Hanyao Zhang

Hanyao Zhang

Behavioral/Experimental Economics

Welcome!

I’m Hanyao, a sixth-year PhD student in economics at Columbia University, where I have the privilege of being advised by Professors Mark Dean, Alessandra Casella, and Michael Woodford. I received my BA from Peking University in 2020.

I am on the job market for the 2025-26 cycle.

I mainly work on behavioral economics, where I employ experimental and econometric methods to test theories of human decision making, especially under uncertainty.

You can find my CV here.

  • Email: hanyao.zhang at columbia.edu
  • Address: 420 W 118th St., New York, NY 10027

Job Market Paper

  • Calculations Behind Lottery Valuations
    Abstract: I introduce a novel experimental design tracking experimental subjects’ calculations when valuing lotteries. The calculations predominantly fall into three groups: expected values, linear functions of monetary outcomes, or those unmatched to lottery primitives. Calculations exhibit remarkable within-subject stability alongside substantial between-subject heterogeneity. Calculations strongly predict valuations: subjects performing expected values-related calculations display near risk-neutrality, while on average, other subjects’ valuations display extreme unresponsiveness to changes in probabilities. An analysis by calculation group reveals distinct behavioral mechanisms driving behaviors: adoption of expected-value calculations is consistent with the reductions in implementation costs from the provided calculator, while the linear functions of monetary outcomes are consistent with the theory of attribute substitution(Kahneman and Frederick, 2002).

Working Papers

  • Recovering Preferences from Mistakes: An Auxiliary Task Approach
    Abstract Risk preferences recovered from lottery valuation data are not robust to unverifiable assumptions about the structure of mistakes in the valuations. To address this, we develop a novel approach utilizing Oprea's (2024) deterministic mirrors -- deterministic payments that preserve key structural features of lotteries. We estimate the mistake structure in deterministic mirrors -- where certain payments enable identification of mistake patterns -- through a mixture model incorporating two types of mistakes frequently observed, then apply these estimates to correct lottery valuations. The corrected valuations are closer to risk neutrality than raw valuations; when they deviate from risk neutrality, they are predominantly risk averse. These patterns align the corrected valuations with expected utility theory, in contrast to the raw valuations which exhibit strong probability weighting. Our approach offers a generalizable framework for preference recovery: researchers can use auxiliary tasks with known correct answers to discipline assumptions about mistakes.
  • Computation Complexity, Elicitation Methods, and Lottery Valuations, with Mark Dean (draft coming soon)

Works in Progress

  • Reference-Dependent Motivated Beliefs, with Zhi Hao Lim

Publication