We estimate a Bayesian learning model in order to assess the value of health plan performance information and the extent to which the explicit provision of information about product quality alters consumer behavior. We take advantage of a natural experiment in which health plan performance information for HMOs was released to employees of a Fortune 50 company for the first time. Our empirical work indicates that the release of information had a small but statistically significant effect on health plan choices, causing 3.1% of employees to switch health plans. Although consumers were willing to pay an extra $267 per year per below average rating avoided, the average value of the information per employee was only $10 per year. The relatively small impact of the ratings arises because the ratings were estimated to be very imprecise measures of quality. More precise measures of quality could have been more valuable.

In this paper we estimate the returns associated with the provision of coronary artery bypass graft (CABG) surgery, by payer type (Medicare, HMO, etc.). Because reliable measures of prices and treatment costs are often unobserved, we seek to infer returns from hospital entry behavior. We estimate a model of patient flows for CABG patients that provides inputs for an entry model. We find that FFS provides a high return throughout the study period. Medicare, which had been generous in the early 1980s, now provides a return that is close to zero. Medicaid appears to reimburse less than average variable costs. HMOs essentially pay at average variable costs, though the return varies inversely with competition.

Determines the presence and causes of network externalities for the automated clearinghouse (ACH) electronic payments system, using a monthly panel data set on individual bank adoption of ACH.

This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random admission leads to spurious inference about hospital quality. This study controls for hospital selection using a model in which distance between the patient's residence and alternative hospitals are key exogenous variables. Bayesian inference in this model is feasible using a Markov chain Monte Carlo posterior simulator, and attaches posterior probabilities to quality comparisons between individual hospitals and groups of hospitals. The study uses data on 74,848 Medicare patients admitted to 114 hospitals in Los Angeles County from 1989 through 1992 with a diagnosis of pneumonia. It finds the smallest and largest hospitals to be of high quality and public hospitals to be of low quality. There is strong evidence of dependence between the unobserved severity of illness and the assignment of patients to hospitals. Consequently a conventional probit model leads to inferences about quality markedly different than those in this study's selection model.

We estimate a bargaining model of competition between hospitals and managed care organizations (MCOs) and use the estimates to evaluate the effects of hospital mergers. We find that MCO bargaining restrains hospital prices significantly. The model demonstrates the potential impact of coinsurance rates, which allow MCOs to partly steer patients towards cheaper hospitals. We show that increasing patient coinsurance tenfold would reduce prices by 16%. We find that a proposed hospital acquisition in Northern Virginia that was challenged by the Federal Trade Commission would have significantly raised hospital prices. Remedies based on separate bargaining do not alleviate the price increases.

Since 1914, incumbent U.S. senators running for reelection have won almost 80% of the time. We investigate why incumbents win so often. We allow for three potential explanations for the incumbency advantage: selection, tenure, and challenger quality, which are separately identified using histories of election outcomes following an open seat election. We specify a dynamic model of voter behavior that allows for these three effects, and structurally estimate the parameters of the model using U.S. Senate data. We find that tenure effects are negative or small. We also find that incumbents face weaker challengers than candidates running for open seats. If incumbents faced challengers as strong as candidates for open seats, the incumbency advantage would be cut in half.

A literature has found that medical providers inflate bills and report more conditions given financial incentives. We evaluate whether Medicare reimbursement incentives are driven more by bill inflation or coding costs. Medicare reformed its payment mechanism for inpatient hospitalizations in 2007, increasing coding costs. We first examine whether increased extra reimbursements from reporting more diagnoses lead hospitals to report more high bill codes. We find that increases in reimbursements within narrow patient groups led to more high bill codes before 2007 but not after. Using the payment reform, we then test for costly coding by comparing hospitals that adopted electronic medical records (EMRs) to others. Adopters reported relatively more top bill codes from secondary diagnoses after the reform, exclusively for medical patients, with a negative effect for surgical patients. This is consistent with EMRs lowering coding costs for medical discharges but increasing them for surgical ones. We further use a 2008 policy where Medicare implemented financial penalties for certain hospital-acquired conditions. EMR hospitals coded relatively more of these conditions following the penalization, lowering revenues. Together, this evidence is contrary to bill inflation but consistent with costly coding. Reducing coding costs may increase inpatient Medicare costs by $1.04 billion annually.

A key problem with solar energy is intermittency: solar generators only produce when the sun is shining. This adds to social costs and also requires electricity system operators to reoptimize key decisions with large-scale renewables. We develop a method to quantify the economic value of large-scale renewable energy. We estimate the model for southeastern Arizona. Not accounting for offset CO2, we find social costs of $138.4/MWh for 20% solar generation, of which unforecastable intermittency accounts for $6.1 and intermittency overall for $46. With solar installation costs of $1.52/W and CO2 social costs of $39/ton, 20% solar would be welfare neutral.