In this paper, we study champions of corporate social responsibility (CSR) performance among the U.S. publicly traded firms and their common characteristics by utilizing machine learning algorithms to identify predictors of firms’ CSR activity. We contribute to the CSR and leadership determinants literature by introducing the first comprehensive framework for analyzing the factors associated with corporate engagement with socially responsible behaviors by grouping all relevant predictors into four broad categories: corporate governance, managerial incentives, leadership, and firm characteristics. We find that strong corporate governance characteristics, as manifested in board member heterogeneity and managerial incentives, are the top predictors of CSR performance. Our results suggest policy implications for providing incentives and fostering characteristics conducive to firms “doing good.”

The recent advances in automation technology, robotics in particular, have sparked a heated debate over the future of labor and human society at large. The ongoing process of robotization may engender profound impacts on various segments of the labor market. Given the far-reaching implications of robots, it is thus very important to understand the scale and scope of robot use and characteristics of robot users. However, the main challenge is the limited availability of robot data at the microeconomic level (Raj and Seamans, 2018). Due to the data constraint, the bulk of the existing literature relies on cross-country industry-level data from the International Federation of Robotics (IFR). The lack of micro-level robot data makes it difficult to paint a comprehensive picture of robotization in industrial settings, and perhaps more importantly, to assess how within-industry firm level heterogeneity manifests itself in robot use and adoption.

Competition in the U.S. appears to have declined. One contributing factor may have been heterogeneity in the availability of credit during the financial crisis. I examine the impact of product market peer credit constraints on long-run competitive outcomes and behavior among non-financial firms. I use measures of lender exposure to the financial crisis to create a plausibly exogenous instrument for product market credit availability. I find that credit constraints of product market peers positively predict growth in sales, market share, profitability, and markups. This is consistent with the notion that firms gained at the expense of their credit constrained peers. The relationship is robust to accounting for other sources of inter-firm spillovers, namely credit access of technology network and supply chain peers. Further, I find evidence of strategic investment, i.e. the idea that firms increase investment in response to peer credit constraints to commit to deter entry mobility. This behavior may explain why temporary heterogeneity in the availability of credit appears to have resulted in a persistent redistribution of output across firms.

Competition in the U.S. appears to have declined. One contributing factor may have been heterogeneity in the availability of credit during the financial crisis. I examine the impact of product market peer credit constraints on long-run competitive outcomes and behavior among non-financial firms. I use measures of lender exposure to the financial crisis to create a plausibly exogenous instrument for product market credit availability. I find that credit constraints of product market peers positively predict growth in sales, market share, profitability, and markups. This is consistent with the notion that firms gained at the expense of their credit constrained peers. The relationship is robust to accounting for other sources of inter-firm spillovers, namely credit access of technology network and supply chain peers. Further, I find evidence of strategic investment, i.e. the idea that firms increase investment in response to peer credit constraints to commit to deter entry mobility. This behavior may explain why temporary heterogeneity in the availability of credit appears to have resulted in a persistent redistribution of output across firms.

This study estimates the firm-level employment effects of investment grants in Germany. In addition to the average treatment effect on the treated, we examine discrimination in the funding rules as potential source of effect heterogeneity. We combine a staggered difference-in-differences approach that explicitly models variations in treatment timing with a matching procedure at the cohort level. The findings reveal a positive effect of investment grants on employment development in the full sample. The subsample analysis yields strong evidence for heterogeneous effects based on firm characteristics and the economic environment. This can help to improve the future design of the program.