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Related Experiment Video

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimal risk adjustment with adverse selection and spatial competition.

William Jack1

  • 1Department of Economics, Georgetown University, Washington DC 20057, United States. wgj@georgetown.edu <wgj@georgetown.edu>

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Paying insurers risk-adjusted prices corrects selection incentives, leading to optimal insurance policies. This requires understanding optimal policies, costs, and market competition for effective risk adjustment.

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Area of Science:

  • Health economics
  • Insurance markets
  • Market regulation

Background:

  • Insurers face selection incentives due to varying individual risks.
  • Market provision of optimal insurance policies is often hindered by adverse selection.
  • Risk-adjusted payments are proposed to align insurer incentives with social welfare.

Purpose of the Study:

  • To determine the conditions under which risk-adjusted pricing can correct selection incentives.
  • To characterize market equilibrium with spatial heterogeneity and adverse selection.
  • To establish a framework for calculating optimal risk-adjusted payments for insurance providers.

Main Methods:

  • Economic modeling of insurance markets with spatial heterogeneity.
  • Analysis of market equilibrium under adverse selection.
  • Derivation of optimal risk-adjusted payment formulas.

Main Results:

  • Risk-adjusted pricing can correct selection incentives and lead to optimal insurance policies.
  • Socially optimal insurance policies can be delivered if providers are paid risk-adjusted fees per individual.
  • Optimal payments should cover expected costs plus a mark-up, adjusted for available risk information.

Conclusions:

  • Risk-adjusted payments are crucial for achieving efficient insurance markets.
  • The calculation of optimal risk-adjusted prices depends on policy optimality, costs, and market competition.
  • Adjustments to risk-based payments are necessary when using partially informative signals for risk assessment.