Knowledge that Transforms

Abstract We examine whether criminal records of CEOs and rank-and-file employees are associated with firms’ likelihood of bankruptcy, and whether lenders adjust their required cost of debt accordingly. We use a nationwide sample of private firms and criminal registers covering all firm employees. We find that the likelihood of bankruptcy is positively associated with the CEO’s criminal record and the proportion of employees with criminal records. We find some, though less robust, evidence that lenders price a firm’s loan higher when the firm’s CEO has a criminal record and when more of the employees have criminal records. The results suggest that the characteristics of firm employees represent a risk that, to some extent, is priced by lenders.

Abstract In this paper, we examine bad debt and charity care reporting by nonprofit hospitals around bond issuance. Given the tax advantages afforded to nonprofit hospitals, including the ability to issue tax-exempt debt, hospital managers encounter stakeholder pressure to provide community benefits. When nonprofits issue debt, they also face economic pressure to meet creditors’ financial performance expectations. We document a reporting strategy that allows nonprofit hospitals to reduce the cost of bond debt while simultaneously alleviating regulators’ and community members’ concerns about inadequate provision of charity care. Using data from public bond issues for California nonprofit hospitals, we find that hospital managers shift costs from bad debt expense to charity care in periods prior to a public bond issuance and that the strategy is associated with a lower cost of debt. Our results inform those relying on accounting measurements to infer nonprofit hospitals’ social good provisions and financial health.

Abstract I examine the relation between firms’ financial conduct and wage theft. Wage theft represents the single largest form of theft committed in the United States and primarily affects firms’ most vulnerable employees. I show that wage theft is more prevalent (i) when firms just meet or beat earnings targets and (ii) when executives’ personal liability for wage theft decreases. Wage theft precedes financial misconduct while the theft is undetected, but once firms are caught engaging in wage theft they are more likely to shift to engaging in financial misconduct. My findings highlight an economically meaningful yet previously undocumented way in which firms’ financial incentives relate to employee treatment.

Abstract This paper investigates how data requirements often encountered in archival accounting research can produce a data-restricted sample that is a non-random selection of observations from the reference sample to which the researcher wishes to generalize results. We illustrate the effects of non-random sampling on results of association tests in a setting with data on one variable of interest for all observations and frequently-missing data on another variable of interest. We develop and validate a resampling approach that uses only observations from the data-restricted sample to construct distribution-matched samples that approximate randomly-drawn samples from the reference sample. Our simulation tests provide evidence that distribution-matched samples yield generalizable results. We demonstrate the effects of non-random sampling in tests of the association between realized returns and five implied cost of equity metrics. In this setting, the reference sample has full information on realized returns, while on average only 16% of reference sample observations have data on cost of equity metrics. Consistent with prior research (e.g., Easton and Monahan The Accounting Review 80, 501–538, 2005), analysis using the unadjusted (non-random) cost of equity sample reveals weak or negative associations between realized returns and cost of equity metrics. In contrast, using distribution-matched samples, we find reliable evidence of the theoretically-predicted positive association. We also conceptually and empirically compare distribution-matching with multiple imputation and selection models, two other approaches to dealing with non-random samples.

Abstract We examine the impact of a disclosure mandate for greenhouse gas emissions on firms’ subsequent emission levels and financial operating performance. For UK-incorporated listed firms a carbon disclosure mandate was adopted in 2013. Our difference-in-differences design shows that firms affected by the mandate reduced their emissions by about 8% relative to a control group of European firms. At the same time, our tests indicate that the treated firms experienced no significant changes in their gross margins. Taken together, our findings indicate that the reporting mandate had a real effect on the variable to be disclosed without adversely affecting the financial operating performance of the treated firms.