Geography

EPAR TECHNICAL REPORT #362
Self Help Groups Prevalence and Coverage
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.

Indicator Cross-Comparisons, All Countries Pivot Tables
EPAR Technical Brief #96
Modeling Tools for Agricultural Development Interventions: A Review of Tools and Research Models
Publication Date: 09/02/2010
Type: Literature Review
Abstract

The purpose of this literature review is to examine research and decision-making tools that model the impacts of agricultural interventions. We begin with a short explanation of what model features are being described. We then review decision-support tools and user-end modeling tools (menu-driven tools with an interface designed for easy use), as well as academic and professional research models for assessing the potential impacts of agricultural interventions. This review also includes decision tools and models for analyzing agricultural and environmental policies outside of technology impacts in Sub-Saharan Africa and South Asia. The other tools mentioned here, for example a tool that considers nutritional intervention impacts, are included to help provide a broader understanding of the structure and availability of user-end, decision-making tools. In the final section of this brief, we review the most complex models used more in academic research than for in-field decision-making.

Literature Review
EPAR Technical Report #65
Environmental Impact of Agricultural Technologies
Publication Date: 03/17/2010
Type: Literature Review
Abstract

Ecosystem services are the benefits people obtain from ecosystems, such as provisioning of fresh water, food, feed, fiber, biodiversity, energy, and nutrient cycling. Agricultural production can substantially affect the functioning of ecosystems, both positively and negatively. The purpose of this report is to provide an overview of the impacts of agricultural technologies and practices on ecosystem services such as soil fertility, water, biodiversity, air, and climate. The report describes the environmental impacts of different aspects of intensive cropping practices and of inputs associated with intensification. We further explore these impacts by examining intensive rice systems and industrial crop processing. Although this report focuses on the impacts of agricultural practices on the environment, many of the practices also have implications for plant, animal, and human health. Farmers and others who come in contact with air, water, and soils polluted by chemical fertilizers and pesticides may face negative health consequences, for instance. By impacting components of the ecosystem, these practices affect the health of plants and animals living within the ecosystem. We find that the unintended environmental consequences of intensive agricultural practices and inputs are varied and potentially severe. In some cases, sustaining or increasing agricultural productivity depends upon reducing impacts to the environment, such as maintaining productive soils by avoiding salinization from irrigation water. However, in other cases, eliminating negative environmental impacts may involve unacceptable trade-offs with food provision or other development goals. Determining the appropriate balance of costs and benefits from intensive agricultural practices is a location-specific exercise requiring knowledge of natural, economic, and social conditions. 

Full Report
EPAR Research Brief #63
Agricultural Research and Development Hours by Crop
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. 

Research Brief