of social integration is based on the former IAB survey “Lebenssituation und Soziale Sicherung” 2005 (LSS 2005). Similar measures have been used, for instance, by Böhnke (2004) and Layte et al. (2010). 4 Empirical identification The aim of this paper is to determine the long-term effects of unemployment on different dimensions of social exclusion over time. The identification of causal effects relies on a comparison of the outcome levels of workers who become unemployed and those of otherwise identical individuals who remain employed. The fundamental challenge of causal inference arises from the inability to simultaneously observe the outcomes of the same individuals both with and without job loss, making the direct observation of causal effects impossible (Imbens and Wooldridge 2009). To address this challenge, I employ a combination of inverse propensity score weighting (IPW) and an event study approach (Miller 2023). The basic idea behind the weighting approach is to make those workers who do not experience job loss comparable in their observed characteristics to those who do experience job loss. This is achieved by downweighting the outcome levels for individuals from 6 The activities captured in the deprivation index also partly address deprivation in social participation. This relates to what is proposed in Suppa (2021b) as a measure of deprivation in social participation based on a set of social activities (such as attending cultural events and meeting friends) and the frequency with which they are performed. While this measure not only captures the financial dimension but also other mechanisms such as the denial of rights, stigmatization, or any combination thereof, I focus solely on the deprivation of activities, including social activities, due to financial constraints. 12 Page 6 of 16 L.Pohlan the control group who are overrepresented, while placing greater weight on the outcome variables of those who are underrepresented. These weights are determined based on the propensity score, or the probability of being unemployed in the subsequent period ( T=1 ), given the observed covariates x: I estimate the propensity score using a logistic regression model based on a large set of determinants of job loss (see Table B.2 in the Appendix).7 This set includes information on prior outcome levels, sociodemographic characteristics, subjective indicators, individual health status and household situation. Moreover, I use information on individual employment histories and previous job characteristics, including firm-level characteristics and whether the position was permanent or not.8 An individual’s past labor market performance should be strongly related to unobserved factors, such as ability and motivation, which are in turn likely to influence individual employment prospects and my outcome variables. Hence, information on individual employment histories may help to identify the effects of becoming unemployed (Heckman etal. 1997). These observed characteristics are measured at the first of two consecutive waves, ensuring that their levels remain unaffected by future unemployment. Subsequently, I use the fitted values of the propensity scores to calculate the weights. In a second step, I estimate the following event study model to eliminate permanent, time-invariant differences between individuals that remain unobserved by the researcher, and examine the effects of unemployment over time: where yi,t is the outcome level of individual i at time t, αi represents individual fixed effects, Ti the treatment dummy taking the value 1 if the individual becomes unemployed at t=1 and 0 otherwise. δc captures calendar year fixed effects and εi,t is the idiosyncratic error term. τ spans from -2 to 4, covering a seven-year period, with point in time1 indicating the start of the treatment. For a fixed point of time τ , βτ captures the average value of outcomes for the control group relative to the reference period (conditional on fixed effects), while γτ (1) p(x)=P(T=1|X=x). (2) yi,t=αi+  τ�=−1 βτI(t=τ)+  τ�=−1 γτI(t=τ)I(Ti=1 ) +δ c +ε i , t , represents the average difference between the treatment and the control group at that specific point in time. The regression is weighted by the inverse propensity score weights determined in the first step and formally given by ˆ p(xi) 1−ˆ p(xi) , where ˆ p(xi) is the predicted probability of becoming unemployed conditional on observed characteristics xi .The IPW-event study approach identifies γτ under the assumption that, in the absence of a job loss event, the average outcomes of both unemployed and stillemployed workers follow a parallel trend. In essence, this approach assumes that both groups undergo similar changes over time, rather than having similar levels of outcome variables when job loss does not occur. The estimated coefficient can then be interpreted as the effect of unemployment at different points in time on the various dimensions of social exclusion of unemployed individuals (average treatment effect on the treated (ATT)). 5 Descriptive statistics andmodel diagnostics Table1 illustrates that the empirical procedure is necessary to render the treatment and control group comparable. It shows selected descriptive statistics of the characteristics used in the analysis, separately for both groups. Mean values of the control group are presented before (column (2)) and after weighting (column (3)). Additional descriptive statistics are reported in Table B.1 in the Appendix. Without weighting, the treatment and control group differ significantly (column (1) vs. column (2)). For individuals transitioning from employment to unemployment between t=0 and t=1 , the outcome levels in t=0 are consistently lower than those of continuously employed individuals before applying any weighting. Accordingly, the deprivation indexes of basic goods and activities indicate a significantly higher level in the treatment group, indicating a lower standard of living. When examining sociodemographic characteristics, the table reveals that men, young employees, employees with a migration background and the low-skilled are more prone to experiencing unemployment. Additionally, the treatment group, on average, faces more severe health limitations. There are also significant differences regarding household characteristics, as those who become unemployed are less likely to be married and less likely to have children. Considering the characteristics of their previous jobs and employment history, individuals who become unemployed have, for instance, shorter job tenure, earn lower wages, and are more frequently affected by interruptions due to periods of unemployment or nonemployment compared to those who remain employed. The empirical approach effectively balances group differences in observed characteristics. After weighting, 7 For a detailed description of the covariates, see Pohlan(2019). 8 The selection of these covariates aligns with the control variables used in other empirical studies on the non-pecuniary effects of job loss (see, e.g., Kassenboehmer and Haisken-DeNew 2009 and Marcus 2013). Page 7 of 16 12 Unemployment’s long shadow: thepersistent impact onsocial exclusion there are no longer statistically significant differences between the treatment and control group (column (1) vs. column (3) of Table1). The last column reports standardized differences, denoted as X , between treated and re-weighted control individuals, serving as a scale-free measure of balancing.9 Given the absence of a universally agreed-upon criterion for determining what constitutes a sufficiently small standardized difference to achieve balance, I adopt the rule of thumb suggested by Austin (2011): �X<|0.1| . The standardized differences reported in Table1 point to no substantial differences between treatment and control group after applying IPW. The large number of non-treated in comparison to treated individuals (a ratio of 1:28) increases the likelihood of finding a suitable control group. This is affirmed by the balancing test, which demonstrates the comparability of the treated and the weighted control group across a wide range of covariates. To assess the validity of the overlap assumption, I compare the distribution of the estimated propensity scores in the treatment and control group, respectively. As shown in Figure B.3 in the Appendix, there is substantial overlap in the sample. Additionally, I examine whether there are treated cases with Table 1 Selected descriptive statistics Notes:Standard. Diff.: Standardized Difference. PQ: Professional qualification. ssc: social security contributions. Scales of the variables are shown in squared brackets. Source: PASS-ADIAB 7520, own computations Treated Control Control P-value Standard. Unweighted Weighted Diff. (1) (2) (3) (1) − (3) (1) − (3) Initial outcome levels Household income [in €] 1383.157 1751.613 1375.832 0.757 0.009 Deprivation of basic goods [0-4.7] 0.183 0.076 0.181 0.886 0.005 Deprivation of activities [0-5.3] 0.522 0.282 0.536 0.612 − 0.019 Social engagement [0-5] 0.506 0.753 0.503 0.927 0.003 Social status [1-10] 5.770 6.264 5.772 0.974 − 0.001 Social integration [1-10] 7.337 7.891 7.304 0.684 0.016 Sociodemographics & household characteristics Female 0.436 0.528 0.432 0.846 0.008 Age [in years] 41.052 44.461 40.968 0.850 0.007 Migrant 0.088 0.044 0.095 0.559 − 0.025 Married 0.383 0.548 0.377 0.755 0.012 Number of own children 1.179 1.426 1.204 0.632 − 0.020 Serious health restrictions 0.254 0.198 0.256 0.909 − 0.004 PQ: no vocational training 0.175 0.096 0.179 0.838 − 0.008 PQ: vocational training 0.615 0.621 0.612 0.872 0.006 PQ: advanced vocational training 0.059 0.095 0.059 0.954 0.002 PQ: academic degree 0.150 0.188 0.150 0.974 − 0.001 East Germany 0.313 0.278 0.312 0.930 0.003 Previous job characteristics & employment history Permanent contract 0.655 0.895 0.647 0.646 0.017 Tenure [in months] 20.707 70.351 21.432 0.537 − 0.020 Daily wage [in €] 59.696 83.726 61.080 0.257 − 0.040 Number of employment periods with ssc 7.548 5.729 7.536 0.964 0.002 Employment duration with ssc [in months] 117.116 184.471 119.188 0.589 − 0.020 Number of unemployment periods 4.501 2.608 4.540 0.826 − 0.010 Unemployment duration [in months] 67.792 36.542 67.342 0.842 0.008 Number of observations 1060 29,375 29,375 9 The standardized difference is computed as � X= ¯ X1− ¯ X0  /  (S 2 1 + S2 0)/2 0.5 , where ¯ Xw represents the sample mean of treated ( w=1 ) or control ( w=0 ) individuals, and S2 w represents the respective sample variances (Austin 2011; Guo and Mark 2015). X offers an advantage over the conventional t-statistic in that it does not mechanically inflate with sample size, thus avoiding the exaggeration of minor imbalances that might still appear significant in a t-test. 12 Page 8 of 16 L.Pohlan propensity score values exceeding the maximum value among the non-treated individuals (Lechner and Strittmatter 2019). In this application, however, such cases do not exist. The model diagnostics indicate that, given the available data, it is feasible to identify an appropriate control group for my empirical analysis. Appendix Figures B.1 and B.2 present the mean values of the outcome variables in levels three years before and four years after treatment start, separately for treated and control individuals before and after applying IPW. The plots already show that individuals who become unemployed experience a substantial and lasting deterioration in both the material and social dimensions of social exclusion. While the trajectories for some outcome variables are quite different between treatment and control group before weighting in the period before job loss, these differences disappear after weighting. This already hints at similar trends in outcomes prior to the onset of treatment, which is required by the event study approach. However, the event study plots presented in the results section below allow for a more systematic assessment of parallel trends in both groups. In addition to the parallel trend assumption, the identification strategy requires that future job loss does not influence the outcome variables before the actual transition in unemployment. For instance, Wunder and Zeydanli (2021) document significant lead effects regarding perceived job insecurity and job satisfaction one year prior to a plant closure. Appendix Figures B.1 and B.2 indicate that the outcome levels in the treatment group are generally on an increasing trajectory, which is reversed by the event of job loss. Thus, there is no clear indication of anticipation. However, if the outcome levels one year prior to job loss are nevertheless negatively influenced by future unemployment, the real costs of unemployment would even be higher. 6 Empirical findings 6.1 Baseline results This section presents the baseline IPW-event study analyses for the effect of unemployment on the material and social dimensions of social exclusion, as defined in Sect.3. Material dimension. Figure 1 shows the estimated effects of unemployment on household income (panel A), deprivation of basic goods (panel B) and deprivation of activities (panel C). Panel A illustrates that experiencing unemployment results in a decline in disposable income and a deterioration in living standards. For individuals who have become unemployed, the average equivalent monthly household income decreases by approximately 270 euros one year after job termination compared to the control group, representing a reduction of 20 percent. The effect diminishes over time but amounts still to 174 euros after four years. Job loss also leads to a significant increase in deprivation of basic goods (by 0.052 scale points) and activities (by 0.200 scale points), reflecting constraints on the standard of living individuals were accustomed to. These findings indicate that individuals are more likely to limit their activities following unemployment rather than cutting back on basic goods. Notably, the effect sizes are substantial. The weighted deprivation index for basic goods and activities is around 0.18 and 0.53, respectively, at time t=0 in both the treatment and the weighted control group. These values correspond to about half the size observed in the unweighted control group (see Table1). Interestingly, these effects are already noticeable one year after becoming unemployed and remain relatively stable over time. This finding speaks against a delayed impact of unemployment, which one would expect regarding durable consumer goods. A more detailed analysis of the individual components of the deprivation index, not presented here, supports this assumption.10 In the case of job loss, affected individuals predominantly cut back on activities like vacation trips, dining out, attending cinema, theater, or concerts. Moreover, they are less inclined to purchase new clothes or furniture, and saving becomes more challenging. They also face increasing difficulties in handling unexpected expenses. In contrast, unemployment does not directly affect the possession of items like a washing machine or a television set in the home. Financial restrictions concerning basic goods are most commonly observed in relation to owning a car or the size of the apartment. Social dimension. Figure2 shows the effects of unemployment on social engagement (panel A), social status (panel B) and social integration (panel C). With regard to social participation, the results are inconclusive. I find that individuals who have lost their jobs do not significantly reduce their social engagement including involvement in clubs, trade unions, political parties, church congregations or other organizations (see panel A of Fig.2). Additionally, there is no noticeable effect on the number of close friends (see Figure B.4 in the Appendix). Several explanations can be considered for this. Firstly, as described earlier, unemployment can make it more challenging to maintain social contacts, but this aspect may not be adequately captured by the measures considered. Furthermore, existing social networks can offer emotional support during crisis situations, which would argue against a decline in friendship relationships. Moreover, lost contacts might be replaced by new acquaintances and friendships. Finally, although the findings do 10 The results are available on request. Page 15 of 16 12 Unemployment’s long shadow: thepersistent impact onsocial exclusion quality of life of individuals facing significant labor market detachment (Achatz etal. 2024). Overall, unemployment-preventing and participation-promoting policy measures can potentially counteract the exclusion experienced by those in precarious circumstances and thereby contribute to strengthening social cohesion. These non-monetary benefits should also be considered when designing such measures. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s1265102400369-8. Supplementary Material 1. Acknowledgements The author would like to thank Matthias Collischon, Nicole Gürtzgen and Stefanie Gundert for helpful comments and discussions. Author contributions The author is solely responsible for this article. The author read and approved the final manuscript. Funding Not applicable. Availability of data and materials This paper uses survey and administrative data from the Research Data Center (FDZ) at the Institute for Employment Research (IAB). Since data access is restricted by the laws of the German Social Code, the data is not freely available to the research community. Members of the research community can apply for and access data products at the FDZ. Access can take place, e.g., via on-site use or via remote data access. More detailed information can be found here: https://fdz.iab.de/en/data-access/. The reproduction codes for the analysis are available from the author upon request. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The author declares that she has no competing interests. Received: 28 November 2023 Accepted: 6 June 2024 References Achatz, J., Bauer, F., Bennett, J., Bömmel, N., Coban, M., Dietz, M., Englert, K., Fuchs, P., Gellermann, J.F.C., Globisch, C., Hülle, S., Zein, K., Kupka, P., Nivorozhkin, A., Osiander, C., Pohlan, L., Promberger, M., Raab, M., Ramos Lobato, P., Schels, B., Schiele, M., Trappmann, M., Tübbicke, S., Wenzig, C., Wolff, J., Zabel, C., Zins, S.: Evaluation des Teilhabechancengesetzes - Abschlussbericht. IAB-Forschungsbericht No 4/2024, Institute for Employment Research (IAB), Nuremberg (2024) Andreß, H.-J., Lipsmeier, G.: Was gehört zum notwendigen Lebensstandard und wer kann ihn sich leisten? Ein neues Konzept zur Armutsmessung. Aus Politik und Zeitgeschichte B 31(32), 35–49 (1995) Arulampalam, W.: Is unemployment really scarring? Effects of unemployment experiences on wages. Econ. J. 111(475), 585–606 (2001) Austin, P.C.: Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm. Statist. 10(2), 150–161 (2011) Bartelheimer, P.: Teilhabe, Gefährdung und Ausgrenzung als Leitbegriffe der Sozialberichterstattung. SOFI-Mitteilungen 32(2004), 47–61 (2004) Bartelheimer, P., Behrisch, B., Daßler, H., Dobslaw, G., Henke, J., Schäfers, M.: Teilhabe – eine Begriffsbestimmung. Springer, Berlin (2020) Berg, M., Cramer, R., Dickmann, C., Gilberg, R., Jesske, B., Kleudgen, M., Beste, J., Dummert, S., Frodermann, C., Schwarz, S., Wenzig, C., Trappmann, M., Altschul, S., Bähr, S., Collischon, M., Coban, M., Gleiser, P., Gundert, S., Küfner, B., Mackeben, J., Malich, S., Müller, B., Stegmaier, J., Teichler, N., Unger, S.: Codebook and documentation of the panel study labour market and social security (PASS). Datenreport Wave 15. FDZ-Datenreport 10/2022 (en), Institute for Employment Research (IAB), Nuremberg and infas Institute for Applied Social Science, Bonn (2022) Böheim, R., Taylor, M.P.: The search for success: do the unemployed find stable employment? Labour Econ. 9(6), 717–735 (2002) Böhnke, P.: Perceptions of social integration and exclusion in an enlarged Europe. Office for official publications of the European communities, Luxembourg (2004) Brand, J.E.: The far-reaching impact of job loss and unemployment. Ann Rev Soc 41, 359–375 (2015) Christoph, B., Lietzmann, T.: Je länger, je weniger? Zum Zusammenhang zwischen der Dauer des ALG-II-Leistungsbezugs und den materiellen Lebensbedingungen der Betroffenen. Zeitschrift Für Sozialreform 59(2), 167–196 (2016) Clark, A., Georgellis, Y., Sanfey, P.: Scarring: the psychological impact of past unemployment. Economica 68(270), 221–241 (2001) Clark, A.E., Diener, E., Georgellis, Y., Lucas, R.E.: Lags and leads in life satisfaction: a test of the baseline hypothesis. Econ. J. 118(529), F222–F243 (2008) Cygan-Rehm, K., Kuehnle, D., Oberfichtner, M.: Bounding the causal effect of unemployment on mental health: nonparametric evidence from four countries. Health Econ. 26(12), 1844–1861 (2017) Dieckhoff, M.: The effect of unemployment on subsequent job quality in Europe: a comparative study of four countries. Acta Sociologica 54(3), 233–249 (2011) Dieckhoff, M., Gash, V.: Unemployed and alone? Unemployment and social participation in Europe. Int. J. Soc. Soc. Policy 35(1/2), 67–90 (2015) Eberl, A., Collischon, M., Wolbring, T.: Subjective wellbeing scarring through unemployment: new evidence from a long-running panel. Soc. Forces 101(3), 1485–1518 (2023) European Commission: Combating poverty and social exclusion. A statistical portrait of the European Union. Publications Office of the European Union, Brussels (2010) Gangl, M.: Scar effects of unemployment: an assessment of institutional complementarities. Am. Sociol. Rev. 71(6), 986–1013 (2006) Gielen, A.C., Van Ours, J.C.: Unhappiness and job finding. Economica 81(323), 544–565 (2014) Guio, A.-C.: What can be learned from deprivation indicators in Europe? Eurostat Methodologies and Working Papers. Publications Office of the European Union, Luxembourg (2009) Gundert, S., Hohendanner, C.: Do fixed-term and temporary agency workers feel socially excluded? Labour market integration and social well-being in Germany. Acta Sociologica 57(2), 135–152 (2014) Guo, S., Fraser, M.W.: Propensity score analysis – statistical methods and applications (2nd ed.). Vol. 12. Sage, Thousand Oaks (2015) Hainmueller, J.: Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Polit. Anal. 20(1), 25–46 (2012) Heckman, J.J., Ichimura, H., Todd, P.E.: Matching as an econometric evaluation estimator: evidence from evaluating a job training programme. Rev. Econ. Stud. 64(4), 605–654 (1997) Hetschko, C., Knabe, A., Schöb, R.: Looking back in anger? Retirement and unemployment scarring. Demography 56(3), 1105–1129 (2019) Imbens, G.W., Wooldridge, J.M.: Recent developments in the econometrics of program evaluation. J. Econ. Liter. 47(1), 5–86 (2009) Ivanov, B., Pfeiffer, F., Pohlan, L.: Do job creation schemes improve the social integration and well-being of the long-term unemployed? Labour Econ. 64, 101836 (2020) 12 Page 16 of 16 L.Pohlan Jahoda, M.: Work employment, and unemployment: values, theories, and approaches in social research. Am. Psychol. 36(2), 184–191 (1981) Jost, O.: Unemployment and its scarring effect on wages in Germany: evidence from linked employer-employee data. Int. J. Manpower 43(5), 1126–1143 (2022) Kassenboehmer, S.C., Haisken-DeNew, J.P.: You’re fired! The causal negative effect of entry unemployment on life satisfaction. Econ. J. 119(536), 448–462 (2009) Kasy, M., Lehner, L.: Employing the unemployed of Marienthal: evaluation of a guaranteed job program. IZA Discussion Paper No. 16088 (2023) Kronauer, M.: Social exclusion and underclass – new concepts for the analysis of poverty. Empirical Poverty Research in a Comparative Perspective, 51–75 (1998) Krug, G., Prechsl, S.: The role of social integration in the adverse effect of unemployment on mental health – testing the causal pathway and buffering hypotheses using panel data. Soc. Sci. Res. 86, 102379 (2020) Krug, G., Drasch, K., Jungbauer-Gans, M.: The social stigma of unemployment: consequences of stigma consciousness on job search attitudes, behaviour and success. J. Labour Market Res. 53(1), 1–27 (2019) Kunze, L., Suppa, N.: Bowling alone or bowling at all? The effect of unemployment on social participation. J. Econ. Behav. Organ. 133, 213–235 (2017) Layte, R., Maitre, B., Whelan, C.T.: Second European quality of life survey: living conditions, social exclusion and mental well-being. Office for Official Publications of the European Communities, Luxembourg (2010) Lechner, M., Strittmatter, A.: Practical procedures to deal with common support problems in matching estimation. Econometr. Rev. 38(2), 193-207 (2019). Lindemann, K., Saar, E.: Contextual effects on subjective social position: evidence from European countries. Int. J. Comp. Sociol. 55(1), 3–23 (2014) Lucas, R.E., Clark, A.E., Georgellis, Y., Diener, E.: Unemployment alters the set point for life satisfaction. Psychol. Sci. 15(1), 8–13 (2004) Marcus, J.: The effect of unemployment on the mental health of spouses–evidence from plant closures in Germany. J. Health Econ. 32(3), 546–558 (2013) Mavridis, D.: The unhappily unemployed return to work faster. IZA J. Labor Econ. 4, 1–22 (2015) Miller, D.L.: An introductory guide to event study models. J. Econ. Perspect. 37(2), 203–230 (2023) Mousteri, V., Daly, M., Delaney, L.: The scarring effect of unemployment on psychological well-being across Europe. Soc. Sci. Res. 72, 146–169 (2018) Nolan, B., Whelan, C.: Measuring poverty using income and deprivation indicators: alternative approaches. J. Eur. Soc. Policy 6(3), 225–240 (1996) Paul, K.I., Batinic, B.: The need for work: Jahoda’s latent functions of employment in a representative sample of the German population. J. Organ. Behav. 31(1), 45–64 (2010) Paul, K.I., Moser, K.: Unemployment impairs mental health: meta-analyses. J. Vocation Behav. 74(3), 264–282 (2009) Pedace, R., Bates, N.: Using administrative records to assess earnings reporting error in the survey of income and program participation. J. Econ. Soc. Measure. 26(3–4), 173–192 (2000) Pohlan, L.: Unemployment and social exclusion. J. Econ. Behav. Organ. 164, 273–299 (2019) Poppitz, P.: Does self-perceptions and income inequality match? The case of subjective social status. IMK Working Paper No. 173. Hans-Böckler-Stiftung (2016) Promberger, M.: Arbeit, Arbeitslosigkeit und soziale Integration. Aus Politik und Zeitgeschichte 40(41), 7–15 (2008) Rauf, T.: Getting a job, again: new evidence against subjective wellbeing scarring. Soc. Forces 100(1), 218–245 (2021) Riddell, W.C., Song, X.: The impact of education on unemployment incidence and re-employment success: evidence from the US labour market. Labour Econ. 18(4), 453–463 (2011) Room, G.: Beyond the threshold: the measurement and analysis of social exclusion. Policy Press, Bristol (1995) Rözer, J.J., Hofstra, B., Brashears, M.E., Volker, B.: Does unemployment lead to isolation? The consequences of unemployment for social networks. Soc. Networks 63, 100–111 (2020) Saar, E., Helemäe, J., Lindemann, K.: Self-placement of the unemployed in the social hierarchy. Evidence from European countries. In: Social inequality in the eyes of the public: a collection of analyses based on ISSP data 1987–2009. Ed. by Edlund, J., Bechert, I., Quandt, M.. Vol. 17. GESIS - LeibnizInstitut für Sozialwissenschaften, Köln, 119–136 (2017) Sen, A.: Commodities and capabilities. Elsevier Science Publishers, Oxford (1985) Shaffer, J.P.: Multiple hypothesis testing. Ann. Rev. Psychol. 46(1), 561–584 (1995) Stauder, J.: Unemployment, unemployment duration, and health: selection or causation. Eur. J. Health Econ. 20(1), 59–73 (2019) Suppa, N.: Unemployment and subjective well-being. Handbook of Labor Human Resources and Population Economics. Springer, Berlin (2021a) Suppa, N.: Walls of glass. Measuring deprivation in social participation. J. Econ. Inequal. 19(2), 385–411 (2021b) Tisch, A., Wolff, J.: Active labour market policy and its outcomes: does workfare programme participation increase self-efficacy in Germany? Int. J. Soc. Soc. Policy 35(1/2), 18–46 (2015) Trappmann, M., Bähr, S., Beste, J., Eberl, A., Frodermann, C., Gundert, S., Schwarz, S., Teichler, N., Unger, S., Wenzig, C.: Data resource profile: panel study labour market and social security (PASS). Int. J. Epidemiol. 48(5), 1411–1411g (2019) Wunder, C., Zeydanli, T.: The early costs of plant closures: evidence on lead effects on workers’ subjective and objective outcomes. J. Econ. Behav. Organ. 188, 489–505 (2021) Young, C.: Losing a job: the nonpecuniary cost of unemployment in the United States. Soc. Forces 91(2), 609–634 (2012) Zhou, Y., Zou, M., Woods, S.A., Wu, C.-H.: The restorative effect of work after unemployment: an intraindividual analysis of subjective well-being recovery through reemployment. J. Appl. Psychol. 104(9), 1195–1206 (2019) Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.