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.

Relative to chronic hunger, seasonal hunger in rural and urban areas of Africa is poorly understood. No estimates are compiled, and limited evidence exists on prevalence, causes, and impacts. This paper contributes to the body of evidence by examining the extent and potential drivers of seasonal hunger using panel data from the Malawi Integrated Household Panel Survey (IHPS). Farmers are commonly thought to use various strategies to smooth consumption, including planting “off-season” crops, investing in post-harvest storage technologies, or generally diversifying farm portfolios including livestock products and/or wild crops. Similarly, when markets are available, farmers may diversify through off-farm income sources in order to purchase food in lean seasons. We investigate whether seasonal hunger – distinct from chronic hunger – exists in Malawi, drawing on two waves of panel data from the LSMS-ISA series. We examine the extent of seasonal hunger, factors associated with variation in seasonal hunger, and how recurring and longer-term seasonal hunger might be associated with various household welfare measures. We find that both urban and rural households report experiencing seasonal hunger in the pre-harvest months, with descriptive evidence suggesting male gender, age, and education of household head, livestock ownership, and storage of crops are associated with lower levels of seasonal hunger. In addition, we find that Malawian households with seasonal hunger harvest crops earlier than average – a short-term coping mechanism that can reduce the crop’s yield and nutritional value, possibly perpetuating hunger.

This four-part analysis describes the current suite of food security measures, then analyzes the respective relationships between food security and poverty, GDP, and crop yields using findings from in-depth literature reviews. Food security measures are criticized for inaccurately characterizing food security at individual, household, and national scales, yet guidelines exist to prescribe a food security measure for a given situation. Some authors see the potential of a combination of indicators that apply at different scales rather than a single, universal food security measure. Limited literature exists on the relationship between food security and poverty, GDP, or crop yields. The relationship between food security and poverty is particularly challenging because neither term has a consistent definition, and the limited literature suggests a lack of consensus among experts. Little empirical research exists on the relationship between food security and GDP, though studies generally note an association between the two Studies that evaluate food security and crop yields provide limited evidence that the two are associated, though many studies use measures of crop yield as food security indicators and vice versa. More research is needed to establish whether there are preferred food security measurement tools for specific scales and situations, and to further explore the relationship between food security and poverty, GDP, and crop yields.

On July 10, 2009 at the Italy G8 summit, attendees issued a joint statement pledging to contribute $20 billion towards agricultural development and food security in the developing world over the next three years. This research brief notes the status of the contributions made to the L’Aquila Food Security Initiative and whether any of the $20 billion will be allocated to agricultural research. We conclude that no declarations have been made as of September 2009 on how much of the $20 billion will be allocated to agricultural research, and which types of research will be funded by the initiative.