Stress Testing Structural Models of Unobserved Heterogeneity: Robust Inference on Optimal Nonlinear Pricing

Aaron L. Bodoh-Creed, Brent R. Hickman, John List, Ian Muir and Gregory K. Sun

No 31647, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We propose a suite of tools for empirical market design in adverse-selection settings where point identification based on exogenous price variation is hampered by multi-dimensional unobserved heterogeneity. Despite significant data limitations, we are able to derive informative bounds on counterfactual consumer demand under out-of-sample price changes. These bounds arise because empirically plausible DGPs must respect the Law of Demand and the observed shift(s) in aggregate demand resulting from a known exogenous price change(s). The bounds facilitate robust policy prescriptions using rich, internal data sources similar to those available in many real- world settings, including our empirical application to rideshare demand. Our partial identification approach enables viable, welfare-improving, nonlinear pricing design while achieving robustness against worst-case deviations from baseline model assumptions. As a side benefit, our framework also provides novel insights into optimal experimental design for pricing RCTs.

JEL-codes: B4 C14 C51 C52 C93 D04 L1 L5 (search for similar items in EconPapers)
Date: 2023-08
New Economics Papers: this item is included in nep-des
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Working Paper: Stress Testing Structural Models of Unobserved Heterogeneity: Robust Inference on Optimal Nonlinear Pricing (2023)
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