The Impact of Gender on Unemployment: Cross‑country and Within‑country Analysis of the European Labour Markets during Economic Recession

Authors

  • Tatiana Medková University of Economics, Prague

DOI:

https://doi.org/10.18778/0208-6018.351.05

Keywords:

gender, labour market, unemployment, logit, Cook’s distance

Abstract

This paper investigates the impact of gender on the individual probability of being unemployed and makes a cross‑country comparison across 13 European countries during the European recession. Applying a general logit model for each country and capital, whilst controlling for the year, as well as for individual and regional characteristics, the probability of unemployment was estimated using individual labour force data from 2011 to 2014. Cook’s distance is used to examine the differences between labour markets of capital regions (or cities) and non‑capital regions. Using the size of Cook’s distance, models are calibrated, and models which include the degree of urbanization and occupation type are evaluated. The results are presented in the form of a spatial map and show that gender affects the probability of unemployment in the majority of the analysed countries. Overall, the effect is lower in capital than in non‑capital regions.

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References

Carruthers E., Lewis K., McCue T., Westley P. (2008), Generalized linear models: model selection, diagnostics, and over dispersion, Memorial University of Newfoundland, http://www.mun.ca/biology/dschneider/b7932/B7932Final4Mar2008.pdf [accessed: 23.09.2018].
Google Scholar

Davison A. C., Snell E. J. (1991), Residuals and diagnostics, [in:] D. V. Hinkley, N. Reid, E. J. Snell (eds.), Statistical Theory and Modelling: In Honour of Sir David Cox D. V. Hinkley, N. Reid, and E. J. Snell, Chapman and Hall, London–New York, pp. 83–106.
Google Scholar

Dobson J. A. (1990), An Introduction to Generalized Linear Models, Chapman and Hall, London.
Google Scholar DOI: https://doi.org/10.1007/978-1-4899-7252-1

Eurofound (2020), What makes capital cities the best places to live?, European Quality of Life Survey 2016 series, Publications Office of the European Union, Luxembourg.
Google Scholar

European Commission (2013), Barcelona objectives, Publications Office of the European Union, Luxembourg, https://ec.europa.eu/info/sites/info/files/130531_barcelona_en_0.pdf [accessed: 3.09.2018].
Google Scholar

Eurostat (2017), http://ec.europa.eu/eurostat/data/database [accessed: 25.05.2017].
Google Scholar

Fox J., Monette G. (1992), Generalized collinearity diagnostics, “Journal of the American Statistical Association”, vol. 87, pp. 178–183.
Google Scholar DOI: https://doi.org/10.1080/01621459.1992.10475190

Knotek E. S. (2007), How useful is Okun’s Law?, “Economic Review”, vol. 4, pp. 73–103.
Google Scholar

Musterd S., Marciaczak S., van Ham M., Tammaru T. (2016), Socio‑Economic Segregation in European Capital Cities: Increasing Separation between Poor and Rich, IZA Discussion Paper No. 9603.
Google Scholar DOI: https://doi.org/10.2139/ssrn.2713024

Tepperova J., Zouhar J., Wilksch F. (2016), Intra‑EU migration: legal and economic view on jobseeker’s welfare rights, “Journal of International Migration and Integration”, no. 17, pp. 1–20.
Google Scholar

Wooldridge J. M. (2002), Econometric Analysis Of Cross Section and Panel Data, MIT Press, Cambridge.
Google Scholar

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Published

2020-12-15

How to Cite

Medková, T. (2020). The Impact of Gender on Unemployment: Cross‑country and Within‑country Analysis of the European Labour Markets during Economic Recession . Acta Universitatis Lodziensis. Folia Oeconomica, 6(351), 81–96. https://doi.org/10.18778/0208-6018.351.05

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