विधियों की तुलना करें

	Asset Index Construction	Living Standards Measurement Study
क्षेत्र	Development Studies	Development Studies
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2001	1980
प्रवर्तक	Deon Filmer & Lant Pritchett	World Bank (Living Standards Measurement Study programme)
प्रकार	Composite socioeconomic-status proxy index	Multi-topic integrated household survey
मौलिक स्रोत	Filmer, D., & Pritchett, L. H. (2001). Estimating Wealth Effects without Expenditure Data—or Tears: An Application to Educational Enrollments in States of India. Demography, 38(1), 115-132.	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
उपनाम	Wealth Index, Asset Index, PCA Wealth Index, Socioeconomic Status Index	LSMS, LSMS Survey, Living Standards Survey, Integrated Household Survey
संबंधित	4	4
सारांश	Asset index construction builds a proxy for household wealth or socioeconomic status from observable possessions — durable goods, housing quality, and access to utilities — when reliable income or consumption data are unavailable. The dominant approach, popularized by Deon Filmer and Lant Pritchett in 2001, applies principal component analysis (PCA) to a set of asset variables and uses the first principal component as a set of weights, producing a single wealth score for each household. The method underlies the wealth quintiles reported in Demographic and Health Surveys and many other household surveys across low- and middle-income countries.	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.