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The role of experience, specialty certification, and practice ownership in the gender wage gap for veterinarians in the United States

Clinton L. NeillDepartment of Population Medicine and Diagnostic Sciences and Center for Business and Entrepreneurship, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.

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Ange Thomas KakpoDepartment of Agricultural and Applied Economics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060.

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Roger MackVirginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060.

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Abstract

OBJECTIVE

To explore the role of various factors in the wage gap between male and female veterinarians in the United States in 2016 and 2017.

SAMPLE

2,760 veterinarians across the United States.

PROCEDURES

Data from the Census of Veterinarians Survey administered by the AVMA Economics Division in 2016 and 2017 were analyzed. The Kolmogorov-Smirnov 2-sample test was used to determine whether a difference existed between male and female income distributions at various levels of experience. Quantile regression was performed separately for male and female respondents to determine the direct effects of individual factors on incomes and compare the effects of practice ownership versus nonownership on income.

RESULTS

Income distributions of men and women were unequal at lower experience ranges but equal at higher experience ranges. Income increased for men with each additional year of experience and with practice ownership. For women, practice owners in the lowest income quantile had a negative return to income; overall, their income benefited most from ownership in the form of partnerships. For certain groups, incomes of both genders were lower when they had absences from the workforce.

CONCLUSIONS AND CLINICAL RELEVANCE

Findings suggested that the largest source of gender income disparity for veterinarians was attributable to female practice owners earning less than their male counterparts. Indeed, women earned more from specialty certifications than from owning a clinic. To decrease income disparities between genders, ways should be identified to support women-owned practices and promote equal pay.

Abstract

OBJECTIVE

To explore the role of various factors in the wage gap between male and female veterinarians in the United States in 2016 and 2017.

SAMPLE

2,760 veterinarians across the United States.

PROCEDURES

Data from the Census of Veterinarians Survey administered by the AVMA Economics Division in 2016 and 2017 were analyzed. The Kolmogorov-Smirnov 2-sample test was used to determine whether a difference existed between male and female income distributions at various levels of experience. Quantile regression was performed separately for male and female respondents to determine the direct effects of individual factors on incomes and compare the effects of practice ownership versus nonownership on income.

RESULTS

Income distributions of men and women were unequal at lower experience ranges but equal at higher experience ranges. Income increased for men with each additional year of experience and with practice ownership. For women, practice owners in the lowest income quantile had a negative return to income; overall, their income benefited most from ownership in the form of partnerships. For certain groups, incomes of both genders were lower when they had absences from the workforce.

CONCLUSIONS AND CLINICAL RELEVANCE

Findings suggested that the largest source of gender income disparity for veterinarians was attributable to female practice owners earning less than their male counterparts. Indeed, women earned more from specialty certifications than from owning a clinic. To decrease income disparities between genders, ways should be identified to support women-owned practices and promote equal pay.

Supplementary Materials

    • Supplementary Figure S1 (PDF 498 KB)
    • Supplementary Figure S2 (PDF 183 KB)
    • Supplementary Figure S3 (PDF 180 KB)

Contributor Notes

Address correspondence to Dr. Neill (cln64@cornell.edu).