Occupational Gender Segregation and Discrimination in Western Europe EPUNet 2006, Barcelona, Spain 8 May 2006 Yekaterina Chzhen Centre for Research in Social Policy

Overview Introduction Theoretical background Research objectives Methodology Findings: descriptive analysis Findings: explanatory analysis Conclusions

Introduction The problem of occupational gender segregation - Horizontal segregation - Vertical segregation - Segregation vs. Concentration Overview of existing research Contribution of present study

Theoretical background Theories of occupational gender segregation - Human capital theories - Gender (feminist) theories Gender segregation regimes theory - Formally egalitarian (e.g. UK) - Substantively egalitarian (e.g. Denmark) - Traditional family-centred (e.g. Germany)

Research objectives Determine the levels of occupational segregation in three countries - H1: highest in Denmark, lowest in the UK Compare the effects of observed worker characteristics on occupational attainment of men and women - H2: presence of children<12 in household has largest effect in Germany Contrast the actual occupational distributions of men and women with the hypothetical ‘discrimination-free’ distributions Compare the levels of ‘discrimination’ across occupations and countries - H3: highest in Germany

Methodology Data and variables - ECHP, 8 th Wave (2001) - Unit of analysis: individual (17+) in paid employment 30+ hrs/wk - Dependent variable: ISCO major groups Methods - Indices of dissimilarity - Multinomial logistic regression - ‘Oaxaca-Blinder’ decomposition

Methodology (cont.) Index of Dissimilarity (ID) Standardised ID s Where - J number of occupational categories - F j number of women in occupation j - M j number of men in occupation j - T j number of workers in occupation j - F and M total of women and men

Methodology (cont.) ‘Sex ratio’ index Where - J number of occupational categories - F j number of women in occupation j - M j number of men in occupation j

Methodology (cont.) Multinomial logistic regression Where - i = 1, … n (individual) - J = 1, … J (occupation) - Xi = vector of explanatory variables - Bi = vector of parameters to be estimated

Methodology (cont.) Dependent variable (occupational category) - Legislators, senior officials and managers - Professionals - Technicians and associate professionals - Clerks - Service workers - Craft and related workers - Plant and machine operators - Elementary occupations (reference) Explanatory variables - Woman (1 – woman; 0 - man) - Age-young (1 – ‘17-25’; 0 – otherwise) - Age-prime (1 – ’26-45’; 0 – otherwise) - Edu-hi (1 – third level or above; 0 – otherwise) - Edu-lo (1 –secondary level; 0 – otherwise) - Industry ‘main activity of employer’ (1 – industry; 2 – services) - Children (number of children under age 12 in household)

Methodology (cont.) Decomposition 1 - ‘actual’ gender differences ln (P fj /P fJ ) – ln (P mj /P mJ ) = X fi β fj – X mi β mj - ‘discrimination-free’ differences Ln (P Fj /P FJ ) – ln (P mj /P mJ ) = X fj β mj – X mi β mj where male β mj applied to female X fj at means - % reduction in gender differences for each j

Methodology (cont.) Decomposition 2 - Estimated probability that a hypothetical female worker is in occupation j P Fj = e X fj β mj /Σ j e X fj β mj - Expected number of female workers in each occupation j E fj = Σ j P Fj - ‘Discrimination-free’ segregation index ID’ = Σ | (E fj /E) – (Mj/M) | * (1/2) Where E – expected number of female workers in labour force

The distribution of workers across eight major occupational groups (2001)

Effects of personal characteristics on occupational attainment, Denmark Note: 1. Managers 2. Professionals 3. Associate professionals 4. Clerks 5. Service workers 6. Crafts/trades 7. Operators 8. Elementary occupations

Effects of personal characteristics on occupational attainment, Germany Note: 1. Managers 2. Professionals 3. Associate professionals 4. Clerks 5. Service workers 6. Crafts/trades 7. Operators 8. Elementary occupations

Effects of personal characteristics on occupational attainment, UK Note: 1. Managers 2. Professionals 3. Associate professionals 4. Clerks 5. Service workers 6. Crafts/trades 7. Operators 8. Elementary occupations

Effects of personal characteristics on occupational attainment Effects of being female (logit coefficients) DenmarkGermanyUnited Kingdom Kids=0Kids=1Kids=0Kids=1Kids=0Kids=1 Managers Professionals Associate professionals Clerks Service workers Crafts Operators Ref: elementary occupations

Effects of personal characteristics on occupational attainment DenmarkGermanyUnited Kingdom MaleFemaleMaleFemaleMaleFemale Managers Professionals Associate professionals Clerks Service workers Crafts Operators Effects of additional child (logit coefficients) Ref: elementary occupations

“Oaxaca-Blinder” decomposition of predicted response probabilities (1)

Highest levels of ‘discrimination’ by country - Germany Managerial Sales/services Operative Elementary - United Kingdom Professional Technicians / associate professionals Crafts/trades - Denmark Clerical Most ‘discriminatory’ category in each country - Germany Professionals (-150%) - United Kingdom Professionals (148%) - Denmark Managerial (-100%)

“Oaxaca-Blinder” decomposition of predicted response probabilities (2) Predicted and actual segregation indices

Conclusions Highest level of segregation (ID s ) in Denmark, lowest in the UK Most ‘discriminatory’ occupations across countries - Clerical (female-dominated) - Operatives (male-dominated) - Managerial, except in Denmark Across occupational categories, levels of ‘discrimination’ highest in Germany and lowest in Denmark BUT broadly similar overall degree of ‘discrimination’

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