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, 8th 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 IDs

• Where

- J number of occupational categories

- Fj number of women in occupation j

- Mj number of men in occupation j

- Tj number of workers in occupation j

- F and M total of women and men

J

ID = Σ│(Fj/F) – (Mj/M)│* (1/2) j=1

J

IDs = Σ│[(Fj/Tj) / Σ (Fj/Tj) ] – [ (Mj / Tj) / Σ (Mj/ Tj)]│* (1/2) j=1

Methodology (cont.)

• ‘Sex ratio’ index

• Where- J number of occupational categories

- Fj number of women in occupation j

- Mj number of men in occupation j

A = exp{ 1/J * Σ[ ln(Fj / Mj) – (1/J * Σ ln(Fj / Mj) )]2}1/2

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

Ln(Pij/PiJ) = ln(eXiβj / eXiβJ) = XiBj

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 (Pfj/PfJ) – ln (Pmj/PmJ) = Xfiβfj – Xmiβmj

- ‘discrimination-free’ differences

• Ln (PFj/PFJ) – ln (Pmj/PmJ) = Xfjβmj – Xmiβmj

• where male βmj applied to female Xfj at means

- % reduction in gender differences for each j

Methodology (cont.)• Decomposition 2

- Estimated probability that a hypothetical female worker is in occupation j

• PFj = eXfjβmj /Σje

Xfjβmj

- Expected number of female workers in each occupation j

• Efj = Σj PFj

- ‘Discrimination-free’ segregation index

• ID’ = Σ | (Efj/E) – (Mj/M) | * (1/2)• Where E – expected number of female workers in labour force

Denmark Germany United Kingdom

Men

%

Wom

en

%

fem

ale

shar

e %

Row

N

Men

%

Wom

en

%

fem

ale

shar

e %

Row

N

Men

%

Wom

en

%

fem

ale

shar

e %

Row

N

Legislators, senior officials and managers

10 4 25 139 5 4 29 178 20 17 37 672

Professionals 22 19 43 389 15 12 31 535 14 14 40 492 Technicians and associate professionals

19 32 59 465 15 30 53 791 11 18 52 482

Clerks 7 21 73 247 7 20 62 441 10 27 65 604 Services workers and shop and market assistants

5 16 72 193 4 16 66 325 8 15 57 388

Craft and related trades workers

18 1 5 192 32 7 10 877 20 2 7 443

Plant and machine operators

11 4 21 147 15 6 18 451 13 3 15 312

Elementary occupations

8 4 33 115 7 6 30 253 5 4 32 168

N 1016 871 46 1887 2478 1373 36 3851 2117 1444 41 3561 Chi square (p value) 355.832 (0.000) 692.428 (0.000) 517.440 (0.000)

ID 0.372 0.401 0.310

IDs 0.441 0.396 0.327

A 3.470 2.522 2.733

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

Effects of personal characteristics on occupational attainment, Denmark

Variable Ln(P1/P8) Ln(P2/P8) Ln(P3/P8) Ln(P4/P8) Ln(P5/P8) Ln(P6/P8) Ln(P7/P8) Intercept (S.E)

-0.784 (0.467)

-1.747* (0.647)

-0.815* (0.390)

-0.675 (0.382)

0.271 (0.381)

-2.402 (0.500)

-0.001 (0.363)

Woman (S.E)

-0.589 (0.398)

0.061 (0.350)

0.816* (0.326)

1.440* (0.343)

1.145* (0.361)

-2.661* (0.580)

-0.419 (0.378)

Age-young (S.E)

-1.557* (0.702)

-1.779* (0.635)

-1.782* (0.530)

-1.077* (0.475)

-0.245 (0.448)

-0.300 (0.535)

-0.585 (0.493)

Age-prime (S.E)

-0.262 (0.346)

-0.536 (0.318)

0.090 (0.300)

0.230 (0.316)

0.004 (0.335)

0.327 (0.348)

0.004 (0.342)

Edu-hi (S.E)

3.974* (0.611)

6.273* (0.757)

4.183* (0.551)

1.734* (0.568)

1.010 (0.589)

1.698 (0.686)

-0.829 (0.770)

Edu-lo (S.E)

1.248* (0.446)

2.484* (0.635)

1.958* (0.346)

1.416* (0.321)

0.815* (0.320)

2.312 (0.400)

-.037 (0.296)

Industry (S.E)

-.429 (0.321)

-1.256* (0.313)

-0.624* (0.271)

-0.981* (0.299)

-3.318* (0.625)

2.493 (0.338)

1.290 (0.294)

Children (S.E)

-0.120 (0.179)

0.067 (0.165)

-0.044 (0.155)

-0.301 (0.192)

0.003 (0.186)

-0.355* (0.169)

-0.135 (0.168)

Children * woman (S.E)

0.052 (0.301)

-0.013 (0.254)

-.026 (0.237)

0.069 (0.264)

0.095 (0.261)

0.409 (0.404)

-0.421 (0.350)

Chi-Square Δ-2 log L

1516.909*

Pseudo R-Square

0.627

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

Variable Ln(P1/P8) Ln(P2/P8) Ln(P3/P8) Ln(P4/P8) Ln(P5/P8) Ln(P6/P8) Ln(P7/P8) Intercept (S.E)

-2.572* (0.476)

-3.704* (0.740)

-1.157* (0.260)

-1.684 (0.285)

-1.014* (0.287)

-1.073* (0.237)

-0.108 (0.232)

Woman (S.E)

0.284 (0.269)

0.431 (0.234)

1.085* (0.206)

1.394 (0.220)

1.363* (0.236)

-1.179* (0.229)

-0.717* (0.235)

Age-young (S.E)

0.157 (0.585)

0.148 (0.539)

1.109* (0.374)

0.991 (0.389)

1.141* (0.398)

1.675* (0.376)

0.682 (0.399)

Age-prime (S.E)

0.113 (0.233)

0.319 (0.201)

0.316 (0.183)

0.341 (0.198)

0.440* (0.214)

0.569* (0.183)

0.056 (0.193)

Edu-hi (S.E)

4.585* (0.512)

7.121* (0.765)

3.796 (0.335)

2.507* (0.365)

1.727* (0.372)

1.448* (0.322)

-0.204 (0.372)

Edu-lo (S.E)

2.112* (0.453)

3.325* (0.731)

2.055 (0.224)

2.010* (0.240)

1.149* (0.237)

1.269* (0.186)

0.346 (0.186)

Industry (S.E)

-0.047 (0.222)

-0.471* (0.195)

-0.506 (0.172)

-0.257 (0.184)

-2.744* (0.340)

2.046* (0.174)

1.154* (0.177)

Children (S.E)

-0.324* (0.139)

-0.180 (0.110)

-0.164 (0.102)

-0.205 (0.121)

-0.147 (0.134)

-0.133 (0.093)

0.011 (0.098)

Children * woman (S.E)

-0.348 (0.365)

-0.274 (0.263)

-0.082 (0.230)

-0.006 (0.244)

0.142 (0.250)

0.143 (0.266)

0.344 (0.252)

Chi-Square Δ-2 log L

3081.260*

Pseudo R-Square

0.572

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

Variable Ln(P1/P8) Ln(P2/P8) Ln(P3/P8) Ln(P4/P8) Ln(P5/P8) Ln(P6/P8) Ln(P7/P8) Intercept (S.E)

0.693* (0.229)

-0.735* (0.289)

-0.134 (0.254)

0.532* (0.234)

0.491 (0.247)

-0.057 (0.251)

0.379 (0.247)

Woman (S.E)

0.521* (0.241)

0.366 (0.251)

0.896* (0.249)

1.421* (0.243)

0.931* (0.254)

-1.632* (0.327)

-0.766* (0.300)

Age-young (S.E)

-0.731* (0.276)

-0.451 (0.293)

-0.133 (0.283)

0.319 (0.269)

0.271 (0.282)

0.126 (0.287)

-0.938* (0.331)

Age-prime (S.E)

0.169 (0.225)

0.429 (0.237)

0.376 (0.237)

0.470* (0.233)

0.173 (0.246)

0.446 (0.243)

0.298 (0.247)

Edu-hi (S.E)

1.526* (0.221)

2.950* (0.277)

1.813* (0.239)

0.260 (0.222)

0.669* (0.235)

0.582* (0.234)

0.133 (0.240)

Edu-lo (S.E)

0.800* (0.251)

1.497* (0.316)

1.066* (0.270)

0.390 (0.243)

0.580* (0.262)

0.667* (0.254)

0.194 (0.266)

Industry (S.E)

-0.560* (0.195)

-0.866* (0.213)

-0.988* (0.214)

-0.910* (0.203)

-2.856* (0.339)

1.299* (0.204)

0.703* (0.208)

Children (S.E)

-0.048 (0.111)

-0.299* (0.122)

-0.198 (0.124)

-0.077 (0.121)

-0.078 (0.128)

-0.106 (0.113)

-0.022 (0.117)

Children * woman (S.E)

-0.359 (0.223)

0.128 (0.229)

0.002 (0.225)

-0.183 (0.217)

-0.100 (0.229)

0.145 (0.295)

-0.094 (0.277)

Chi-Square Δ-2 log L

1636.670*

Pseudo R-Square

0.392

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)

Denmark Germany United Kingdom

Kids=0 Kids=1 Kids=0 Kids=1 Kids=0 Kids=1

Managers -0.589 -0.537 0.284 -0.064 0.521 0.162

Professionals 0.061 0.048 0.431 0.157 0.366 0.494

Associate professionals

0.816 0.790 1.085 1.003 0.896 0.898

Clerks 1.440 1.509 1.394 1.388 1.421 1.238

Service workers 1.145 1.240 1.363 1.505 0.931 0.831

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

Effects of personal characteristics on occupational attainment

Denmark Germany United Kingdom

Male Female Male Female Male Female

Managers -0.120 -0.068 -0.324 -0.672 -0.048 -0.407

Professionals 0.067 0.054 -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 workers 0.003 0.098 -0.147 -0.005 -0.078 -0.178

Crafts -0.355 0.054 -0.133 0.010 -0.106 0.039

Operators -0.135 -0.556 0.011 0.355 -0.022 -0.116

Effects of additional child (logit coefficients)

Ref: elementary occupations

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

Category / Country Pmj

(1) Pfj

(2) PFj

(3) Pfj -Pmj

(2) - (1) PFj -Pmj

(3) - (1) % Δ

DK 0.186 0.057 0.191 -0.129 0.005 -1.040 GE 0.054 0.046 0.055 -0.008 0.002 -1.217

Managers

UK 0.224 0.207 0.215 -0.017 -0.009 -0.470 DK 0.031 0.149 0.056 0.118 0.025 -0.791 GE 0.084 0.058 0.096 -0.026 0.012 -1.475

Professionals

UK 0.133 0.109 0.147 -0.025 0.013 -1.544 DK 0.342 0.404 0.357 0.062 0.015 -0.762 GE 0.198 0.406 0.249 0.208 0.051 -0.755

As-te profs.

UK 0.125 0.190 0.137 0.065 0.012 -0.818 DK 0.086 0.246 0.105 0.161 0.019 -0.881 GE 0.086 0.244 0.112 0.158 0.026 -0.833

Clerks

UK 0.105 0.300 0.129 0.194 0.024 -0.877 DK 0.052 0.104 0.093 0.052 0.041 -0.214 GE 0.017 0.154 0.053 0.137 0.036 -0.736

Services

UK 0.056 0.141 0.102 0.085 0.046 -0.462 DK 0.115 0.000 0.055 -0.115 -0.060 -0.477 GE 0.337 0.028 0.225 -0.309 -0.113 -0.635

Crafts

UK 0.170 0.013 0.116 -0.157 -0.054 -0.657 DK 0.113 0.000 0.078 -0.113 -0.035 -0.691 GE 0.153 0.026 0.131 -0.127 -0.022 -0.826

Operatives

UK 0.131 0.009 0.102 -0.122 -0.029 -0.763 DK 0.075 0.039 0.065 -0.036 -0.009 -0.741 GE 0.071 0.038 0.078 -0.033 0.007 -1.216

Elementary

UK 0.056 0.032 0.052 -0.024 -0.004 -0.849

Highest levels of ‘discrimination’ by country

• 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)

ID ID’ % Δ IDs IDs’ % Δ Denmark 0.372 0.113 -0.700 0.441 0.137 -0.690

Germany 0.401 0.112 -0.720 0.396 0.104 -0.738

Great Britain 0.310 0.105 -0.662 0.327 0.104 -0.683

Predicted and actual segregation indices

Conclusions

• Highest level of segregation (IDs) 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’

Centre for Research in Social PolicySchofield Building

Loughborough UniversityLoughboroughLeicestershire

LE11 3TU

Telephone: +44 (0)1509 223372crsp@lboro.ac.ukwww.crsp.ac.uk