This study analyzes factors associated with physician income distribution using quantile regression analyses, and to our knowledge, is one of few such studies (Kugler and Sauer 2005
). This method allowed us to examine issues beyond the “mean” or “typical” level of inquiry normally associated with physician income studies. As we anticipated, quantile regression analysis permits the identification of variables that affect physician income on a different scale at various levels of the income distribution.
In some instances, variables exhibiting statistically insignificant differences at the mean were found to be significant at other levels. For example, the income of academic PCPs was found to be compatible with, and higher than that of nonacademic physicians at the mean, and the lower tail, respectively. This finding suggested that academic PCPs may receive more favorable income at entry level, but that their income may grow at a much slower rate than the nonacademic physicians; consequently, their income advantages diminished at the higher income levels. Findings like this not only provide more information to help researchers disentangle the intricacy of factors associated with physicians' income distribution, they represent the situations where policy recommendations based on the conventional method may fail to recognize some effective policy parameters, thus, missing real opportunities to “make a difference” in decision making. A good example from our data is whether policy makers should target IMG specialists to recruit to ease the shortage of specialists in under-served communities. Our results showed that the incomes of IMG specialists were not statistically different from those of non-IMGs at the mean; but at the 10th percentile, IMG specialists earned significantly less than non-IMGs. This finding suggests that programs providing financial incentives for physicians to relocate to underserved areas are likely to be attractive to a group of lower-income IMG specialists. Using only information from the least squares model, policy makers would not know that a group of IMG specialists may be responsive to such programs.
In other instances, variables found to be significant at the mean were not found to be significant at some other levels. For example, autonomy was significantly positively associated with incomes at the mean for specialists, but not at the higher levels of the distribution; a finding suggested that specialists who valued autonomy highly may exhibit certain personality traits that were more important to one's financial success at the early stage of one's career but less so at the late stage. In these cases, policy makers may design policies around certain parameters based on the information from the mean estimated from the conventional method, anticipate seeing an impact but find the policy to be ineffective. The variable of MSA in the analysis of specialists can be used to illustrate our point. At the mean level, a significant difference was found between urban and rural specialists. Some policy makers may decide that the shortage of rural specialists is due to the lower income of specialists in those regions and decide to provide financial subsidies or bonuses to increase specialists' income in rural area as a way to retain specialists in rural communities. However, our study suggested that at the 10th percentile level, there was no difference in incomes between MSA and non-MSA specialists, but the incomes of these two groups were significantly different at the middle range (the 25th percentile and median) of the distribution. Therefore, unless the amount of subsides or bonuses offered is large enough to be financially attractive to rural physicians at the middle range of their income distribution, these policies are unlikely to succeed.
Our findings have produced data that will assist health workforce policy makers to project the impact of proposed policy initiatives, whether regulatory or market-based, on the physician labor market. Information on the local variations across health care markets that may affect physicians' income distribution may, in turn, assist policy analysts to assess the political reaction of physicians' professional organizations in support of or opposition to policy initiatives that are intended to encourage a more rational allocation of health care resources. Most importantly, these analyses can help identify appropriate parameters for workforce policies targeted at reducing income disparities between segments of the physician workforce while simultaneously improving the geographic distribution of physicians and influencing the relative proportion of specialists and generalists in the physician workforce. One concrete example that builds on our findings is a better design of incentive-based recruitment policies for physician relocation. Recall that our analyses reported a higher income among specialists in MSAs at the middle range of the income distribution and a lower income of IMGs at the 10th percentile of specialists' incomes. This finding suggests that policies providing financial incentives to IMGs at lower income levels are likely to be effective and relatively less costly relocation strategies to alleviate the shortage of specialists in rural communities. In fact, that may be what successful state and federal loan repayment programs have been doing by offering to pay off the education debts of relatively young, lower-income physicians in return for a period of service in rural or underserved practice settings (Pathman et al. 2000
Findings from our study contribute baseline observations for longitudinal analyses to examine how changes in health policies impact physician income distribution. These findings are pertinent for the community of health workforce policy makers attempting to respond with meaningful policy initiatives to the systematic changes produced by the trend of managed care. We found that at all levels of income, the effects of managed care penetration are demonstrable but are more pronounced at the higher levels of physician income. This is consistent with a major objective of managed care plans in the 1990s—to reduce utilization of higher priced medical services, presumably by targeting those with higher fees or procedure volumes. However, within markets driven by managed care, the individual physician's participation in more managed care plans is associated with higher income. This is consistent with the notion that physicians can resist economic pressures where actual competition occurs between health plans for purchase of medical services rather than in near-monopsony arrangements, which erode their bargaining power. Consequently, managed care is related to income level, but in a relatively complex manner, and the modeling approach we have taken to examine these relationships enables a more subtle and informed interpretation of how various segments of the physician workforce are affected.
A study based on the 2001 AMA Patient Care Physician Survey reported unadjusted median income differences by various physician characteristics employment type, specialty, census division, board certification status, gender, age, and country of medical school graduation (Kane and Loeblich 2003
). Our study, after adjusting for many possible confounding factors, found similar associations with the majority of the above variables but on a much smaller scale. A disturbing trend found in the AMA study was a widening gender gap of earnings among physicians in the late 1990s, and continuing into the current millennium. Our analyses showed that even after accounting for differences in working hours, ownership status, and specialty, the female/male income disparity persisted but was more pronounced at lower levels of the income distribution. Male physicians on average worked more hours than female physicians, thus, when we explored the gender difference in hourly wages, the gender differentials in median wages of PCPs and specialists were reduced but remained statistically significant. This finding indicated that part, but not all, of the observed gender differences in physician income can be attributed to male physicians working more hours per week. Future studies should apply the quantile regression method to disentangle the factors contributing to this apparently increasing income disparity by gender.
The nonlinear relationship between age and income observed in the univariate analyses by Kane and Loeblich (2003)
was supported at most levels in our multivariate analyses. A similar pattern persisted when examining the relationship between age and wages among specialists, although the magnitude of differences between middle-aged and older specialists decreased, suggesting that the lower working hours observed in older specialists attributed to part, but not all, of the differences in income distribution across various age categories. Among PCPs, a nonlinear relationship was not found between age and wages. Although a significant lower wage was found in younger PCPs when compared with older PCPs at most levels, there was no difference in wages between middle-aged and older PCPs, indicating that the observed nonlinear relationship between age and income was possibly due to older PCPs practicing fewer hours.
Our study encountered several methodological challenges. The first challenge involved incorporating complex survey designs in the estimation of quantile regressions; we addressed this issue by obtaining weighted estimates with standard errors generated from bootstrapping with resampling within PSUs (see footnote 2
). The second methodological issue concerned the top coding of income variables. In the restricted CTS-PS data, physician income was top coded at $400,000, which was close to the 99th and the 92nd percentile of the income distributions of PCPs and specialists, respectively. Therefore, the truncation in income caused by top coding should be less problematic for analyses of PCPs, especially at the lower percentiles. At the 75th percentile and higher of the income distribution of specialists, approximately 34 percent of the conditional quantiles were above the censoring point ($400,000) and the proportion increased to 86 percent for the 90th percentile and higher of the distribution. Therefore, estimates of specialists' incomes at the higher percentiles were likely to be biased due to top coding. One solution is to use a censored quantile regression (Buchinsky 1998
); however, methods to apply these algorithms to complex survey data have not yet been developed.
A third methodological problem concerned potential endogeneity. The endogeneity of hours worked per week could be addressed using instruments such as marital status or number of kids in the family; however, none of these variables were collected in the CTS-PS. Therefore, we isolated the effect of hours by comparing the results between regression models using income versus wage as the dependent variable. Unobserved characteristics may motivate physicians to self-select into certain practice types or ownership status, and these characteristics are likely to place physicians at different points of the income distribution. One advantage of quantile regression is that it allows us to estimate income at various points of the distribution, which may reflect the distribution of these unobserved characteristics (Arias, Hallock, and Sosa-Escudero 2001
). Therefore, by examining the impact of these potentially endogenous variables at various percentiles throughout the income distribution, we would be able to infer the direction of biases at the mean. If we believed that “entrepreneurship” was the unobserved variable that correlated with physicians' decisions to become full- or partowners, then we could infer the effect of ownership on physicians' incomes by exploring the relationship between “entrepreneurship” and income. The increasing difference in incomes from lower to higher percentiles between PCPs who were fullowners and those who were nonowners may be due to the fact that business risk tended to have stronger financial impact on the more entrepreneurial physicians, making a more dramatic effect on their losses and gains. If entrepreneurship could be measured, then the difference between fullowner and nonowner PCPs would likely decrease once entrepreneurship was added to the least square regression model. Although some of the biases caused by endogeneity may be mitigated or conjectured through the use of quantile regression, a more recognized approach is to utilize a panel data set to remove the unobserved individual-specific effects by differencing between two periods (Kyriazidou 1997
; Askildsen, Baltagi, and Holmas 2003
). The more recent rounds of the CTS-PS (Round Two for 1998–1999 and Round Three for 2000–2001) contain a subset of physicians who were interviewed at each round of the survey; future research can utilize the panel data formed by this subset to address the endogeneity bias caused by self-selection.
This study provides a successful demonstration of the feasibility of an analytical framework using the quantile regression method to study physician income distribution. While one-time cross-sectional studies can be useful, more and better information can be obtained with repeated cross-sectional or true longitudinal designs. Such approaches can isolate winners and losers and assess how market dynamics and health policy initiatives affect the overall physician income distribution over various time intervals.