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THE GENDER PAY GAP IN THE UK 1995-2007:

PART 2 FINDINGS

Cathie Marsh Centre For Census and Survey Research

Wendy Olsen, Vanessa Gash, Leen Vandecasteele, Pierre Walthery, and Hein Heuvelman

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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

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Part 2 Main Findings1. 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 overqualificationEducation is protective against doing career interruptionsBut high education is also in the background of overqualification

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Definitions• Flexible working• Training• Overqualification

– A latent factor was created for training, taking up to three stints per year for 4 years 2004-7

– 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

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2. The Age-Specificity of the Pay Gap is a Proxy for Life Stages and Contraints on

Women: notice the red central curve here

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20 30 40 50 60 70Age

3rd pctile Mean GPG 97th pctile

Predicted Log Gender Pay Gap by Age Range,Scaled to Approx. % Pay Gap

28% GPG for age 45

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2. The confidence interval was developed using bootstrapping: notice the width of the 94%

confidence interval

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20 30 40 50 60 70Age

3rd pctile Mean GPG 97th pctile

Predicted Log Gender Pay Gap by Age Range,Scaled to Approx. % Pay Gap

28% GPG for age 45

+/- 12% Confidence Interval at Peak for Single Years of Age

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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%: {.15 . . . .25}– Likely to be even smaller if we use a larger

data set such as Annual Population Survey– Wage confidence intervals are <2%

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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 %

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A Wage Model

The regression coefficients are from a path model.

This is a partial picture of a structural equation model.

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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

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Latent Variables-

Here, we used data for 2004-7 as X1, X2, X3, etc.

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The Best Model Has 5

Equations

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Institutional Factors

• Firm size – helps male wages.

• Job tenure – not a very strong effect.

• Public sector – helps to protect women’s wages. Important near the lower end of the wage spectrum.

• Being in a trade union – helps everyone’s wages, especially women’s.

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Sex Segregation

• The male percent in one’s 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.

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Human Capital is Sometimes Wasted via Overqualification

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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

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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.

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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

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Key References

– Fuller, B., et al. (2002). "Does maternal employment influence poor children's social development?" Early Childhood Research Quarterly 17(4): 470-497. (Uses SEM)

– Oaxaca, R. L. and M. R. Ransom (1994). "On Discrimination and the Decomposition of Wage Differentials." Journal of Econometrics 61(1): 5-21.

– J Swaffield and A Manning. "The Gender Gap in Early-Career Wage Growth.", Economic Journal , 2008, 118(530), 983-1024