Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment

Arbeitspapier

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.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 16792

Classification and Topics

• Classification: Wirtschaft ; Statistical Simulation Methods: General ; Measurement and Analysis of Poverty ; Economic Development: Human Resources; Human Development; Income Distribution; Migration
• Subject: consumption ; poverty ; survey-to-survey imputation ; household surveys ; Tanzania

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Dang, Hai-Anh ; Kilic, Talip ; Hlasny, Vladimir ; Abanokova, Kseniya ; Carletto, Calogero
• Event: Veröffentlichung
• (who): Institute of Labor Economics (IZA)
• (where): Bonn
• (when): 2024