Estimating Salary Compression in an ARL Institution: A University of Colorado at Boulder Case Study

Scott Seaman

Abstract

Salary compression is the narrowing of the pay differentials between people in the same job but with widely varying years of experience. Within academics, the most commonly asserted cause for salary compression is that of a labor shortage. When institutions compete in a job market with more vacancies than candidates, salaries for vacant positions increase faster than salaries for filled positions. As resources are directed at recruiting new hires rather than annual merit increases, productive senior staff find themselves earning similar salaries as new hires. While this has been common in disciplines such as business, nursing, and engineering, there is also anecdotal evidence that the conditions may have existed for this to happen in librarianship during the late 1990s. This case study defines salary compression, reviews the context in which it may arise, and discusses those conditions in which compression may be beneficial or may be detrimental, and examines the statistical tools used to detect evidence of compression within an organization. Multiple regression analysis is used to determine if there is evidence of salary compression among the librarians at the University of Colorado at Boulder.

Full Text:

PDF
Copyright Copyright © The Author(s)


Article Views (Last 12 Months)

No data available

Contact ACRL for article usage statistics from 2010-April 2017.

Article Views (By Year/Month)

2022
January: 0
February: 1
March: 0
April: 0
May: 0
2021
January: 1
February: 0
March: 0
April: 3
May: 2
June: 1
July: 1
August: 1
September: 0
October: 4
November: 1
December: 0
2020
January: 1
February: 0
March: 0
April: 1
May: 3
June: 0
July: 0
August: 1
September: 0
October: 0
November: 1
December: 0
2019
January: 1
February: 1
March: 1
April: 1
May: 1
June: 2
July: 1
August: 2
September: 0
October: 0
November: 1
December: 2
2018
January: 7
February: 1
March: 3
April: 2
May: 4
June: 1
July: 3
August: 2
September: 1
October: 0
November: 1
December: 1
2017
April: 1
May: 1
June: 1
July: 3
August: 3
September: 1
October: 1
November: 1
December: 3