Corporate Codes of Conduct: The Effects of Code Content and Quality on Ethical Performance

Corporate codes of conduct are a practical corporate social responsibility (CSR) instrument commonly used to govern employee behavior and establish a socially responsible organizational culture. The effectiveness of these codes has been widely discussed on theoretical grounds and empirically tested in numerous previous reports that directly compare companies with and without codes of conduct. Empirical research has yielded inconsistent results that may be explained by multiple ancillary factors, including the quality of code content and implementation, which are excluded from analyses based solely on the presence or absence of codes. This study investigated the importance of code content in determining code effectiveness by examining the relationship between code of conduct quality and ethical performance. Companies maintaining high quality codes of conduct were significantly more represented among top CSR ranking systems for corporate citizenship, sustainability, ethical behavior, and public perception. Further, a significant relationship was observed between code quality and CSR performance, across a full range of ethical rankings. These findings suggest code quality may play a crucial role in the effectiveness of codes of conduct and their ability to transform organizational cultures. Future research efforts should transcend traditional comparisons based on the presence or absence of ethical codes and begin to examine the essential factors leading to the effective establishment of CSR policies and sustainable business practices in corporate culture.     

A decomposition of US business sector TFP growth into technical progress and cost efficiency components

A problem with index number methods for computing TFP growth is that during recessions these methods show declines in TFP. This is rather implausible since it implies technological regress. We develop a new method to decompose TFP growth into technical progress and inefficiency arising from the short run fixity of capital and labour, and apply this to new data on the US corporate nonfinancial sector and the noncorporate nonfinancial sector. The analysis sheds light on sources of the productivity growth slowdowns over the period 1960–2014.     

Fund Liquidation, Self-selection, and Look-ahead Bias in the Hedge Fund Industry

A wide range of empirical biases hampers hedge fund databases.In this paper we focus upon survival-related biases and disentanglelook-ahead biases due to self-selection of funds and due tofund termination. Self-selection arises because funds voluntarilyreport their information to data vendors and may decide to stopdoing so. By extending existing methodology, we analyze persistencein hedge fund performance over the period 1994–2000, takinginto account the above biases. The results show that look-aheadbiases due to liquidation and self-selection enforce each otherand may lead to overestimating expected returns by as much as8% per year. Overall, the results are consistent with positivepersistence in hedge fund returns at horizons of two and fourquarters.     

Learning about fundamentals: The widening of the French ERM bands in 1993

Learning about Fundamentals: The Widening of the French ERM Bands in 1993. — The authors incorporate a Bayesian learning model into a fairly general model of exchange rate determination in discrete time. The model is applied to the period following the widening of the French-German ERM bands in August 1993, in which a systematic underprediction of the franc can be observed until February 1994. A (substantial) part of these forecast errors can be mimicked by a Bayesian learning process. Simulations with our model show that, after the widening of the bands, agents, contrary to their initial expectations, gradually learned that the true process driving monetary conditions in France had not changed notably.     

Two-step estimation of panel data models with censored endogenous variables and selection bias

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing, for two forms of endogeneity and/or sample selection bias. Furthermore, it incorporates roles for dynamics and state dependence in the reduced form. Finally, we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions.