Abstract
I analyze how the imperfectly competitive market for obstetricians and gynecologists (ob-gyns) clears in the face of an excess demand for female ob-gyns. This excess demand arises because all ob-gyn patients are women, many women prefer a female ob-gyn, and only a small portion of ob-gyns are female. I find that both money and non-money prices adjust: female ob-gyns charge higher fees and also have longer waiting times. Furthermore, institutional structure matters: waiting times adjust more when fees are inflexible. In the end, female ob-gyns capture some but not all of the value of the preferred service they provide.
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Notes
1. American Medical Association, 2002.
3. The female share of ob-gyns increased from 7 percent in 1975, to 22 percent in 1990, and to 35 percent in 2000; the female share of ob-gyn residents increased from 13 percent in 1975, to 47 percent in 1990, and to 70 percent in 2000, data from the AMA and the American College of Obstetricians and Gynecologists.
4. A similar non-price rationing using waiting times occurred in gasoline markets in the US in the late 1970s; see Frech and Lee [1987].
5. Moreover, obstetrics and gynecology, a surgical sub-specialty with an arduous training process, has not historically been particularly welcoming to women physicians, and may have presented additional barriers to women's entry.
6. Both a patient's sex role [Weyrauch et al. 1990] and her attitude towards the appropriate role of the physician [Elstad 1994] are strongly related to her preference for a female physician. As these roles and attitudes change, it is likely that patients' preferences for female physicians will change as well.
7. Anecodotal evidence on this point is strong. Thorne [1994], Haar et al. [1975], Chandler et al. [2000], and Ossorio [1999] provide a broad perspective. Quotes from a web forum at http://forums.obgyn.net/ also reflect these preferences: “A female doctor understands — blank —”; “I feel more comfortable confiding my concerns with a woman. This isn't the case for other types of physicians.”
8. The American College of Obstetricians and Gynecologists, in its policy statement “Women's health care: a career in obstetrics & gynecology,” specifically states that “a higher percentage of female obstetrician-gynecologists reflects the new balance in medical schools and residency education programs in the United States… However, male students should not be discouraged from choosing ob-gyn as a career; there are numerous practice opportunities for competent clinicians of both genders.”
9. Whether this discrimination is in any sense legitimate is an interesting question, but one that lies beyond the scope of this paper. Academic and legal discussion on this point has been mixed. See Ossorio [1999].
10. Bickel [2000] provides a useful overview of issues facing women in medicine.
11. The dissimilarity index, representing the share of individuals who would need to switch specialties to make the distributions equal for men and women, was 0.20 in the 1990s. This is small relative to dissimilarity indices for overall gender occupational segregation, which generally exceed 0.60. See Cutler et al. [1999] for details about the dissimilarity index, and Goldin [1990] for more discussion of gender segregation.
12. The main types of managed care organizations include Preferred Provider Organizations (PPO), Health Maintenance Organizations (HMO), and Independent Practice Arrangements (IPA). In a PPO, a physician agrees with an insurance company to accept specific fees in exchange for being part of their network of approved providers. In a staff-model HMO, physicians are employed by the HMO and provide care to all patients who are members without charging fees. A physician may also remain in private practice and yet agree to provide services to an HMO through an IPA. Although numerous and diverse structures have developed in recent years, these three types of organizations effectively describe the geography of managed care in the time period under study: the late 1980s and early 1990s.
13. The 13 medical specialty categories are: general family practice, general internal medicine, specialty with no subspecialties, medical subspecialty, general surgery, surgical specialty/subspecialty, pediatrics, ob-gyn, radiology, psychiatry, anesthesiology, pathology, and other. Details of specialty classification can be found in the series Physician Characteristics and Distribution, published by the AMA.
14. The base case is a male physician in general practice in the Northeast. As discussed below, weights provided with the YPS data are used. All dollar values in pooled specifications are in 1986 dollars. For waiting times and standard fees, only those specialties that have relevant values for these variables were included in the analysis.
15. Because it appeared that some individuals were counting 5-day weeks while others were counting 7-day weeks, waiting times have been recoded from days to weeks. Waiting times have also been truncated at a maximum of 26 weeks. This does not affect the results.
16. It is crucial for the current analysis that the data include a substantial number of female ob-gyns. Because female ob-gyns represented only 1.2 percent of all physicians in 1990, available data sets covering physicians of all ages generally include very few female ob-gyns. A sample of young physicians includes many more female ob-gyns.
17. The 1991 survey included both new observations and repeat observations on some individuals interviewed in 1987. Data counts are as follows: 2,744 observations with data only in 1987; 2,267 with data only in 1991, and 2,334 with data in 1987 and 1991. While a third YPS survey was conducted in 1997, it did not include several key variables and consequently is not appropriate for this paper.
18. The need to weight the sample data derives from two characteristics of the sample: differential response rates within the eligible sample across identifiable sample subgroups, and the selection of an oversample of minority physicians. Consequently, to allow the combined sample to be analyzed as a representative sample of young physicians in the target population of the study, the investigators for the YPS data assigned individual weights. Weights were constructed in two steps. First, a standard cell-based weighting methodology was performed using 104 cells defined by 13 large specialty categories, 2 age categories (under 35 and 35–39), AMA membership, and foreign/domestic medical school graduation. Second, observations from the minority oversample were down-weighted so that the final weighted set of respondents would approximate a random sample. More details can be found in the codebooks to the YPS data.
19. The definitions of the types of contract arrangements have evolved over time, as have physicians' knowledge of these definitions. For these reasons, it seems appropriate to use a variable indicating any type of contract arrangement rather than a specific type of arrangement.
20. An Oaxaca decomposition of the income shows that most of the 30–45 percent difference in annual income can be explained by differences in characteristics. Specifically, 1/4 to 1/3 of the difference is explained by specialty and practice-setting characteristics, while 1/2 to 2/3 can be explained by adding personal and physician demographics. The remaining differential is largely attributable to differential returns to family variables such as marriage, children, and a non-working spouse.
21. Men are also more likely to sub-specialize within ob-gyn. However, because they are more likely to be in the higher-paying sub-specialties this does not explain the difference in fees.
22. The primary results are robust to alternate definitions of contracting. Analysis on a cross-section within each survey year confirms all quoted results. Three versions of a random effects unbalanced panel specification have been run (pooled OLS with clustered errors, maximum likelihood random effects, feasible generalized least squares) and these also confirm all quoted results. In addition, a more detailed specification including additional covariates (number of children; interactions of marriage, parent, or children with gender; past malpractice claims) was tested, and the results were unchanged.
23. This differential is similar in a Tobit specification. A Tobit specification is used for waiting times because many physicians appear to report actual waiting times of zero days (which have been recoded into weeks), and the Tobit corrects for left truncation at zero. It might also be expected that waiting time results would be non-linear as waiting times become excessively large, but further analysis reveals that this is not a significant issue.
24. I run separate regression segregated by contract arrangement (rather than including interactions with contract arrangement) because there are reasons to expect that many aspects of the labor market are different inside and outside of contract arrangements.
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Acknowledgements
I thank David Autor, David Cutler, Martin Feldstein, Claudia Goldin, Daniel Hammermesh, Caroline Minter Hoxby, Christopher Jencks, Lawrence Katz, Joseph Newhouse, Justin Wolfers, the editor, several anonymous referees, and participants in the Harvard Labor and Public Economics Lunch for valuable advice. Any remaining errors are my own. This research was supported by the National Science Foundation, the National Bureau of Economic Research, and the National Institute on Aging.
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Reyes, J. Gender Preference and Equilibrium in the Imperfectly Competitive Market for Physician Services. Eastern Econ J 34, 325–346 (2008). https://doi.org/10.1057/palgrave.eej.9050033
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DOI: https://doi.org/10.1057/palgrave.eej.9050033