Explaining Gender Differences in Unemployment with Micro Data on Flows in Post-Communist Economies

Post-communist labor markets provide an interesting laboratory since unemployment rates grew from zero to double digits and gender differences began to vary greatly across these countries. We provide the first systematic analysis of the determinants of the gender unemployment gap in the Czech Republic using a method that decomposes unemployment rates into transition probabilities (flows) between labor market states, which we calculate using Labor Force Survey data. We extend the analysis to other post-communist economies by evaluating the flows available from existing studies with the decomposition framework. We further examine the flows in the Czech Republic by estimating gender-specific multinomial logit models to learn which factors (demographic, regional, cyclical) other than gender and marital status affect unemployment. We find that women’s lower probability of exiting unemployment for a job explains the lion’s share of the gender gap in the unemployment rates in the Czech Republic and the other post-communist countries for which studies exist. This is also the principal factor explaining married women’s higher unemployment rates compared to married men in the Czech Republic. On the other hand, single men and women’s rates are higher than married men and women’s because they are twice as likely to lose/leave a job for unemployment. We find that age and education are systematically important in explaining flows of both men and women in all these economies, as it is in the more developed industrial economies. The less educated are more likely to be laid off or quit and less likely to find a job. Whereas younger individuals are more likely to be laid off or quit, they are also more likely to find a job.Unemployment, Gender, Transition Probabilities, Flow Analysis, Post-communist economies, Czech Republic