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The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

The authors investigate the implications of environmental, social and governance (ESG) practices of firms for the pricing of their credit default swaps (CDS). In doing so, the authors compare European and US firms and consider nonlinear and indirect effects. This complements the previous literature focusing on linear and direct effects using bond yields and credit ratings of US firms.

For this purpose, the authors apply fixed effects regressions on a comprehensive panel data set of US and European firms. Further, nonlinear and indirect effects are investigated utilizing quantile regressions and a path analysis.

The evidence indicates that higher ESG ratings mitigate credit risks of US and European firms from 2007 to 2019. The risk mitigation effect is U-shaped across ESG quantiles, which is consistent with opposing effects of growing stakeholder influence capacity and diminishing marginal returns on ESG investments. The authors further reveal a mediating indirect volatility channel that substantially amplifies the direct effect of ESG on credit risk. A one-standard-deviation improvement in ESG ratings is estimated to reduce CDS spreads of low, medium and high ESG firms by approximately 4%, 8% and 3%, respectively.

This is the first study to examine whether credit markets reflect regional differences between Europe and the US with regard to the ESG-CDS-relationship. In addition, this paper contributes to the existing literature by investigating differences in the response of CDS spreads across ESG quantiles and to study potential indirect channels connecting ESG and CDS spreads using structural credit risk variables.