UC Berkeley

This dissertation uses tools and models from labor economics to study two information problems in healthcare markets: the uncertainty of patients regarding the quality of medical care and the asymmetry of information between physicians and patients. These problems may lead to market failure and impact patient care, but our current understanding of the consequences of each is imperfect.

I first consider patients' difficulty in determining the quality of medical services, focusing on technical skill of cardiac specialists. While it is difficult for patients to judge the skill of cardiac specialists due to information problems, referring doctors may have access to quality information unavailable to patients. This chapter considers whether the referral relationship between primary care physicians and specialists mitigates problems arising from patients' lack of information in this context. In particular, I measure the extent to which referring doctors learn about specialist quality by observing patient outcomes and use this information to select specialists on patients' behalf.

This chapter presents a model of the referral relationship with public learning by PCPs about specialist quality. The model makes predictions for specialists' careers. In general terms, the model predicts that careers of specialists should diverge by quality over time. I test predictions of the model using the universe of Medicare claims filed by cardiac specialists in the U.S. from 1996-2005. Specifically, I compare careers of higher and lower quality specialists using a new measure of specialist quality that is robust to nonrandom patient sorting. The evidence suggests some degree of learning by PCPs: lower quality specialists are significantly more likely to drop out of the labor market and to change geographic markets over time. For young cohorts, learning also results in improved sorting of patients to providers based on risk characteristics over time.

The next chapter, which is joint work with M. Marit Rehavi, addresses the asymmetry of information between physicians and patients. Specifically, it measures the extent of agency problems arising from this inequality, focusing on the decision to perform C-sections. We do this by comparing the probability of receiving a C-section for physician-patients with the probability for non-physician professionals. The research design exploits the fact that physicians are better informed regarding the appropriateness of recommendations and treatments than the average professional. As such, treatments for this group provide a near-fully-informed baseline that allows us to isolate the effects of information and agency problems.

We carry out this analysis using vital statistics data from the state of Texas, including every registered birth from 1995-2008. We find evidence consistent with agency problems in the physician-patient relationship. Physician-patients are approximately 5% less likely to have a C-section than other highly educated patients, controlling for relevant medical factors. This difference is even larger when the mother is the physician, and it comes almost entirely from non-emergecy C-sections. Findings are consistent with significant agency problems, and these appear to have increased in importance over the sample period.