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2. Optimal Long-Term Health Insurance Contracts: Characterization, Computation, and Welfare Effects.

Author

Ghili, Soheil, Handel, Ben, Hendel, Igal, and Whinston, Michael D

Subjects
INSURANCE policies, HEALTH insurance, NATIONAL health insurance, INSURANCE companies, HEALTH insurance exchanges, PRICE regulation
Abstract

Reclassification risk is a major concern in health insurance where contracts are typically 1 year in length but health shocks often persist for much longer. While most health systems with private insurers pair short-run contracts with substantial pricing regulations to reduce reclassification risk, long-term contracts with one-sided insurer commitment have significant potential to reduce reclassification risk without the negative side effects of price regulation, such as adverse selection. We theoretically characterize optimal long-term insurance contracts with one-sided commitment, extending the literature in directions necessary for studying health insurance markets. We leverage this characterization to provide a simple algorithm for computing optimal contracts from primitives. We estimate key market fundamentals using data on all under-65 privately insured consumers in Utah. We find that dynamic contracts are very effective at reducing reclassification risk for consumers who arrive at the market in good health, but they are ineffective for consumers who come to the market in bad health, demonstrating that there is a role for the government insurance of pre-market health risks. Individuals with steeply rising income profiles find front-loading costly, and thus relatively prefer ACA-type exchanges. Switching costs enhance, while myopia moderately compromises, the performance of dynamic contracts. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Internal versus External Growth in Industries with Scale Economies: A Computational Model of Optimal Merger Policy

Author

Mermelstein, Ben, Nocke, Volker, Satterthwaite, Mark A, Whinston, Michael D, Mermelstein, Ben, Nocke, Volker, Satterthwaite, Mark A, and Whinston, Michael D

Abstract

© 2019 by The University of Chicago. We study merger policy in a dynamic computational model in which firms can reduce costs through investment or through mergers. Firms invest or propose mergers according to the profitability of these strategies. An antitrust authority can block mergers at some cost. We examine the optimal policy for an antitrust authority that cannot commit to its future policy and approves mergers as they are proposed. We find that the optimal policy can differ substantially from a policy based on static welfare. In general, antitrust policy can greatly affect firms’ investment behavior, and firms’ investment behavior can greatly affect the optimal antitrust policy.

Published
2022

21. Concentration Thresholds for Horizontal Mergers

Author

Massachusetts Institute of Technology. Department of Economics, Sloan School of Management, Nocke, Volker, Whinston, Michael D, Massachusetts Institute of Technology. Department of Economics, Sloan School of Management, Nocke, Volker, and Whinston, Michael D

Abstract

Concentration-based thresholds for horizontal mergers, such as those in the US Horizontal Merger Guidelines, play a central role in merger analysis but their basis remains unclear. We show that there is both a theoretical and an empirical basis for focusing solely on the change in concentration, and ignoring its level, in screening mergers for whether their unilateral price effects will harm consumers. We also argue that current threshold levels likely are too lax, unless one expects efficiency gains of 5 percent or greater, or other factors such as entry and product repositioning to significantly constrain the exercise of market power postmerger. (JEL D43, G34, G38, K21, L13, L41)

Published
2022

41. Internal versus External Growth in Industries with Scale Economies: A Computational Model of Optimal Merger Policy

Author

Mermelstein, Ben, Nocke, Volker, Satterthwaite, Mark A, Whinston, Michael D, Mermelstein, Ben, Nocke, Volker, Satterthwaite, Mark A, and Whinston, Michael D

Abstract

© 2019 by The University of Chicago. We study merger policy in a dynamic computational model in which firms can reduce costs through investment or through mergers. Firms invest or propose mergers according to the profitability of these strategies. An antitrust authority can block mergers at some cost. We examine the optimal policy for an antitrust authority that cannot commit to its future policy and approves mergers as they are proposed. We find that the optimal policy can differ substantially from a policy based on static welfare. In general, antitrust policy can greatly affect firms’ investment behavior, and firms’ investment behavior can greatly affect the optimal antitrust policy.

Published
2021

42. A multi-risk SIR model with optimally targeted lockdown

Author

Acemoglu, Daron, Chernozhukov, Victor, Werning, Iván, and Whinston, Michael D.

Subjects
Coronavirus, Sterblichkeit, Altersgruppe, Wirtschaftlicher Schaden, ddc:330, Impfung, Modellierung, Epidemie, Shutdown
Abstract

We develop a multi-risk SIR model (MR-SIR) where infection, hospitalization and fatality rates vary between groups-in particular between the "young", "the middleaged" and the "old". Our MR-SIR model enables a tractable quantitative analysis of optimal policy similar to those already developed in the context of the homogeneousagent SIR models. For baseline parameter values for the COVID-19 pandemic applied to the US, we find that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter lockdown policies on the oldest group. For example, for the same economic cost (24.3% decline in GDP), optimal semi-targeted or fully-targeted policies reduce mortality from 1.83% to 0.71% (thus, saving 2.7 million lives) relative to optimal uniform policies. Intuitively, a strict and long lockdown for the most vulnerable group both reduces infections and enables less strict lockdowns for the lower-risk groups. We also study the impacts of social distancing, the matching technology, the expected arrival time of a vaccine, and testing with or without tracing on optimal policies. Overall, targeted policies that are combined with measures that reduce interactions between groups and increase testing and isolation of the infected can minimize both economic losses and deaths in our model.

Published
2020

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