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Marin Clarkberg, DirectorWilliam Searle, Research Associate

Institutional Research and Planning

Faculty Salary Analyses

• In the Division of Budget & Planning, and part of the Provost’s Office

• Mission is to “provide official, accurate, and unbiased information and analysis about the university in support of institutional planning, decision-making, and reporting obligations.”

• IRP provides annual analyses relating to faculty salaries to the Provost and the college deans.

Institutional Research & Planning

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Explain some of what we do to help:

• Ensure salaries are competitive with peers

• Assess the equity of salaries within Cornell

Sorry, but I hope that nothing we present today will be particularly helpful to you in negotiating your own salary.

Goals for today

• Internal data sources

• AAUP faculty salary reporting (as seen in the Chronicle of Higher Education in past years)

• IPEDS (what is now used in the Chronicle)

• Confidential data exchanges with other universities

Faculty Salary Data

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AAUP: Economic Status of the Faculty Report

Chronicle (IPEDS data)

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Chronicle (AAUP data)

Careful: shift in what Cornell was reporting

Chronicle (AAUP data)

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• AAU includes sixty largest research universities– Mix of public and private

– Ivies excluding Dartmouth

• Faculty salary exchange by CIP – “Classification of Instructional Programs”

• 11.0101 Computer and Information Sciences

• 45.0201 Anthropology

• 50.0701 Art, General

AAU Data Exchange

• Out of anti-trust concerns, salary data from private institutions is collected for prior years only

• Specific figures (e.g. average faculty salary for philosophers at Institution X) are restricted to “highest level of university administration (deans and above)”– as per the terms of our exchange agreement

AAUDE Protections

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AAUDE Salaries… for Leadership

ONE SPECIFIC DISCIPLINE

For each institution:

High

Average (*)

Low

AAUDE Salaries… for HLUA

ONE SPECIFIC DISCIPLINE

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2013-14 AAUP vs AAUDE

$-

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

$180,000

Assistant Associate Full

Cornell

Peers (AAUP)

Peers (AAUDE, by discipline)

Comparison with peers

• Internal “equity analysis”

Salary Analyses

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Chronicle (IPEDS data)

$-

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

$180,000

Assistant Associate Full

Men Women

Chronicle (IPEDS data)

95% 96% 95%

80%

85%

90%

95%

100%

105%

110%

115%

120%

125%

$-

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

$180,000

Assistant Associate Full

Men Women Ratio

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• Rank

• Years since PhD

• Endowed chair

• Discipline

• Productivity (but we don’t have it)

• Not sex

• Not race

Factors which should explain salary

Equity analysis: regression

Sal

ary

e.g. Years since PhD

Totally fake data created for illustration purposes only

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Equity analysis: regressionA

ctua

l Sal

ary

PREDICTED SALARY (based on basic things we know)

Totally fake data created for illustration purposes only

Equity analysis: regression

Act

ual S

alar

y

Predicted Salary

WomenMen

Observed gender pay gap because women tend to be below the line

Totally fake data created for illustration purposes only

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Equity analysis: regressionA

ctua

l Sal

ary

Predicted Salary

WomenMen

Observed gender pay gap because women have othercharacteristics that are less well compensated

Totally fake data created for illustration purposes only

Equity analysis: regression

Act

ual S

alar

y

Predicted Salary (based on basic stuff we know)

WomenMen

Totally fake data created for example purposes only

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Equity analysis: regressionA

ctua

l Sal

ary

Predicted Salary (based on basic stuff we know)

“Let’s talk about this

guy”

“What’s going on with this person?”

WomenMen

Totally fake data created for illustration purposes only

Outliers highlighted for discussion with deans:

Equity analysis: regression

Act

ual S

alar

y

Predicted Salary (based on basic stuff we know)

“Let’s talk about this

guy”

“What’s going on with this person?”

“And this one”

??

WomenMen

??

“Why?” Totally fake data created for illustration purposes only

Outliers highlighted for discussion with deans:

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• Rank is huge• Endowed chair, similarly• Discipline is very important in some areas• Without any measures of productivity, a

simple regression model explains 70-80% of the variation in salary

• On occasion, sex has turned up as a “statistically significant” determinant of salary– neither systematic nor sustained

Sex and salary?

Thanks!

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Operating & Capital FY 2015https://dpb.cornell.edu/documents/1000559.pdf