American Economic Journal:
Microeconomics
ISSN 1945-7669 (Print) | ISSN 1945-7685 (Online)
Supervised Machine Learning for Eliciting Individual Demand
American Economic Journal: Microeconomics
vol. 15,
no. 4, November 2023
(pp. 146–82)
Abstract
The canonical direct-elicitation approach for measuring individuals' valuations 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 learning (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 percent over using the stated WTP, with the same data.Citation
Clithero, John A., Jae Joon Lee, and Joshua Tasoff. 2023. "Supervised Machine Learning for Eliciting Individual Demand." American Economic Journal: Microeconomics, 15 (4): 146–82. DOI: 10.1257/mic.20210069Additional Materials
JEL Classification
- C45 Neural Networks and Related Topics
- C91 Design of Experiments: Laboratory, Individual
- D12 Consumer Economics: Empirical Analysis
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