%0 Report
        %A Dang, Hai-Anh H
%A Kilic, Talip
%A Hlasny, Vladimir
%A Abanokova, Kseniya
%A Carletto, Calogero
        %T Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment
        %D 2024
        %8 2024 Feb
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 16792
        %U https://www.iza.org/index.php/publications/dp16792
        %X Survey data on household consumption are often unavailable or incomparable over time in many low- and middle-income countries. Based on a unique randomized survey experiment implemented in Tanzania, this study offers new and rigorous evidence demonstrating that survey-to-survey imputation can fill consumption data gaps and provide low-cost and reliable poverty estimates. Basic imputation models featuring utility expenditures, together with a modest set of predictors on demographics, employment, household assets and housing, yield accurate predictions. Imputation accuracy is robust to varying survey questionnaire length; the choice of base surveys for estimating the imputation model; different poverty lines; and alternative (quarterly or monthly) CPI deflators. The proposed approach to imputation also performs better than multiple imputation and a range of machine learning techniques. In the case of a target survey with modified (e.g., shortened or aggregated) food or non-food consumption modules, imputation models including food or non-food consumption as predictors do well only if the distributions of the predictors are standardized vis-à-vis the base survey. For best-performing models to reach acceptable levels of accuracy, the minimum-required sample size should be 1,000 for both base and target surveys. The discussion expands on the implications of the findings for the design of future surveys.
        %K consumption
%K poverty
%K survey-to-survey imputation
%K household surveys
%K Tanzania