The ability to determine whether an individual or a household is poor is crucial for development institutions concerned with poverty alleviation. Governments and Non-Governmental Organizations (NGOs) "need to know who is poor" in order to track the poverty status of their beneficiaries. Recently, innovations have emerged from the microfinance field that may make it feasible for small-scale institutions to measure poverty via a short, statistically powerful survey. Using the general methodology of proxy-means testing, several Poverty Measurement Tools (PMTs) have been created, allowing organizations to quickly, and cost effectively, measure the poverty statuses of their beneficiaries.
Among the most widely used PMTs are the Poverty Assessment Tool (PAT) designed by USAID and IRIS Center, the Progress Out of Poverty Index (PPI) jointly designed by Mark Schreiner, Director of Microfinance Risk Management, and Grameen Foundation, and the Multidimensional Poverty Index (MPI), designed by Oxford Poverty & Human Development Initiative.
Catholic Relief Services (CRS), one of the largest international development NGOs, is considering adopting one of these PMTs as a standard tool to both target beneficiaries and track changes in poverty across several different development programs. We provide a detailed literature review on the available PMTs, including alternative poverty measurement approaches such as Participatory Wealth Ranking. Using the El Salvador household survey of multiple purposes 2008, we test the accuracy and precision levels of the PPI and PAT estimates of poverty and estimates of household targeting. By implementing a composite survey in El Salvador and Guatemala we compare the poverty incidences and relative ranking ability of single-dimension (PPI & PAT) and multidimensional (MPI) poverty tools. Finally, we provide a discussion on the feasibility of PMT implementation.
The El Salvador accuracy results suggest that single dimension poverty tools give relatively accurate estimates of the poverty incidence across different areas and regions (when the "true" incidence of poverty is determined by the same single dimension). Compared to the PAT, the PPI is a slightly more accurate tool for targeting purposes (at specific cutoffs). The El Salvador and Guatemala fieldwork results suggest that the single dimension poverty tools estimate similar poverty incidences and predict a majority of the same households as poor. On the contrary, the results also revealed that single dimension and multidimensional PMTs estimate less than a majority of the same households as poor. Finally, for CRS the PPI is the most practical and easy-to-use PMT among the three. In order to accurately and feasibly capture the multiple layers of poverty, we recommend that CRS adopt a 'hybrid' PMT, which includes the country specific PPI and a formatted version of the MPI.
|School||UNIVERSITY OF CALIFORNIA, DAVIS|
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