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Gender Differences in Unemployment Dynamics and Initial Wages over the Business Cycle

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Abstract

Using administrative data from Spanish Social Security for the period 2002–2013, we explore differences between unemployed men and women in: their probabilities to find a job, their initial wages if they find a new job, and the likelihood to fall back into unemployment. We estimate bivariate proportional hazard models for unemployment duration and for the consecutive job duration for men and women separately, and decompose the gender gap using a non-linear Oaxaca decomposition. Gender differentials in labour market outcomes are procyclical, probably due to the procyclical nature of typically male occupations. While a higher level of education protects women in particular from unemployment, having children hampers women’s employment and initial wages after unemployment. There are lower gender gaps in the public sector and in high technology- firms. Decompositions show that the gender gaps are not explained by differences in sample composition. Indeed, if women had similar characteristics to men, the gender gap would be even wider.

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Notes

  1. See Fig. 6 in the Appendix.

  2. The principle of Europe 2020: reinforce mutually a Strategy for smart, sustainable and inclusive growth and the Strategy for Equality between women and men. See www.eesc.europa.eu

  3. For a review of this literature, see Altonji and Blank (1999).

  4. Economic models identify two main sources of discrimination. The first one is associated to the prejudice that employers might have against women. Statistical discrimination refers to the underestimation of women’s skills, productivity, and labour market attachment in the presence of imperfect information.

  5. Petrongolo (2004) analyses gender segregation in employment contracts in 15 EU countries using microdata from the European Community Household Panel.

  6. Rebollo-Sanz (2012) points out that the use of an administrative dataset in this type of analysis avoids the seam bias associated with misreported transitions.

  7. See Guner et al. (2014) for a review.

  8. We use the LWLS version with fiscal data that contains information on wages.

  9. Civil servants are not included in the LWLS.

  10. Historical information containing the type of contract is available since 1991 approximately.

  11. To ensure the representativeness of our sample we merge the LWLS data from 2004 to 2013.

  12. We avoid exits through early retirement.

  13. Workers that have contributed to other regimes at any time during the period 1997–2013, such as the Self-employment Special Regime or the Agrarian Special Regime, etc., are excluded from our sample since they follow specific rules in the use of unemployment benefits.

  14. For details on the Spanish Unemployment Insurance System see, e.g., Nagore and van Soest (2014).

  15. Table available upon request (see Supplementary material).

  16. Most of them are in line with Cebrián and Moreno (2007).

  17. Available upon request (see Supplementary material).

  18. To ensure the probability is between zero and one we assume\( \kern0.5em {p}_k=\frac{ \exp \left({a}_k\right)}{\left(1+{\sum}_{l=1}^{K-1} \exp \left({a}_l\right)\right)} \).

  19. The model with three mass points does not converge for the male sample.

  20. According to our goals, we focus on analyzing unemployment exits to any job.

  21. The fact that larger firms pay higher wages is also predicted by the Burdett and Mortensen (1998) model.

  22. Available upon request (see Supplementary material).

  23. This is in line with Guner et al. (2014) but not with Murillo and Simón (2014).

  24. In this context, exits to non-employment include transitions to unemployment (with and without benefits) and out of the labour force.

  25. Estimations with three mass points do not converge.

  26. From job matching theory, jobs are experienced goods and good job matches are those that survive longer.

  27. In line with this, Rebollo-Sanz (2012) finds a spike in the probability of leaving employment corresponding to the moment in which the employee qualifies for unemployment benefits.

  28. According to our goals, the decomposition analysis is studied for the job to non-employment transition.

  29. The crisis in Spain led to a fall of construction and manufacturing jobs (male concentrated sectors).

  30. Based on other estimations (not shown), differences in the return to individual characteristics exist in the two different economic periods.

  31. Given the lower gender gap found in high technology industry and the gender dimension of Europe 2020 seeking for involving more women in IT jobs.

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Acknowledgements

The author wishes to thank the Spanish Social Security Administration for providing the data for this research and Arthur van Soest for his excellent guidance and useful comments and suggestions. I would also like to thank María Rochina Barrachina, Olga Cantó Sánchez, Hans Bloemen, Adriaan Kalwij, Jan van Ours and seminar participants in Tilburg, Luxembourg and Valencia for useful comments. Amparo Nagore acknowledges financial support from University of Valencia (UV-INV-EPDI15-275059). The author received a research grant from the University of Valencia (UV-INV-EPDI15-275059) for a two months research stay in Tilburg University for working on this paper.

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  1. LISER, Campus Belval, Maison des Sciences Humaines, 11, Porte des Sciences, L-4366, Esch-Sur-Alzette, Luxembourg

    Amparo Nagore García

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Correspondence to Amparo Nagore García.

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Appendix

Appendix

Fig. 6

Fig. 6
figure 6

Evolution of unemployment rate by gender in Spain. 2002Q1–2014Q3

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Table 10

Table 10 Definition of explanatory variables
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Nagore García, A. Gender Differences in Unemployment Dynamics and Initial Wages over the Business Cycle. J Labor Res 38, 228–260 (2017). https://doi.org/10.1007/s12122-017-9244-9

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