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The constant rate discounted utility model is commonly used to represent intertemporal preferences in health care program evaluations. This paper examines the appropriateness of this model, and argues that the model fails both normatively and descriptively as a representation of individual intertemporal preferences for health outcomes. Variable rate discounted utility models are more flexible, but still require restrictive assumptions and may lead to dynamically inconsistent behaviour. The paper concludes by considering two ways of incorporating individual intertemporal preferences in health care program evaluations that allow for complementarity of health outcomes in different time periods. [ABSTRACT FROM AUTHOR]

The paper focuses on two estimation methods that have been widely used to address endogeneity in empirical research in health economics and health services research—two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI). 2SPS is the rote extension (to nonlinear models) of the popular linear two-stage least squares estimator. The 2SRI estimator is similar except that in the second-stage regression, the endogenous variables are not replaced by first-stage predictors. Instead, first-stage residuals are included as additional regressors. In a generic parametric framework, we show that 2SRI is consistent and 2SPS is not. Results from a simulation study and an illustrative example also recommend against 2SPS and favor 2SRI. Our findings are important given that there are many prominent examples of the application of inconsistent 2SPS in the recent literature. This study can be used as a guide by future researchers in health economics who are confronted with endogeneity in their empirical work. [Copyright &y& Elsevier]

Abstract: Quality reports about health plans and providers are becoming more prevalent in health care markets. This paper casts the decision about what information to report to consumers about health plans as a policy decision. In a market with adverse selection, complete information about quality leads to inefficient outcomes. In a Rothschild–Stiglitz model, we show that averaging quality information into a summary report can enforce pooling in health insurance, and by choice of the right weights in the averaged report, a payer or regulator can induce first-best quality choices. The optimal quality report is as powerful as optimal risk adjustment in correcting adverse selection inefficiencies. [Copyright &y& Elsevier]

The paper presents a stylised model of contracting for a specific health service. The benefit of this service differs across patients. A purchaser, National Health Service (NHS, insurer) offers a contract to providers (hospitals, GP's), under the constraint of limited information about the provider's costs and the contract specifies the payment as a function of the number of cases treated. A number of features of the optimal contract are derived. Some of these are surprising: for example, the price per case generally increases with the efficiency of the provider. [ABSTRACT FROM AUTHOR]

This paper develops implications of Arrow's hypothesis that nonprofit organizations are prevalent in health care because of quality uncertainty. The model analyzes the ability of nonprofits to mitigate market failures created by asymmetric information in an environment characterized by potential competition from both explicitly for-profit firms and for-profits in disguise (profit-motivated firms who obtain nonprofit status in order to exploit the perceived trustworthiness of the nonprofit sector). Under certain conditions, it is shown that nonprofit status can serve as a credible signal of quality and that nonprofits can decrease the underprovision of quality both by providing high quality services and indirectly via a spillover effect on quality in the for-profit sector. Applicability to long-term care and implications for empirical research and policy towards nonprofits in health care are discussed. [ABSTRACT FROM AUTHOR]

Previous discussions of methods for the efficient allocation of health care resources subject to a budget constraint have relied on unnecessarily restrictive assumptions. This paper makes use of established optimization techniques to demonstrate that a general mathematical programming framework can accommodate much more complex information regarding returns to scale, partial and complete indivisibility and program interdependence. Methods are also presented for incorporating ethical constraints into the resource allocation process, including explicit identification of the cost of equity. [ABSTRACT FROM AUTHOR]