Elena Simintzi, University of North Carolina-Chapel Hill
Sheng-Jun Xu, University of Alberta
Ting Xu, University of Toronto
Abstract: We study the effect of government-subsidized childcare on women’s career outcomes and firm performance using linked tax filing data. Exploiting a universal childcare reform in Quebec in 1997 and the variation in its timing relative to childbirth across cohorts of parents, we show that earlier access to childcare increases not just new mothers’ employment, but their active reallocation of careers across firms. New mothers are more likely to switch to firms they find traditionally unattractive (e.g., offering demanding, inflexible jobs), leading to higher earnings and productivity. Such firms benefited from the reform, drawing more young female workers and experiencing better performance. Our results suggest that childcare frictions hamper women’s career progression and their allocation of human capital in the labor market.
Discussant: Irem Demirci, Nova School of Business and Economics
Devin Shanthikumar, University of California-Irvine
Dayin Zhang, University of Wisconsin-Madison
Abstract: Research suggests that minorities continue to face lower mortgage application approval rates, and, if approved, higher interest rates. At the same time, research suggests that disparities between minority and white borrowers have narrowed in recent years. We examine the role of diversity policies in addressing mortgage lending disparities, using both within-lender analyses and an event study design. We find that diversity policies significantly reduce race-related gaps in borrowing costs, captured by effective interest rate spreads. However, they drive larger gaps in approval rates. Additional analyses provide insights into the mechanisms through which diversity policies affect loan costs and approval rates. The increase in application completion rates and the reduction in loan costs, both driven by the front office, are in part driven by better matching of borrower and loan officer race. The reduction in approval rates is in part driven by an increase in higher-risk loan applications from minority borrowers. However, the increase in risk does not fully explain the approval rate effect, suggesting an overreaction from the back office – in which the back office increases approval standards for minorities. Examination of ex post loan performance is consistent with an overreaction, with a significant decrease in ex post defaults and an increase in prepayment from minority borrowers. Together, our results suggest that diversity policies in part address race-related disparities in lending. However, the policies also induce wider loan approval gaps. We discuss possible explanations.
Abstract: This paper investigates the impact of female analyst coverage on firms’ environmental and social (E&S) performance. Exploiting broker closures as a quasi-exogenous shock to female analyst coverage, we show that firms experiencing an exogenous drop in female analyst coverage subsequently suffer a 7% decline in E&S scores and a deterioration in real E&S outcomes. To uncover the mechanisms, we develop novel machine learning models to analyze a large corpus of over 2.4 million analyst reports and 120,000 earnings call transcripts. Our analysis reveals that female analysts are more likely to discuss E&S issues in their research reports and during earnings conference calls compared to their male counterparts. Moreover, female analysts are more likely to take actionable steps, such as downgrading stock recommendations and lowering target prices, following negative E&S discussions in their reports. By tracing the path from analyst gender to differences in behaviour and ultimately to covered firms’ E&S performance, we find evidence that gender diversity among analysts is a key driver of corporate E&S practices. Our findings highlight the importance of promoting gender diversity in the finance industry and offer novel insights into the role of female analysts in shaping corporate E&S practices.
Discussant: Anya Mkrtchyan, University of Massachusetts-Amherst
