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Living Standards Measurement Study×Poverty Mapping (Small-Area Estimation)×
OborDevelopment StudiesDevelopment Studies
RodinaProcess / pipelineProcess / pipeline
Rok vzniku19802003
TvůrceWorld Bank (Living Standards Measurement Study programme)Chris Elbers, Jean O. Lanjouw & Peter Lanjouw
TypMulti-topic integrated household surveyCensus-survey small-area poverty estimation method
Původní zdrojGrosh, M., & Glewwe, P. (Eds.). (2000). Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 Years of the Living Standards Measurement Study. Washington, DC: World Bank. ISBN: 9780821345283Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-Level Estimation of Poverty and Inequality. Econometrica, 71(1), 355-364. DOI ↗
Další názvyLSMS, LSMS Survey, Living Standards Survey, Integrated Household SurveyELL Method, Poverty Mapping, Census-Survey Poverty Estimation, Small-Area Poverty Estimation
Příbuzné44
ShrnutíThe Living Standards Measurement Study (LSMS) is a multi-topic integrated household survey programme launched by the World Bank in 1980 to improve the quality of household data for measuring and analysing welfare in developing countries. Built around a modular questionnaire that links a detailed household interview to community and price questionnaires, the LSMS measures living standards through consumption expenditure rather than income, and connects welfare outcomes to their determinants — employment, education, health, agriculture, and access to services — within a single, internally consistent dataset.ELL poverty mapping, named after Chris Elbers, Jean Lanjouw, and Peter Lanjouw, is a small-area estimation method that produces poverty and inequality estimates for geographic units far smaller than a household survey can support on its own. It combines two data sources: a detailed household survey that measures consumption but covers too few households per locality, and a population census that covers everyone but does not measure consumption. The method estimates a model of consumption on variables common to both, imputes consumption into the census, and simulates to generate poverty estimates — with statistically valid standard errors — for districts, communes, or even villages, which are then drawn as poverty maps.
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