1

Modelling the Gender Pay Gap

By Wendy Olsen and Sylvia Walby

(Part of a 3-part project on Modelling Gendered Pay and Productivity, EOC

2003-5)

2

Publication

• www.eoc.org.uk

Working Paper No. 17

• For the report that was dated 2002, by the same authors, using similar techniques with 2000 data, see:

• http://www2.umist.ac.uk/management/ewerc/equalpay/walbyolsenreport.pdf

3

Introduction

• Re-thinking the dichotomy between human capital and discrimination– Regression was used.– Then fixed effects modelling,– And decomposition of the pay gap’s causes.

• Critique of Oaxaca • Using simulation to do decomposition• What accounts for the gender wage gap?

4

Human capital and discrimination are not mutually exclusive

• Re-thinking the dichotomy• Human capital theory is re-estimated

– Part-time work is associated with no rise in wage– Interruptions are associated with lower wages

• What is the place of institutions?– Re-interpretation of the coefficients:

• One interpretation focuses on the variables• Other interpretations are suffused with theory,

– E.g. the ‘labour market rigidities’ interpretation– And the EOC’s ‘discrimination and other factors’ interpretation

– which is misleading

5

Regression results:

The main factors influencing wage rates for women and men

•Female 8.9% lower wages if female

•Education (years) 5.7% higher wages for each year of FT education

•Years of full-time employment (curved) 2.6% higher wages for each year of FT work

•Years of part-time employment (curved) 0.8% lower wages for each year of PT work

•Unemployment (years) 2.2% lower wages per year of unemployment

•Family care (years) 0.8% lower wages for each year of interruptions to employment for childcare and other family care

•Recent education not employer funded 5.9% lower among those funding their own training

6

Regression results:

Further (institutional) factors influencing wage rates

•Segregation (male percent x10) 1.3% higher wages per 10% more males in that occupation

•Firm size 500+ workers 11.7% higher wages if firm size is over 500 workers

•Firm size 50-499 workers 6.2% higher wages if firm size is 50-499 workers

•In public sector 8.0% higher wages if working in public sector

•In union or staff association 6.2% higher wages if union member

(These are the same regression continued. That regression also has SIC and REGION in it)

7

Regression results:The results for ‘female’ of –9% are re-affirmed using ten years of

data. (See Appendix of EOC Working Paper No. 17)

•Panel data set for 1992/3, 1993/4, 1998/9, 1999/2000, 2000/2001, and 2001/2 from BHPS

•I merged the annual work-life histories for the people who are in this data set continuously or who enter the data-set as young people later in the panel.

•The work-life history data and annual data are used together, to re-calculate a fixed-effects regression, which shows a huge female factor (a) due to ‘preferences or motivation or discrimination’ (Kim & Polachek). We calculated the 9% figure from their technique for estimation of the gender component of the fixed-effects individual heterogeneity.

8

Figure 2.2 Life-time working patterns of women and men of working age

Source: British Household Panel Survey, 2001/2.

0

2

4

6

8

10

12

14

16

Women Men

Yea

rs

Family

Unemployment

Part-time

Full-time

9

The Human Capital ResultsVariables: Education (Scaled in years)

The length of the working-life that was spent in full-time work

The length of the working-life that was spent in part-time work

The length of time spent in interruptions of the working-life for caring and family work

Other periods: Unemployment; Longterm sick/disabled periods.

Training on the job that is employer-funded or at the place of employment

Training during the past year that is not employer funded nor on the premises of the employer

10

Figure 2.3 Actual wages, work experience and years spent on family

care

0

Source: British Household Panel Survey, 2001/2.

4

5

6

7

8

9

10

11

0 2 4 6 8 10 12 14 16 18 20

Years

Wa

ge

s

Family care years Years worked part-time Years worked full-time

Years worked full-time

Years worked part-time

Family care years

11

Figure 2.1 Wage rates (predicted by model) and formal education Source: British Household Panel Survey, 2001/2.

£0

£2

£4

£6

£8

£10

£12

£14

8 10 12 14 16 18

Education, formal, in years

Wag

e in

pou

nds/

hour

12

13

14

Oaxaca

• Operationalises the dichotomy between human capital and discrimination

• Poor grasp of institutional causes of gender wage gap (Juhn, Pierce Murphy extension)

• Estimates of discrimination unstable and arbitrary, depending on choice of comparator: men, women, all. (O&Ransom; Neumann)

• Inclusion of 3rd term to represent ‘average’ improves but does not eliminate problems

• Separate regressions omit ‘gender’ despite its significance and considerable effect.

15

Equations

• Traditional Oaxaca two-term equation:• Men’s wage rate relative to women’s wage rate = human-capital

effect + a residual discrimination effect. • The full decomposition of the wage gap equation is offered by:

• ln wm – ln wf = (Xm - Xf) m + (m - f)Xf (Eq. 2)• where the Xi's refer to the mean for men and women of each

variable. The i are the slope coefficients for the men and women respectively.

• Hence wm/wf = exp[(Xm - Xf) m + (m - f)Xf ](Eq. 3)

16

Equations

• Oaxaca three-term equation (O&R, 1988, 1994):

• Ln (gap+1) = (Xm - Xf) * + (m - *)Xm + (* - f)Xf

(Eq. 4)

= “productivity differential” + male wage advantage + female wage disadvantage

17

Beyond Oaxaca: Originality in the Research So Far

• A single, full (integrated by sex) regression, with institutional as well as individual factors included

• Gender a variable in that regression• Heckman to eliminate potential sample

selection bias [also done in panel]• Simulation to estimate size of components

of gender wage gap

18

Table A4.1 Details of the decomposition by simulation

X X

Men's average

Women's average

Change factor

Overall coefficient

Simulation effect

Simulated change as a % of the pay

gap

Pence/hour £ equivalent

Female 0.00 1.00 -1.00 -0.09 0.09 0.38 0.87 Currently mothering 0.00 0.28 -0.28 -0.02 0.01 0.03 0.06 Education (years) 12.63 12.31 +.32 0.06 0.02 0.08 0.18 Years of full-time work 13.10 7.57 +5.53 0.026 0.14 0.61 1.40

19

Table A4.1 Details of the decomposition by simulation

X X

Men's average

Women's average

Change factor

Overall coefficient

Simulation effect

Female 0.00 1.00 -1.00 -0.09 0.09 Currently mothering 0.00 0.28 -0.28 -0.02 0.01 Education (years) 12.63 12.31 +.32 0.06 0.02 Years of full-time work 13.10 7.57 +5.53 0.026 0.14 Years of full-time work squared

4035.90 1476.88 -0.00004

-0.10

Years of part-time work 0.26 3.35 -3.09 -0.008 0.02 Years of part-time work squared 1.95 42.52 0.00041 -0.02 Months of unemployment 8.37 4.43 -0.0018 Months of family care 0.77 48.57 -47.8

-0.00069 0.03

20

Table 3.1 Simulation decomposition of the GB hourly gender pay gap, 2002

Variable £ per hour Per cent

Education: 0.18 8

- Education (years) 0.18 8

Life-time working-time patterns: 0.84 36

- Years of full-time employment 0.44 19

- Years of part-time employment 0.08 3

- Years of family care 0.32 14

Labour market institutions: 0.39 18

- Segregation (male% x 10) 0.22 10

- Firm size 500+ workers 0.04 2

- Firm size 50+ workers 0.05 2

- In union or staff association 0.01 1

- Other institutional factors1 0.07 3

Other female: 0.87 38

- Other female 0.87 38

Total gender pay gap 2.28 100

21

Problems with Oaxaca-Blinder

1) The labelling of slope and levels components(endowments: Oaxaca and Ransom 1999;discrimination vs. productivity, O&R 1994);2) Interpretive contradictions

a) descriptive contradictions, where the operationalisation of discrimination is found both in both the “discrimination” and the “productivity” termsb) normative contradictions, where the approval of one term has as its dual the disapproval of the other term

22

3) Arbitrary reference point of the male wage equation (Applies only to two-term Oaxaca, not to 3-term version found in O&R 1988; Neilsen 2000)

4) Arbitrary reference point of one category, e.g. lowest level of educational qualification;

5) Oaxaca discourages adding up the three terms (or two terms) horizontally to see the net effect of each associated factor

6) Not well adapted to the factors other than human capital: inherently individualistic.

23

7) Does not handle nicely the factors which are present for one sex but not for the other;

8) Considers women’s slopes only in relation to other women’s returns -- but the slope is higher whilst the intercept is lower [than men]

9)Considers men’s slopes only in relation to other men: lacks a sex term in equation.

24

Summary: What makes a difference to rates of pay?

• Gender• Motherhood (current and former)• Employment experience (nuanced)

– Part-time (not pro-rata, not neutral, but negative)– Interruptions for child and other family care– Training, tenure

• Segregation• Institutions: firm size, public sector, union

membership• Region and industry

25

The Next Two Stages of Research

• 1. We have simulated the effects of changing the values of X-variables, e.g. education, training, occupational segregation, and the work-histories.

• 2. We give results for each type of woman.• 3. The aggregation of results is costed out

(as a cost-benefit analysis) for 4 stakeholder groups.