This paper develops a dynamic framework for analyzing an individual's choice between a Preferred Provider Organization (PPO) and a Health Maintenance Organization (HMO) under uncertainty regarding future health. We explicitly model health as a stochastic process whose fluctuations arise from three sources, one deterministic and two stochastic. Health evolves over time with a downward drift over the lifespan. In addition, health is subject to small, mean zero random fluctuations. Finally, there exists a small possibility every period of a serious illness resulting in a large, discrete fall in health. Under this characterization of health uncertainty, we develop a Real Options model valuing flexibility in health plan choice which takes into account the embedded flexibility to receive coverage for outof-network care if the PPO health plan is chosen. Our model suggests that greater health problems increase the value of the option to go out of network for the PPO enrollee.
In health care markets, moral hazard is conventionally viewed as a demand-side phenomenon in which insurance causes patients to use more care because it reduces the price they have to pay for care. However, demand-side moral hazard cannot explain why U.S. per capita health care costs are much higher than those of countries with universal coverage and lower out-of-pocket charges. Instead, blame rests with a phenomenon that may be called supply-side moral hazard, which occurs when third-party payment removes the constraints the demand curve would otherwise exert over the prices providers charge, and the quantity of expensive services they can sell. Public institutions are better positioned than private entities to address supply-side moral hazard. This helps explain why the other wealthy democracies—both those with single-payer systems, like Canada, and those with multipayer systems and all-payer procedures for setting provider rates, like Germany and Switzerland—spend much less per capita than the United States. Although managed care achieved some success in controlling U.S. provider prices in the 1990s, in the longer term, it motivated increasing market concentration among providers, which vitiated cost control. Furthermore, managed care exacerbates inequity and complexity, problems that public price regulation avoids.
During the 1992 campaign, candidate Bill Clinton made the strategic decision to embrace many aspects of an economic model called “managed competition” in his approach to health‐care reform. Managed competition ideas did not die when Clinton’s health plan expired. Instead, they informed the 1997 expansion of a demonstration project into the Medicare + Choice program. Today, the Medicare + Choice legacy lives on in the Medicare Advantage and prescription drug programs created by the Medicare Prescription Drug, Improvement and Modernization Act of 2003. Shortcomings of the managed competition approach received little public attention during the debate over the Medicare Modernization Act, despite the fact that the record of Medicare + Choice is disappointing in a number of respects. There are serious questions about the ability of the managed competition approach to control costs and achieve equitable access to high‐quality care. Nevertheless, the approach remains influential largely because of political considerations.
There is growing evidence of risks associated with excessive technology use, especially among teens and young adults. However, little is known about the characteristics of those who are at elevated risk of being problematic users. Using data from the 2012 Current Population Survey Internet Use Supplement and Educational Supplement for teens and young adults, this study developed a conceptual framework for modeling technology use. A three-part categorization of use was posited for utilitarian, social and entertainment purposes, which fit observed data well in confirmatory factor analysis. Seemingly unrelated regression was used to examine the demographic characteristics associated with each of the three categories of use. Exploratory factor analysis uncovered five distinct types of users, including one user type that was hypothesized to likely be at elevated risk of problematic use. Regression results indicated that females in their twenties who are in school and have greater access to technology were most likely to fall into this higher-risk category. Young people who live with both parents were less likely to belong to this category. This study highlighted the importance of constructing models that facilitate identification of patterns of use that may characterize a subset of users at high risk of problematic use. The findings can be applied to other contexts to inform policies related to technology and society as well. Supplementary Information The online version of this article (10.1007/s11293-020-09683-1) contains supplementary material, which is available to authorized users.
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