Types of Research
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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.
Studies of improved seed adoption in developing countries almost always draw from household surveys and are premised on the assumption that farmers are able to self-report their use of improved seed varieties. However, recent studies suggest that farmers’ reports of the seed varieties planted, or even whether seed is local or improved, are sometimes inconsistent with the results of DNA fingerprinting of farmers' crops. We use household survey data from Tanzania to test the alignment between farmer-reported and DNA-identified maize seed types planted in fields. In the sample, 70% of maize seed observations are correctly reported as local or improved, while 16% are type I errors (falsely reported as improved) and 14% are type II errors (falsely reported as local). Type I errors are more likely to have been sourced from other farmers, rather than formal channels. An analysis of input use, including seed, fertilizer, and labor allocations, reveals that farmers tend to treat improved maize differently, depending on whether they correctly perceive it as improved. This suggests that errors in farmers' seed type awareness may translate into suboptimal management practices. In econometric analysis, the measured yield benefit of improved seed use is smaller in magnitude with a DNA-derived categorization, as compared with farmer reports. The greatest yield benefit is with correctly identified improved seed. This indicates that investments in farmers' access to information, seed labeling, and seed system oversight are needed to complement investments in seed variety development.
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.
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.
This report provides a general overview of trends in public and private agricultural research and development (R&D) funding and expenditures in Sub-Saharan Africa (SSA). The request is divided into two sections, covering public funding and private funding. Within each section, relevant data is presented on historical funding patterns, the types of research conducted, and which countries within SSA are financing R&D at the highest level. We find that the majority of growth in African public agricultural research funding took place in the 1960s, when real public spending on agricultural research increased 6% a year. From 1971 to 2000 annual growth averaged 1.4% a year. Public financing of agricultural R&D experienced a moderate shift in the 1990s from bilateral and multilateral donor funding to domestic government financing. The shift varied by country, but donor funding dropped for all SSA countries an average of 10%. Private research and development funding is heavily concentrated in developed countries with the United States and Japan the two biggest spenders. Within SSA, private R&D expenditures comprise 2% of all R&D spending. The main private actors in SSA are companies based in South Africa and Nigeria. The private sector is focused on research areas that involve marketable inputs, such as chemicals, seeds, and machines/
This brief presents an in depth analysis of the FAO’s methodology behind their calculations for hunger. The analysis includes a review of the key assumptions made by the FAO in their calculations, critiques of their methodology, and recommendations for future research. The critiques include opinions from the literature on the subject as well as from the authors of the request.
Special Economic Zones (SEZs) are generally defined as geographically delimited areas administered by a single body, offering certain incentives (duty-free importing and streamlined customs procedures, for instance) to businesses that physically locate within the zone. This literature review provides a baseline analysis of SEZs and their potential impacts on smallholder farmers in SSA. Criticism on SEZs is distinctly divided between those who criticize on social or environmental grounds versus those who question the economic impact of SEZs. SEZs are often criticized based on perceived negative socio-economic impacts—particularly their negative impact on women, labor, and working conditions. This review includes several country-specific studies that find evidence that SEZs actually have higher environmental standards and higher worker satisfaction than outside the SEZ. Most responses to criticisms do note, however, that the case studies’ results are not necessarily generalizable to SEZs throughout the world. The literature review includes key elements of successes and failures pulled from the case studies of SEZs in SSA. Though the evidence is insufficient to conclusively determine if smallholder farmers receive direct benefits from SEZs and their associated agroindustrial contracts, this review finds that resources provided to farmers (credit at rates lower than bank rates, technical or managerial assistance, pesticides, seeds, and fertilizer on credit) tend to be concentrated among larger farmers. The report concludes with a note on donor involvement as well as recommendations for further research.
This brief presents an initial examination of the possibility of using Disability Adjusted Life Years (DALYs) as a way to evaluate agricultural interventions. We review DALYs, their formulation, and the data necessary to compute values. A review of relevant literature suggests that to use DALYs as an evaluative tool, an agricultural intervention must be tied to a specific disease, and from there, impacts on DALYs can be assessed.