Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/281990 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
GLO Discussion Paper No. 1392
Verlag: 
Global Labor Organization (GLO), Essen
Zusammenfassung: 
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.
Schlagwörter: 
consumption
poverty
survey-to-survey imputation
household surveys
Tanzania
JEL: 
C15
I32
O15
Dokumentart: 
Working Paper

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