1 pay differences by gender of university faculty jenny hunt, daniel parent, and michael r. smith

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Pay Differences by Gender of University Faculty

Jenny Hunt, Daniel Parent, and Michael R. Smith

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Institutional context

Non-union staff association (MAUT) discusses salary with the University administration.

Everyone’s a professor (sort of) – about 1,400 in the various analyses.

Normal career progression involves Assistant-Associate-Full Professor.

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ControlsCoeff. P-value

Arts -5654 0.0003 Two department groups, years since Ph.D., rank.Medicine -4230 0.0145 Three department groups, age, rank.Music -7013 0.0603 Years of service, rankUniversity -4852 0.0001 Ph.D., age, faculty, rank.No significant differences in other faculties* but all coefficients negative.

Arts includes the Faculty of Religious StudiesMedicine includes the Faculty of Dentistry*Education, Engineering, Management, Law, Science

Gender effectESTIMATED FEMALE PAY DISADVANTAGES AND CONTROLS, 2000 STUDY*

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The response

A little less than $1,000,000 assigned over three years to correct female salaries, informed by position of individual salary with respect to relevant regression line.

It was a agreed that the study would be repeated in 2003 to determine whether or not the gap had re-emerged

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Slippage between 2003 and 2009 (1)

A report on regression analysis commissioned from a faculty member (Fall, 2003).

Responsibility for the analysis assigned to a private consulting firm – contract signed in 2005: “The distance and lack of familiarity with McGill data and structure has also, in my opinion, hampered [the firm’s] ability to interpret the data.” Academic Salary Gender Study Status Report, July 25th.

In early 2006 the contract with the private consulting firm was terminated and responsibility for the production of an analysis assigned to Mary Mackinnon and Michael Smith, who approached Jenny Hunt and Daniel Parent for assistance.

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Slippage between 2003 and 2009 (2)

Sometime during 2006 the project was unilaterally appropriated by the Office of the Provost. Various fragments of analysis were presented to the Committee on Academic Salary Policy but no report was produced.

At the request of the SSCOW, in December 2007 a question was asked in Senate on the whereabouts of the report.

Shortly after that, responsibility for the analysis was returned to a group of people associated with the MAUT.

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Coeff. P-value Coeff. P-value Coeff. P-valueArts -2663 0.1209 -2913 0.0839 -2402 0.1472Medicine -4180 0.0391 -4112 0.0379 -3305 0.0818Science 673 0.7478 -2070 0.3895 -2769 0.2779University -4336 0.0001 -2921 0.0036Controls for faculty analyses: experience, department groups, rank.Controls for University analyses: experience, faculty, rank

2002 2006 2007Gender effect

The 2000 model (approximately) applied to more recent data

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The 2000 study was of great value, but

It didn’t tell us much about the sources of gender differences in earnings.

There may have been biases in the 2000 analyses leading to either an underestimate or overestimate of the gender disadvantage.

This sort of analysis usually examines log earnings. CRCs and related awards have become more important

since 2000. A premise: a single coefficient is unlikely to suitably

inform us on pay differences by gender.

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Possible sources of bias

Bias that may increase the measured gender effect …. Department groupings may not fully reflect market

differences across fields of study.

Bias that may reduce the measured gender effect …. Rank was controlled in these models but may be

endogenous with respect to gender. (“If you’re going to discriminate against me in pay, why wouldn’t you discriminate against me in promotion?”)

Department groupings may reflect downward pressure on pay through the devaluation of women’s work.

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Possible sources of gender difference:

systematic disadvantage of some sort or another; differences in mobility potential (Blackaby, Booth, and

Frank, 2005); differences in quality.

We bear these in mind in the analysis and return to them in the conclusion.

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McGill pay policy is likely to influence outcomes ….

Entry-level pay varies substantially, matching market processes common across North American research universities.

Annual pay increases are substantially tied to merit judgments.

Pay may be increased to retain a faculty member who has received an offer from another university.

Pay has (erratically) increased with promotion to full professor.

Pay is further increased through the award of federal and McGill chairs.

Plus, heterogeneity in practices across McGill.

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Choosing how to describe the data

Avoid complicated ways of incorporating indirect effects – e.g., that gender may have a direct effect on earnings but also an indirect effect through time to promotion.

Instead, look at some descriptive information on parts of the McGill pay determination process that might account for differences in earnings by gender.

Then regress log pay on gender and controls. Include awards. Exclude administrative stipends. Exclude ‘part timers’.

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Average merit score, 2003-2006

0 0.5 1 1.5 2 2.5 3 3.5 4

Assistant

Associate

Full

Total

Merit score (reversed rank = 5-rank)

Men

Women

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Proportions of female and male faculty members and of female and male award holders

0 10 20 30 40 50 60 70 80 90

Women

Men

Women

Men

Pro

port

ion

in to

tal

Pro

port

ion

of a

war

d ho

lder

s

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What do these slides tell us?

(Remember, the slides include no controls!) Longer time to promotion may contribute to lower female pay: i) the

proportion of women in Arts and Education is higher than it is in Engineering, Science, and Medicine and the time to promotion is longer in Arts and Education; ii) women in Medicine take longer to be promoted than men.

Merit pay appears not to be a cause of lower pay for female faculty. Men are more likely to have their pay increased with an external

award. Much of the average difference in pay between males and females

is produced by the very high pay received by a small number of male faculty members.

Anomaly and retention – no satisfactory evidence on this.

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No controls in the charts – so move on to regression analysis

Log salary – evidently a technical advantage where the distribution is skewed.

Experience - consistently measured as years since Ph.D. – both number of years, and the square of number of years.

Enter the variables of interest consecutively to see what happens to the gender effect, as consecutive controls are added, adding potentially endogenous factors at the end

Two regression techniques: OLS and median regression. The first is sensitive to extreme values, the second isn’t.

Maximum control for department.

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Statistical significance

P-values routinely reported in this work and used in interpretation.

We have a population. However, the population in 2007 could be viewed as a

sample from a hypothetical set of possible McGills. In practice: I shall take 0.1 (two-tailed) as the

significance threshold. (Cf. Oxana Marmer and Walter Sudmant, “Statistical analysis of UBC faculty salaries: Investigation of differences due to sex or visible minority status”, UBC Panning and Institutional Research.

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Coeff. P-value Coeff. P-valueGender 9.62 0.0001 9.50 0.0001Experience 4.27 0.0003 3.23 0.0026Departments 3.20 0.0021 1.78 0.0669Rank 1.84 0.0315 0.63 0.4283Start Full Professor (without award) 1.47 0.0783 0.41 0.5974Award (without start Full Professor) 1.09 0.1536 0.00 0.9716*When added, the control for departments includes all departments.

Percentage earnings disadvantage of women, with consecutive controls, 2007*

OLS MedianUniversity Level

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What does the university level analysis show?

Using OLS, controlling for all departments: women earn 3% less than men before controlling for rank; Women still earn almost 2% less than men after rank is controlled; The difference becomes weaker or insignificant after either

‘appointed as full professor’ or ‘holds award’ is added;

Using median regression, controlling for all departments: the pay disadvantage of women becomes clearly insignificant once

rank is added. The differences between the OLS and median regression results

show that the extreme values we saw in the density functions are influencing the OLS results.

Neither OLS nor median regression is ‘right’.

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What about within (large) faculty results?

In the previous study and in the reanalysis using the same model, disadvantage was concentrated in the faculties of Arts and Medicine. Our analysis modifies the method used in the previous study in potentially significant ways. In particular:

we use the log of salaries rather than salary in dollars; So far we’ve reported results that have controlled for all

departments.

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Coeff. P-value Coeff. P-valueGender 14.61 0.0001 16.13 0.0001Experience 4.45 0.0257 3.62 0.0622Departments 4.00 0.0402 2.47 0.1918Rank 2.36 0.1153 1.59 0.2653Start Full Professor (without award) 1.53 0.2861 1.01 0.4720Award (without start Full Professor) 1.72 0.2099 0.04 0.7715


OLS Median

with consecutive controls (including detailed departments), 2007

Science

Percentage earnings disadvantage of women within faculties,

ArtsOLS Median

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MedicineOLS Median

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What do these tables show?

Median regressions eliminate disadvantage in all three faculties. OLS suggests female disadvantage in Arts, up to addition of rank.

But remember, years to promotion does not seem to be longer for women in Arts and Education.

There is no evidence of a female earnings disadvantage in Science. In Medicine, after controlling for departments, the OLS result

becomes insignificant. This is surprising given that, using the 2000 model, there were significant or approximately significant differences in Medicine in 2006 and 2007, in which the controls were: experience, departments, and rank. Possible explanations: i) the department dummies used; ii) the switch from raw to log dollars.

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Coeff. P-value Coeff. P-valueDepartments 1 -4,220 0.0656 -978 0.6119Departments 2 -4,691 0.0354 -2,003 0.2900Departments 5 -3,996 0.0973 -107 0.9579Departments 6* -4,281 0.0769 -236 0.9091*This version controls for each department

Earnings disadvantage of women in dollars , Medicine, 2007( with different department controls, and controlling for work experience)

OLS Median

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The previous table replaces log earnings with dollar earnings …

Using OLS, with the following variables in the model: gender, experience, various department controls.

Women are disadvantaged – they earn about $4,000 less - in all models.

The different department specifications do not substantially change the conclusion.

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Some other effects

At University level, maternity leave has no significant effect.

At University level, (married to a professor)*(gender) has no significant effect.

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Back to possible explanations for disadvantage …

Quality – merit awards suggest, if anything, higher female quality.

Limits on mobility of women: i) gender*married to McGill professor interaction – the right sign, but insignificant; ii) the effect of hired at rank of full professor on the gender coefficient might be consistent with this.

Systematic disadvantage: i) women have a lower rate of promotion in Medicine + larger proportions not promoted in faculties with more women (Arts, Education); ii) fewer women get awards; iii) external recruitment brings in fewer women.

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Some broader conclusions

The relative advantages of institutionally specific versus general surveys.

The issue of the tails of the distribution:

1. Income earnings – data sources ‘top coded’ or extreme values deleted.

2. Most analyses focus on central tendency.

3. Some interesting ‘action’ concentrated in the tails; e.g.; Frenette, Green, and Milligan, CJE (2007) – Census data with tax adjustments imputed – overall inequality influenced by falls in relative income at the bottom of the distribution, not picked up by surveys.

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Some broader conclusions

There is marked heterogeneity within the University. Different mechanisms to produce the same outcome:

e.g. the University of Montreal.

1 pay differences by gender of university faculty jenny hunt, daniel parent, and michael r. smith

Documents

smith slide

sources of gender differences

reemerged slide

gender disadvantage

sort of analysis

regression analysis

measured gender effect

market differences