We use a vignette-based survey experiment to elicit respondents’ assessments of the fairness of race-based hiring decisions and compare these assessments with the predictions of four preregistered ethical frameworks. While conservative respondents are much more accepting of discriminatory actions than others, respondents of all political leanings rate the relative fairness of different actions in a very similar way. A two-group framework in which one group (mostly self-described conservatives) values employers’ decision rights, the other has utilitarian concerns, and both groups use the same race-blind rules to assign relative fairness levels to actions explains our data well.

We study the influence of multi-sector conglomerate firms on sectoral comovement. Using an innovative network model of firms and industries, we derive a novel measure of the co-concentration of industries in which two industries are more co-concentrated if they share greater exposure to the same conglomerate firms. Using time-series, cross-sectional, and longitudinal tests on establishment-level data from nearly all US firms over 1991 to 2019, we find that industries with higher co-concentration exhibit stronger comovement in employment, sales, and asset growth. Controlling for alternative explanations, a one-standard deviation increase in co-concentration corresponds to a 0.32-standard deviation increase in the comovement of employment growth. In variance-covariance decompositions, we find that firm-specific shocks explain nearly half of aggregate volatility and industry comovement and that conglomerates play a significant role in sectoral comovement. Our framework helps explain how idiosyncratic, firm-level shocks contribute to aggregate fluctuations and influence business cycles.

Competitive threats motivate firms to use convertible debt because the possibility of future conversion enhances financial flexibility. Consistent with this intuition, we find that the intensity of competitive threats is positively associated with convertible debt financing at both the extensive and intensive margins. By using large tariff reductions as exogenous shocks to competition we show that this relation is likely causal. Convertible debt usage in response to competitive threats strongly depends on a firm’s relative financial and competitive conditions. In addition, firms increase the probability of future conversion by tailoring convertible debt features.

Drawing on current literature, this study develops a simple external financing model and provides international evidence of the causal effects of corporate governance improvements on trade credit. We introduce an internal governance perspective, hypothesizing that weak internal governance, which fosters managerial agency problems, allows firm managers to misuse trade credit. Specifically, poor governance may lead firms to rely on supplier financing as a substitute for traditional financing when they face financing constraints, a practice the literature argues raises concerns about shifting a firm's financial burdens onto its suppliers. Using a decade of data surrounding governance-enhancing board reforms in 38 countries, our difference-indifferences analyses strongly support these predictions. We find that strengthening board oversight via exogenous reforms reduces firms' reliance on supplier financing. Improved internal governance decreases firms' dependence on supplier financing and limits the manipulation of payables through real earnings management activities, such as inventory overproduction, which affects accounts payable. We also find that the effect of internal governance reforms is most pronounced in countries with stronger external governance mechanisms, suggesting a complementary effect. Additionally, our findings show that enhanced governance leads to better investment decisions and improved firm performance, especially for financially constrained firms and those with high agency costs.

We examine the cross-sectional predictability of corporate bond returns using a novel international dataset and a set of machine learning techniques. We find strong predictability in both U.S. and non-U.S. markets, with differing predictive factors. Bonds in developed markets show greater integration with the U.S. market and stronger ties to equity markets. Predictive performance of machine learning models varies over time and is greater before the onset of the COVID-19 pandemic and during periods of deteriorating business conditions, reduced market liquidity, elevated investor sentiment, and heightened risk aversion. The results offer insights into bond pricing and global diversification opportunities.

We examine the relationship between shareholder leverage constraints and corporate risk-taking, focusing on its impact on debtholders. Our findings show that mutual fund leverage constraints are related to more risk-taking activities of portfolio companies, inducing higher credit risk and greater risk-shifting concerns for the firms’ debtholders. In response, the debtholders raise borrowing costs and tighten lending conditions. These effects intensify for firms facing higher levels of conflict between debtholders and shareholders and when mutual funds exert greater influence over firms. Econometric analyses, including instrumental variable specifications and asset management company mergers, support a causal interpretation.

Downside risks are ubiquitous and can profoundly impact firm operations and valuation. Failure to adequately assess and manage target firms' downside risks hinders acquirers' ability to integrate and manage these businesses. This article introduces a novel measure of firms' downside risk similarity (DRS) based on risk factor descriptions and examines its implications for mergers and acquisitions (M&A) outcomes. We first validate that the measure is distinct from existing similarity measures and that it captures similarity in firms' potential significant downside. Using the new measure, we find that the market reacts more positively to deals in which acquirers and targets share more downside risks. Additional analyses show that this beneficial effect of DRS is driven primarily by risks that are idiosyncratic or firm‐specific, consistent with these risks requiring acquirers' relevant expertise to manage. Last, we document that in deals with more similar downside risks, the acquirers experience fewer risk profile changes and are less likely to suffer from adverse outcomes, such as deal‐specific goodwill impairment, divestitures, and significant profitability declines. Overall, we conclude that DRS plays a significant role in the M&A process.