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Ching-to Albert Ma and Henry Y. Mak

Health services providers receive payments mostly from private or public insurers rather than patients. Provider incentive problems arise because an insurer misses information about the provider and patients, and has imperfect control over the provider’s treatment, quality, and cost decisions. Different provider payment systems, such as prospective payment, capitation, cost reimbursement, fee-for-service, and value-based payment, generate different treatment quality and cost incentives. The important issue is that a payment system implements an efficient quality-cost outcome if and only if it makes the provider internalize the social benefits and costs of services. Thus, the internalization principle can be used to evaluate payment systems across different settings. The most common payment systems are prospective payment, which pays a fixed price for service rendered, and cost reimbursement, which pays according to costs of service rendered. In a setting where the provider chooses health service quality and cost reduction effort, prospective payment satisfies the internalization principle but cost reimbursement does not. The reason is that prospective payment forces the provider to be responsible for cost, but cost reimbursement relieves the provider of the cost responsibility. Beyond this simple setting, the provider may select patients based on patients’ cost heterogeneity. Then neither prospective payment nor cost reimbursement achieves efficient quality and cost incentives. A mixed system that combines prospective payment and cost reimbursement performs better than each of its components alone. In general, the provider’s preferences and available strategies determine if a payment system may achieve internalization. If the provider is altruistic toward patients, prospective payment can be adjusted to accommodate altruism when the provider’s degree of altruism is known to the insurer. However, when the degree of altruism is unknown, even a mixed system may fail the internalization principle. Also, the internalization principle fails under prospective payment when the provider can upcode patient diagnoses for more favorable prices. Cost reimbursement attenuates the upcoding incentive. Finally, when the provider can choose many qualities, either prospective payment and cost reimbursement should be combined with the insurer’s disclosure on quality and cost information to satisfy the internalization principle. When good healthcare quality is interpreted as a good match between patients and treatments, payment design is to promote good matches. The internalization principle now requires the provider to bear benefits and costs of diagnosis effort and treatment choice. A mixed system may deliver efficient matching incentives. Payment systems necessarily interact with other incentive mechanisms such as patients’ reactions against the provider’s quality choice and other providers’ competitive strategies. Payment systems then become part of organizational incentives.


Matteo Lippi Bruni, Irene Mammi, and Rossella Verzulli

In developed countries, the role of public authorities as financing bodies and regulators of the long-term care sector is pervasive and calls for well-planned and informed policy actions. Poor quality in nursing homes has been a recurrent concern at least since the 1980s and has triggered a heated policy and scholarly debate. The economic literature on nursing home quality has thoroughly investigated the impact of regulatory interventions and of market characteristics on an array of input-, process-, and outcome-based quality measures. Most existing studies refer to the U.S. context, even though important insights can be drawn also from the smaller set of works that covers European countries. The major contribution of health economics to the empirical analysis of the nursing home industry is represented by the introduction of important methodological advances applying rigorous policy evaluation techniques with the purpose of properly identifying the causal effects of interest. In addition, the increased availability of rich datasets covering either process or outcome measures has allowed to investigate changes in nursing home quality properly accounting for its multidimensional features. The use of up-to-date econometric methods that, in most cases, exploit policy shocks and longitudinal data has given researchers the possibility to achieve a causal identification and an accurate quantification of the impact of a wide range of policy initiatives, including the introduction of nurse staffing thresholds, price regulation, and public reporting of quality indicators. This has helped to counteract part of the contradictory evidence highlighted by the strand of works based on more descriptive evidence. Possible lines for future research can be identified in further exploration of the consequences of policy interventions in terms of equity and accessibility to nursing home care.