Compara mètodes

	Poverty Probability Index	Microfinance Impact Assessment
Camp	Development Studies	Development Studies
Família	Process / pipeline	Process / pipeline
Any d'origen	2005	2010
Autor original	Mark Schreiner; Grameen Foundation (now Innovations for Poverty Action)	Dean Karlan, Jonathan Zinman; Banerjee, Duflo, Glennerster & Kinnan; J-PAL
Tipus	Poverty-likelihood scoring instrument	Programme impact evaluation
Font seminal	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.	Banerjee, A., Duflo, E., Glennerster, R., & Kinnan, C. (2015). The Miracle of Microfinance? Evidence from a Randomized Evaluation. American Economic Journal: Applied Economics, 7(1), 22–53.
Àlies	PPI, Progress out of Poverty Index, Poverty Scorecard, Poverty Likelihood Scorecard	Microcredit Impact Evaluation, Microfinance Impact Evaluation, Microcredit Impact Assessment, Microsavings Impact Assessment
Relacionats	4	4
Resum	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.	Microfinance impact assessment is the set of methods used to measure the causal effects of small loans, savings, and related financial services — long promoted as a tool against poverty — on borrowers' income, business activity, consumption, and empowerment. After two decades in which observational studies reported large gains, a wave of randomized evaluations from around 2010 onwards, exemplified by Banerjee, Duflo, Glennerster, and Kinnan's Hyderabad study with Spandana and Karlan and Zinman's randomised credit-scoring work, delivered a more sober and credible verdict.