Evans School Policy Analysis & Research Group (EPAR)

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.

This report summarizes current trends in the application of Development Finance Institution (DFI)-based returnable capital finance in developing countries, with an emphasis on “pro-poor” development initiatives. We begin by reviewing the financial instruments used by DFIs. We then review the major DFI providers of returnable-capital based finance, drawing on past and present peer-reviewed articles and published reports exploring trends in the uses of different returnable capital instruments over time. Finally, we conclude by further examining recent efforts to use returnable capital to finance development initiatives explicitly targeting the poor.

How development organizations, NGOs, and governments can best allocate scarce resources to those in need has long been debated. As opposed to universal allocation of resources, a more targeted approach attempts to minimize program costs while maximizing benefits among those with the greatest need or market opportunity. Drawing on literature from several sectors,this brief presents two categories of beneficiary targeting in the development context: administrative targeting and self-targeting. The paper includes a brief overview of targeting and segmentation in development, a summary of reasons for targeting, theoretical and practical critiques of targeting, and a discussion of targeting methods in research and practice, including examples from the literature. Implementation examples cited in this body of research include food aid program targeting by self-reported household income in Egypt; fertilizer use in low-potential zones of Uganda; and seven strategic initiatives to improve drought and disease resistance in crops in Asia and Sub-Saharan Africa.  We find that beneficiary segmentation has several theoretical advantages.  Improved targeting may increase the efficiency and equity of organizational and program efforts and help better match interventions to recipient preferences, increasing the likelihood of adoption and participation. Development organizations may improve the focus of both their strategic priorities and budgets through customized targeting methods. However, concerns exist regarding the accuracy, reliability, cost, and time-constraints of targeting methodologies. Creating valid and reliable target groups with implementation potential remains a significant challenge. 

How development organizations, NGOs, and governments can best allocate scarce resources to those in need has long been debated. As opposed to universal allocation of resources, a more targeted approach attempts to minimize program costs while maximizing benefits among those with the greatest need or market opportunity. Many international development organizations strategically target clients based on geographic location (e.g., community, region, country) or socio-economic indicators, such as the World Bank’s “$1 a day” poverty line. Drawing on literature from several sectors, this brief presents additional methods of beneficiary targeting that international development organizations might consider. We find that beneficiary targeting/segmentation has the potential to make organizational and program efforts more equitable and efficient. With limited resources, smaller organizations have tended to use single robust indicators or simple heuristics, whereas agribusinesses and private sector firms have used more data-intensive marketing tools to position their products. Technological innovation and better access to data have made targeting more prevalent and potentially more affordable in agricultural development. However, creating valid and reliable target segments remains the most significant challenge.