Comparing Methods for Targeting Water Subsidies to the Poorest Households
In Ghana, the poorest households tend to have lower access to safe drinking water, in part due to the cost of improved sources such as piped water systems. Subsidizing safe water services for the poorest can help to address these inequities, but water subsidies are commonly ineffective due to the financial constraints of service providers and unsuccessful targeting that benefits high-income groups. It is critical to find appropriate targeting methods that can accurately predict household poverty, are acceptable to community members and other stakeholders, and can be scaled efficiently.
WHAT IS THE BEST METHOD TO IDENTIFY POOR AND VULNERABLE HOUSEHOLDS FOR WATER SUBSIDIES?
In this study, we compared the performance of five methods for targeting the poorest for water subsidies, identified through the literature and existing practice in Ghana.
This study took place in three small towns in the Ahafo and Ashanti Regions of southwestern Ghana. We held six community consultation meetings in neighborhoods within these towns and surveyed all 818 households in these communities. Surveys included questions to assess eligibility with respect to all three proxy-means tests (PMT) and enrollment in the Livelihood Empowerment Against Poverty (LEAP) program. To assess the acceptability of each method, we also conducted qualitative interviews with nine households identified as poor through community consultation, eight households not identified as poor, and six local government officials. We also tracked the costs of implementing each approach.
The five methods identified very different proportions of households as poor, ranging from 4% (LEAP) to 61% (PPI) of all households. Our AI-based PMT and the DHS wealth index identified much smaller proportions (20-27%) than the PPI, while community consultation identified only 12% of households. Only two households (<1%) were identified as poor by all five methods, while 18 (2%) were identified by all the methods excluding LEAP (which was very restrictive). 109 households (13%) were identified by all three PMTs. The small overlap between PMTs and Community consultation revealed a different poverty concept: communities tended to more effectively identify households affected by chronic poverty that may not be associated with assets and other standard proxies for wealth, while PMTs identified poor households based on their assets and their expenditures explaining the larger overlap between these three methods.