This paper addresses a key methodological challenge in the modeling of individual poverty dynamics--the influence of measurement error. Taking the US and Britain as case studies and building on recent research that uses latent Markov models to reduce bias, we examine how measurement error can affect a range of important poverty estimates. Our data are taken from the British Household Panel Survey and the US Panel Study of Income Dynamics, for working-aged adults over the period 1993-2003. For both national samples we ask how common vulnerability to poverty was over the period in question, what the entry and exit probabilities were for the group likely to transition into or out of poverty, and how effective redistributive programs were at protecting those most at risk. Crucially, in answering these questions we estimate and remove the effects of error in the measurement of poverty status. Throughout, we compare our results with estimates that do not take this error into account, and assess the implications for understanding poverty dynamics both within and between the two countries. Our modeling strategy extends previous research in several respects, enabling us to make stronger statements about measurement error and individual poverty dynamics. We find that correcting for error affects conclusions in important ways: Poverty is less temporary and risks are less widely dispersed than otherwise assumed, while cross-national differences are more pronounced.