| Abstract: |
Recent work has shown that response times contain information about willingness to pay (Cotet and Krajbich, 2021), preference intensity (Alós-Ferrer, Fehr and Netzer, 2021), happiness (Liu and Netzer, 2023), product quality (Card, DellaVigna and Taubinsky, 2023), and other latent variables that drive individual decision-making but are not directly observable. Based on a chronometric effect, decisions are faster when the latent intensity is larger. Response time data can therefore be used to learn about properties of latent distributions instead of assuming these properties. This can help to solve severe identification problems that the econometric literature has noticed (e.g., Bond and Lang, 2019).
In this talk, I will begin by providing an overview of the technique developed by Liu and Netzer (2023) for analyzing response time data. I will then present some preliminary findings from our ongoing project, where we ask the general question which properties of distributions can be identified with the help of response time data. We provide a general characterization result which relates the identifiable properties to the set of admissible chronometric functions. Many of the existing results in the literature follow as corollaries from this result. It also gives rise to several new applications, such as how to identify the shape of the income-happiness relation without making arbitrary assumptions on the shape of the reporting function (Kaiser and Oswald, 2022).
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Biography:
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Shuo Liu is Associate Professor of Economics at the Peking University Guanghua School of Management. He earned his PhD in Economics from the University of Zurich in 2019. His research focuses on industrial and organizational economics, game theory, and mechanism design. His scholarly contributions have been featured in leading academic journals in economics, including the American Economic Review, Theoretical Economics, Journal of Economic Theory, Economic Journal, RAND Journal of Economics, and Management Science.
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