Monitoring poverty trends on a timely and consistent basis is a priority for policymakers. These objectives are difficult to achieve in practice when household consumption (income) data are neither frequently collected, nor collected using consistent criteria. This paper develops and applies a simple framework for survey-to-survey poverty imputation in an attempt to overcome these obstacles. The framework introduced here imposes few restrictive assumptions, works with simple variance formulas, provides general guidance on the selection of control variables for model building, and can be applied to imputation involving surveys with either the same, or differing, sampling designs. Results from combining Jordan's Household Expenditure and Income Survey (HEIS) with its Unemployment and Employment Survey (LFS) are quite encouraging, with imputation-based poverty estimates closely tracking direct estimates of poverty.