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.

Many low- and middle-income countries remain challenged by a financial infrastructure gap, evidenced by very low numbers of bank branches and automated teller machines (ATMs) (e.g., 2.9 branches per 100,000 people in Ethiopia versus 13.5 in India and 32.9 in the United States (U.S.) and 0.5 ATMs per 100,000 people in Ethiopia versus 19.7 in India and 173 in the U.S.) (The World Bank 2015a; 2015b). Furthermore, only an estimated 62 percent of adults globally have a banking account through a formal financial institution, leaving over 2 billion adults unbanked (Demirgüç–Kunt et al., 2015). While conventional banks have struggled to extend their networks into low-income and rural communities, digital financial services (DFS) have the potential to extend financial opportunities to these groups (Radcliffe & Voorhies, 2012). In order to utilize DFS however, users must convert physical cash to electronic money which requires access to cash-in, cash-out (CICO) networks—physical access points including bank branches but also including “branchless banking" access points such as ATMs, point-of-sale (POS) terminals, agents, and cash merchants. As mobile money and branchless banking expand, countries are developing new regulations to govern their operations (Lyman, Ivatury, & Staschen, 2006; Lyman, Pickens, & Porteous, 2008; Ivatury & Mas, 2008), including regulations targeting aspects of the different CICO interfaces. 

EPAR's work on CICO networks consists of five components. First, we summarize types of recent mobile money and branchless banking regulations related to CICO networks and review available evidence on the impacts these regulations may have on markets and consumers. In addition to this technical report we developed a short addendum (EPAR 355a) which includes a description of findings on patterns around CICO regulations over time. Another addendum (EPAR 355b) summarizes trends in exclusivity regulations including overall trends, country-specific approaches to exclusivity, and a table showing how available data on DFS adoption from FII and GSMA might relate to changes in exclusivity policies over time. A third addendum (EPAR 355c) explores trends in CICO network expansion with a focus on policies seeking to improve access among more remote or under-served populations. Lastly, we developed a database of CICO regulations, including a regulatory decision options table which outlines the key decisions that countries can make to regulate CICOs and a timeline of when specific regulations related to CICOs were introduced in eight focus countries, Bangladesh, India, Indonesia, Kenya, Nigeria, Pakistan, Tanzania, and Uganda.

EPAR’s Political Economy of Fertilizer Policy series provides a history of government intervention in the fertilizer markets of eight Sub-Saharan African countries: Côte d’Ivoire, Ghana, Kenya, Malawi, Mozambique, Nigeria, Senegal, and Tanzania. The briefs focus on details of present and past voucher programs, input subsidies, tariffs in the fertilizer sector, and the political context of these policies. The briefs illustrate these policies’ effect on key domestic crops and focus on the strengths and weaknesses of current market structure. Fertilizer policy in SSA has been extremely dynamic over the last fifty years, swinging from enormous levels of intervention in the 1960s and 70s to liberalization of markets of the 1980s and 1990s. More recently, intervention has become more moderate, focusing on “market smart” subsidies and support. This executive summary highlights key findings and common themes from the series.

In Tanzania, agriculture represents approximately 50 percent of GDP, 80 percent of rural employment, and over 50 percent of the foreign exchange earnings. Yet poor soil fertility and resulting low productivity contribute to low economic growth and widespread poverty. Chemical fertilizer has the potential to contribute to crop yield increases. Yet high prices and weaknesses in the fertilizer market keep fertilizer use low. This literature review examines the history of government interventions that have intended to increase access to fertilizers, and reviews current policies, market structure, and challenges that contribute to the present conditions. We find that despite numerous strategies over the last fifty years, from heavy government involvement to liberalization, major weaknesses in Tanzania’s fertilizer market prevent efficient use of fertilizer. High transportation costs, low knowledge level of farmers and agrodealers, unavailability of improved seed, and limited access to credit all contribute to the market’s problems. The government’s current framework, the Tanzania Agriculture Input Partnership (TAIP), acknowledges this interconnectedness by targeting multiple components of the market. This model could help Tanzania tailor solutions relevant to specific road, soil, and market conditions of different areas of the country, contributing to enhanced food security and economic growth.