Cowles Foundation for Research in Economics

We link high-frequency geocoded protest data during Bangladesh’s 2024 Monsoon Revolution, Facebook measures of protest activity and social connectivity, university enrollment and demographics to establish: (1) campus protest precedes off-campus mobilization; (2) student concentration predicts both on- and off-campus protest, while non-student youth, educated adults, urbanization, and locations of “placebo” universities authorized but not yet operating do not; and (3) social connectivity to campus protests predicts off-campus diffusion one week later. Universities appear to incubate mass uprising by making dissent visible, coordinated, and capable of spreading beyond campus. Such patterns are also evident in U.S. Vietnam War protests and in global data. These results can rationalize why governments in Hungary, Turkey, Iran, India, Russia, Bangladesh, Israel, and the United States have targeted campuses to suppress dissent.

The enormous gravity-model-of-trade literature has illustrated important advantages of estimation via Poisson Pseudo Maximum Likelihood (PPML). That literature’s prioritization of parameter estimation over hypothesis testing has left questions of testing in the PPML framework underexplored. In this paper we show analytically that scaling the dependent variable can affect the outcome of some joint hypothesis tests, but not others. Likelihood-ratio, model-based Wald, and model-based Lagrange Multiplier test statistics depend on scale, and therefore do not support scale-invariant inference. Wald and Lagrange Multiplier tests constructed with heteroskedasticity-robust sandwich adjustments are invariant to scale. We illustrate these points empirically with an application from the literature on Revealed Comparative Advantage.

We develop a unified approach to studying cost-price dynamics in the cross-section of firms in order to jointly explain the time series of aggregate inflation and the frequency of price changes, both during normal times and inflation surges. A key novelty is the use of microdata on firms’ prices and production costs to construct an empirical measure of price gaps—the deviation between a firm’s listed and optimal price. Conditional on the path of aggregate cost shocks extracted from the data, a state-dependent pricing model with strategic complementarities accounts well for both the linear cost-price dynamics of the pre-pandemic period and the nonlinear increase in inflation and frequency of price adjustment that followed.

This paper provides evidence of how the beliefs of investors who step out of the market, or “non-marginal” investors, influence asset prices. Using more than two decades of respondent-level investor surveys, we construct wedge measures that quantify the distance between subjective investor beliefs and option-implied benchmarks. We use self-identified proxies for market participation to document that these wedges correspond with the beliefs of non-marginal investors. Non-marginal investors are generally more pessimistic in their market return forecasts and perceive greater crash risks than the pricing population. Subjective crash beliefs are a key determinant of participation, even when controlling for expected return beliefs. This heterogeneity, in part, explains the negative association between average expected returns estimated from surveys and future realized market returns. In the cross-section, stocks with greater sensitivity to non-marginal beliefs earn lower returns, particularly where disagreement is high. Taken together, the results provide evidence of a composition channel in which belief-driven exit concentrates risk among fewer investors.

We study efficient dynamic mechanism design with independent private values when agents do not share a common prior over the stochastic environment. Each agent privately observes the kernel governing her own type evolution and may hold arbitrary beliefs about others’ kernels. We include kernels in agents’ type spaces and show that the dynamic team mechanism of Athey and Segal (2013) and the dynamic pivot mechanism of Bergemann and Välimäki (2010) implement the socially efficient allocation in periodic ex-post equilibrium. We further show that kernels need be elicited only at the outset and that these mechanisms induce the efficient private acquisition of kernels.

We develop a mechanism design framework for AI agents whose alignment (preferences) and capabilities (feasible actions and information) are unknown. We want such agents to act on our behalf so mechanisms must incentivize both honesty and obedience. A one-sided imitation structure—capabilities can be concealed but not counterfeited—yields a revelation principle, a characterization of implementable policies via nested cyclical monotonicity, and conditions under which eliciting higher-order beliefs can discipline multiple agents. We apply our framework to stylized examples of (i) sandbagging in which a more capable agent pretends to be less capable; (ii) an alignment–interpretability trade-off, where the two are substitutes in the instrument but complements in value; (iii) discipline via peer scoring; (iv) coupling rewards to induce competition among multiple agents; and (v) scalable oversight and reward shaping.        

Do elections aggregate the private information of office-motivated candidates? Our answer stems from a general result for two-player constant-sum Bayesian games with type-independent payoffs. Under a “completeness” statistical condition, every “identifiable” equilibrium is an ex-post equilibrium. Applied to Downsian elections, the ex-post property implies a sharp bound on information aggregation: equilibrium voter welfare is at best equal to the efficient use of a single candidate’s information. In canonical specifications, politicians may “anti-pander” (overreact to their information), whereas some degree of pandering would be socially beneficial. We discuss other applications of the ex-post result.

This paper proposes a semi-endogenous growth theory that incorporates technology vintages and the endogenous evolution of multiple technological paradigms through innovation. It provides a characterization of both balanced growth equilibrium and transitional dynamics in an environment where new technologies continuously emerge. From a positive perspective, the model rationalizes two distinct empirical patterns. Using two centuries of US patent data, I first document that the age profile of patents has a pronounced hump shape: most contemporary patents build upon technologies that are between 50 and 100 years old. Second, this age profile has remained stable throughout the past century. From a normative standpoint, the theory underscores a misallocation of research effort induced by the tendency among profit-maximizing firms to overinvest in further developing mature technologies. This yields a suboptimally slow development of emerging technologies. According to a calibrated version of the model, correcting such misallocation could generate welfare gains of 7%.