This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the na?ve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate...

Propensity score matching provides an estimate of the effect of a 'treatment' variable on an outcome variable that is largely free of bias arising from an association between treatment status and observable variables. However, matching methods are not robust against 'hidden bias' arising from...

Publicly traded firms in the U.S. typically determine C.E.O. compensation by benchmarking the pay of their C.E.O.s against the pay of C.E.O.s in “peer” firms. The naming of particular peer companies by individual firms constitutes a supra-firm relational structure (network) in which an...

This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naı̈ve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate...

The disclosure of compensation peer groups is argued to provide shareholders with valuable information that can be used to scrutinize CEO compensation. However, research suggests that there are substantial incentives for executives and directors to bias the compensation peer group such that the...