Gender Equity Salary Studies:

The Good, the Bad, and the Ugly

Presentation April 3, 2008Presentation April 3, 2008University of Illinois at University of Illinois at

ChicagoChicagoCarol LivingstoneCarol Livingstone

livngstn@uiuc.edu

Gender Equity Studies:

The Bad, the Better, and the Ugly

Why should salaries be equitable?

Fairness – the “right thing to do”

Retention of best faculty

It’s the law

What are our goals in studying salary equity?

• To identify and correct any systematic bias

• To identify and correct any individual salary errors• To emphasize the institutional commitment to gender equity

Some BAD Ways to Study Gender Equity

• Anecdotal evidence

• Simple campus-wide averages

Simpson’s Paradox(The Fallacy of the Averages)

The average salary of female faculty members at one institution is 64% of the average male's salary.

Does this institution discriminate against women?

Suppose the institution has just two colleges, Engineering and Social Work

Fact: Engineers are paid more than Social Workers.

Fact: Engineering is predominantly a male field, and Social Work is predominately female.

College

Number Average Salary

Men Women

Men Women

Women as %

of men

Engr 90 10 100,000

110,000

110%

Social Work

10 30 40,000

44,000

110%

Campus total

100 40

94,000 60,500 64%

Averages are misleading

A BETTER Way to Look at Gender A BETTER Way to Look at Gender EquityEquity

Multiple regression analysisMultiple regression analysis Dependent variable = constant +

independent variable 1 * coefficient 1 +

independent variable 2 * coefficient 2 +

independent variable 3 * coefficient 3 + …

Using Multiple regression to Using Multiple regression to look for systematic look for systematic

discriminationdiscrimination

Include gender or Include gender or race/ethnicity as an race/ethnicity as an

independent variable.independent variable.

A coefficient statistically A coefficient statistically different from zero implies a different from zero implies a correlation between gender correlation between gender

and salary.and salary.

Using Multiple regression to Using Multiple regression to look for individual look for individual

discriminationdiscrimination•Exclude gender and Exclude gender and race/ethnic code race/ethnic code from from independent variables. independent variables. •Find the regression Find the regression

equation.equation.•For each person, see what For each person, see what

salary the salary the regression regression

equation predicts.equation predicts.

Assumptions of Multivariate Assumptions of Multivariate RegressionRegression

•Factors are independentFactors are independent•Each factor is linearly Each factor is linearly related to related to dependent dependent variablevariable

•Variables can be measured Variables can be measured accuratelyaccurately•Populations are sufficiently Populations are sufficiently largelarge•All relevant factors are All relevant factors are includedincluded

Urbana’s History of Gender Equity Urbana’s History of Gender Equity StudiesStudies

•Chancellor commissioned Chancellor commissioned first one in early 90’s. Took a first one in early 90’s. Took a year to complete. year to complete.

•Found some systematic Found some systematic bias, bias, individual bias based individual bias based on genderon gender•Resulted in many salary Resulted in many salary correctionscorrections•Repeated many times since Repeated many times since then; then; results varyresults vary

BOT Gender Equity ReportBOT Gender Equity Report

• All three campuses were All three campuses were asked to submit a gender asked to submit a gender equity report in June, 2000equity report in June, 2000• Included a regression Included a regression analysis of salaries, analysis of salaries, retention and promotion retention and promotion studies, comparisons with studies, comparisons with national benchmarksnational benchmarks

Urbana Gender Equity StudiesUrbana Gender Equity Studies

Nine studies since Nine studies since 1990’s1990’s

(hmmm, 8 ½)(hmmm, 8 ½)

http://http://www.dmi.uiuc.edu/regwww.dmi.uiuc.edu/reg

Urbana ProcessUrbana Process•Tenure-system faculty onlyTenure-system faculty only

•On-going salary, no lump On-going salary, no lump sums sums

•Much manual data Much manual data collection/fixingcollection/fixing

•Periodic revisions, especially Periodic revisions, especially with with input from CSWinput from CSW

Urbana Independent VariablesUrbana Independent Variables•RankRank•DepartmentDepartment•Years from degreeYears from degree•Having a Ph.D.Having a Ph.D.•Administrator flagAdministrator flag•Hired in as assistant Hired in as assistant professorprofessor•GenderGender•Race/ethnic groupRace/ethnic group•Years to reach associate Years to reach associate professorprofessor•Years to reach full Years to reach full professorprofessor

Urbana RegressionsUrbana Regressions

•All faculty combinedAll faculty combined

•Assistant ProfessorsAssistant Professors

•Associate ProfessorsAssociate Professors

•New Assistant ProfessorsNew Assistant Professors

•Others - appendixOthers - appendix

Regression EvaluationRegression Evaluation

R2 – usually about 0.6-0.9

Model significant at the 0.0001 level

Significance of Gender term & Significance of Gender term & RegressionsRegressions

(2004)(2004)

All faculty n.s. 0.74Full professors

n.s. 0.62

Associate professors

n.s 0.77

Assistant professors

Men paid

$1459 more

0.97

New assist profs

n.s. 0.98

Regression Gender effect R2

Coefficients from 2004Coefficients from 2004FY04

Prob > |T|

Full Professor=Y 25,743 0.0001Associate Prof=Y 2,795 0.0294Administrator=Y 17,159 0.0001Number of depts 4,041 0.0001

First hired as an asst prof=Y -12,348 0.0001Doctorate=Y n/s 0.191

Years from degree 355 0.0001Race=Native American n/s 0.718Race=African American n/s 0.5008

Race=Hispanic 4,926 0.0398Race=Asian n/s 0.995Gender=male n/s 0.1057

Y-axis intercept (b0) 71,199 0.0001

A1. All Faculty Combined FY04

Dept factor ranged from –$30,000 to $66,000

Actual Salaries as % of Predicted Actual Salaries as % of Predicted (2006)(2006)

Other regressions runOther regressions run

•Using peer salaries instead of department dummy factor

•Using log(salary) instead of salary as dependent variable

•Added terms interacting gender with other variables: significant but small interactions found with years to reach full professor & number of other departments

Publication/Follow-upPublication/Follow-up• Report, general statistics, Report, general statistics, outcomes reported to Provost, outcomes reported to Provost, Deans and posted on webDeans and posted on web

• Deans & business managers Deans & business managers get list of faculty with actual get list of faculty with actual and predicted salariesand predicted salaries

• Deans must fix or justify Deans must fix or justify salaries 7% or more below salaries 7% or more below predictionprediction

The UglyThe Ugly

• Claiming to have a precise Claiming to have a precise answeranswer

• Taking individual predictions as Taking individual predictions as truthtruth

• Confusing correlation with Confusing correlation with causalitycausality

• Selecting one regression (e.g. Selecting one regression (e.g. all faculty) result over another all faculty) result over another

The UglyThe Ugly

Data wars! Data wars!

Adversarial attitudes from Adversarial attitudes from

administration or faculty administration or faculty

are counterproductive.are counterproductive.

Beyond Salary Equity: HiringBeyond Salary Equity: Hiring

• Who is in the pool?Who is in the pool?• Who applies?Who applies?• Who is on the hiring Who is on the hiring committee?committee?• Who is a finalist?Who is a finalist?• Who gets an offer?Who gets an offer?• What salary is offered?What salary is offered?• Who actually accepts?Who actually accepts?

Beyond Salary Equity: RetentionBeyond Salary Equity: Retention• PromotionsPromotions• Teaching & advising Teaching & advising workloadworkload• Committee assignmentsCommittee assignments• Salary increases, esp. Salary increases, esp. matchesmatches• Administrative Administrative appointmentsappointments• SabbaticalsSabbaticals• Awards/ChairsAwards/Chairs• ClimateClimate

Beyond Salary Equity: Policy Beyond Salary Equity: Policy AnalysisAnalysis

Some data gathering is Some data gathering is helpful, but don’t get bogged helpful, but don’t get bogged down in data.down in data.

Spend your time thinking Spend your time thinking about processes, policies, about processes, policies, and decision pointsand decision points

Questio

ns

Questio

ns

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