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

Tatiana Medková

doi:10.18778/0208-6018.351.05

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.

Highlights

Gender equality is currently an important and frequently discussed topic
The gender impact is most likely caused by the uncertainty in the labour markets, along with a higher unemployment rate in the recession period, with female work‐ ers enduring a higher negative impact
Eurofound (2020) recently pointed out that: “in Europe, people living in the capital city generally have a better quality of life than people living in oth‐ er parts of a country

Summary

Introduction

Gender equality is currently an important and frequently discussed topic. The Eu‐ ropean Union has defined targets to achieve equality in labour market participa‐ tion in the EU and has established a roadmap for increased participation of women in the workforce (European Commission, 2013). There are differences between capital cities and other cities in a country Cap‐ itals are often more heterogeneous than other types of cities, they host the gov‐ ernment, they are likely to be national industrial, cultural and commercial centres with concentration of headquarters of companies. They tend to be economical‐ ly stronger, for example, London has a much higher GDP per capita than the rest of the UK. The probability of being unemployed πi for an individual i is assumed to be dependent on these exogenous variables: 1) x1si a first difference of regional GDP per capital standardised using (8); 2) x2si a first difference of regional population density standardised using (8); 3) x3i and x4i are dummies for a degree of area urbanization: a) densely popu‐ lated area (the base), b) intermediate area (X3i), c) thinly populated area (X4i); 4) x5i to x7i are year dummies for analyzed time period 2011–2014: year 2011 is the base, x5i is the dummy variable with the value 1 for the survey year 2012, x6i is the dummy variable with the value 1 for the survey year 2013, x7i is the dum‐ my variable with the value 1 for the survey year 2014; 5) x8i a dummy variable immig, which takes value 1 for immigrant and 0 for na‐ tive; 6) x9i a gender dummy variable female, with 1 for female and 0 for male; 7) x10i a marital status variable married, with 1 for a married individual and 0 otherwise; 8) x11i to x14i are dummies for 5 age groups: up to years (the base), individu‐ als age to (x11i), individuals age to (x12i), individuals age to (x13i), and individuals age to 66 (x14i); 9) x15i to x16i are dummies for highest educational attainment level, with three categories: primary (the base), secondary (x15i) and university (x16i); 10) x17i to x24i are dummies for profession of an individual as an occupation group by current employment or last employment before becoming unemployed (9 groups by NACE classification with group 1 as the base)

Occupations were created by ISCO with levels

Empirical results

Conclusion