Measuring Effort-Based Moral Hazard with Wearables: A Structural-Semiparametric Approach

Recruit enrollees into voluntary data-sharing with incentives and instrument wearable adoption with randomized incentives. Estimate a model where observed effort enters health production, allowing clean separation of risk heterogeneity from effort-driven behavior after coverage changes. Test whether insurance reduces preventive effort (behavioral moral hazard) and whether provider-side supply responses amplify or dampen the effect. This design overcomes classic identification struggles by making effort observable at high frequency, turning a key latent driver into data. It also allows testing the selection on anticipated moral hazard mechanism by examining if individuals with low baseline effort select plans with lax utilization management. The research advances the disentangling agenda by adding direct effort measurement and complements findings of little utilization response by probing moral hazard on the prevention margin. It intersects with information economics by generating consensual information used for welfare-improving pricing or incentives. The approach supports causal designs around enrollment and benefit changes, is scalable, and relevant to employers and public insurers. It can quantify welfare-relevant trade-offs between privacy, incentives, and risk sharing. The impact includes enabling targeted incentive design (e.g., effort-contingent rebates) that reduces moral hazard without restricting care and informing privacy-conscious data policies by demonstrating measurable social value from opt-in effort data.

References:

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