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Factors Associated with the Income Distribution of Full-Time Physicians: A Quantile Regression Approach

Authors

  • Ya-Chen Tina Shih,

    1. Section of Health Services Research, Department of Biostatistics, Division of Quantitative Sciences, The University of Texas, M.D. Anderson Cancer Center, TX
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    • Address correspondence to Ya-Chen Tina Shih., Ph.D., 1515 Holcombe Blvd., Box 447, Houston, TX 77030. Dr. Shih is with the Section of Health Services Research, Department of Biostatistics, Division of Quantitative Sciences, The University of Texas, M.D. Anderson Cancer Center, TX. Thomas R. Konrad, Ph.D., is with the Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, NC.

  • Thomas R. Konrad

    1. Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, NC
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Abstract

Objective. Physician income is generally high, but quite variable; hence, physicians have divergent perspectives regarding health policy initiatives and market reforms that could affect their incomes. We investigated factors underlying the distribution of income within the physician population.

Data Sources. Full-time physicians (N=10,777) from the restricted version of the 1996–1997 Community Tracking Study Physician Survey (CTS-PS), 1996 Area Resource File, and 1996 health maintenance organization penetration data.

Study Design. We conducted separate analyses for primary care physicians (PCPs) and specialists. We employed least square and quantile regression models to examine factors associated with physician incomes at the mean and at various points of the income distribution, respectively. We accounted for the complex survey design for the CTS-PS data using appropriate weighted procedures and explored endogeneity using an instrumental variables method.

Principal Findings. We detected widespread and subtle effects of many variables on physician incomes at different points (10th, 25th, 75th, and 90th percentiles) in the distribution that were undetected when employing regression estimations focusing on only the means or medians. Our findings show that the effects of managed care penetration are demonstrable at the mean of specialist incomes, but are more pronounced at higher levels. Conversely, a gender gap in earnings occurs at all levels of income of both PCPs and specialists, but is more pronounced at lower income levels.

Conclusions. The quantile regression technique offers an analytical tool to evaluate policy effects beyond the means. A longitudinal application of this approach may enable health policy makers to identify winners and losers among segments of the physician workforce and assess how market dynamics and health policy initiatives affect the overall physician income distribution over various time intervals.

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