Adverse selection without single crossing

Screening models are used to analyze contracting in many subfields of economics like regulation, labor economics, monopoly pricing, taxation or finance. Most models assume single crossing. This simplifies the analysis as local incentive compatibility is in this case sufficient for global incentive compatibility. If single crossing is violated, global incentive compatibility constraints have to be taken into account. This paper studies monotone solutions in a model where single crossing is violated. It is shown that local and non-local incentive constraints distort the solution in opposite directions. Therefore, the optimal decision might involve distortions above as well as below the first best decision. Furthermore, the well known“no distortionat the top”propertydoes not necessarilyhold. Sufficient conditions for existence, monotonicity and continuity of the solution and an algorithm to obtain such a solution are derived. Some results can be readily applied. For example, overinsurance, i.e. insurance levels above first best as in“Cadillac”insurance plans, can be rationalized. In a non-linear pricing framework, the model also provides an explanation for marginal prices below marginal costs as observed in flat rate offers

Health Insurance without Single Crossing:Why Healthy People Have High Coverage

Standard insurance models predict that people with high risks have high insurance coverage. It is empirically documented that people with high income have lower health risks and are better insured. We show that income differences between risk types lead to a violation of single crossing in an insurance model where people choose treatment intensity. We analyse different market structures and show the following: if insurers have market power, the violation of single crossing caused by income differences and endogenous treatment choice can explain the empirically observed outcome. Our results do not rely on differences in risk aversion between types

Competing with Big Data

We study competition in data-driven markets, where the cost of quality production decreases in the amount of machine-generated data about user preferences or characteristics. This gives rise to data-driven indirect network effects. We construct a dynamic model of R&D competition, where duopolists repeatedly determine innovation investments. Such markets tip under very mild conditions, moving towards monopoly. After tipping, innovation incentives both for the dominant firm and the competitor are small. We show when a dominant firm can leverage its dominance to a connected market, thereby initiating a domino effect. Market tipping can be avoided if competitors share their user information