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Unemployment Scarring Effects: An Overview and Meta-analysis of Empirical Studies

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Abstract

This article reviews the empirical literature on the scarring effects of unemployment, by first presenting an overview of empirical evidence relating to the impact of unemployment spells on subsequent labor market outcomes and then exploiting meta-regression techniques. Empirical evidence is homogeneous in highlighting significant and often persistent wage losses and strong unemployment state dependence. This is confirmed by a meta-regression analysis under the assumption of a common true effect. Heterogeneous findings emerge in the literature, related to the magnitude of these detrimental effects, which are particularly penalizing in terms of labor earnings in case of unemployment periods experienced by laid-off workers. We shed light on further sources of heterogeneity and find that unemployment is particularly scarring for men and when studies’ identification strategy is based on selection on observables.

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Data Availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. Moreover, further outcomes discussed by the literature on scarring are family formation, crime and negative psychological implications in terms of well-being, life satisfaction and mental health (see e.g. Helbling and Sacchi 2014; Strandh et al. 2014; Mousteri et al. 2018; Clark and Lepinteur 2019).

  2. A further strand of the recent literature focuses on the effect of adverse labor market conditions at graduation, for example focusing on the effect of local unemployment rate or graduating during a recession (see e.g. Raaum and Roed, 2006; Kahn 2010; Oreopoulos et al. 2012; Kawaguchi and Murao 2014; Altonji et al. 2016). The consequences of economic downturns on wages, labor supply and social outcomes for young labor market entrants have been recently surveyed by Cockx (2016), Von Wachter (2020) and Rodriguez et al. (2020).

  3. The stigma effect means that individuals who have been unemployed face lower chances of being hired because employers may use their past history of unemployment as a negative signal.

  4. Thus, papers using traditional multivariate descriptive analysis, duration models, or OLS regressions with a reduced number of controls which do not properly address endogeneity issues and are unlikely to have a causal interpretation (endogeneity issues are discussed in SubSect. 3.2).

  5. For intergenerational scars we mean that studies focused on the effect of parents’ unemployment experiences on the children’ future employment status (see e.g. Karhula et al. 2017). For macroeconomic conditions at graduation we mean that we excluded that literature focused on the local unemployment rate at graduation or other local labor market conditions, rather than on individual unemployment experience and state dependence (see e.g. Oreopoulos et al. 2012; Raaem and Roed, 2006).

  6. When we could not directly retrieve the t-statistics because not reported among the study results, we computed them as the ratio between the estimated unemployment effects (\({\beta }_{i}\)) and their standard errors. If studies only displayed the estimated effects and their 95% confidence intervals, the standard error can be calculated by SE = (ub − lb)/(2 × 1.96), where ub and lb are the upper bound and the lower bound, respectively.

  7. We removed from the meta-regression analysis 8 articles because they did not contain sufficient information to compute the t-statistic of the estimated scarring effect. They are reported in italics in Tables 5 and 6.

  8. For employment outcomes we mean the likelihood of experiencing future unemployment, the probability to have a job later (employability), the fraction of days spent at work or the hours worked during the following years (labor market participation), the call-backs from employers in case of field experiment. Earning outcomes include hourly wages, labor earnings, income, etc.

  9. Since many studies did not provide precise information on the number of covariates, we approximated \({dk}_{i}\) with the number of observations minus 2. Indeed, given that in microeconometric applications the sample sizes are very often much larger than the number of the parameters, the calculation of the partial correlation coefficient is quite robust to errors in deriving \({dk}_{i}\) (Picchio 2022).

  10. The publication bias is the bias arising from the tendency of editors to publish more easily findings consistent with a conventional view or with statistically significant results, whereas studies that find small or no significant effects tend to remain unpublished (Card and Krueger 1995).

  11. We employed the Precision Effect Estimate with Standard Error (PEESE) specification because its quadratic form of the standard errors has been proven to be less biased and often more efficient to check for heterogeneity than the FAT-PET specification when there is a nonzero genuine effect (Stanley and Doucouliagos 2014). Nevertheless, the results from the FAT-PET specification are very similar to the ones from the PEESE model.

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Acknowledgements

The author acknowledges financial support from the Cariverona Foundation Ph.D. research scholarship. He is particularly grateful to Matteo Picchio and Claudia Pigini for their comments and suggestions on a preliminary version of this paper. He also thanks the Associate Editor Massimiliano Bratti and two anonymous reviewers, whose comments were very useful for an important improvement of the paper.

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  1. Department of Economics and Social Sciences, Marche Polytechnic University, Piazzale Martelli 8, 60121, Ancona, Italy

    Mattia Filomena

  2. Department of Public Economics, Masaryk University, Lipová 41a, 60200, Brno, Czech Republic

    Mattia Filomena

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Appendix

Appendix

See Tables 5 and 6 .

Table 5 Effect on future employment
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Table 6 Effect on future labor earnings
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Filomena, M. Unemployment Scarring Effects: An Overview and Meta-analysis of Empirical Studies. Ital Econ J (2023). https://doi.org/10.1007/s40797-023-00228-4

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