This essay engages with Florian Grisel’s The Limits of Private Governance and uses it as a springboard to rethink the theoretical foundations of the social norms and private governance literature. Drawing on Grisel’s historical study of the Prud’homie de pêche of Marseille—a centuries-old institution that governed fisheries—as well as my own research on property and neighborhood governance in China, including Chinese Small Property and The Authoritarian Commons, the essay proposes a new framework: “social norms 2.0.” This approach views governance as a process of co-evolution in which norms, legal institutions, and political authority continuously reshape one another. By reframing private governance as a dynamic and relational process, the co-evolutionary model moves the field beyond conventional debates about whether governance emerges primarily from informal norms or from state support. The essay suggests that future research should focus instead on the recursive interactions through which social norms, legal institutions, and political authority jointly produce and transform systems of governance.

This paper studies how donors respond to merit when giving to families facing unaffordable medical expenses. Using data from a leading crowdfunding platform in China, I find that campaigns receive larger donations from repeat donors when recipients report attending more selective colleges. To examine how perceived deservingness shapes donor behavior, I conduct a survey experiment that elicits willingness to donate to fundraising vignettes. Both academic merit and financial need increase donor generosity, but there is substitution between merit-based and need-based considerations. The college selectivity effect is more pronounced for the top 50 institutions and when donors have better knowledge of the rankings.

This paper compares China's parallel and sequential admissions mechanisms under the dummy-quota policy that limits how many applicants each college can screen. Although neither mechanism is stable or strategy-proof, their weaknesses are structurally different. Under the parallel mechanism, priority violations and profitable deviations are confined to a policy-determined buffer: students routed into colleges' screening pools by exam-score priority but rejected when colleges finalize admissions based on their preferences. The size of this buffer is bounded by the gap between screening and admission capacities. Under the sequential mechanism, by contrast, violations are driven by the round-by-round structure and can expand sharply with congestion and preference concentration. We derive tight upper bounds that characterize these exposures and quantify their magnitudes in calibrated simulations. The results reveal a clear distinction: institutional risk is capped ex ante by design under the parallel mechanism, whereas under the sequential mechanism it scales with market pressure-providing a complementary explanation for China's transition from sequential to parallel admissions.

Why do consumers express strong preference for Geographical Indication (GI) products yet hesitate to pay a premium? This study addresses this paradox by conceptualizing GI consumption as a staged decision process involving purchase intention, premium participation, and premium intensity. Drawing on signaling theory, we argue that premium formation depends on both trust in certification and consumers’ ability to decode label value under market disorder. Using survey data from 521 Chinese urban consumers and a Two-Part Model, we find a significant attitude–behavior gap: high stated preference does not automatically translate into premium payment. Institutional trust determines whether consumers enter the premium market, while GI label knowledge drives premium intensity. Perceived market disorder further strengthens the effect of institutional trust on premium intensity. These findings suggest that strengthening certification credibility and consumer education is essential for enhancing GI premium realization in emerging markets.

Salience theory posits that investors’ attention is disproportionately drawn to returns that stand out relative to a reference point. Building on this insight, we empirically examine how a stock’s salience theory value (ST) affects its future price crash risk. Using a comprehensive sample of Chinese A-share listed firms from 2005 to 2024, we find that stocks with higher ST are significantly more prone to subsequent price crashes. Mechanism analysis suggests that this relation operates through transient retail speculation and impaired pricing efficiency. High-ST stocks attract retail speculative demand and experience temporary overvaluation, which delays the incorporation of negative information into prices and amplifies the likelihood of abrupt price corrections. We further show that the effect is more pronounced during high-sentiment market conditions and is largely driven by stocks exhibiting salient upside return realizations. Overall, our findings highlight stock salience as a novel behavioral driver of crash risk and offer cautionary insights for investors and regulators.

Using data from Chinese non-financial listed firms over the 2009-2021 period, this study examines the link between cybersecurity and total factor productivity. The results suggest that cybersecurity can improve total factor productivity, a finding robust to multiples tests. Further investigation confirms that this relationship arises from information multiplication and capital deepening. It is particularly pronounced after the implementation of the Cybersecurity Law or among firms with strong digital innovation activities. In contrast, for firms whose senior executives have an information technology background, the moderating effect is not significant. However, the relationship is more pronounced for firms with larger scale, that are more technology-intensive, or operating in industries with above-average competition. Policy wise, these findings provide new evidence on the economic value of cybersecurity and offer policy implications for improving cybersecurity governance and aligning security investment with information and communications technology development to enhance resource allocation efficiency and sustainable digital growth.

This study examines how changes in auction timing affect bidding behavior, using data from Chinese land market transactions. In 2021, a new policy mandated that 22 cities shift from sequential to simultaneous auctions for residential land. Using an event study approach, I find simultaneous auctions lead to higher prices and a greater auction failure rate. Additionally, I examine testable implications related to the mechanism behind the policy effect: auction prices rise more for higher-quality land and in cities with higher land quality variance; moreover, the auction failure rate increases more for lower-quality land. I then developed a model of bidding that rationalizes my main findings and offers some policy suggestions.

Using Chinese A-share listed firms from 2015 to 2022, we construct a firm-level AI Narrative Gap — the gap between a firm's LLM-scored AI strategic narrative intensity and its subsequent two-year growth in real AI capital expenditure. We show that a wider AI Narrative Gap is associated with significantly more optimistic and less accurate analyst earnings forecasts. These distorted expectations, in turn, predict lower subsequent stock returns, lower industry-adjusted abnormal returns, and weaker future accounting performance. A double-sort portfolio placing firms simultaneously in the highest quintile of the AI Narrative Gap and highest quintile of analyst optimism earns a mean return 22.8 percentage points below that of the lowest quintile on both dimensions (t = −5.10). The return reduction in the AI Narrative Gap coefficient is attenuated but not eliminated after controlling for optimism, consistent with a partial expectation-distortion channel. Together, the evidence points to AI-themed narrative inflation as a mechanism that inflates investor and analyst expectations before precipitating return reversals (Lee et al., 2026; Jia et al., 2025; Chehimi, 2025).

This paper utilizes data from Chinese A-share listed companies and, based on the limited attention theory, reexamines the impact of data asset disclosure on capital market pricing efficiency. The study finds that the economic consequences of data asset disclosure are not static but exhibit a significant marginal decreasing feature as the overall market disclosure level rises. Specifically, when the overall market disclosure level is low, corporate disclosure of data asset information can significantly reduce stock price synchronization and enhance capital market pricing efficiency by increasing the supply of idiosyncratic information, enhancing information diffusion, and accelerating information integration. However, with the recent widespread disclosure behavior, there is no evidence that the above mechanisms remain effective. Further analysis indicates that disclosure behavior can maintain its positive effect only when the overall market disclosure level of data assets is low, and the firm's disclosed idiosyncratic information is relatively limited, the information dissemination environment is favorable, or institutional investor attention is not excessively diffused. This paper expands the boundaries of the limited attention theory, extending the research perspective to the dimension of the dispersion of investor attention by homogenized information, and reveals the value dilution problem faced by data asset information disclosure in the context of universalization.

We investigate whether China's 2021 mining ban transformed Bitcoin from a speculative vehicle into a macro-sensitive asset. Using daily data from 2017-2025, we document a decisive structural break. Pre-2021, volatility was endogenous, driven by raw trading volume rather than fundamentals. Post-ban, however, internal microstructure noise loses predictive power. Instead, volatility is now driven by macroeconomic anxiety: Wikipedia searches for "Inflation" improve forecast accuracy by over 7%. We further uncover a "dual narrative" where inflation attention predicts crash risk, while "Recession" queries (pivot speculation) drive rallies. These findings suggest the regulatory shock successfully curtailed noise trading, allowing macro-fundamentals to dominate price discovery.