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

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

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

4. 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)

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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)

11. 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

12. 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

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

14. 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

15. 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

16. 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

17. Effects of personal characteristics on occupational attainment Effects of being female (logit coefficients) DenmarkGermanyUnited Kingdom Kids=0Kids=1Kids=0Kids=1Kids=0Kids=1 Managers-0.589-0.5370.284-0.0640.5210.162 Professionals0.0610.0480.4310.1570.3660.494 Associate professionals 0.8160.7901.0851.0030.8960.898 Clerks1.4401.5091.3941.3881.4211.238 Service workers1.1451.2401.3631.5050.9310.831 Crafts-2.661-2.252-1.179-1.036-1.632-1.487 Operators-0.419-0.840-0.717-0.373-0.766-0.860 Ref: elementary occupations

18. Effects of personal characteristics on occupational attainment DenmarkGermanyUnited Kingdom MaleFemaleMaleFemaleMaleFemale Managers-0.120-0.068-0.324-0.672-0.048-0.407 Professionals0.0670.054-0.180-0.454-0.299-0.171 Associate professionals -0.044-0.070-0.164-0.246-0.198-0.196 Clerks-0.301-0.232-0.205-0.211-0.077-0.260 Service workers0.0030.098-0.147-0.005-0.078-0.178 Crafts-0.3550.054-0.1330.010-0.1060.039 Operators-0.135-0.5560.0110.355-0.022-0.116 Effects of additional child (logit coefficients) Ref: elementary occupations

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

20. 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%)

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

22. 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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