Research Topics

EPAR TECHNICAL REPORT #424
Publication Date: 09/01/2022
Type: Research Brief
Abstract

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

EPAR RESEARCH BRIEF #385
Publication Date: 03/17/2019
Type: Research Brief
Abstract

Much literature discusses the importance of investing in human capital—or “the sum of a population’s health, skills, knowledge, experience, and habits” (World Bank, 2018, p. 42)—to a country’s economic growth. For example, the World Bank reports a “chronic underinvestment” in health and education in Nigeria, noting that investing in human capital has the potential to significantly contribute to economic growth, poverty reduction, and societal well-being (World Bank, 2018). This research brief reports on the evidence linking investment in human capital—specifically, health and education—with changes in economic growth. It reviews the literature for five topic areas: Education, Infectious Diseases, Nutrition, Primary Health Care, and Child and Maternal Health. This review gives priority focus to the countries of Bangladesh, Burkina Faso, Democratic Republic of Congo, Ethiopia, India, Kenya, Madagascar, Nigeria, Rwanda, and Tanzania. For each topic area, we report the evidence in support of a pathway from investing in human capital to economic growth.

EPAR Technical Report #363
Publication Date: 02/10/2019
Type: Data Analysis
Abstract

Studies of improved seed adoption in developing countries almost always draw from household surveys and are premised on the assumption that farmers are able to self-report their use of improved seed varieties. However, recent studies suggest that farmers’ reports of the seed varieties planted, or even whether seed is local or improved, are sometimes inconsistent with the results of DNA fingerprinting of farmers' crops. We use household survey data from Tanzania to test the alignment between farmer-reported and DNA-identified maize seed types planted in fields. In the sample, 70% of maize seed observations are correctly reported as local or improved, while 16% are type I errors (falsely reported as improved) and 14% are type II errors (falsely reported as local). Type I errors are more likely to have been sourced from other farmers, rather than formal channels. An analysis of input use, including seed, fertilizer, and labor allocations, reveals that farmers tend to treat improved maize differently, depending on whether they correctly perceive it as improved. This suggests that errors in farmers' seed type awareness may translate into suboptimal management practices. In econometric analysis, the measured yield benefit of improved seed use is smaller in magnitude with a DNA-derived categorization, as compared with farmer reports. The greatest yield benefit is with correctly identified improved seed. This indicates that investments in farmers' access to information, seed labeling, and seed system oversight are needed to complement investments in seed variety development.

EPAR TECHNICAL REPORT #362
Publication Date: 01/16/2019
Type: Data Analysis
Abstract

Self-Help Groups (SHGs) in Sub-Saharan Africa can be defined as mutual assistance organizations through which individuals undertake collective action in order to improve their own lives. “Collective action” implies that individuals share their time, labor, money, or other assets with the group. In a recent EPAR data analysis, we use three nationally-representative survey tools to examine various indicators related to the coverage and prevalence of Self-Help Group usage across six Sub-Saharan African countries. EPAR has developed Stata .do files for the construction of a set of self-help group indicators using data from the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA), Financial Inclusion Index (FII), and FinScope.

We compiled a set of summary statistics for the final indicators using data from the following survey instruments:

  • Ethiopia:
    • Ethiopia Socioeconomic Survey (ESS), Wave 3 (2015-16)
  • Kenya:
    • Kenya FinScope, Wave 4 (2015)
    • Kenya FII, Wave 4 (2016)
  • Nigeria
    • Nigeria FII, Wave 4 (2016)
  • Rwanda:
    • Rwanda FII, Wave 4 (2016)
  • Tanzania:
    • Tanzania National Panel Survey (TNPS), Wave 4 (2014-15)
    • Tanzania FinScope, Wave 4 (2017)
    • Tanzania FII, Wave 4 (2016)
  • Uganda:
    • Uganda FinScope, Wave 3 (2013)
    • Uganda FII, Wave 4 (2016)

The raw survey data files are available for download free of charge from the World Bank LSMS-ISA website, the Financial Sector Deepening Trust website, and the Financial Inclusion Insights website. The .do files process the data and create final data sets at the household (LSMS-ISA) and individual (FII, FinScope) levels with labeled variables, which can be used to estimate summary statistics for the indicators.

All the instruments include nationally-representative samples. All estimates from the LSMS-ISA are household-level cluster-weighted means, while all estimates from FII and FinScope are calculated as individual-level weighted means. The proportions in the Indicators Spreadsheet are therefore estimates of the true proportion of individuals/households in the national population during the year of the survey. EPAR also created a Tableau visualization of these summary statistics, which can be found here.

We have also prepared a document outlining the construction decisions for each indicator across survey instruments and countries. We attempted to follow the same construction approach across instruments, and note any situations where differences in the instruments made this impossible.

The spreadsheet includes estimates of the following indicators created in our code files:

Sub-Populations

  • Proportion of individuals who have access to a mobile phone
  • Proportion of individuals who have official identification
  • Proportion of individuals who are female
  • Proportion of individuals who use mobile money
  • Proportion of individuals who have a bank account
  • Proportion of individuals who live in a rural area
  • Individual Poverty Status
    • Two Lowest PPI Quintiles
    • Middle PPI Quintile
    • Two Highest PPI Quintiles

Coverage & Prevalence

  • Proportion of individuals who have interacted with a SHG
  • Proportion of individuals who have used an SHG for financial services
  • Proportion of individuals who depend most on SHGs for financial advice
  • Proportion of individuals who have received financial advice from a SHG
  • Proportion of households that have interacted with a SHG
  • Proportion of households in communities with at least one SHG
  • Proportion of households in communities with access to multiple farmer cooperative groups
  • Proportion of households who have used an SHG for financial services

Characteristics
In addition, we produced estimates for 29 indicators related to characteristics of SHG use including indicators related to frequency of SHG use, characteristics of SHG groups, and individual/household trust of SHGs.

EPAR Research Brief #360
Publication Date: 02/05/2018
Type: Research Brief
Abstract

In this brief, we report on measures of economic growth, poverty and agricultural activity in Ethiopia. For each category of measure, we first describe different measurement approaches and present available time series data on selected indicators. We then use data from the sources listed below to discuss associations within and between these categories between 1994 and 2017. 

EPAR Technical Report #335
Publication Date: 11/21/2017
Type: Data Analysis
Abstract
EPAR has developed Stata do.files for the construction of a set of agricultural development indicators using data from the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA). We are sharing our code and documenting our construction decisions both to facilitate analyses of these rich datasets and to make estimates of relevant indicators available to a broader audience of potential users. 
Code, Code, Code, Code
EPAR Technical Report #339
Publication Date: 09/28/2017
Type: Data Analysis
Abstract

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. 

Code
EPAR Research Brief #167
Publication Date: 10/07/2011
Type: Data Analysis
Abstract

This is "Section B" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of household characteristics by gender and by administrative zone, considering landholding size, number of crops grown, yields, livestock, input use, and food consumption.

EPAR Technical Report #160
Publication Date: 09/30/2011
Type: Data Analysis
Abstract

This is "Section C" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of the basic characteristics of household heads and other household members, as well as our analyses of education for adults, children, and household heads by gender and zone.

EPAR Research Brief #63
Publication Date: 02/05/2010
Type: Research Brief
Abstract

This research brief reports on full time equivalent (fte) positions devoted to research and development of major food and cash crops in Sub-Saharan Africa (SSA). Data on fte by country and crop were collected from individual Agricultural Science and Technology Indicator (ASTI) country briefs. ASTI data are obtained from unpublished surveys conducted by CGIAR centers. Our report includes 23 countries in SSA.