Gender unemployment in the Czech and Polish labour market

Abstract

Making use of EU-Labour Force Survey data, the authors estimated logistic regressions with a maximum likelihood method and found that gender unemployment risk was largely explained by human capital, marital status, receiving financial support, job experience and gender discrimination in both Poland and the Czech Republic. The gender unemployment risk gap amounted to 8% and 10% in Poland and the Czech Republic, respectively. Although the impact of marital status was significant and considerable, married women in the Czech Republic benefited from their marital status on average three times less than men in the Czech Republic, and men and women in Poland. In both countries only women aged below 30 were ‘rewarded’, while women beyond 50 years of age were penalized in terms of unemployment risk. As opposed to that, men up to 60 years old have their unemployment risk reduced all else equalled. The authors argue that this form of possible discrimination in some respects is a better measure of injustice than the commonly used pay gap and it constitutes an alternative dimension of ‘gender inequality’. The results can contribute to better targeted policies against discriminatory practices by enhancing the career paths demanded in the labour market and by breaking the stereotypes rooted in the cultures of Polish and Czech societies

The impact of macroeconomic and structural factors on the unemployment of young women and men

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Comparative Study of Predictive Classification Models on Data with Severely Imbalanced Predictors

“…Ref. [14]- [16] concluded that education and gender affect the employment status of the educated population in Indonesia. Other studies by [17]- [19] highlighted age as factor of unemployment.…”

The Role of Personal Characteristics in Shaping Gender-Biased Job Losses during the COVID-19 Pandemic: The Case of South Africa

“…Given the binary nature of the dependent variable (losing job or not) we used logistic regression to assess the impact of personal characteristics on gender-biased unemployment in South Africa. Logistic regressions and odds ratios have been a popular technique for unemployment studies where the dependent variable was binary and factors relating to the unemployment status have been assessed [31][32][33][34]. Logistic regression estimates the probability of an event occurring.…”