Dutey-Magni, Peter

Southampton (e-Prints Soton)

Introduction Methods Results Discussion Summary ReferencesOccupational Segregation and EarningsDifferentials between Women and Men:Evidence from the UK Labour Force SurveyPeter Dutey-MagniGeography / Social StatisticsUniversity of SouthamptonLFS User Meeting, 10 December 2012p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesOutlineIntroductionMethodsResultsDiscussionp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesIntroductionDescriptivesDescriptive statistics are the main tools at our service:I Index of dissimilarity If k is the industrial sector, division orgroup drawn from an industrial classification containing Ksuch headings:D =12×K∑k=1∣∣∣∣∣N fkN f − NmkNm∣∣∣∣∣Yet, such statistics have important limitations.p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Fig. 1. – Duncan’s dissimilarity index across industriesSource: LFS 1975-1990 ; QLFS 1992-20120%10%20%30%40%50% SIC 80 ~180 units SIC 90 ~200 units SIC 07 ~270 units SIC 80 ~60 units SIC 90 ~17 units SIC 07 ~21 unitsFig. 2. – Dissimilarity index across occupationsSource: LFS 1975-1990 ; QLFS 1992-20120%20%40%60%80% KOS (376) KOS condensed (160) SOC 90 (353) KOS groups (20) SOC 90 groups (80)Introduction Methods Results Discussion Summary ReferencesIntroductionIntroduction— Rising participation of (increasingly qualified) women in thelabour market.— Persistence of gender segregation across the board— Persistence of a large unexplained inequality of earnings,along with the rise of wage dispersion.p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesIntroductionResearch questionsI Is it possible to measure how much of the gender pay gapis due to occupational segregation?I To what extent is this estimate interpretable?Focus on industrial sectors, with minor controls for occupationalstatus.p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesOutlineIntroductionMethodsResultsDiscussionp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesMethodsModel Random-effect model nesting individuals within 268industrial groups.Data Quarterly Labour Force Survey (2009, Q1 – 2012, Q3),wave 1 only.Response variable ln(hourpay) (Self-reported gross hourlyearnings).Three models:A. Fixed effects.B. Random intercepts.C. Random gender effects.— Modified Oaxaca-Blinder decomposition.— Heckman 2-step correction for selection bias.p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesMethodsExplanatory variables (Level 1 only)I SexI AgeI Months continuously employedI Whether has childrenI Whether works part-timeI QualificationsI Whether works in the public sectorI Whether has a LLTII NS-SEC, 9 headingsI Sampling weightsI Heckman two-stepI interaction terms between gender and most covariatesp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesMethodsRandom effect modelsModel specifications pooled wage regression with a genderdummy δmale, an industrial-level random error term υk and anindividual-level random error term εikln(Wik ) = β0 + βmaleδmale +J∑j=1[βjXij]+ υk + εikυk ∼ N(0, σ2υ) εik ∼ N(0, σ2ε ) Cov(υk , εik ) = 0p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesMethodsRandom effect modelsAdvantages compared to fixed-effect models:I No reference category: assuming a latent overall tendency;I Allows to break down industries into more categories, inorder to capture the whole segregation effect (Kidd &Shannon, 1996) ;I Men and women are treated together in variancecomponents: addressing the identification issue?— Used by Haberfeld et al. (1998) and De Ruijter & Huffman(2003).p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesMethodsOaxaca-Blinder decomposition: original method2 separate OLS regressions are estimated. Male and femalemean wages may then be expressed as follows:ln(W¯m) = βˆm0 +n∑j=1βˆmj X¯mj ; ln(W¯f ) = βˆf0 +n∑j=1βˆfj X¯fjThe pay gap is then decomposed:ln(W¯mW¯ f)= ln(W¯m)− ln(W¯ f )=n∑j=1[βˆmj (X¯mj − X¯ fj )]︸ ︷︷ ︸Structural component+n∑j=1[X¯ fj (βˆmj − βˆfj )]+ (βˆm0 − βˆf0)︸ ︷︷ ︸Returns componentp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesOutlineIntroductionMethodsResultsDiscussionp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesResultsModel ∅Variance partitionLevels SIC headings σ S.E % tot. var.Level 4 Industry divisions (88 units) 0.042 (0.014) 12.8 %Level 3 Industry group (272 units) 0.012 (0.005) 3.7 %Level 2 Industry class (615 digits) 0.014 (0.003) 4.3 %Level 1 Individual 0.258 (0.001) 79.2%p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesResultsModel A: OLS-0.3-0.2-0.10.00.10.20.30.4Structural ReturnsC ont ri but i on t o l og (Wm) - log (Wf ) QualificationsExperienceChildrenWorking part-timeLongstanding illnessPublic sectorOccupation (NS-SEC)Heckman (self-selection)Sampling correctionResidual4.8%3.4% 18.8%14.7%-7.7%-5.7%Figurep.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesResultsModel B: Random intercepts-0.3-0.2-0.100.10.20.30.4Structural ReturnsC ont ri but i on t o l og (Wm) - log (Wf ) QualificationsExperienceChildrenWorking part-timeLongstanding illnessPublic sectorOccupation (NS-SEC)Heckman (self-selection)Sampling correctionResidualSIC07 division4.66%15.0%-6.75%5.67%4.56%2.68%-4.35%p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesResultsModel C: Random gender effect-0.3-0.2-0.100.10.20.30.4Structural ReturnsC ont ri but i on t o l og (Wm) - log (Wf ) QualificationsExperienceChildrenWorking part-timeLongstanding illnessPublic sectorOccupation (NS-SEC)Heckman (self-selection)Sampling correctionResidualSIC07 division4.72%5.80%14.0%-4.58%-6.75%5.30%2.90%Figurep.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesOutlineIntroductionMethodsResultsDiscussionp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesDiscussionModel assumptionsp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesDiscussionEndogeneityDefinition: corr(εi ,Xi) 6= 0Endogeneity leads to an identification problem.I Often overlooked by the literature... unsurprisingly! OnlyBrown et al. (1980) use a self-selection correction. But it isfound to do a poor job (Kidd & Shannon, 1996).I ‘it is quite possible that the sorting process of men andwomen into certain occupations is not exogenous to theearnings model’ (Haberfeld et al., 1998, p. 109).I Miller (1987) argues that occupations used as covariatesraise an issue. It is taking people’s distribution on thelabour market for granted. This ignores pre-marketdiscrimination, which may in turn miss on the actual drivesfor both occupational segregation and wage inequality.p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesDiscussionEndogeneityp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesDiscussionEndogeneityp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesDiscussionEndogeneitySegregation in qualifications?Unequal aspirations?Unequal distribution of housework and childcare?Attitudes?Discrimination?p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesDiscussionInterpretationThe contribution of uk to the wage decomposition may bethought of as between-industry unobserved variation.This is a ‘magic wand’ estimate, based on a series ofassumptions.Where this variation comes from is undetermined.Implications: gender inequality, professional mobility, unequalexposure to economic conditions.p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesSummarySummary— One fourth of the earnings differential between women andmen is associated with between-industry variations.— This comes to saying that the estimated premium enjoyed bymen due to relative segregation across industrial sectors is+5.7 % (including bias).— This estimate is a theoretical one and does not account forsecond order effects.In an ideal world:I either the dissimilarity index is 0⇔ no segregationI or the correlation between δ and uk is 0⇔ neutral effectp.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesSummaryFurther researchAddressing bias:— Recognising the informative sampling design:within-household income correlation.— Endogeneity and unobserved heterogeneity.Adding controls: centralised bargaining, degree subject area,institutional factors.p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesSummaryBrown, R. S., Moon, M., & Zoloth, B. S. (1980). ‘Occupational Attainment andSegregation by Sex’, The Journal of Human Resources, 33(4), pp. 506-517.De Ruijter, J. M. & Huffman, M. L. (2003). ‘Gender Composition Effects in theNetherlands: A Multilevel Analysis of Occupational Wage Inequality’, Social ScienceResearch, 32(2), pp. 312-334.Duncan, O. D. & Duncan, B. (1955). ‘A Methodological Analysis of SegregationIndexes’, American Sociological Review, 20(2), pp. 210-217.Fields, J., & Wolff, E. N. (1995). ‘Interindustry Wage Differentials and the Gender WageGap’, Industrial & Labor Relations Review, 49(1), pp. 105-120.Haberfeld, Y., Semyonov, M., & Addi, A. (1998). ‘A Hierarchical Linear Model forEstimating Gender-Based Earnings Differentials’, Work and Occupations, 25(1), pp.97-112.Horrace, W. C., & Oaxaca, R. L. (2001). ‘Inter-Industry Wage Differentials and theGender Wage Gap: An Identification Problem’, Industrial and Labor RelationsReview, 54(3), pp. 611-618.p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012Introduction Methods Results Discussion Summary ReferencesSummaryKidd, M. P. & Shannon, M. (1996). ‘Does the Level of Occupational Aggregation AffectEstimates of the Gender Wage Gap?’, Industrial and Labor Relations Review, 49(2),pp. 317-329.Miller, P. W. (1987). ‘The Wage Effect of the Occupational Segregation of Women inBritain’, The Economic Journal, 97(388), pp. 885-896.O’Neill, J., & Polachek, S. W. (1993). ‘Why the Gender Pay Gap Narrowed in the1980s’, Journal of Labor Economics, 11(1), pp. 205-228.Smyth, E., & Steinmetz, S. (2008). ‘Field of Study and Gender Segregation inEuropean Labour Markets’, International Journal of Comparative Sociology,49(4-5), pp. 257-281.Yun, M.-S. (2006). ‘Revisiting Inter-Industry Wage Differentials and the Gender WageGap: An Identification Problem’, IZA Discussion Paper No 2427. Available online:http://ftp.iza.org/dp2427.pdf [Accessed 22 November 2012].p.dutey-magni@soton.ac.uk LFS User Meeting, 10 December 2012

Occupational segregation and earnings differentials between women and men: evidence from the UK labour force survey

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