Types of Research
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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 Socioeconomic Survey (ESS), Wave 3 (2015-16)
- Kenya FinScope, Wave 4 (2015)
- Kenya FII, Wave 4 (2016)
- Nigeria FII, Wave 4 (2016)
- Rwanda FII, Wave 4 (2016)
- Tanzania National Panel Survey (TNPS), Wave 4 (2014-15)
- Tanzania FinScope, Wave 4 (2017)
- Tanzania FII, Wave 4 (2016)
- 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:
- 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
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.
According to AGRA's 2017 Africa Agriculture Status Report, smallholder farmers make up to about 70% of the population in Africa. The report finds that 500 million smallholder farms around the world provide livelihoods for more than 2 billion people and produce about 80% of the food in sub-Saharan Africa and Asia. Many development interventions and policies therefore target smallholder farm households with the goals of increasing their productivity and promoting agricultural transformation. Of particular interest for agricultural transformation is the degree to which smallholder farm households are commercializating their agricultural outputs, and diversifying their income sources away from agriculture. In this project, EPAR uses data from the World Bank's Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) to analyze and compare characteristics of smallholder farm households at different levels of crop commercialization and reliance on farm income, and to evaluate implications of using different criteria for defining "smallholder" households for conclusions on trends in agricultural transformation for those households.
Labor is one of the most productive assets for many rural households in developing countries. Despite the importance of labor—and time use more generally—little research has empirically examined the quality of time-use data in household surveys. Many household surveys rely on respondent recall, the reliability of which may decrease as recall length increases. In addition, respondents often report on time allocation for the entire household, which they may not know or recall as clearly as their own time allocation. Finally, simultaneous activities such as tending children while preparing dinner, may lead to the systematic underestimation of certain activities, particularly those that tend to be performed by women. This paper examines whether the identity of the survey respondent affects estimates of time allocation within the household. Drawing on the Ugandan LSMS-ISA household survey, we find that individuals responding for themselves report higher levels of time use over the previous week than when responding for other household members. Moreover, male respondents tend to underreport time allocation for females over the age of 15 as compared to female respondents, especially time spent on domestic activities. In addition, an analysis of the effects of two economics shocks—having a baby and floods or droughts—suggests that the identity of the respondent can affect substantive conclusions about the effects of shocks on household time use.
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.
The purpose of this analysis is to provide a measure of marketable surplus of maize in Tanzania. We proxy marketable surplus with national-level estimates of total maize sold, presumably the surplus for maize producing and consuming households. We also provide national level estimates of total maize produced and estimate “average prices” for Tanzania which allows this quantity to be expressed as an estimate of the value of marketable surplus. The analysis uses the Tanzanian National Panel Survey (TNPS) LSMS – ISA which is a nationally representative panel survey, for the years 2008/2009 and 2010/2011. A spreadsheet provides our estimates for different subsets of the sample and using different approaches to data cleaning and weighting. The total number of households for Tanzania was estimated with linear extrapolation based on the Tanzanian National Bureau of Statistics for the years 2002 and 2012. The weighted proportions of maize-producing and maize-selling households were multiplied to the national estimate of total households. This estimate of total Tanzanian maize-selling and maize-producing households was then multiplied by the average amount sold and by the average amount produced respectively to obtain national level estimates of total maize sold and total maize produced in 2009 and 2011.
In this brief we analyze patterns of intercropping and differences between intercropped and monocropped plots among smallholder farmers 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). Intercropping is a planting strategy in which farmers cultivate at least two crops simultaneously on the same plot of land. In this brief we define intercropped plots as those for which respondents answered “yes” to the question “Was cultivation intercropped?” We define “intercropping households” as those households that intercropped at least one plot at any point during the year in comparison to households that did not intercrop any plots. The analysis reveals few significant, consistent productivity benefits to intercropping as currently practiced. Intercropped plots are not systematically more productive (in terms of value produced) than monocropped plots. The most commonly cited reason for intercropping was to provide a substitute crop in the case of crop failure. This suggests that food and income security are primary concerns for smallholder farmers in Tanzania. A separate appendix includes the details for our analyses.