Employment and Adverse Selection in Health Insurance

We construct and test a new model of employer-provided health insurance provision in the presence of adverse selection in the health insurance market. In our model, employers cannot observe the health of their employees, but can decide whether to offer insurance. Employees sort themselves among employers who do and do not offer insurance on the basis of their current health status and the probability distribution over future health status changes. We show that there exists a pooling equilibrium in which both sick and healthy employees are covered as long as the costs of job switching are higher than the persistence of health status. We test and verify some of the key implications of our model using data from the Current Population Survey, linked to information provided by the U.S. Department of Labor about the job-specific human capital requirements of jobs.

Do Instrumental Variables Belong in Propensity Scores?

Propensity score matching is a popular way to make causal inferences about a binary treatment in observational data. The validity of these methods depends on which variables are used to predict the propensity score. We ask: "Absent strong ignorability, what would be the effect of including an instrumental variable in the predictor set of a propensity score matching estimator?" In the case of linear adjustment, using an instrumental variable as a predictor variable for the propensity score yields greater inconsistency than the naive estimator. This additional inconsistency is increasing in the predictive power of the instrument. In the case of stratification, with a strong instrument, propensity score matching yields greater inconsistency than the naive estimator. Since the propensity score matching estimator with the instrument in the predictor set is both more biased and more variable than the naive estimator, it is conceivable that the confidence intervals for the matching estimator would have greater coverage rates. In a Monte Carlo simulation, we show that this need not be the case. Our results are further illustrated with two empirical examples: one, the Tennessee STAR experiment, with a strong instrument and the other, the Connors' (1996) Swan-Ganz catheterization dataset, with a weak instrument.

Competition among Hospitals

We examine competition in the hospital industry, in particular the effect of ownership type (for-profit, no-for-profit, government). We estimate a structural model of demand and pricing in the hospital industry in California, then use the estimates to simulate the effect of a merger. California hospitals in 1995 face an average price elasticity of demand of -4.85. Not-for-profit hospitals face less elastic demand and act as if they have lower marginal costs. Their prices are lower than for-profits, but markups are higher. We simulate the effects of the 1997 merger of two hospital chains. In San Luis Obispo County, where the merger creates a near monopoly, prices rise by up to 53%, and the predicted price increase would not be substantially smaller were the chains not-for-profits.analysis of health care markets

Entry and Competition in Local Hospital Markets

There has been considerable consolidation in the hospital industry in recent years. Over 900 deals occurred from 1994-2000, and many local markets, even in large urban areas, have been reduced to monopolies, duopolies, or triopolies. This surge in consolidation has led to concern about its effect on competition in local markets for hospital services. In this paper we examine the impact of market structure on competition in local hospital markets -- specifically, does competition increase with the number of firms? We extend the entry model developed by Bresnahan and Reiss to make use of quantity information, and apply it to data on the U.S. hospital industry. The results from the estimation are striking. In the hospital markets we examine, entry leads to markets becoming competitive quickly. Entry reduces variable profits and increases quantity. Indeed, most of the effects of entry come from having a second and possibly a third firm enter the market. The use of quantity information allows us to infer that entry is consumer welfare increasing.

Is Drug Coverage a Free Lunch? Cross-Price Elasticities and the Design of Prescription Drug Benefits

Recently, many U.S. employers have adopted less generous prescription drug benefits. In addition, the U.S. began to offer prescription drug insurance to approximately 42 million Medicare beneficiaries in 2006. We use data on individual health insurance claims and benefit data from 1997-2003 to study the effects of changing consumers' co-payments for prescription drugs on the quantity demanded and expenditure on prescription drugs, inpatient care and outpatient care. We allow for effects both in the year of the co-payment change and in the year following the change. Our results show that increases in prescription drug prices reduce both the use of and spending on prescription drugs. However, consumers substitute the use of outpatient care and inpatient care for prescription drug use, and about 35% of the expenditure reductions on prescription drugs are offset by the increases in other spending.

Is Drug Coverage a Free Lunch? Cross-Price Elasticities and the Design of Prescription Drug Benefits

Recently, many US employers have adopted less generous prescription drug benefits. In addition, the U.S. began to offer prescription drug insurance to approximately 42 million Medicare beneficiaries in 2006. We use data on individual health insurance claims and benefit data from 1997-2003 to study the effects of changing consumers’ co-payments for prescription drugs on the quantity demanded and expenditure on prescription drugs, inpatient care and outpatient care. We allow for effects both in the year of the co-payment change and in the year following the change. Our results show that increases in prescription drug prices reduce both the use of and spending on prescription drugs. However, consumers substitute the use of outpatient care and inpatient care for prescription drug use, and the expenditure reductions on prescription drugs are largely offset by the increases in outpatient spending.drugs, elasticity, substitution, cost-sharing, insurance

Entry and Competition in Local Hospital Markets

There has been considerable consolidation in the hospital industry in recent years. Over 900 deals occurred from 1994-2000, and many local markets, even in large urban areas, have been reduced to monopolies, duopolies or triopolies. This surge in consolidation has led to concern about its effect on competition in local markets for hospital services. In this paper we examine the impact of market structure on competition in local hospital markets – specifically, does competition increase with the number of firms? We extend the entry model developed by Bresnahan and Reiss to make use of quantity information and apply it to data on the US hospital industry. The results from the estimation are striking. In the hospital markets we examine, entry leads to markets quickly becoming competitive. Entry reduces variable profits and increases quality. Indeed, most of the effects of entry come from having a second and possibly a third firm enter the market. The use of quantity information allows us to infer that entry is welfare increasing.analysis of health care markets

Household Demand for Employer-Based Health Insurance

We use the 1996 Medical Expenditure Panel Survey to estimate a model of household demand for employer-based health insurance, explicitly investigating differences in behavior between households with two potential sources of coverage and those with one source. Own and cross-price elasticities are estimated for three types of health plans, including exclusive provider organizations, any provider organizations, and mixed provider organizations. We find that the premium, family size, income, and wealth significantly affect demand. Our elasticity estimates reveal an overall, small behavioral response to changes in price with respect to health plan switching and take-up. Finally, we discuss the implications of our findings with respect to employer benefit design.

Does the Profit Motive Make Jack Nimble? Ownership Form and the Evolution of the U.S. Hospital Industry

We examine the evolving structure of the U.S. hospital industry since 1970, focusing on how ownership form influences entry and exit behavior. We develop theoretical predictions based on the model of Lakdawalla and Philipson, in which for-profit and not-for-profit hospitals differ regarding their objectives and costs of capital. The model predicts for-profits would be quicker to enter and exit than not-for-profits in response to changing market conditions. We test this hypothesis using data for all U.S. hospitals from 1984 through 2000. Examining annual and regional entry and exit rates, for-profit hospitals consistently have higher entry and exit rates than not-for-profits. Econometric modeling of entry and exit rates yields similar patterns. Estimates of an ordered probit model of entry indicate that entry is more responsive to demand changes for for-profit than not-for-profit hospitals. Estimates of a discrete hazard model for exit similarly indicate that negative demand shifts increase the probability of exit more for for-profits than not-for-profits. Finally, membership in a hospital chain significantly decreases the probability of exit for for-profits, but not not-for-profits.

Does the Profit Motive Make Jack Nimble? Ownership Form and the Evolution of the U.S. Hospital Industry

We examine the evolving structure of the U.S. hospital industry since 1970, focusing on how ownership form influences entry and exit behavior. We develop theoretical predictions based on the model of Lakdawalla and Philipson, in which for-profit and not-for-profit hospitals differ regarding their objectives and costs of capital. The model predicts for-profits would be quicker to enter and exit than not-for-profits in response to changing market conditions. We test this hypothesis using data for all U.S. hospitals from 1984 through 2000. Examining annual and regional entry and exit rates, for-profit hospitals consistently have higher entry and exit rates than not-for-profits. Econometric modeling of entry and exit rates yields similar patterns. Estimates of an ordered probit model of entry indicate that entry is more responsive to demand changes for for-profit than not-for-profit hospitals. Estimates of a discrete hazard model for exit similarly indicate that negative demand shifts increase the probability of exit more for for-profits than not-for-profits. Finally, membership in a hospital chain significantly decreases the probability of exit for for-profits, but not not-for-profits.