Pay%20Differences%20by%20Gender%20of%20University%20Faculty

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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.
• Everyones a professor (sort of) about 1,400 in
  the various analyses.
• Normal career progression involves
  Assistant-Associate-Full Professor.

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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 firms 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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The 2000 study was of great value, but

• It didnt 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 youre
  going to discriminate against me in pay, why
  wouldnt you discriminate against me in
  promotion?)
• Department groupings may reflect downward
  pressure on pay through the devaluation of
  womens 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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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 isnt.
• 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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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 weve reported results that have
  controlled for all departments.

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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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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) gendermarried 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.