Compară metode

	Poverty Probability Index	Poverty Mapping (Small-Area Estimation)
Domeniu	Development Studies	Development Studies
Familie	Process / pipeline	Process / pipeline
Anul apariției	2005	2003
Autorul original	Mark Schreiner; Grameen Foundation (now Innovations for Poverty Action)	Chris Elbers, Jean O. Lanjouw & Peter Lanjouw
Tip	Poverty-likelihood scoring instrument	Census-survey small-area poverty estimation method
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.	Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-Level Estimation of Poverty and Inequality. Econometrica, 71(1), 355-364.
Denumiri alternative	PPI, Progress out of Poverty Index, Poverty Scorecard, Poverty Likelihood Scorecard	ELL Method, Poverty Mapping, Census-Survey Poverty Estimation, Small-Area Poverty Estimation
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.	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.