Compare methods
Review your selected methods side by side; rows that differ are highlighted.
| Poverty Probability Index× | Asset Index Construction× | |
|---|---|---|
| Field | Development Studies | Development Studies |
| Family | Process / pipeline | Process / pipeline |
| Year of origin≠ | 2005 | 2001 |
| Originator≠ | Mark Schreiner; Grameen Foundation (now Innovations for Poverty Action) | Deon Filmer & Lant Pritchett |
| Type≠ | Poverty-likelihood scoring instrument | Composite socioeconomic-status proxy index |
| Seminal source≠ | 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. link ↗ | 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. DOI ↗ |
| Aliases | PPI, Progress out of Poverty Index, Poverty Scorecard, Poverty Likelihood Scorecard | Wealth Index, Asset Index, PCA Wealth Index, Socioeconomic Status Index |
| Related | 4 | 4 |
| Summary≠ | 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. |
| ScholarGateDataset ↗ |
|
|