Ao Wang

Welcome! I am an Assistant Professor of Economics at the National University of Singapore. I received my PhD in Economics from UC Berkeley.

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,  development economics, and market design.

My CV is here.

Email: at

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.

We present a general approach to experimentally testing candidate reference points. This approach builds from Prospect Theory's prediction that an increase in payoffs is perfectly offset by an equivalent increase in the reference point. Violation of this prediction can be tested with modifications to existing econometric techniques in experiments of a particular design. The resulting approach to testing theories of the reference point is minimally parametric, robust to broad classes of heterogeneity, yet still implementable in comparatively small sample sizes. We demonstrate the application of this approach in an experiment that tests the role of salience in setting reference points.

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.