Research has shown that female physicians continue to earn less than their male counterparts. From both social justice and feminist perspectives, laws requiring equal pay should provide just income for females as compared to males. However, the literature continues to indicate that in general females earn less than males, a trend that is also true for physicians. Theoretically informed postulates are measured here with structural equation modeling to test the influence of the unique latent construct "specialization" on the income gap while controlling for demographic and contextual variables. The analysis tests the assumption that the influence of specialization is the same for females and males. If the influence of specialization and other variables differs by gender, gender bias in physician income may be conceptually implied. The study uses three waves of data from the Community Tracking Study Physician Survey (CTS). The study finds an income gap between females and males in three waves of the CTS. Gini coefficients show females continue to experience greater income inequality than males, with the Lorenz curves for males being closer to the equality lines. Using 1999 income data, there is a statistically significant income gap between female and male physicians when controlling for weeks worked. Information Technology (IT) use was found to be the most reliable construct measuring the unique latent variable specialization. Structural equation modeling showed indicators of specialization have an influence on the income gap. The variables in the CTS Physician Survey made for a poor construct that failed to measure specialization as a uni-dimensional construct. The variables that influence the income gap were different for females than for males. As policy makers revise or create better laws to protect income equality, gender differences must be taken into consideration.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-1940 |
| Date | 01 January 2006 |
| Creators | Bolyard, Wendy |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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