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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 )

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2 Publication Working Paper No. 17 For the report that was dated 2002, by the same authors, using similar techniques with 2000 data, see: ualpay/walbyolsenreport.pdf

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3 Introduction Re-thinking the dichotomy between human capital and discrimination –Regression was used. –Then fixed effects modelling, –And decomposition of the pay gaps causes. Critique of Oaxaca Using simulation to do decomposition What accounts for the gender wage gap?

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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 EOCs discrimination and other factors interpretation – which is misleading

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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 funded5.9% lower among those funding their own training

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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+ workers11.7% higher wages if firm size is over 500 workers Firm size workers6.2% higher wages if firm size is workers In public sector8.0% higher wages if working in public sector In union or staff association6.2% higher wages if union member ( These are the same regression continued. That regression also has SIC and REGION in it)

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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.

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9 The Human Capital Results Variables: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

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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 3 rd term to represent average improves but does not eliminate problems Separate regressions omit gender despite its significance and considerable effect.

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15 Equations Traditional Oaxaca two-term equation: Mens wage rate relative to womens wage rate = human-capital effect + a residual discrimination effect. The full decomposition of the wage gap equation is offered by: ln w m – ln w f = (X m - X f ) m + ( m - f )X f (Eq. 2) where the X i '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 w m /w f = exp[(X m - X f ) m + ( m - f )X f ] (Eq. 3)

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16 Equations Oaxaca three-term equation (O&R, 1988, 1994): Ln (gap+1) = (X m - X f ) * + ( m - * )X m + ( * - f )X f (Eq. 4) = productivity differential + male wage advantage + female wage disadvantage

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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

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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 terms b) normative contradictions, where the approval of one term has as its dual the disapproval of the other term

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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.

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23 7) Does not handle nicely the factors which are present for one sex but not for the other; 8) Considers womens slopes only in relation to other womens returns -- but the slope is higher whilst the intercept is lower [than men] 9)Considers mens slopes only in relation to other men: lacks a sex term in equation.

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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

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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.

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