Year Published
- 2008 (0)
- 2009 (0)
- 2010 (0)
- 2011 (2) Apply 2011 filter
- 2012 (1) Apply 2012 filter
- 2013 (0)
- 2014 (0)
- 2015 (0)
- 2016 (2) Apply 2016 filter
- (-) Remove 2017 filter 2017
- 2018 (0)
- 2019 (2) Apply 2019 filter
- 2020 (0)
- (-) Remove 2021 filter 2021
Research Topics
Populations
Types of Research
- (-) Remove Data Analysis filter Data Analysis
- Literature Review (0)
- Portfolio Review (0)
- Research Brief (1) Apply Research Brief filter
Geography
- (-) Remove East Africa Region and Selected Countries filter East Africa Region and Selected Countries
- Global (1) Apply Global filter
- (-) Remove South Asia Region and Selected Countries filter South Asia Region and Selected Countries
- Southern Africa Region and Selected Countries (0)
- (-) Remove Sub-Saharan Africa filter Sub-Saharan Africa
- West Africa Region and Selected Countries (2) Apply West Africa Region and Selected Countries filter
Dataset
Current search
- (-) Remove East Africa Region and Selected Countries filter East Africa Region and Selected Countries
- (-) Remove 2021 filter 2021
- (-) Remove Research & Development filter Research & Development
- (-) Remove 2017 filter 2017
- (-) Remove Data Analysis filter Data Analysis
- (-) Remove Sub-Saharan Africa filter Sub-Saharan Africa
- (-) Remove Poverty filter Poverty
- (-) Remove Finance & Investment filter Finance & Investment
- (-) Remove Information & Mobile Technology filter Information & Mobile Technology
- (-) Remove South Asia Region and Selected Countries filter South Asia Region and Selected Countries
- (-) Remove Labor & Time Use filter Labor & Time Use
- (-) Remove Education & Training filter Education & Training
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.
Suggested Citation:
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
In this report we analyze three waves nationally-representative household survey data from Kenya, Uganda, Tanzania, Nigeria, Pakistan, Bangladesh, India, and Indonesia to explore sociodemographic and economic factors associated with mobile money adoption, awareness, and use across countries and over time. Our findings indicate that to realize the potential of digital financial services to reach currently unbanked populations and increase financial inclusion, particular attention needs to be paid to barriers faced by women in accessing mobile money. While policies and interventions to promote education, employment, phone ownership, and having a bank account may broadly help to increase mobile money adoption and use, potentially bringing in currently unbanked populations, specific policies targeting women may be needed to close current gender gaps.
An ongoing stream of EPAR research considers how public good characteristics of different types of research and development (R&D) and the motivations of different providers of R&D funding affect the relative advantages of alternative funding sources. For this project, we seek to summarize the key public good characteristics of R&D investment for agriculture in general and for different subsets of crops, and hypothesize how these characteristics might be expected to affect public, private, or philanthropic funders’ investment decisions.