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The dynamic of the gender gap in the European labour market in the years of economic crisis

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Abstract

Closing the gender gap in the labour market is one of the main goals of European Union and part of a wider effort to eliminate social inequalities. In recent decades, all developed countries have suffered a deep global economic crisis, that has increased social and economic inequalities. In Europe, the crisis involved problems of European stability and growth, but the crisis did not affect the euro-area countries to the same extent, and the consequences and recovery were correspondingly asymmetrical. In this paper, we analyse the changes that occurred in the gender gap in the European labour markets from 2007 to 2012 to understand if the recession has further increased or reduced the gender differentials. At this aim, we combine the use of two different statistical methodologies. Through the composite indicator methodology, we test how the rank of countries in relation to gender equality has changed in these years. In addition, the Dynamic Factor Analysis allows us to identify the factors that drive these changes. Moreover, the contextual analysis of the measures that were utilized to face the crisis could give policy makers some useful suggestions on the most efficacious actions to take.

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Notes

  1. For sake of brevity, the results obtained for each aggregation method are not reported, but are available on request by authors.

  2. The indicators choice was inspired to the need of considering all the measurable differences between female and male employees arising from labour market, in terms of presence and condition reserved them. In this view, no compensatory effects have been considered, for example, between horizontal and vertical segregation indexes. Also the possible overlapping between the different contractual forms, for example who has both a part-time and a temporary job contract, is not controlled for, because they measure two different aspects of the labour condition.

  3. For sake of brevity, the values assumed by indicators are not reported here, but they are available by Authors on request.

  4. The steps followed to bootstrap the 95 % confidence interval for the median value are: (1) To resample with replacement from the original data set, creating 2500 bootstrapped data sets. (2) To independently compute the median value for each bootstrapped data set. (3) To compute the 95 % confidence interval from the set of computed median values from the bootstrapped data sets using the normal approximation method (Xu and Long 2005).

  5. Note that in country projection through ACP we do not correct indicators for direction because, despite the composite indicator methodology, where compensatory issues could arise, here the interpretation of results is based on the contrapositions of country-points in the four quadrants of factorial plane.

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Authors and Affiliations

  1. Department of Management and Quantitative Studies, University of Naples “Parthenope”, Via Generale Parisi, 13, Naples, Italy

    Rosalia Castellano & Antonella Rocca

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  1. Rosalia Castellano
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  2. Antonella Rocca
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Correspondence to Antonella Rocca.

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Castellano, R., Rocca, A. The dynamic of the gender gap in the European labour market in the years of economic crisis. Qual Quant 51, 1337–1357 (2017). https://doi.org/10.1007/s11135-016-0334-1

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