Knowledge that Transforms

We construct a measure ( fLMA ) of the extent to which neighboring firms hire similar types of workers, based on the similarity between the labor profile of a firm and that of its locality. We show that a firm’s innovation is positively related to fLMA. The enhanced labor mobility induced by higher fLMA is an important channel for this positive relation. This relation is stronger when firms have increased outside job opportunities for employees, increased knowledge spillovers via coworkership, and more employee stock options. Innovation is higher when intellectual property ownership is with employers, not employees. This effect increases in fLMA.

We analyze the effects of limited investor attention on the stock market reaction to innovation announcements and develop a new measure of patents’ economic value. We hypothesize that, when some investors pay delayed attention to innovation announcements, there will be a post-announcement drift in addition to the announcement effect, with the former decreasing and the latter increasing in investor attention. Using media coverage and abnormal Google search volume as investor attention proxies, we find consistent evidence. Our new attention-weighted measure of patents’ economic value has greater predictive power for future firm performance than measures based on the announcement effect alone.

Using high-frequency disagreement data from the investor social network StockTwits, we find that greater unsophisticated disagreement facilitates informed buying and selling. During periods of overvaluation, the facilitating effect of disagreement on trading is dampened for informed buyers but is amplified for informed sellers. These findings are unexplained by sentiment, news, and retail order flow, and they remain when we measure disagreement overnight and disagreement of technical investors, which alleviates the concern that disagreement and informed trading respond to a common shock. These findings suggest that informed traders respond meaningfully but differently to valuation changes induced by unsophisticated disagreement.

This article shows that exchange-traded funds (ETFs) “sample” their indexes, systematically underweighting or omitting illiquid index stocks. As a result, arbitrage activity between the ETF and its index has heterogeneous effects on underlying asset markets. Using an instrumental variables approach, we find that the trading activity of ETFs reduces liquidity and price efficiency and increases volatility and co-movement for liquid stocks but has no effect on illiquid stocks. Our results demonstrate that the effects of passive investing on asset markets depend on how passive funds replicate their target index.

Using a proprietary data set covering all foreign investors’ daily trades in the Chinese stock market from 2016 to 2019, we find that foreign order flows, facilitated by regulatory liberalization through several channels, present strong predictive power for future stock returns, implying that these order flows are likely informed. We track the source of this informativeness and find that foreign order flows significantly predict firm-level news and news-day returns, suggesting that foreign investors can effectively process local firm information. Finally, we find that regulatory reforms that generally relax investment access requirements further improve foreign investors’ predictive power.

We investigate how the government, as a customer, affects a supplier’s environmental policies. We find that government contractors have significantly lower levels of toxic pollution. Exploring the mechanisms, we show that government contractors reduce pollution by strengthening internal environmental governance and increasing investments in pollution abatement. Further analysis rules out alternative explanations related to reduced economic activities and financial constraints. Overall, our results highlight the government’s important role in disciplining corporate environmental misbehavior.

The identification of disaster risk has remained a significant challenge due to the rarity of macroeconomic disasters. We show that the interbank market can help characterize the time variation in disaster risk. We propose a risk-based model in which macroeconomic disasters are likely to coincide with interbank market failure. Using interbank rates and their options, we estimate our model via maximum likelihood estimation (MLE) and filter the short-run and long-run components of disaster risk. Our estimation results are independent of the stock market and serve as an external validity test of rare disaster models, which are typically calibrated to match stock moments.

This study examines the impact of recreational marijuana laws (RMLs) on firm-level employment using an imputation-based difference-in-differences (DiD) approach across U.S. states. RMLs significantly reduce employment, particularly among firms with high-skilled labor, strong union presence, permissive corporate cultures, and in states with greater dispensary density. Alternative explanations—including economic crises, COVID-19, fiscal changes, labor regulations, and related policies such as smoking bans and right-to-work (RTW) laws—are systematically ruled out through a series of placebo and robustness tests. RMLs also reduce investment, sales growth, and innovation, suggesting that legalization introduces labor-related frictions with broad implications for firm performance and long-term dynamism.

Stablecoins are a new form of private money. They are fragile but largely trade at par. How? We present a model and empirical work to examine a novel source of demand for stablecoins. Stablecoin owners are indirectly compensated for run risk by lending their coins to crypto speculators. The stablecoin can then support its $1 peg, but this arrangement links crypto speculation to traditional financial markets where stablecoins invest their reserves.

This paper examines the effect of the All‐Star award on the performance of Chinese financial analysts. Leveraging unique voting data from 2007 to 2016 and a regression discontinuity design (RDD), we find that the All‐Star award significantly enhances recipients' fundamental analysis. Awarded analysts issue more accurate earnings forecasts, and their stock recommendations convey greater information content for firms with higher information asymmetry. RDD results also indicate that award recipients gain increased resources and greater flexibility in reallocating time and effort. Post award, analysts concentrate on fewer industries, cover more firms within each industry, issue forecasts more frequently, expand their teams, and conduct more site visits. Surveys of analysts and institutional investors corroborate these findings, highlighting increases in site visits and roadshows following the award. Overall, the results suggest that the All‐Star award boosts analyst performance by fostering more concentrated coverage and improving access to both internal and external resources.