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Individual Welfare Analysis: Random Quasilinear Utility, Independence, and Confidence Bounds

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  • Junlong Feng
  • Sokbae Lee

Abstract

We introduce a novel framework for individual-level welfare analysis. It builds on a parametric model for continuous demand with a quasilinear utility function, allowing for heterogeneous coefficients and unobserved individual-product-level preference shocks. We obtain bounds on the individual-level consumer welfare loss at any confidence level due to a hypothetical price increase, solving a scalable optimization problem constrained by a new confidence set under an independence restriction. This confidence set is computationally simple, robust to weak instruments, nonlinearity, and partial identification. In addition, it may have applications beyond welfare analysis. Monte Carlo simulations and two empirical applications on gasoline and food demand demonstrate the effectiveness of our method.

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  • Junlong Feng & Sokbae Lee, 2023. "Individual Welfare Analysis: Random Quasilinear Utility, Independence, and Confidence Bounds," Papers 2304.01921, arXiv.org, revised Aug 2023.
    • Handle: RePEc:arx:papers:2304.01921
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    References listed on IDEAS

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