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The effects of parental leave extension on training for young women

Abstract

Using three datasets for West Germany, we estimate the effect of the extension of parental leave from between 10 and 18 to 36 months on young women’s participation in job-related training. Specifically, we employ difference-in-differences identification strategies using control groups of older women and young and older men. We find that parental leave extension negatively affects job-related training for young women, even if they do not have children, especially when the focus is on employer-arranged training. There is tentative evidence that young women partly compensated for this reduction in employer-arranged training by increasing training on their own initiative.

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Notes

  1. Since 1986, maternity leave in Germany can be taken by either the mother or the father and is, therefore, called parental leave. But according to the German Federal Ministry of Families, Seniors, Women, and Youth, only 1.5% of fathers made use of this opportunity up to 2001, a share that has risen to about 5% by 2004 (see the Ministry’s website http://www.bmfsfj.de/bmfsfj/generator/BMFSFJ/familie,did=20212.html, in German). If men were seen by employers as treated by the reforms that we subsequently analyze, the effects we estimate in the following would be lower bounds for the reform effects. However, given the small share of men taking leave during our observation period and anecdotal evidence that men usually took very short leaves, we do not believe that employers perceived men as being par of the treatment group of the parental leave extension reforms.

  2. Present discounted value of earnings, of which wage profiles and employment histories are major ingredients, might be the most appropriate outcome variable for the financial impact of parental/maternity leave. However, measurement of the impacts on overall lifecycle wage and employment profiles is complicated by the frequent lack of long panel data. Conversely, impacts on wages at a certain point in the lifecycle may fail to take account of effects like steepened wage profiles. For example, when women have to bear a higher share of the costs of firm-specific training because of extended parental/maternity leave, their early-career wages may fall, although Hashimoto’s (1981) model would predict that they will also reap a higher share of the returns later in their careers. Thus, without data on lifecycle wage profiles, estimates with wages as the outcome might be difficult to interpret.

  3. See http://www.childpolicyintl.org/issuebrief/issuebrief5table1.pdf.

  4. Job stability and thus the opportunity for investment in firm-specific human capital is much higher in Germany than it is in the USA. For example, Auer and Cazes (2000) report that, in 1990, the annual job failure rate of jobs with up to 1 year of tenure was 63.4% and 24% in the USA and Germany, respectively.

  5. During parental leave, child-raising benefits were paid during our observation period, as described in Table 1 (about 300 euros per month). These benefits partly depended on the income of the parents and there were some smaller changes concerning the income thresholds. For more details, see Ondrich et al. (1996). Eligible for parental leave are all parents who work as employees. However, temporary contracts do not have to be renewed during the leave period.

  6. According to administrative birth records for Germany, 8.3 percent of all new mothers in 1990 were 36 years of age or older. This share is rising over time. For example, in the year 2000, it was already 11.5 percent. However, the share of all new mothers aged 40 or older is much lower at 1.8 and 2.5 percent in the years 1990 and 2000, respectively.

  7. More information on these data is available from the Central Archive for Empirical Social Research, University of Cologne, web site: http://info1.za.gesis.org/DBKSearch12/SDesc.asp.

  8. The GSOEP is probably the most frequently used individual-level dataset for Germany. For more information, see http://www.diw.de/english/soep/29012.html.

  9. The Qualification and Careers Survey (IAB-BIBB), which specializes in job descriptions, was also used by Spitz-Öner (2006). More information is available at http://www.gesis.org/Datenservice/Themen/38Beruf.htm.

  10. According to OECD statistics, the labor force participation rate of women in Germany is somewhat below the rate in the U.S., namely at 63.6 and 70.8 percent in the year 2000, respectively. By 2008, these figures virtually equalized, however, at 70.3 and 70.4 percent for Germany and the U.S., respectively.

  11. The question in the BSW asks whether job-related training was a) arranged by the company, b) arranged on the recommendation of a supervisor, or c) on your own initiative. We subsume answers a) and b) under ‘employer-arranged training’ in contrast to ‘training on one’s own initiative’.

  12. There are different regulations for civil servants when it comes to unpaid leave (whether for parental or other reasons) with a right to return. They can take up to 12 (15 since February 2009) years of leave and this was left unchanged by the reform discussed here as far as the extension of the parental leave duration is concerned. However, civil servants were affected by the extension of the duration of parental benefit payments just as other employees. Point estimates for the sample excluding civil servants are comparable to (in fact a bit higher than) the ones including civil servants, but standard errors are also higher. Therefore, and because we want to estimate the effect for the entire West German workforce, we do not exclude civil servants from our sample.

  13. The Micro Census (MZ) is a one-percent sample of the population (the scientific community receives only a 70 percent sample of that one percent) and asks questions similar to a census. For political reasons, there has been no census in Germany since 1987, so the Micro Census acts as a substitute. For more information, see http://www.destatis.de/jetspeed/portal/cms/Sites/destatis/Internet/EN/press/abisz/Mikrozensus__e,templateId=renderPrint.psml.

  14. Nevertheless, the Micro Census data clearly suggest that the long-run trend in catching up with young men stalled after 1992 (when the parental leave period was doubled from 18 to 36 months) until about 2000. Hence, the short time series presented here is consistent with a permanent reduction in the labor force attachment of young women relative to their male peers.

  15. In results not shown here, it turns out, however, that despite of the catch-up in labor supply of young women in relation to young men, young women have lost in terms of employer-arranged training in relation to young men after the extension of parental leave. When considering training in general, however, they have caught up. Yet, consistent with the relative labor supply trends shown here, this catch-up in terms of training in general was slowed down in the period when parental leave was extended (compared to a placebo period).

  16. For the same reason that larger firms might more easily find substitutes for women on leave, extension of parental leave might also have a smaller effect in larger firms. It is thus an empirical question whether the effect is really larger in larger firms, where training incidence is higher.

  17. We have also checked whether the reform affected the selection into white-collar jobs at larger firms. To this end, we regressed an indicator for being a white-collar worker in a larger firm on the treatment indicator and all other control variables using all five control group designs described in the previous section. It turned out that the parental leave extension had no significant effect on the selection into white-collar jobs at larger firms: all point estimates were insignificant. Most point estimates were also close to zero.

  18. Again, because control variables do not make a noteworthy difference to the estimates, we only report the specifications for the full set of control variables.

  19. The GSOEP provides information on these training aspects, but the questions are not comparable across years.

  20. For more information, please see: http://www.bmbf.de/pub/berichtssystem_weiterbildung_9.pdf.

  21. In our analysis, training participation in the Micro Census was only less than half as high as in the other three datasets.

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Acknowledgements

This research was supported by German Research Foundation within the project “Labour Market Effects of Social Policy” which is part of the research initiative “Flexibility in Heterogeneous Labour Markets.” We are grateful to Christian Dustmann, Lena Edlund, Bernd Fitzenberger, Uta Schönberg, Alfonso Sousa-Poza, Marie Waller, two anonymous referees, and seminar participants at the Universities of Darmstadt, Hannover, Hohenheim, Paris II (ERMES), at the research initiative’s IAB and ZEW meetings in Nuremberg and Mannheim, respectively, and participants at the Ausschuss für Bildungsökonomie (Bern), EEA (Milan), EALE (Amsterdam), RES (Guildford), SOLE (Boston), and Verein für Socialpolitik (Graz) meetings for helpful comments. All remaining errors are our own.

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Correspondence to Patrick A. Puhani.

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Appendices

Appendix 1: Data description

The three datasets used are the Report System [on] Further Education (Berichtssystem Weiterbildung, BSW), the German Socioeconomic Panel (GSOEP), and the Qualification and Careers Survey (Qualifikation und Berufsverlauf, IAB-BIBB).

The BSW is relatively unknown compared to the other datasets. The BSW survey was conducted seven times (1979, 1982, 1985, 1988, 1991, 1994, 1997, 2000, and 2003) by the Federal Ministry for Education and Research (Bundesministerium für Bildung und Forschung); data are provided by the Central Archive for Empirical Social Research, University of Cologne. Each survey year, about 7,000 persons between 19 and 64 years are interviewed orally (this includes employed and non-employed people). The BSW dataset is at present the only regular representative survey containing all kinds of training incidences in Germany.Footnote 20 In contrast to the other datasets, questions on training are the focus of this survey. We take the year 1988 as observations before and 1994, 1997, and 2003 as observations after the reform. Questions on job-related training refer to the last 12 months.

The GSOEP is an individual-level dataset with panel structure. It is the largest representative longitudinal study of private households in Germany. The same private households, persons, and families have been surveyed annually since 1984. In this dataset, we have information on whether a person took part in job-related training in the last 3 years. Observations before the reform refer to 1989 and observations after the reform to the years 2000 and 2004. The GSOEP has been conducted since 1984, but questions on job-related training started in 1989 and were only repeated in 1993, 2000, and 2004. We do not use 1993 because in asking for training during the last 3 years, this wave barely covers the 1992 reform.

The IAB-BIBB data are a representative survey of employed persons, which was conducted in 1985, 1991, and 1998. It focuses on job descriptions and detailed information on qualification profiles and occupational development. Each survey wave consists of more than 34,000 observations; questions on job-related training refer to the last 5 years.

Although there are some questions on job-related training in the German Micro Census (Mikrozensus, MZ), this dataset is not suitable for this analysis, because training participation is underrepresented there.Footnote 21 As pointed out by Wohn (2007), there are several reasons why training participation in the MZ is underrepresented compared to the BSW training participation. Since the other two datasets (GSOEP and IAB-BIBB) have comparable training incidences to the BSW, we focus on these three datasets in the regression analyses and use the Micro Census data only for descriptive analyses (see Fig. 2a–c).

The choice of datasets is driven by information on job-related training at the individual level both before and after the parental leave extension of 1992. Because the treatment group comprises all women of childbearing age, actual information on parental leaves of the mother was not required for a dataset to be used here. Nevertheless, problems do arise in the dataset comparison. First, all three datasets measure the outcome variable, job-related training, for a different period of time: the last 5 years in the IAB-BIBB data, the last 3 years in the GSOEP, and the last 12 months in the BSW. The second difficulty stems from the needs of our difference-in-differences analysis. Not only does it require training incidence observations before and after the parental leave extension, but these can only be done properly by focusing on the most drastic reform, that which lengthens parental leave from 18 to 36 months. However, the post-1992 reform surveys differ enormously in timing: 1994 for the BSW, 1998 for the IAB-BIBB, and 2000 for the GSOEP. Variation also exists in the timing of the pre-1992 reform surveys, which refer to the following years: BSW, 1988; GSOEP, 1989; and IAB-BIBB, 1991. Obviously, these differences must be taken into account. For example, by asking for training in the 5 years previous to 1991, the pre-reform survey refers to a period during which three smaller extensions of parental leave benefits occurred (see the gray-shaded boxes in Fig. 2a–c). The surveys also differ somewhat in their sample sizes, with the largest, the IAB-BIBB, containing more than 16,000 observations per wave. GSOEP and BSW are smaller, the former with over 2,700 observations in 1989 but more than 5,000 in 2000 because of refreshment samples, and the latter with more than 3,000 and 2,000 observations before and after the reform, respectively.

Appendix 2

Table 9 Summary statistics

Appendix 3

Table 10 Before–after estimates (including control variables stepwise)

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Puhani, P.A., Sonderhof, K. The effects of parental leave extension on training for young women. J Popul Econ 24, 731–760 (2011). https://doi.org/10.1007/s00148-009-0295-y

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Keywords

  • Policy
  • Evaluation
  • Education

JEL Classification

  • J16
  • J24
  • J83