Knowledge that Transforms

We explore the effects of a blockchain-based environmental monitoring technology on emissions. Our model of firm competition in the presence of regional regulators reveals that blockchain adoption reduces industrial pollution but triggers business relocation, creating trade-offs between local emission reduction and economic contraction. When pollution-induced social losses are highly dispersed across cities, a partial-adoption equilibrium fails to mitigate aggregate emissions because of the pollution leakage. We further present the first piece of empirical evidence corroborating model predictions, by taking advantage of a recent regulation change in China. The concentrations of SO2, NO2, and CO in blockchain-adopting cities are on average 16.6 percent, 7.9 percent, and 4.6 percent, respectively, lower than other cities. However, blockchain-based monitoring disproportionately hurts the industrial sector, and the average economic growth are 1.8–2.6 percent lower than other cities. Firms in adopting cities open more non-local plants to avoid regulation.

Institutional investors affected by hurricanes subsequently support environmental proposals in non-affected firms even if they never voted for similar initiatives. Affected investors raise their holdings in firms where their pro-environment votes are consequential. The increased voting support after hurricanes has real effects as environmental proposals endorsed by more hurricane-afflicted investors are more likely to pass. Moreover, both market capitalization and analysts’ recommendations decline after firms pass environmental proposals. Our evidence suggests that natural disasters raise institutional investors’ concerns about the environment and about potential fund flow disruptions. These concerns, in turn, influence environmental activism, corporate policies, and firm performance.

Using unique data on Italian households from 2011 to 2015, we examine how investor demand-side and bank supply-side characteristics relate to households’ holdings of bonds issued by their own bank. Households with a higher concentration of own bank bonds tend to have a lower education level, a shorter investment horizon, and less wealth. Controlling for investment and investor attributes, households exhibit high bank bond concentration when the issuing bank has high funding needs, low profitability, or a high branch market share. Furthermore, we show that a buy-and-hold strategy on bank bonds yields lower returns than one on government bonds over the same period. These findings offer insights into retail bond issuance, an important source of funding for banks in times of market stress.

This article highlights a novel channel through which the level of deposits matters for bank fragility and efficiency. We augment a global-game model of bank runs with a consumption-saving choice that determines deposit size in the initial period. We derive two key results. First, depositors’ incentives to run increase with the amount of savings held as bank deposits. Second, a saving externality emerges because individual depositors fail to internalize the impact of their deposit decisions on the likelihood of a bank run. This leads to depositors’ over-saving and inefficient bank liquidity provision, as well as excessive bank fragility. Finally, we characterize the optimal policy to implement the efficient allocation.

In a tender offer by a value-increasing raider, voting shareholders face a free-rider problem. However, when they are not atomistic, they do not completely free-ride. In contrast, non-voting shareholders, who are never pivotal for the success of the offer, are absolute free-riders. Hence, in this case there is a gain from departing from one share/one vote. This departure also has a cost; there is an increased vulnerability to value-decreasing raiders, and the optimal governance structure balances the cost and the gain.

Using a comprehensive dataset of privacy policies, firm characteristics, consumer tracking, and cybersecurity incidents, we document several stylized facts about the heterogeneity of firms’ data extraction practices and the influence of privacy regulations. Rather than adopting standardized boilerplate privacy policies, we find substantial within-industry differences correlated with firms’ technical sophistication; firms engaging in data extraction have lengthier policies, seeking to hedge legal risks. Firms with intermediate technical sophistication appear to follow a “collect and share” model, collecting large amounts of consumer data and sharing it with third parties for processing, thus creating cybersecurity risks. Conversely, high sophistication firms appear to implement a “receive and process” model, consistent with a two-tier data market in which data flow from intermediate to high sophistication firms.

The more trust investors place in a money manager, the more confident they are to take risk. We test this theory in a laboratory experiment using the amount returned from a trust game as measure of trustworthiness. Investors increase the share invested in risky assets with high-cost money managers compared to those with low costs when the high-cost money managers are more trustworthy than the low-cost ones. The willingness to take more risk with high-cost money managers is increasing in the difference in trustworthiness. Up to a third of the difference in trustworthiness translates into an increasing risky share. Vice versa, investors are willing to accept higher costs for investments made through more trustworthy money managers. Our findings are robust to alternative explanations, demonstrating that the risk-aversion channel can be sufficient for trust to influence behavior.

This article proposes an innovative method to assess borrowers’ creditworthiness in consumer credit markets by conducting machine-learning-based analyses on real-time video information that records borrowers’ behavior during the loan application process. We find that the extent of borrowers’ micro-facial expressions of happiness is negatively associated with loan delinquency likelihood, while the degree of fear expressions is positively associated with delinquency risk. These results are consistent with two economic channels relating to the adequacy and uncertainty of borrowers’ future income, drawn from the extant psychology and economics literature. Our study provides important practical implications for fintech lenders and policymakers.

In four large online experiments, we study how investors assess the relationship between stock portfolios and the market. Participants select or are randomly assigned a portfolio of stocks from a market index. They state portfolio return expectations conditional on different market outcomes, revealing implied beliefs about portfolio beta. We find general underestimation of beta which is stronger for downside beta. This asymmetry is amplified for participants who select their portfolio. They believe their portfolio goes up with the market but does not come down with it. We confirm biased beliefs about beta with financial professionals, monetary incentives, and alternative belief elicitation methods.

This article examines whether more bank presence in underserved areas can improve households’ health. Leveraging a 2005 Reserve Bank of India policy and a regression discontinuity design, I demonstrate that 5 years post-policy, treatment districts have twenty-seven more bank branches than control districts. This expansion increases household employment and access to savings accounts, enhancing health investments. On the healthcare supply side, hospitals utilize more credit and expand services. Six years after the policy, households in treatment districts are nineteen percentage points less likely to suffer from non-chronic illnesses in a given month. Chronic diseases remain unaffected.