Ao Wang

Welcome! I am a behavioral economist who is interested in applied microeconomics questions. Most of my works center around how cognitive limitations affect decision making under risk and uncertainty, and the implications of such limitations in labor economics, market design, and development economics.

To answer these questions, I design and apply a wide variety of tools, including structural modeling, theory-based reduced-form analysis, nonparametric econometrics, and randomized control trials in the field and lab, to identify the cognitive biases that I hypothesize.

I am a Ph.D. candidate in Economics at UC Berkeley, and will be available for job interviews at both the EJME 2021 and the ASSA 2022 Annual Meeting.

My CV is here.


Cognitive Distortions in Complex Decisions: Evidence from Centralized College Admission (With Shaoda Wang and Xiaoyang Ye)

Constructing optimal rank-order lists in centralized matching systems often entails sophisticated risk-taking consideration. We empirically study an admission system that employs a constrained Deferred Acceptance Algorithm to understand how students construct their lists. Students appear overly cautious with their top choices and most of them do not always put safer choices at a lower-ranked spot on the list. We propose that the Model of Directed Cognition could explain such choices. Applicants using the model myopically focus on the spot they are contemplating and neglect its impact on the rest of the list. To differentiate from alternative hypotheses, we deploy an in-field experiment that pinpoints a core prediction of our model concerning framing effects and find clear evidence of it. Structural estimation suggests that 45%∼55% of the sample are better described by our model and that this boundedly rational decision rule explains 83% of outcome inequality across socioeconomic groups.

The application of reference-dependent models is often complicated by the modeler's uncertainty regarding the reference point (referent) that agents adopt. We develop a powerful and minimally parametric approach to testing whether decisions could be rationalized by a general reference-dependent model with a specific referent. Our approach builds from the observation that, when both payoffs and the true referent are randomly varied, a marginal increase in all payoffs will have an equivalent effect as a marginal decrease in the referent. The observation that this equivalence holds at all payoff/referent combinations, when applied to decisions over properly constructed gambles, allows us to generate our test through modifications to existing tools for rejecting single-index representations. We assess the performance of this test in a simulation study and find that it is highly diagnostic even in the comparatively small sample sizes that are common in experimental economics. We then utilize this approach in an online experiment in which we randomly vary the salience of both goal-based and expectations-based referents. In this experiment, we confirm the common assumption that salient goals could serve as reference points. Illustrating the importance of salience, we reject that either reference point is adopted when it is not salient. Furthermore, and perhaps surprisingly, we reject the adoption of expectations as a reference point even when they are salient.

This paper reports a field experiment that tests the effect of motivated cognition on information acquisition. When the high-stakes College Entrance Exam is held in the month of Ramadan, Chinese Muslim students not only underestimate the cost of fasting when uninformed, but further, misread clear empirical evidence of the cost, which we obtain by analyzing administrative data on past students' exam performance. Inspired by the theory of motivated cognition, we tackle this learning failure by randomly offering a subset of the students reading materials in which well-respected Muslim clerics explain that it is permissible to postpone the fast until after the exam. Students who receive the material are substantially less likely to misread our empirical analysis and more willing to postpone the fast.

This paper investigates agents' simultaneous learning about multiple interacting technologies in the context of fertilizer application in China. We first present experimental evidence that, relative to the personalized fertilizer recommendations based on plot-level soil analysis, farmers simultaneously overuse nitrogen fertilizers and underuse phosphorus and potassium fertilizers. Our first-phase interventions that provide customized fertilizer recommendations lead to reduced nitrogen application and increased phosphorus/potassium use. Average yields and revenues are 5-7% higher and greenhouse gas (N2O) emissions are lower, while total fertilizer costs remain unchanged. Survey data suggest that farmers overestimate the return to nitrogen because it produces a salient signal on crops by increasing greenness, but they underestimate the effectiveness of phosphorus and potassium because their effects are barely observable. Motivated by these facts, we then propose a model of misspecified learning in which agents face two technologies. In learning about the effectiveness of both technologies, the overestimation of the return to the first technology causes an undervaluation and underuse of the second technology. To further test the model, we design a second-phase intervention that distributes leaf color charts to farmers to correct their overestimation of the return to greenness. Consistent with the model prediction, the intervention not only reduces farmers’ nitrogen use immediately, but also induces gradual learning of phosphorus and potassium; the proportion of farmers using phosphorus and potassium both increase by 6 percentage points, relative to 4% and 9% at baseline.

Pooled Testing Efficiency Increases with Test Frequency (with Ned Augenblick, Jonathan Kolstad, Ziad Obermeyer) Proceedings of the National Academy of Sciences 119.2 (2022).

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around the square root of x. However, this calculation omits a further benefit of frequent testing: removing infections from the population lowers intra-group transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms, etc.) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.

We investigate the causal impact of collegiate economics courses on students’ decision-making. By exploiting a Chinese college-admission system that quasi-randomly assigns students to economics/business majors given students’ preferences and the College Entrance Exam’s cutoff scores for economics/business majors, we are able to isolate the treatment effects of an economics education on students’ responses to a decision-making survey. Specifically, we compare the survey responses of students who narrowly meet the cutoffs for the economics/business majors to those who do not and find that students educated in economics/business courses are more likely to be risk neutral and less prone to common biases in probabilistic beliefs. While students in economics/business majors do not show significant changes in social preferences, they appear more inclined to believe that others behave selfishly.