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...

We examine the determinants of low income transitions using first-order Markov models that control for initial conditions effects (those found to be poor in the base year may be a non-random sample) and for attrition (panel retention may also be non-random). Our econometric model is a form of...