Measuring poverty trends and dynamics is an important undertaking for poverty reduction policies, which is further highlighted by the SDG goal 1 on eradicating poverty by 2030. We provide a broad overview of the pros and cons of poverty imputation in data-scarce environments, update recent review papers, and point to the latest research on the topics. We briefly review two common uses of poverty imputation methods that aim at tracking poverty over time and estimating poverty dynamics. We also discuss new areas for imputation.