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 reviews the current body of peer-reviewed scholarship exploring the impacts of morbidity on economic growth. This overview seeks to provide a concise introduction to the major theories and empirical evidence linking morbidity – and the myriad different measures of morbidity – to economic growth, which is defined primarily in terms of gross domestic product (GDP) and related metrics (wages, productivity, etc.). Through a systematic review of published manuscripts in the fields of health economics and economic development we further identify the most commonly-used pathways linking morbidity to economic growth. We also highlight the apparent gaps in the empirical literature (i.e., theorized pathways from morbidity to growth that remain relatively untested in the published empirical literature to date).

This brief presents our analysis of market access in Tanzania using data 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). The TZNPS asked few direct questions about market access. However, farmers reported information about market participation that sheds light on several important components of the value chain: input markets, including both goods and services; crop storage, processing, and transport; and sales of outputs. A separate appendix includes additional detail on our analyses.

Agriculture is a principal source of livelihood for the Tanzanian population. Agriculture provides more than two-thirds of employment and almost half of Tanzania‘s GDP. Women play an essential role in agricultural production. The sector is characterized as female-intensive, meaning that women comprise a majority of the labor force in agriculture (54%). This brief reviews the academic and grey literature on gender and agriculture in Tanzania, providing an overview on the structure of households, the household structure of agricultural production, information on women’s crops, and gender and land rights in Tanzania. We conclude with a summary of challenges to women in agriculture, and of potential implications for women of advancements in production technology and other economic opportunities at the household level.