Job vacancy duration reflects the time a firm spends searching, selecting, and hiring for a job opening. Capturing vacancy duration using the creation and deletion dates of job postings by US public firms, we examine the informativeness of vacancy duration for future firm profitability. We find that while firms that quickly fill low-skill job vacancies exhibit higher future profitability, firms that take more time to fill high-skill jobs exhibit higher future profitability. Our cross-sectional analyses across the benefits and costs of candidate selection and performance expectations suggest that the informativeness of vacancy duration comes from its reflection of firms’ hiring strategies. That is, firms expecting higher profitability recruit more intensively to avoid the opportunity cost associated with vacancies for low-skill jobs and to ensure the selection of high-quality workers for high-skill jobs. Further analyses show that the implication of job vacancy duration for future profitability is not incorporated timely in the capital markets, as evidenced by pessimistic analyst forecasts and positive earnings announcement returns in future quarters for firms with short (long) durations for low-skill (high-skill) jobs. These results demonstrate the informativeness of job vacancy duration for firm profitability and advance the understanding of firms’ hiring strategies.

We analyze the standard error bias associated with the use of generated regressors—independent variables generated from first‐step regressions—in accounting research settings. Under general conditions, generated regressors do not affect the consistency of coefficient estimates. However, commonly used generated regressors can cause standard errors to be understated. Problematic generated regressors include predicted values, coefficient estimates, and measures derived from these estimates. Widely used generated regressors in accounting include measures of earnings persistence, normal accruals, litigation risk, and conditional conservatism. Using simple regression models and simulation, we demonstrate how generated regressors can produce understated standard errors in accounting research settings. We also demonstrate how the magnitude of the standard error bias is inversely related to the precision of the generated regressor. Finally, we discuss bootstrapping as a correction for the bias and demonstrate the pairs cluster bootstrap as a tool to improve inferences in common accounting settings involving generated regressors.

We examine the real effects of lease-capitalization rules (i.e., standards that require firms to capitalize finance leases) on corporate investment. We show that the introduction of these rules leads to a decrease in investment, which is more pronounced for firms with high reliance on leases. We posit and find that lease capitalization affects investment via a learning channel and a contracting channel. Regarding the first channel, we argue that managers identify areas of overinvestment and activities that should be discontinued or downsized because of the information they collect and analyze to comply with lease-capitalization rules. Accordingly, we find that the effect of lease capitalization is stronger when learning opportunities are higher. Regarding the second channel, we argue that lease capitalization affects investment via its effect on contracts. Accordingly, we document an increase in the likelihood of covenant breaches and a stronger decline in investment for financially constrained firms.