11 THE GENDER PAY GAP IN THE UK : PART 2 FINDINGS Cathie Marsh Centre For Census and Survey Research Wendy Olsen, Vanessa Gash, Leen Vandecasteele, Pierre Walthery, and Hein Heuvelman

2 Aims To review the main findings of the Part 2 report To show the age-specificity of the pay gap bootstrapping methods were carefully applied To show an explanatory model of hourly pay which is based on a structural equation model –latent factor for training Conclusions

3 Part 2 Main Findings 1.Flexible working is not associated with a higher pay gap 2.The only exception is term-time working which causes 6% lower wages Here, employees have holidays during school holidays. This is nearly all done by women. 9% of women had term-time working hours compared with just 1% of men. 3.On-the-job training and employer-funded training are common, are more frequent among women, and are associated with 9% higher wages. 4. Human capital is sometimes wasted via overqualification Education is protective against doing career interruptions But high education is also in the background of overqualification

4 Definitions Flexible working Training Overqualification –A latent factor was created for training, taking up to three stints per year for 4 years –A latent factor for flexible working definitely could not be discerned Discrete conditions of flexibility were highly heterogenous –Overqualification also was not a latent factor Overqualification is a temporary or permanent part of trajectories

5 2.The Age-Specificity of the Pay Gap is a Proxy for Life Stages and Contraints on Women: notice the red central curve here 28% GPG for age 45

6 2. The confidence interval was developed using bootstrapping: notice the width of the 94% confidence interval 28% GPG for age 45 +/- 12% Confidence Interval at Peak for Single Years of Age

7 The Size of the Pay Gap Confidence Interval + / - 12% for individual years on the age axis + / - 8% for 5-year periods on the age axis + / - 5% overall for the UK, given the BHPS data –for example,.20 +/- 5%: { } –Likely to be even smaller if we use a larger data set such as Annual Population Survey –Wage confidence intervals are <2%

8 3.Explanatory Modelling: Introduction to Variables in the X Set These are just some of the covariates used: Age Work experience –years of full-time work experience including self-employment –years of part-time work experience including self-employment –[however those currently self-employed in 2007 were dropped out] Whether working public/private sector Whether working full-time/part-time Highest level of qualifications Industry Occupation Firm size Region Gender composition of occupation (Sex segregation), Male %

9 A Wage Model The regression coefficients are from a path model. This is a partial picture of a structural equation model.

10 A Structural Model A structural model has several outcomes. Each can be related to the other outcomes, with interdependency not in all directions. Exogenous and endogenous variables can be depicted in a path diagram. A HYPOTHETICAL ILLUSTRATION FOLLOWS

11 Latent Variables- Here, we used data for as X1, X2, X3, etc.

12 The Best Model Has 5 Equations

13 Institutional Factors Firm size – helps male wages. Job tenure – not a very strong effect. Public sector – helps to protect womens wages. Important near the lower end of the wage spectrum. Being in a trade union – helps everyones wages, especially womens.

14 Sex Segregation The male percent in ones occupation is associated with higher wages. The size of the effect is substantial. 15% of the overall pay gap is due to this one factor. It benefits males who dominate in male- dominated occupations.

15 Human Capital is Sometimes Wasted via Overqualification

16 Overqualification – Not Just a Gender Issue The dilemma we now face is that being over average education for a given job is not restricted to women at all Definition and measurement (own educ. minus average education as a scale; dummy) Weaknesses of this measure Absence of correlation year-on-year

17 Further research is possible: Extensions: –Regional differentials can be explored through matched samples; –Ethnic gaps (use Annual Population Survey or the raw ASHE data); –Gender combined with other gaps by equality strands; –Structural model of trajectories, using the work histories.

18 Conclusions UK: A lot of progress on the gender pay gap Numerous policy levers exist. Eg.: –Making private sector firms do pay bargaining and pay transparency in approximately the way that public-sector and unionised workplaces do it –Avoiding long domestic career interruptions by making child care cheaper and easily available

19 Key References –Fuller, B., et al. (2002). "Does maternal employment influence poor children's social development?" Early Childhood Research Quarterly 17(4): (Uses SEM) –Oaxaca, R. L. and M. R. Ransom (1994). "On Discrimination and the Decomposition of Wage Differentials." Journal of Econometrics 61(1): –J Swaffield and A Manning. "The Gender Gap in Early-Career Wage Growth.", Economic Journal, 2008, 118(530),