方法对比

	Living Standards Measurement Study	Poverty Mapping (Small-Area Estimation)
领域	Development Studies	Development Studies
方法族	Process / pipeline	Process / pipeline
起源年份	1980	2003
提出者	World Bank (Living Standards Measurement Study programme)	Chris Elbers, Jean O. Lanjouw & Peter Lanjouw
类型	Multi-topic integrated household survey	Census-survey small-area poverty estimation method
开创性文献	Grosh, 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: 9780821345283	Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-Level Estimation of Poverty and Inequality. Econometrica, 71(1), 355-364.
别名	LSMS, LSMS Survey, Living Standards Survey, Integrated Household Survey	ELL Method, Poverty Mapping, Census-Survey Poverty Estimation, Small-Area Poverty Estimation
相关	4	4
摘要	The 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.