Working Paper: NBER ID: w31647
Authors: Aaron L. Bodoh-Creed; Brent R. Hickman; John A. List; Ian Muir; Gregory K. Sun
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.
Keywords: No keywords provided
JEL Codes: B4; C14; C51; C52; C93; D04; J02; L1; L5
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| observed price changes (E30) | shifts in aggregate demand (E00) |
| rank stability holds (C62) | identify utility functions and demand-type distributions (D11) |
| violations of rank stability (P37) | complicate inference (D80) |
| approach enables estimation of robust lower bounds on price responsiveness (D40) | optimal nonlinear pricing design (D40) |
| optimal subscription offerings can be adjusted (D40) | hedge against worst-case scenarios of unobserved consumer behavior (D11) |