Knowledge that Transforms

Using variation in the timing and location of branches of failed banks we analyze its effect on income inequality. Employing a difference-in-differences specification we find that bank failures increased the GINI by 0.3 units (or 0.7%). We show that the rise in inequality is due to a decrease in the incomes of the poor that outpaces declines of the rest. We further show that individuals with lower levels of education exhibit a relatively greater decline in real wages and weekly hours worked. Exploring channels of transmission, we find income inequality is explained by a general decline in small business loans. This in turn reduces net new small business formation and their job creation capacity, a sector that hires a substantial share of low-income earners.

We study entry into entrepreneurship during the COVID-19 recession of 2020 using new data from an extensive survey of more than 24,000 Spanish households, conducted between June and November 2020. We find that while the overall decline in the startup rate in 2020 was large, and of a similar magnitude as that during the Great Recession, the differential impact depending on ex ante income was starkly different. During 2020, the drop in firm entry was entirely concentrated among low- and medium-income households. We show that the entrepreneurship gap between these households and their high-income counterparts is not directly explained by social distancing, since it is mostly driven by the sectors not directly affected by lockdown measures, and it is larger among households that did not suffer a negative income shock during the pandemic. Our results instead indicate that high-income households performed relatively better during the COVID-19 recession because they had the means to exploit new business opportunities, thanks to their larger wealth and better access to external finance.

Using U.S. Department of Justice (DOJ) data on corruption convictions of government officials, we study the effect of public corruption on analyst forecast quality. We find that analyst earnings forecasts for firms headquartered in more corrupt states are less accurate. Our results are robust to endogeneity checks and several alternative corruption measures. In our cross-sectional analysis, we find that the negative effect of corruption on analyst forecast accuracy is more pronounced in government contractor firms and firms with weaker internal governance or external monitoring. We further identify two channels through which corruption negatively influences analyst forecast accuracy: Firms in more corrupt states exhibit lower earnings quality and issue less frequent management guidance.

As of April 2018, several of the largest U.S. mutual fund firms have been imposed to disclose a measure of fund manager activeness to retail investors. We evaluate the effectiveness of this intervention. We find that, even for those funds with a large overlap of holdings with the benchmark, no measurable effort to increase management activeness can be observed subsequent to the imposed disclosure. By contrast, investors strongly respond to the intervention. However, an analysis of investor reaction suggests that they do not rationally trade on the newly available information. We discuss our results and propose potential disclosure improvements.

Originating and managing debt are cognitively demanding tasks that have become more challenging in the past few decades with the increasing variety and complexity of available financial products. We examine how cognitive ability is related to debt burdens among older adults and whether this relationship has changed over time with the increasingly complex financial landscape. We find that cognitive ability is an important predictor of debt burdens in older age and that individuals with higher cognitive ability have taken on higher debt levels relative to their counterparts in more complex financial environments, leading to increased financial fragility among relatively more sophisticated consumers. Our findings are broadly inconsistent with financial intermediaries systematically pushing increasingly complicated financial products onto unsophisticated borrowers.

Using the universe of business development companies (BDCs), a unique publicly traded segment of U.S. Private Equity (PE), for the period 1998–2017, we provide the first in-depth examination of their performance and risk-adjusted characteristics and compare our results to contrasting evidence derived from recently developed time series proxies for unlisted PE returns. BDCs exhibit zero alpha, beta of one, and significant exposure to SMB, HML, and CMA factors of 0.5, 0.7, and -0.3, respectively. BDC performance and market beta are sensitive to fund size and leverage. We provide evidence that BDC returns capture both the asset selection and PE ownership elements of the unlisted PE investment strategy. Finally, an event study analysis shows that NAV disclosures become informative only after the adoption of the Statement of Financial Accounting Standards 157 (SFAS 157). We posit that BDCs provide a readily available market-based PE benchmark for use by regulators, market participants, and academics.

We exploit a provision in Spanish labor laws whereby employment protection is more stringent for firms with 50+ employees. Firm-level evidence suggests that during the credit crunch of 2008-09, healthy firms with less than 50 employees borrowing from troubled banks grew faster in sectors where production factors were sufficiently substitutable. This effect is made possible by firms’ substituting labor for capital when the rental cost of capital increases. Our analysis sheds new light on the importance of labor regulation and the technological substitutability of the factors of production in enabling firms to adjust to financial shocks.

Earlier research indicates that attracting pre-offer investor attention yields long-term benefits to an initial public offering (IPO) issuer. For investors with limited attention, we model a way in which firms may attract attention: through underpricing their IPO, and using the expected allocations of underpriced shares to induce investors to attend the road show and consider the offering. Our model generates a novel set of predictions regarding the relationship between initial returns and attention, retention, expansion, and the benefits of attention, plus the asymmetry of the relationship with attention. Consistent with our model, investors’ attention is positively related to both initial returns and the magnitude of price revision. The relationship between attention and underpricing is asymmetric, and stronger when ex ante uncertainty is greater. Our work has implications regarding direct listings, is consistent with partial adjustment to public information, explains the relative unpopularity of gray market/when-issued trading and predicts that, even if the JOBS Act leads to more active pre-IPO trading (through crowdinvesting/equity crowdfunding), underpricing will still occur.

We study daily information shares for markets with partially overlapping trading hours. The established methodologies consider price discovery measures computed either for exactly overlapping trading hours or in sequential markets. In contrast, we develop a framework that exploits all price information generated during a full trading day in which any market can be open or closed at any time and propose a contribution-weighted information share. We apply this new method to the S&P 500 and NASDAQ-100 ETF and E-mini futures markets. It turns out that conventional information shares for the ETF markets are overestimated. E-mini futures are traded almost continuously throughout the trading day and process additional pricing relevant information when the ETF markets are closed.

We consider the problem of assessing and mitigating fire sales risk for banks under partial information. Using data from the European Banking Authority's stress tests, we consider the matrix of asset holdings of different banks. We first analyse fire sales risk under both full and partial information using different matrix reconstruction methods. We then investigate how well some policy interventions aimed at mitigating fire sales risk perform if they are applied based on only partial information. We find that even under partial information, using suitable network reconstruction methods to decide on policy interventions can significantly mitigate risk from fire sales. Furthermore, we show that some interventions based on reconstructed networks significantly outperform ad hoc methods that decide on interventions only based on the size of an institution and do not account for overlapping portfolios.