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.

Our initial agriculture capacity building search revealed best practices including institutional partnership building, cross-border opportunities such as ‘twinning,’ and views that these practices are most effective when accompanied by appropriate policies and regulatory frameworks to incentivize return on education to home countries. In addition, the literature explained the historical and political context  in which some countries successfully built higher educational capacity, suggesting a set of socio-political conditions necessary for a ‘surge’ in capacity building to occur.  Our results raised questions about challenges shaping these best practices (e.g. “brain drain” leading to the need for cross-border opportunities) as well as possible approaches to address these underlying issues. To further examine identified challenges from our initial findings, we re-oriented our search to investigate retention strategies, regional or intra-national network capacity building approaches, and whether there is in fact a need for higher education capacity in all countries through comparative advantage or otherwise. This report presents a review of the literature on the best and worst practices for national agricultural capacity building when investing in a country's higher education system or when investing directly in national or relevant global research capacity. We find that several countries have successfully employed a variety of retention, return, and diaspora strategies to build capacity by capitalizing on the feedback loops of international mobility.  In addition, several countries in Africa have employed strategies to address the rural-to-urban “brain drain” by prioritizing education of students with post-secondary rural agricultural work experience and strong ties to rural communities in order to return the benefit of this education to local communities. The report discusses these and other strategies as well as analysis related to the ‘whole system effect’ of higher education and subsequent ‘need’ for Higher Agricultural Education (HAE) capacity in all countries.

This brief provides an overview of the national and zonal characteristics of agricultural production in Tanzania using the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). More detailed information and analysis is available in the separate EPAR Tanzania LSMS-ISA Reference Report, Sections A-G.

This brief analyzes the indicators used by the World Bank in its Project Appraisal Documents (PAD) to measure the outputs and outcomes of 44 Water, Sanitation and Hygiene projects in Africa and Asia from 2000-2010.  This report details the methods used to collect and organize the indicators, and provides a brief analysis of the type of indicators used and their evolution over time. A searchable spreadsheet of the indicators used in this analysis accompanies this summary. We find that some patterns emerge over time, though none are very drastic. The most common group of indicators used by the World Bank are “management” oriented indicators (28% of indicators). Management indicators are disproportionately used in African projects as compared to projects in Asia. Several projects in Africa incorporate indicators relating to legal/regulatory/policy outcomes, while projects in Asia do not. In recent years, the World Bank has used fewer indicators that measure service delivery, health, and education and awareness.