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

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


1
Pay Differences by Gender of University Faculty
  • Jenny Hunt, Daniel Parent, and Michael R. Smith

2
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

5
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.

6
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.

9
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.

10
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.

11
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.

12
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.

32
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.

33
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.

36
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.

42
Some other effects
  • At University level, maternity leave has no
    significant effect.
  • At University level, (married to a
    professor)(gender) has no significant effect.

43
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.

44
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.

45
Some broader conclusions
  • There is marked heterogeneity within the
    University.
  • Different mechanisms to produce the same outcome
    e.g. the University of Montreal.
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