Data and Code for: Supervised Machine Learning for Eliciting Individual Demand

Principal Investigator(s): View help for Principal Investigator(s) John Clithero, University of Oregon; Jae Joon Lee, Stanford University; Joshua Tasoff, Claremont Graduate University

Version: View help for Version V1

Name	File Type	Size	Last Modified
AEJ_revision_221003			11/30/2022 01:53:AM

Project Citation:

Clithero, John, Lee, Jae Joon, and Tasoff, Joshua. Data and Code for: Supervised Machine Learning for Eliciting Individual Demand. Nashville, TN: American Economic Association [publisher], 2023. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-10-19. https://doi.org/10.3886/E180561V1

Project Description

Summary:

The canonical direct-elicitation approach for measuring individuals’ valu- ations for goods is the Becker-DeGroot-Marschak procedure, which generates willingness-to-pay (WTP) values that are imprecise and systematically biased. We show that enhancing elicited WTP values with supervised machine learn- ing (SML) can improve estimates of peoples’ out-of-sample purchase behavior. Furthermore, swapping WTP data with choice data generated from a simple task leads to comparable performance. We quantify the benefit of using various SML methods in conjunction with using different types of data. Our results suggest that prices set by SML would increase revenue by 29% over using the stated WTP, with the same data.

Scope of Project

JEL Classification:
      C81 Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
      C91 Design of Experiments: Laboratory, Individual
      D12 Consumer Economics: Empirical Analysis

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Published Versions

V1 [2023-10-19]

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