This study uses data from the Community Tracking Study's (CTS) Physician Survey sponsored by the Robert Wood Johnson Foundation and conducted by the Center for Studying Health System Change. The survey, which is conducted to better understand how health care delivery in the United States is changing over time, uses the American Medical Association and American Osteopathic Association master files to sample active nonfederal office- and hospital-based physicians practicing a minimum of 20 hours per week in direct patient care. Residents and fellows are excluded.
I combined the restricted-use data files for 1996–1997, 1998–1999, 2000–2001, and 2004–2005 to generate a pooled cross-sectional dataset. There were 12,528 physicians surveyed during year 1 of the study, 12,304 physicians surveyed during year 2, 12,406 physicians surveyed during year 3, and 6,628 physicians surveyed during year 4, for a total of 43,866 physician-year observations in the initial pooled sample. After excluding a supplemental national random sample for which site identifiers were not available, 38,826 observations remained.
The dependent variable of interest in this study, hours of charity care, is a continuous variable defined as the number of hours in the past month that the physician provided free or reduced fee health care (excluding discounted fee-for-service) to a patient because of the patient's financial need. It does not include time spent providing services for which the physician expected but did not receive payment (i.e., bad debt).
The key independent variable of interest in this study is physicians' hourly wage. This variable was constructed by dividing physicians' total annual income from the practice of medicine by the total number of medically related hours worked in the last year, excluding charity care as follows:
An alternate construction of hourly wage is discussed in the limitations.
All dollar figures were adjusted for inflation using the Consumer Price Index and are reported using 2005 dollars. Physician's income in the CTS is defined as net income in the previous year, reported to the nearest U.S.$1,000 and top coded at U.S.$400,000. A total of 1,575 observations (4.1 percent) were top coded. This should have a minimal impact on the construction of the hourly wage variable. To capture nonlinear effects of hourly wage on the provision of charity care, hourly wage squared is included. The percent of physician's income from managed care and percent of physician's income from Medicaid are included. Similarly, control variables are included for percent of physician's income from capitated sources and percent of physician's income from Medicare. These revenue categories are not mutually exclusive (e.g., Medicaid managed care). In addition to the percent of physician's income from managed care, a variable for the number of managed care contracts a physician has is also included. To gauge the impact of managed care restrictions, a variable defined by level of agreement with the question “I can make clinical decisions in the best interests of my patients without the possibility of reducing my income” is included.
Other factors previously identified as being associated with either the likelihood of providing charity care or the amount of charity care provided are also controlled for in the model. These include ownership of practice, practice type, physician specialty, allopathic or osteopathic physician, and physician age, gender, and years of practice experience (Ohsfeldt 1985
; Blumenthal and Rizzo 1991
;). In particular, practice ownership is included because those who own their practice are free to decide how to allocate their time and thus may provide more charity care, while those who are employees of a practice are less autonomous (Reed, Cunningham, and Stoddard 2001
Physician age and years of practice are calculated by subtracting the year of birth and the year in which the physician began practicing medicine, respectively, from the year in which the survey was administered. After careful consideration, age was dropped from the model because it was highly correlated with years of practice (0.94) and no additional explanations for the effect of age on the provision of charity care beyond years of practice were intuitively apparent. Additionally, a test of functional form found that the effect of years of practice was nonlinear and significant. Thus, a variable years of practice squared is also included in the model.
Because the J-1 Visa Program permits foreign medical graduates entry to the United States to practice in underserved areas, and because the demand for charity care is greater in these areas, a positive association between foreign medical graduate status and the provision of charity care seems likely and is controlled for. Then, because the pooled data come from four different study years, three time dummies are included in the model. These variables allow the intercept to shift over time, capturing any time trends in the provision of charity care not accounted for elsewhere in the model. Lastly, to capture differences in the effect of physician wage on charity care over time, the wage variable is interacted with each of the time dummies.
T-tests of summary statistics between observations with and without missing data did not reveal systematic differences in missing data. Therefore, I decided to use complete case analysis. Starting with the 38,826 physician-year sample, I dropped individual observations with noninstructive or impossible values for the following variables (number of observations dropped in parentheses): hours of charity care >744 per month (1), missing data on whether physician is salaried (80), fewer than 0 or more than 52 weeks of work in the last year (68), negative years of practice (1), hourly wage missing or <0 (104), physician did not know or refused to answer if they face financial disincentives (450), income not ascertained (26), international medical graduate not ascertained (9). After these adjustments, 38,087 individuals remained in the sample.