Year Published


Publication Date: 12/28/2020
Type: Literature Review

Recent research has used typologies to classify rural households into categories such as “subsistence” versus “commercialized” as a means of targeting agricultural development interventions and tracking agricultural transformation. Following an approach proposed by Alliance for a Green Revolution in Africa, we examine patterns in two agricultural transformation hallmarks – commercialization of farm output, and diversification into non-farm income – among rural households in Ethiopia, Nigeria, and Tanzania from 2008-2015. We classify households into five smallholder farm categories based on commercialization and non-farm income levels (Subsistence, Pre-commercial, Transitioning, Specialized Commercial, and Diversified Commercial farms), as well as two non-smallholder categories (Largeholder farms and Non-farm households). We then summarize the share of households in each of these categories, examine geographic and demographic factors associated with different categories, and explore households’ movement across categories over time. We find a large amount of “churn” across categories, with most households moving to a different (more or less commercialized, more or less diversified) category across survey years. We also find many non-farm households become smallholder farmers – and vice versa – over time. Finally, we show that in many cases increases in farm household commercialization or diversification rates actually reflect decreased total farm production, or decreased total income (i.e., declines in the denominators of the agricultural transformation metrics), suggesting a potential loss of rural household welfare even in the presence of “positive” trends in transformation indicators. Findings underscore challenges with using common macro-level indicators to target development efforts and track progress at the household level in rural agrarian communities.

Publication Date: 01/16/2019
Type: Data Analysis

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:


  • 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.

EPAR Technical Report #335
Publication Date: 11/21/2017
Type: Data Analysis
EPAR has developed Stata do.files for the construction of a set of agricultural development indicators using data from the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA). We are sharing our code and documenting our construction decisions both to facilitate analyses of these rich datasets and to make estimates of relevant indicators available to a broader audience of potential users. 
Code, Code, Code, Code
EPAR Technical Report #357
Publication Date: 08/01/2017
Type: Literature Review

Land tenure refers to a set of land rights and land governance institutions which can be informal (customary, traditional) or formal (legally recognized), that define relationships between people and land and natural resources (FAO, 2002). These land relationships may include, but are not limited to, rights to use land for cultivation and production, rights to control how land should be used including for cultivation, resource extraction, conservation, or construction, and rights to transfer – through sale, gift, or inheritance – those land use and control rights (FAO, 2002). In this project, we review 38 land tenure technologies currently being applied to support land tenure security across the globe, and calculate summary statistics for indicators of land tenure in Tanzania and Ethiopia.

EPAR Technical Report #347
Publication Date: 03/17/2017
Type: Literature Review

A growing body of evidence suggests that empowering women may lead to economic benefits (The World Bank, 2011; Duflo, 2012; Kabeer & Natali, 2013). Little work, however, focuses specifically on the potential impacts of women’s empowerment in agricultural settings. Through a comprehensive review of literature this report considers how prioritizing women’s empowerment in agriculture might lead to economic benefits. With an intentionally narrow focus on economic empowerment, we draw on the Women’s Empowerment in Agriculture Index (WEAI)’s indicators of women’s empowerment in agriculture to consider the potential economic rewards to increasing women’s control over agricultural productive resources (including their own time and labor), over agricultural production decisions, and over agricultural income. While we recognize that there may be quantifiable benefits of improving women’s empowerment in and of itself, we focus on potential longer-term economic benefits of improvements in these empowerment measures.

EPAR Research Brief #190
Publication Date: 03/30/2012
Type: Data Analysis

This brief presents a comparative analysis of men and women and of male- and female-headed households 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). We compare farm activity, productivity, input use, and sales as well as labor allocations by gender of the respondent and of the household head. In households designated “female-headed” a woman was the decision maker in the household, took part in the economy, control and welfare of the household, and was recognized by others in the household as the head. For questions regarding household labor (both non-farm and farm), the gender of the individual laborer is recorded, and we use this to illustrate the responsibilities of male and female household members. An appendix provides the details for our analyses.