Year Published
- 2008 (0)
- 2009 (0)
- 2010 (0)
- 2011 (0)
- 2012 (0)
- 2013 (0)
- 2014 (0)
- 2015 (0)
- 2016 (0)
- 2017 (2) Apply 2017 filter
- 2018 (0)
- 2019 (0)
- 2020 (0)
- 2021 (1) Apply 2021 filter
Research Topics
Populations
Types of Research
- (-) Remove Data Analysis filter Data Analysis
- Literature Review (18) Apply Literature Review filter
- Portfolio Review (0)
- (-) Remove Research Brief filter Research Brief
Geography
- East Africa Region and Selected Countries (4) Apply East Africa Region and Selected Countries filter
- (-) Remove Global filter Global
- South Asia Region and Selected Countries (1) Apply South Asia Region and Selected Countries filter
- Southern Africa Region and Selected Countries (0)
- Sub-Saharan Africa (3) Apply Sub-Saharan Africa filter
- (-) Remove West Africa Region and Selected Countries filter West Africa Region and Selected Countries
Dataset
Current search
- (-) Remove Household Well-Being & Equity filter Household Well-Being & Equity
- (-) Remove West Africa Region and Selected Countries filter West Africa Region and Selected Countries
- (-) Remove Global filter Global
- (-) Remove Development Finance & Policy filter Development Finance & Policy
- (-) Remove Poverty filter Poverty
- (-) Remove Data Analysis filter Data Analysis
- (-) Remove Research Brief filter Research Brief
Key Takeaways
-
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.
-
The selected 36 poverty indicators relied primarily on 26 data sources, mainly household surveys and administrative government data.
-
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.
-
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.
-
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).
-
No single indicator dominates on considerations of reliability, dimensions, depth or intensity, comparability, etc., but rather each measure involves tradeoffs.
-
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.
-
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
An ongoing stream of EPAR research considers how public good characteristics of different types of research and development (R&D) and the motivations of different providers of R&D funding affect the relative advantages of alternative funding sources. For this project, we seek to summarize the key public good characteristics of R&D investment for agriculture in general and for different subsets of crops, and hypothesize how these characteristics might be expected to affect public, private, or philanthropic funders’ investment decisions.