We examine how banks manage carbon transition risk by selling loans given to polluting borrowers to less regulated shadow banks in securitization markets. Exploiting the election of Donald Trump as an exogenous shock that reduces carbon risk, we find that banks’ securitization decisions are sensitive to borrowers’ carbon footprints. Banks are more likely to securitize brown loans when carbon risk is high but swiftly change to keep these loans on their balance sheets when carbon risk is reduced after Trump’s election. Importantly, securitization enables banks to offer lower interest rates to polluting borrowers but does not affect the supply of green loans. Our findings are more pronounced among domestic banks and banks that do not display green lending preferences. We discuss how securitization can weaken the effectiveness of bank climate policies through reducing banks’ incentives to price carbon risk.

We examine how firms adapt their organization when they go public. To conform with the requirements of public capital markets, we expect IPO firms to become more organized, making the firm more accountable and its human capital more easily replaceable. We find that IPO firms transform into a more hierarchical organization with smaller departments. Managerial oversight increases. Organizational functions dedicated to accounting, finance, information and communication, and human resources become much more prominent. Employee turnover is sizeable and directly related to changes in hierarchical layers. New hires are better educated, but younger and less experienced than incumbents, which reflects the staffing needs of a more hierarchical organization. Wage inequality increases as firms become more hierarchical. Overall, going public is associated with a comprehensive transformation of the firm's organization which becomes geared towards efficiently operating a public firm.

The effects of private equity buyouts on employment, productivity, and job reallocation vary tremendously with macroeconomic and credit conditions, across private equity groups, and by type of buyout. We reach this conclusion by examining the most extensive database of U.S. buyouts ever compiled, encompassing thousands of buyout targets from 1980 to 2013 and millions of control firms. Employment shrinks 12% over two years after buyouts of publicly listed firms—on average, and relative to control firms—but expands 15% after buyouts of privately held firms. Postbuyout productivity gains at target firms are large on average and much larger yet for deals executed amid tight credit conditions. A postbuyout tightening of credit conditions or slowing of gross domestic product growth curtails employment growth and intrafirm job reallocation at target firms. We also show that buyout effects differ across the private equity groups that sponsor buyouts, and these differences persist over time at the group level. Rapid upscaling in deal flow at the group level brings lower employment growth at target firms. We relate these findings to theories of private equity that highlight agency problems at portfolio firms and within the private equity industry itself.

We examine the effects of the big five personalities of CEOs (openness, conscientiousness, extroversion, agreeableness, and neuroticism) on their annual compensation. We hand-collect the tweets of S&P 1500 CEOs and use IBM's Watson Personality Insights to measure their personalities. CEOs with high ratings of agreeableness and conscientiousness get more compensation. We further find that the firms with these CEOs outperform their peers due to better investment efficiency. Firms are willing to pay higher compensation for talent, especially for firms with better operations, located in states with higher labor unionization, or facing higher competition in the product market. Overall, CEO personality is a valid predictor of CEOs' compensation.

This study employs synthetic control methods to estimate the effect of the Iberian exception mechanism on wholesale electricity prices and consumer inflation, for both Spain and Portugal. We find that the intervention led to an average reduction of approximately 40% in the spot price of electricity between July 2022 and June 2023 in both Spain and Portugal. Regarding overall inflation, we observe notable differences between the two countries. In Spain, the intervention has an immediate effect, and results in an average decrease of 3.5 percentage points over the twelve months under consideration. In Portugal, however, the impact is small and generally close to zero. Different electricity market structures in each country are a plausible explanation.

We study how deposit competition affects a bank's decision to securitize mortgages. Exploiting the state-specific removal of deposit market caps across the U.S. as a source of competition, we find a 7.1 percentage point increase in the probability that banks securitize mortgage loans. This result is driven by an 11 basis point increase in deposit costs and corresponding reductions in banks' deposit holdings. Our results are strongest among banks that rely more on deposit funding. These findings highlight a hitherto undocumented and unintended regulatory cause that motivates banks to adopt the originate-to-distribute model.

This study investigates the impact of inaccurate assumptions on economic forecast precision. We construct a new dataset comprising an unbalanced panel of annual German GDP forecasts from various institutions, taking into account their underlying assumptions. We explicitly control for different forecast horizons to reflect the information available at the time of release. Our analysis reveals that approximately 75% of the variation in squared forecast errors can be attributed to the variation in squared errors of the initial assumptions. This finding emphasizes the importance of accurate assumptions in economic forecasting and suggests that forecasters should transparently disclose their assumptions to enhance the usefulness of their forecasts in shaping effective policy recommendations.

Using the universe of high school and college admissions data in Croatia, we geocoded nearly half a million students’ residential addresses to investigate how their college and major choices are influenced by older neighbors and peers. Using an RDD to exploit time and program variation in admission cutoffs, we find that having an older neighbor who was admitted to and enrolled in a program increases a student’s probability of applying to the program by about 20%. We find that this effect consistently holds only for the closest neighbors, both in terms of distance and age difference. Female students are more likely to be influenced by older neighbors’ choices, and male older neighbors’ admission has a larger impact on both male and female students compared to female older neighbors. The effect is stronger if the student-neighbor pair lives in a region that does not have its own university, implying that the value of information in rural areas is higher. We find evidence that students don’t follow their older neighbors to less competitive programs; instead, they are more likely to apply for the same programs their older neighbors were admitted to when the program is more prestigious. Next, we utilize the variation in weight scheme of Croatia’s college study programs to show evidence, beyond college choices, of how older neighbors affect the human capital formation of their younger peers. The main channel through which we observe this effect is during high school, through specialization in the subjects needed to gain admittance to older neighbors’ college programs. These findings shed light on the intricate dynamics shaping educational decisions and underscores the significant role older neighbors play in guiding younger peers toward specific academic pathways.

How do beliefs on admission probability influence application choices? In this study, we empirically investigate whether and how admission probability is reflected in application choices in a centralized admission system. We exploit a novel setting of a dynamic deferred acceptance mechanism as employed in Croatia with hourly information updates and simultaneous application choices. This setting allows us to explore within-applicant strategic adjustments as a reaction to changing signals on admission probability. We show in an RDD analysis that applicants react to negative signals on admission probability with an increased propensity to adjust their application choices by 11-23%. Additionally, we show how application strategies evolve over time, while applicants learn about their admission probability. The group most-at-risk to remain unmatched improves their application choices by applying to programs with a higher admission probability towards the application deadline. Yet, we also identify a popular and potentially harmful strategy of applying to safer programs before applying to more risky “reach” programs. About a quarter of applicants have the potential to improve their application choices by resorting their application choices.

We investigate how global banks’ macroeconomic expectations for borrower countries influence their credit supply. Utilizing granular data on varying expectations among banks lending to the same firm at the same time, combined with an instrumental variable approach, we find that more optimistic GDP growth expectations for a borrower country are strongly linked to increased credit supply. Specifically, a one standard deviation increase in a lender’s GDP growth expectation for the borrower’s country corresponds to an increase of 8.46 percentage points in the loan share, equivalent to approximately 0.75 standard deviations of the loan share and $75.35 million in loan amount. In contrast, global banks’ short-term inflation expectations do not show a significant impact on their credit supply.