Gaussian empirical Bayes methods usually maintain a precision independence assumption: The unknown parameters of interest are independent from the known standard errors of the estimates. This assumption is often theoretically questionable and empirically rejected. This paper proposes to model the conditional distribution of the parameter given the standard errors as a flexibly parameterized location‐scale family of distributions, leading to a family of methods that we call close . The close framework unifies and generalizes several proposals under precision dependence. We argue that the most flexible member of the close family is a minimalist and computationally efficient default for accounting for precision dependence. We analyze this method and show that it is competitive in terms of the regret of subsequent decision rules. Empirically, using close leads to sizable gains for selecting high‐mobility Census tracts.

I document that the right tail of the firm size distribution systematically thickens with economic development. To rationalize this fact, I develop a parsimonious idea search model in which both aggregate growth and the firm size distribution are endogenously determined. The model features an asymptotic balanced growth path along which Gibrat's law holds at each date, and the right tail of the firm size distribution thickens monotonically toward Zipf's law. The model also implies that policies favoring large firms can improve welfare by better utilizing the diffusion externalities arising from idea search.

This paper develops a dynamic model of the attention economy featuring two interlinked markets: a product market, where consumers have limited awareness of firms, and an attention market, where platforms attract attention by investing in free services (e.g., social media, streaming, podcasts) and sell targeted ads using data on consumer preferences. The model provides a tractable framework in which platform investment, ad-driven discovery, product demand, and ad revenue are jointly determined. The effects of data and interoperability policies can reverse over time, platform quality may be inefficiently high or low, and the rise of data-rich digital advertising can coincide with a declining ad revenue-to-GDP ratio.

Policy makers and researchers are concerned that the expected credit loss (ECL) approach may exacerbate procyclicality. Using administrative loan‐level and firm‐level data in China, we find that banks adopting the ECL model reduced their credit supply and became more prudent in lending decisions after the onset of the COVID‐19 pandemic, compared to banks using the incurred credit loss (ICL) approach. Our findings are more pronounced for banks that experienced greater loan loss provisions induced by ECL and for firms with higher credit risk. The credit contraction persisted throughout our sample period. We further document that firms more exposed to ECL banks experienced larger reductions in loans, assets, liabilities, and revenue after the pandemic began than those more exposed to ICL banks. These findings support the conjecture that the ECL approach may exacerbate procyclicality.

This paper examines how climate risk transparency influences house prices and credit supply. I contend that inadequate property-level climate risk disclosures induce homebuyers to demand a risk aversion discount on house prices, creating a potential market for lemons. Employing a stacked difference-in-differences design, I find that flood risk disclosure laws, by enhancing dwelling-specific flood risk transparency, raise house prices on average by 7.6%. Results strengthen in states with stricter disclosure requirements. Exploiting within-state heterogeneity and controlling for housing market trends, I find that the effects persist and intensify in regions with higher aggregate exposure to flood risk, greater information frictions, and more attention to climate risks. Conversely, the effectiveness of flood risk disclosure laws attenuates in regions where households are less concerned about climate change. Additionally, less sophisticated lenders extend credit to financially constrained borrowers in response to the laws but experience lower profitability.

This paper investigates a sequential social learning problem in which individuals face ambiguity about others’ signal structures and have max-min expected utility preferences, thereby exhibiting ambiguity aversion. Unlike previous findings, which suggest that learning outcomes depend on the specifics of the learning environment, this study establishes information cascades as a robust outcome under ambiguity. With sufficient ambiguity, cascades arise almost surely, regardless of the statistical properties of signal structures. Moreover, standard results predicting the absence of cascades can easily break down: Even minimal ambiguity can trigger cascades when signals are bounded and lead to incorrect herding when signals are unbounded.

This study examines the influence of global board reforms on cross-border mergers and acquisitions (CBMAs). Using a difference-in-differences methodology, we find that CBMA flows increase significantly following board reforms in both home and host countries. The effect is more pronounced for countries with relatively weaker external governance mechanisms compared to their counterparts. Our findings suggest that board reforms enhance board functions, thereby facilitating firms' outbound investments. Simultaneously, improved board governance mitigates acquisition risks, attracting inward investments and, consequently, stimulating CBMA flows.

Our study analyses 6 million civil judgments in China from 2014 to 2018, documenting gender disparities that disfavour female litigants. We investigate the impact of an open justice reform that mandated courts to broadcast legal proceedings live on a centralized online platform. By exploiting variations in its implementation across courts and over time and employing both difference-in-differences and Bartik IV approaches, we find that gender disparities in chances of winning decrease as broadcast intensity increases. Analysis of the textual content of judicial decisions provides further evidence that these changes in judicial outcomes stem from altered judge behaviours (i.e. attention and effort) under enhanced judicial transparency. Our results demonstrate how information technology shapes judges’ conduct, underscoring its broader potential to improve accountability in public institutions.

This paper studies the productivity of nurse practitioners (NPs) and physicians, two professions performing overlapping tasks but with starkly different backgrounds, training, and pay. Using quasi-experimental variation in patient assignment to NPs versus physicians in Veterans Health Administration emergency departments, we find that, on average, NPs use more resources and exhibit a higher 30-day preventable hospitalization rate than physicians. However, the NP-physician performance difference varies by case complexity and severity. Importantly, even larger productivity variation exists within each profession, leading to substantial overlap between the productivity distributions of the two professions; NPs outperform physicians in 38 percent of random pairs.