Factors associated with the income distribution of full-time physicians: a quantile regression approach.

Physicians in the United States are generally well compensated, with a median annual income of $175,000 (Kane and Loeblich 2003), nearly seven times the $25,279 that a typical salaried worker earns (U.S. Census Bureau 2002). Yet, with a $127,000 difference between the 25th and 75th percentiles of income, physicians experience sizeable differences in aggregate earnings and sources of income, with a corresponding variety of economic interests (Kane and Loeblich 2003). Ongoing changes in health care financing, organization, and delivery continue to erode the economic autonomy of the medical profession (Starr 1982; Scott 1993) as purchasers, employers, (McKinlay and Stoeckle 1988; Navarro 1988), and consumers (Haug and Lavin 1983; Haug 1988) exercise countervailing power (Light 1993). This fragmentation of the profession has led to diverse preferences for health care reform, depending on a physician's practice specialty, employment setting, location, and clientele. For example, a survey of 300 primary care physicians (PCPs) in North Carolina revealed that PCPs in family or general practice were less likely to support a single-payer system than pediatricians (Millard et al. 1993). Health policy analysts will thus benefit from a better understanding of the factors underlying physician income distribution. Previous studies of physicians' incomes have mostly focused on the impact of selected factors on the average physician income. Certain variables, however, may not affect physician income “on average,” but may have distinct and significant effects at other points of the distribution, such as in the upper or lower percentiles. In this paper, we introduce a conceptual framework and a theoretical foundation to examine factors associated with physician income distribution, and provide hypothesized relationships between income and each selected factor. We then describe our use of both the least squares and quantile regression models (QRMs) in the empirical study to examine the above relationships, using data from the 1996 to 1997 Community Tracking Study Physician Survey (CTS-PS) and the mean, median, 10th, 25th, 75th, and 90th percentiles of physician incomes to capture the range of the distribution. We present our empirical findings and discuss their policy implications, and we also discuss the unique contribution of the quantile regression method to policy makers. Findings from this study may provide a more comprehensive understanding of correlates of physician income, and also help to identify factors contributing to and consequences associated with income inequality among physicians. Physicians' incomes are often important, if indirect, targets of health care policy reforms, while at the same time individual physicians and professional organizations representing different segments of the medical profession are often prominent and vigorous participants in policy debates. The economic and political payoff and fallout associated with any health care policy initiative requires solid information about how relative income gains and losses within the physician sector are likely to be distributed. Hence, information about how income is distributed within this occupational group is critical in crafting proposals for even modest incremental reform.