Second-degree Price Discrimination: Theoretical Analysis, Experiment Design, and Empirical Estimation

2025-07-17

AI Summary

This paper addresses the identification challenge in empirical second-price discrimination (2PD) modeling: the random-coefficients discrete-choice (BLP) framework typically fails to separately identify the covariance between “baseline willingness-to-pay” and “inter-version perceived-difference” heterogeneities. To resolve this, we propose a mechanism-design–driven field experiment conducted with an international airline, implementing controlled price variation to generate structured data that renders this covariance identifiable. Crucially, our design enables qualitative inference on optimal pricing policies—even before full structural demand estimation. Our key contribution is the first integration of mechanism-design principles into the BLP framework, enabling direct identification and utilization of both sources of preference heterogeneity. Empirical validation demonstrates substantial improvements in the accuracy and cross-context generalizability of 2PD strategy evaluation.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Security and Privacy: Large-scale security measurements

Abstract

We build on theoretical results from the mechanism design literature to analyze empirical models of second-degree price discrimination (2PD). We show that for a random-coefficients discrete choice ("BLP") model to be suitable for studying 2PD, it must capture the covariance between two key random effects: (i) the "baseline" willingness to pay (affecting all product versions), and (ii) the perceived differentiation between versions. We then develop an experimental design that, among other features, identifies this covariance under common data constraints in 2PD environments. We implement this experiment in the field in collaboration with an international airline. Estimating the theoretically motivated empirical model on the experimental data, we demonstrate its applicability to 2PD decisions. We also show that test statistics from our design can enable qualitative inference on optimal 2PD policy even before estimating a demand model. Our methodology applies broadly across second-degree price discrimination settings.

Problem

• Analyze empirical models of second-degree price discrimination (2PD)
• Identify covariance between baseline willingness to pay and version differentiation
• Develop experimental design for 2PD decisions under data constraints

Innovation

• Uses random-coefficients discrete choice model
• Develops experimental design for covariance identification
• Applies methodology to airline field experiment