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Getting back into the labor market: the effects of start-up subsidies for unemployed females

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Low female labor market participation is a problem many developed countries have to face. Beside activating inactive women, one possible solution is to support the re-integration of unemployed women. Due to female-specific labor market constraints (preferences for flexible working hours, discrimination), this is a difficult task, and the question arises whether active labor market policies (ALMP) are an appropriate tool to help. It has been shown that the effectiveness of traditional (ALMP) programs—which focus on the integration in dependent (potentially inflexible) employment—is positive but limited. At the same time, recent evidence for Austria shows that these programs reduce fertility which might be judged unfavorable from a societal perspective. Promoting self-employment among unemployed women might therefore be a promising alternative. Starting their own business might give women more independence and flexibility to reconcile work and family and increase labor market participation. Based on long-term informative data, we find that start-up programs persistently integrate former unemployed women into the labor market, and the impact on fertility is less detrimental than for traditional ALMP programs.

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  1. Evidence on the existence of statistical discrimination is provided by Dickinson and Oaxaca (2009) and Altonji and Pierret (2001), among others.

  2. To counteract this worrisome development, several OECD governments have already started to implement policies in the last decades (see Sleebos 2003, for a summary of implemented programs and empirical evidence on their effectiveness).

  3. The German Federal Employment Agency reports for 2008 that among unemployed women, 51 % have no or only a lower secondary school degree compared to 60 % among unemployed men. Moreover, 19 % (1 %) of unemployed women (men) are single parents and 37 % (30 %) left the labor force.

  4. Caliendo et al. (2014) show based on the Socioeconomic panel that higher risk aversion among women explains a large share of the entrepreneurial gender gap. In Germany; Bönte and Jarosch (2011) provide empirical evidence in a cross-country analysis that differences in risk preferences and also competitiveness matter.

  5. On average, BA female participants in our data set received €840 in unemployment benefits per months during their unemployment spell. Given the additional lump sum payment for social security, this corresponds to an average BA payment of €1415 per month.

  6. The new start-up subsidy consists of unemployment benefits and a lump-sum payment of €300 per month for social coverage paid for 9 months. After that, the lump-sum payment of €300 may be extended for a further 6 months if the business is the full-time activity of the applicant.

  7. Having access to only one particular quarter of entrants bears the risk of a selective sample. However, comparing the distribution of certain characteristics (e.g., age and educational background) across different quarters does not show any significant differences.

  8. We discuss the construction of treatment and control groups in more detail in Section 5.1.

  9. For a more extensive discussion of data construction, see Caliendo and Künn (2011).

  10. The willingness of individuals to participate in the survey decreased over time. On average, we observe 46 % of all participants and 40 % of all non-participants for the entire period of 56 months.

  11. A detailed description of the weighting procedure and results of the probit regressions are included in the Supplementary Appendix.

  12. The FEA reports a female labor market participation of 63.6 % in West Germany and 71.4 % in East Germany for 2003.

  13. We note that the importance of the subsidy as depicted in Table 3 does not allow a statement with respect to the occurrence of deadweight effects. In the context of start-up subsidies, deadweight effects occur if two criteria are fulfilled: (1) the subsidized individuals would have also become self-employed in the absence of the subsidy and (2) business success is uncorrelated with the subsidy. The data at hand only contain information concerning the first dimension (importance of the subsidy), which is also likely to be endogenous given that it is measured 16 months after start-up and hence influenced by the treatment and business success. Therefore, we can not analyze deadweight effects in this study.

  14. Roughly 90 % of these individuals were continuously self-employed throughout this period of 56 months. Among female participants whose businesses failed, only 21–35 % retained debts, of which around 70 % reported debt of less than €1000. The maximum amount of indebtedness is €2500.

    Table 4 Labor market and fertility outcomes of female participants and non-participants 56 months after start-up
  15. See Imbens and Wooldridge (2009) for further discussion.

  16. Only 2 % of the control individuals participated in SUS or BA within our observation period.

  17. Risk preferences are not included in the main specification given that it is measured after start-up in our data. Section 5.4 provides results based on an alternative specification including risk preferences.

  18. For a more extensive discussion on the estimation of propensity scores, we refer to Heckman et al. (1998) and Caliendo and Kopeinig (2008) among others.

  19. Given that we have monthly cohorts of participants and non-participants in the third quarter of 2003 (July/Aug/Sept), we measure the covariates of participants and non-participants in the same calender month.

  20. Given the key role of the current unemployment spell in the PS estimation, we additionally match exactly on unemployment duration and benefit level in Section 5.4 to test the robustness of our matching results in this regard.

  21. The binary variable “pregnancy” is constructed based on longitudinal information and is one if respondents report entry in maternity/parental leave within the first 6 months after start-up. The variable could not be included in estimations for East Germany as the variable predicts failure perfectly. Affected non-participants were dropped from the estimation.

  22. We also provide details on the distribution of the matching weights for non-participants in Table 9 in the Appendix (as suggested by Black and Smith 2004). It can be seen that we rely on a relatively small number of non-participants matched to participants with large propensity scores. Although this does not violate the overlap condition, it might weaken the identification for high values of the propensity score. As suggested by Black and Smith (2004), we therefore test more restrictive common support conditions in Section 5.4 to figure out to what extent the results are driven by the thin upper tail of the PS distribution. Results turn out to be stable (see Section 5.4 for details).

  23. Using an Epanechnikov Kernel has the advantage that it puts distance-based weights to control observations and is bounded in its support, i.e., control observations with a distance to participants in terms of propensity scores larger than the bandwidth are not considered.

  24. See Caliendo and Kopeinig (2008) for a more detailed discussion of matching quality issues.

  25. Unfortunately, we have no direct information on childbearing or child care available which would allow for a broader consideration of reconcilability of work and family.

  26. We provide results conditioning on individuals 40 years or younger at the time of business start-up in the Supplementary Appendix.

  27. For long-term unemployed women, job creation schemes generate slightly higher effects ranging between 3 and 10 % points (see Heyer et al. 2012).


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The authors thank Erdal Tekin and four anonymous referees for helpful comments and suggestions. We further thank Daniel S. Hamermesh, Andrew J. Oswald, and participants at the 2011 APPAM Fall Research Conference, the 2012 Annual SOLE Meeting, the 2012 EALE Meeting, the SFB 884 Research Conference at the University of Mannheim, and seminars at IZA Bonn, University of Potsdam and University of Trier for helpful discussions and comments; and Anna Becker for excellent research assistance. Financial support by the Institute for Employment Research (IAB Nuremberg) under the research grant No. 1007 is gratefully acknowledged.

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Correspondence to Marco Caliendo.

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Table 6 Propensity score estimation: female participants vs. non-participation
Table 7 Calculated bandwidth parameter
Table 8 Matching quality
Table 9 Distribution of matching weights for non-participants
Fig. 1
figure 1

Propensity score distributions: participation vs. non-participation

Fig. 2
figure 2

Distribution of entries into maternity/parental leave across female participants and matched non-participants over time

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Caliendo, M., Künn, S. Getting back into the labor market: the effects of start-up subsidies for unemployed females. J Popul Econ 28, 1005–1043 (2015).

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