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

TL;DR

This paper combines theoretical analysis, experimental design, and empirical estimation to better understand second-degree price discrimination, highlighting the importance of covariance in demand models and demonstrating practical application in airline pricing.

Contribution

It introduces a new empirical model capturing covariance in demand for 2PD, along with an experimental design to identify this covariance in real-world settings.

Findings

The empirical model effectively captures demand in 2PD environments.

Experimental design successfully identifies key demand covariance.

Methodology enables qualitative inference on optimal 2PD policies.

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.