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Key Takeaways
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A survey of poverty indicators surfaced 139 candidates, of which 36 were ultimately selected for inclusion in the study based on indicator construction, use, and timeliness.
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The selected 36 poverty indicators relied primarily on 26 data sources, mainly household surveys and administrative government data.
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Most indicators relied on household survey data and used multidimensional indices to comprehensively measure poverty, aside from poverty line and poverty gap measures which relied exclusively on income and consumption.
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Indicators or indicator components were typically based on quantitative estimates of income or consumption, although an increasing number of measurements are instead classifying households according to deprivation of assets, food, or access to services and basic infrastructure.
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Overall, critics find that an emphasis on poverty line measurements has led to an incomplete understanding of poverty’s prevalence and trends over the last several decades (UN Special Rapporteur, 2020).
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No single indicator dominates on considerations of reliability, dimensions, depth or intensity, comparability, etc., but rather each measure involves tradeoffs.
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If the goal is to increase the utility of commonly used indicators, including those considering multiple dimensions of poverty, then investments focused on expanding the coverage, frequency, or scope of nationally representative household surveys is a necessary first step.
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Making cross-country comparisons using any poverty indicator runs the risk of using a common metric based on different data sources and collected in different years that may not fully reflect a household’s welfare. Indices which include multiple subcomponents may be more holistic, but even less reliable as the number of components requiring data increases.
Suggested citation:
Landscape Review of Poverty Measures. EPAR Technical Report #424 (2022). Evans School of Public Policy & Governance, University of Washington. Retrieved <Day Month Year> from https://epar.evans.uw.edu/research
In this report we analyze three waves nationally-representative household survey data from Kenya, Uganda, Tanzania, Nigeria, Pakistan, Bangladesh, India, and Indonesia to explore sociodemographic and economic factors associated with mobile money adoption, awareness, and use across countries and over time. Our findings indicate that to realize the potential of digital financial services to reach currently unbanked populations and increase financial inclusion, particular attention needs to be paid to barriers faced by women in accessing mobile money. While policies and interventions to promote education, employment, phone ownership, and having a bank account may broadly help to increase mobile money adoption and use, potentially bringing in currently unbanked populations, specific policies targeting women may be needed to close current gender gaps.