Types of Research
- (-) Remove Technology Adoption filter Technology Adoption
- (-) Remove Poverty filter Poverty
- (-) Remove Aid & Other Development Finance filter Aid & Other Development Finance
- (-) Remove Health filter Health
In this dataset, we compile current project data from three major international financial institutions (or IFIs) - the World Bank, African Development Bank, and the International Fund for Agricultural Development - to understand
- how much countries are borrowing from each institution. and
- how much of that funding is devoted to small scale producer agriculture.
We begin by gathering publicly accessible data through downloads and webscraping Python and R scripts. These data are then imported into the statistical software program, Stata, for cleaning and export to Excel for analysis. This dataset contains rich information about current projects (active, in implementation, or recently approved), such as project title, project description, borrowing ministry, commitment amount, and sector. We then code relevant projects into two categories: On Farm (projects pertaining directly to small scale producer agriculture) and Rural/Agricultural Economies (inclusive of On Farm, but broader to include projects that impact community livelihoods and wellbeing). Finally, we annualize and aggregate these coded projects by IFI and then by country for analysis. Bilateral funding, government expenditures on agriculture, and development indicators are also included as supporting data to add context to a country's progress towards agricultural transformation.
The primary utility of this dataset is having all projects collected in a single spreadsheet where it is possible to search by key terms (e.g. commodity, market, financial, value chain) for lending by IFI and country, and to get some level of project detail. We have categorized projects by lending category (e.g. irrigation, livestock, agricultural development, research/extention/training) to aggregate across IFI so that the total funding for any country is easier to find. For example, Ethiopia and Nigeria receive the most total lending from these IFIs (though not on a per capita basis), with each country receiving more than $3 billion per year on average. Ethiopia receives the most lending devoted to On Farm projects, roughly $585 million per year. Overall, these data provide a snapshot of the magnitude and direction of these IFI's lending over the past several years to sub-Saharan Africa.
Figone, K., Porton, A., Kiel, S., Hariri, B., Kaminsky, M., Alia, D., Anderson, C.L., and Trindade, F. (2021). Summary of Three International Financial Institution (IFI) Investments in Sub-Saharan Africa. EPAR Technical Report #411. Evans School of Public Policy & Governance, University of Washington. Retrieved <Day Month Year> from https://epar.evans.uw.edu/research/tracking-investment-landscape-summary-three-international-financial-institutions-ifis
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.
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.
The private sector is the primary investor in health research and development (R&D) worldwide, with investment annual investment exceeding $150 billion, although only an estimated $5.9 billion is focused on diseases that primarily affect low and middle-income countries (LMICs) (West et al., 2017b). Pharmaceutical companies are the largest source of private spending on global health R&D focused on LMICs, providing $5.6 billion of the $5.9 billion in total private global health R&D per year. This report draws on 10-K forms filed by Pharmaceutical companies with the U.S. Securities and Exchange Commission (SEC) in the year 2016 to examine the evidence for five specific disincentives to private sector investment in drugs, vaccines and therapeutics for global health R&D: scientific uncertainty, weak policy environments, limited revenues and market uncertainty, high fixed costs for research and manufacturing, and imperfect markets. 10-K reports follow a standard format, including a business section and a risk section which include information on financial performance, investment options, lines of research, promising acquisitions and risk factors (scientific, market, and regulatory). As a result, these filings provide a valuable source of information for analyzing how private companies discuss risks and challenges as well as opportunities associated with global health R&D targeting LMICs.
In this brief, we report on measures of economic growth, poverty and agricultural activity in Ethiopia. For each category of measure, we first describe different measurement approaches and present available time series data on selected indicators. We then use data from the sources listed below to discuss associations within and between these categories between 1994 and 2017.
The share of private sector funding, relative to public sector funding, for drug, vaccine, and diagnostic research & development (R&D) differs considerably across diseases. Private sector investment in overall health R&D exceeds $150 billion annually, but is largely concentrated on non-communicable chronic diseases with only an estimated $5.9 billion focused on "global health", targeting diseases that primarily affect low and middle-income countries (LMICs). We examine the evidence for five specific disincentives to private sector global health R&D investment: scientific uncertainty, weak policy environments, limited revenues and market uncertainty, high fixed and sunk costs, and downstream rents from imperfect markets. Though all five may affect estimates of net returns from an investment decision, they are worth examining separately as each calls for a different intervention or remediation to change behavior.
Donor countries and multilateral organizations may pursue multiple goals with foreign aid, including supporting low-income country development for strategic/security purposes (national security, regional political stability) and for short-and long-term economic interests (market development and access, local and regional market stability). While the literature on the effectiveness of aid in supporting progress on different indicators of country development is inconclusive, donors are interested in evidence that aid funding is not permanent but rather contributes to a process by which recipient countries develop to a point that they are economically self-sufficient. In this report, we review the literature on measures of country self-sufficiency and descriptive evidence from illustrative case studies to explore conditions associated with transitions toward self-sufficiency in certain contexts.