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Supervised Machine Learning for Eliciting Individual Demand

Author

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  • John A. Clithero
  • Jae Joon Lee
  • Joshua Tasoff

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.

Suggested Citation

  • John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2023. "Supervised Machine Learning for Eliciting Individual Demand," American Economic Journal: Microeconomics, American Economic Association, vol. 15(4), pages 146-182, November.
    • Handle: RePEc:aea:aejmic:v:15:y:2023:i:4:p:146-82
    DOI: 10.1257/mic.20210069
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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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