Evans School Policy Analysis & Research Group (EPAR)

Much literature discusses the importance of investing in human capital—or “the sum of a population’s health, skills, knowledge, experience, and habits” (World Bank, 2018, p. 42)—to a country’s economic growth. For example, the World Bank reports a “chronic underinvestment” in health and education in Nigeria, noting that investing in human capital has the potential to significantly contribute to economic growth, poverty reduction, and societal well-being (World Bank, 2018). This research brief reports on the evidence linking investment in human capital—specifically, health and education—with changes in economic growth. It reviews the literature for five topic areas: Education, Infectious Diseases, Nutrition, Primary Health Care, and Child and Maternal Health. This review gives priority focus to the countries of Bangladesh, Burkina Faso, Democratic Republic of Congo, Ethiopia, India, Kenya, Madagascar, Nigeria, Rwanda, and Tanzania. For each topic area, we report the evidence in support of a pathway from investing in human capital to economic growth.

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 research project examines the traits of Tanzanian farmers living in five different farming system-based sub-regions: the Northern Highlands, Sukumaland, Central Maize, Coastal Cassava, and Zanzibar. We conducted quantitative analysis on data from the Tanzania National Panel Survey (TNPS). We complimented this analysis with qualitative data from fieldwork conducted in the summer of 2011 and September 2013 to present a quantitatively and qualitatively informed profile of the “typical” agricultural household’s land use patterns, demographic dynamics, and key issues or production constraints in each sub-region.

Agriculture and Climate Change: Part I

With estimated global emissions of 5,969-6,615 metric tons (Mt) of carbon dioxide (CO2) per year, agriculture accounts for about 13.5% of total global anthropogenic emissions of greenhouse gases (GHG). Deforestation contributes about 11.8% of total GHG emissions, releasing about 5,800 Mt CO2 per year. Developing countries are largely responsible for emissions from agriculture and deforestation, with the developing countries of South Asia and East Asia accounting for 17% and 25% of global agricultural emissions respectively. Sub-Saharan Africa (SSA) accounts for about 13% of global emissions from agriculture and 15% of emissions from land use change and forestry. This report examines the biophysical and economic potential of mitigating agriculture and land use GHG emissions, and provides a summary on the current and projected impact of global carbon market mechanisms on emission reductions. 

Agriculture and Climate Change: Part II

This report covers two topics related to agriculture and climate change in developing countries. The first section discusses the role of agricultural offsets in mitigating greenhouse gas emissions. Recent negotiations around a post-Kyoto Protocol agreement have included debate about whether agricultural carbon sequestration projects should be eligible under the Clean Development Mechanism (CDM). We examine the reasons for supporting or opposing this type of CDM reform and how these reasons relate to impacts on development goals and smallholder farmers, scientific uncertainty about carbon sequestration, and philosophical disagreement about the use of emission offsets. The second section covers proposed agricultural adaptation activities in Africa and other developing countries. While the majority of developing countries have outlined immediate adaptation needs in National Adaptation Programs of Action (NAPAs), few have made progress in implementing adaptation activities. We find that issues related to financial resources, scientific and technical information, and capacity building continue to challenge developing countries in preparing for the impacts of climate change.