Abstract
The Great Recession had led to gender convergence in unemployment rates. In this paper we seek its sources to assess whether this convergence will remain once the Great Recession ends. We use Social Security records to study the determinants of unemployment ins and outs for men and women separately, over the course of a whole business cycle, i.e. 2000–2013. We focus on Spain—a country hit hard by unemployment increases in downturns. We find that unemployment outs are crucial in understanding changes in unemployment rates in Spain as well as to understand the gender convergence in unemployment rates. Among the determinants of the large drop in unemployment outs, lack of demand and negative state dependence emerge as key sources, which affect men more negatively than women. In a scenario of upcoming recovery, unemployment outs will increase for short-term unemployed, particularly for males. On the contrary, both male and female long-term unemployed workers will face enormous difficulties to access a job, as the job access rates for long-term unemployed is not sensitive to the economic cycle. Hence, we expect that the gender convergence in unemployment rates will persist only when considering the long-term unemployed.
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Notes
To avoid odd behaviour in the estimated baseline hazard functions due to the scarcity of observations spanning longer durations, we right-censor spells of unemployment longer than 48 months and spell of employment longer than 240 months.
A monthly spell database becomes extremely demanding from the computational point of view given the long time span considered in the paper. Nevertheless, as robustness checks we compare the results using monthly versus quarterly transitions when possible but we do not find any qualitative differences with respect to the determinants of gender gaps in job loss or job access.
We restrict our study to those who experience a layoff, which fits the concept of job loss better. Moreover, the contribution of quits to the dynamic of the unemployment rate and to the dynamics of the gender gap in the unemployment rate is negligible.
Table 2 presents layoff rates by gender and by job and individual characteristics. Upturn and downturn periods are presented separately.
To put the results in an international context, the Spanish worker flows, as other continental European countries, are characterized by lower values of the job finding and separation rates than the ones computed on US data (Elsby et al. 2013). For instance, Shimer (2012) finds a job finding probability of around 30% and a separation probability of around 2%. Using the LFS, the French job finding probability amounts to 7.5% whereas the separation probability is 1.22%.
Though in the statistical section we have shown the time interval 1997–2013, in the estimation we will restrict the analysis for the upturn to the years 2000–2007.
Bachmann and Sinning (2012) also use the linear probability model to apply the Oaxaca–Blinder decomposition on the estimated transition probabilities. Nevertheless, as a robustness test, we also estimate the hazard rates using the conditional log–log function which is the standard link function for discrete time duration models. We can not compare the detailed decomposition but we do compare the aggregate decomposition one and very similar results are obtained.
A problem related to the detailed decomposition of dummy variables is the arbitrary choice of the reference categories that are omitted from the regression model.
Using the conditional log–log function to estimate the layoff rate, the compositional effects are estimated to be −0.0257 for females and −0.0171 for males whereas the differences in coefficients are estimated to be 0.0262 and 0.0337, respectively.
Within brackets we displayed the covariates more relevant to understand the results obtained. They are derived from a detailed decomposition—not shown in the article for sake of concreteness but they are offered upon request—made by each covariate.
Indeed, in the detailed composition for men, the non-compositional factors are closely related to low educated workers with low job skills in small firms. That is, in the recession men located in these jobs are less protected from being layoff and in the expansionary period.
Table 7 presents the detailed estimation of the job finding probabilities by gender and for expansion and recession. Based on these estimations, we use the GU correction to identify the contribution of each category of dummy variables to explaining gender differences in differences in job access in recession compared to expansion. Though not shown, we have also estimated the same model but restricting the sample to workers aged 25–55 years old, which are highly attached to the labour market. Results are almost identical so we do not report them although are available upon request.
Using the conditional log–log function to estimate the unemployment hazard rate, the compositional effects are estimated to be −0.0465 for females and −0.0348 for males whereas the differences in coefficients are estimated to be −0.0798 and 0.0693, respectively.
That is, if groups that typically expect relatively longer durations enter unemployment in proportionately greater number during a recession, the aggregate average unemployment duration will increase, though average unemployment duration at the individual level will remain hardly the same. We obtain that the variation in the composition of entrants is insufficient to drive the variation observed in aggregate unemployment duration.
Using the information provided by the European Commission, the OECD and the FMI, annual expected growth in GDP for 2015 varies between 2.6 and 3%. For 2016 the official forecasts are very similar. Hence in our simulations, we use a quarterly GDP growth of 0.8%, i.e. an optimistic scenario.
It can be checked how far the Spanish scenario by the end of 2014 (which is already known) resembles any of those depicted in Fig. 6: average quarterly flows of Spanish workers from unemployment to employment in 2014 amounted to 20% for men and 18% for women (see http://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176907&menu=resultados&idp=1254735976595). From our simulated model we find that the job-finding probability in the first year, i.e. 2014, is likely also to be around 18.5% for men and 19% for women.
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The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.
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We are indebted to participants in the seminar at the Bank of Spain and in the SOLE/EALE 2015 meeting for their useful comments and suggestions.
We gratefully acknowledge financial support from research projects from the Spanish Ministry of Science (ECO2012-35820, ECO2013-43526-R), the Basque Government (IT793-13) and the Andalusian regional government (PAI-SEJ479). The usual disclaimer applies.
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De la Rica, S., Rebollo-Sanz, Y.F. Gender Differentials in Unemployment Ins and Outs during the Great Recession in Spain. De Economist 165, 67–99 (2017). https://doi.org/10.1007/s10645-016-9288-x
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DOI: https://doi.org/10.1007/s10645-016-9288-x