Compară metode

	Poverty Probability Index	Asset Index Construction
Domeniu	Development Studies	Development Studies
Familie	Process / pipeline	Process / pipeline
Anul apariției	2005	2001
Autorul original	Mark Schreiner; Grameen Foundation (now Innovations for Poverty Action)	Deon Filmer & Lant Pritchett
Tip	Poverty-likelihood scoring instrument	Composite socioeconomic-status proxy index
Sursa seminală	Schreiner, M. (2016). The Poverty Probability Index (PPI): A Brief on Calculating Annual Poverty Rates and Movement Across a Poverty Line. Innovations for Poverty Action / PovertyIndex.org.	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.
Denumiri alternative	PPI, Progress out of Poverty Index, Poverty Scorecard, Poverty Likelihood Scorecard	Wealth Index, Asset Index, PCA Wealth Index, Socioeconomic Status Index
Înrudite	4	4
Rezumat	The Poverty Probability Index (PPI), formerly the Progress out of Poverty Index, is a simple, country-specific scorecard that estimates the likelihood that a household is living below a given poverty line. Developed by Mark Schreiner and disseminated first by the Grameen Foundation and later by Innovations for Poverty Action, it reduces poverty measurement to ten easy-to-answer, verifiable questions about household characteristics. The answers produce a score from 0 to 100, which a calibration table converts into the probability that the household falls below national or international poverty lines — a low-cost alternative to a full consumption survey for organizations that need to track the poverty profile of the people they serve.	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.