We present the first large-scale empirical evidence on the effects of adopting a dynamic matching mechanism, in replacement of the Immediate Acceptance (IA) mechanism, on matching stability in college admissions in China. In 2007, the Inner Mongolia Autonomous Region introduced the "Real-time Dynamic Mechanism", which allowed college applicants to change their college choices as many times as they want during a restricted time interval while seeing their tentative admission outcome when they made each choice. Using administrative data on test scores and admission outcomes of the universe of National College Entrance Exam (NCEE) takers from 2005 to 2011, we construct measures of justified envy, an indicator of matching stability. We use a generalized difference-in-differences framework and, in contradiction to the theoretical and experimental predictions from previous studies, find no evidence that the real-time dynamic mechanism improved matching stability in the first four years after its implementation. Our findings suggest that the real-time dynamic mechanism is much less effective in eliminating justified envy than the parallel mechanism, a hybrid of IA and the Deferred Acceptance (DA) mechanism, which is now widely adopted in other provinces in China.

The past few years have seen a shift in many universities' admission policies from test-required to either test-optional or test-blind. This paper uses laboratory experiments to examine students' reporting behavior given their application package and the school's interpretation of non-reported standardized test scores. We find that voluntary disclosure is incomplete and selective, supporting both the incentive of partial unraveling (students with higher scores are more likely to report) and the incentive of reverse unraveling (students facing a better school's interpretation of non-reporting are less likely to report). Subjects exhibit some ability to learn about the hidden school interpretation, though their learning is imperfect. Using a structural model of student reporting behavior, we simulate the potential tradeoff between academic preparedness and diversity in a school's admission cohort. We find that, if students have perfect information about the school's interpretation of non-reporting,  test-blind is the worst and test-required is the best in both dimensions, while test-optional lies between the two extremes. When students do not have perfect information, some test-optional policies can generate more diversity than test-required, because some students with better observable attributes may underestimate the penalty on their non-reporting. This allows the school to admit more students that have worse observable attributes but report. The results are robust to a variety of extensions, including when schools have access to alternative signals of academic ability and standardized test score is a noisy but sufficiently informative measure of student ability.

This paper studies China's nationwide ban on private academic tutoring for students in compulsory education. Using a novel nationally representative household panel survey on education expenditure from 2017 to 2023, we find that the ban was associated with a substantial decline in private tutoring spending, but also a significant increase in in-school education spending. Households in the lower and middle tiers of the income distribution responded to the ban with lower private academic tutoring spending and students from these families were less likely to be ranked at the top of the class. Further analysis reveals that (i) 87.5 percent of private academic tutoring facilities exited the market after the ban, (ii) the declines in private tutoring expenditures were concentrated among urban households, and (iii) highly educated parents may turn to home tutoring as a substitute to private tutoring. Our findings highlight the substitution toward alternative forms of academic support under the ban and the heterogeneous impacts of the policy.

We use laboratory experiments to examine strategic disclosure and deception in a sender–receiver game modeling the data economy. Firms (senders) are privately informed of their quality and decide how to disclose it to consumers (receivers). They may signal higher quality through exaggeration, but face penalties if misrepresentation is detected. First, we vary penalty severity and returns to perceived quality, and find that exaggeration declines with stronger penalties and lower returns. We then compare two verification regimes: exogenous detection, which mimics probabilistic data breaches, and endogenous detection, in which consumers (receivers) incur a cost to verify the firm’s claim mimicking privacy audits. Contrary to equilibrium predictions, exaggeration is more prevalent under exogenous detection than under endogenous detection. This is driven by the finding that some receivers function as watchdogs and have strong desire to detect regardless of detection cost.