Managerial ability and climate change exposure

Purpose - This study aims to investigate how a firm's management team's capacity to efficiently use its resources affects the firm's exposure to climate change. Specifically, the authors investigate the intriguing question – does managerial ability affect a firm's climate change exposure? Design/methodology/approach - The authors use an unbalanced panel dataset of 4,230 US based firms listed on Compustat from 2002–2019 and test the hypothesis by panel regression analysis. To mitigate endogeneity concerns, difference-in-differences and instrumental variable approaches are used. Findings - The baseline analysis shows a negative, statistically significant impact of managerial ability on climate change exposure. The findings hold after controlling for endogeneity using two-stage least squares regression and difference-in-differences tests. The authors find the negative effect is stronger for managers engaged in socially responsible activities, and after climate change issues receiving greater public awareness following the 2006 release of the Stern Review and the 2016 signing of the Paris Accord. Research limitations/implications - Motivated by the resource-based theory and the natural resource-based view of the firm model, the empirical results support the view that greater managerial ability protects the firm against environmental challenges through efficient use of firm resources. Compared with traditional climate change measures that are plagued by disclosure issues, the use of the Sautner, Van Lent, Vilkov and Zhang's machine learning based dataset utilizing earning conference calls provides stronger, robust findings that will be useful to management and investors in environmental performance assessments. Originality/value - Motivated by the resource-based theory and the natural resource-based view of the firm model, the empirical results support the view that greater managerial ability protects the firm against environmental challenges through efficient use of firm resources. Compared with traditional climate change measures that are plagued by disclosure issues, the use of the machine learning based dataset utilizing earning conference calls provides stronger, robust findings that will be useful to management and investors in environmental performance assessments.