Types of Research

EPAR TECHNICAL REPORT #411
Publication Date: 05/24/2021
Type: Data Analysis
Abstract

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

  1. how much countries are borrowing from each institution. and
  2. 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

Code
EPAR Technical Report #339
Publication Date: 09/28/2017
Type: Data Analysis
Abstract

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. 

Code
EPAR Technical Report #341
Publication Date: 08/03/2017
Type:
Abstract
Data on public expenditures on agriculture are not systematically collected in any one database. Rather, a variety of sources collect and publish data on certain aspects of agricultural public expenditures. These sources vary in their data collection methods, their frequency of data collection, and the specific expenditures they report on. We collected data on agricultural public expenditures and conducted preliminary analyses for four countries: India (with a focus on Bihar, Odisha, and Uttar Pradesh), Ethiopia, Nigeria, and Tanzania. The data are disaggregated in a variety of ways depending on the source, but we include disaggregated data where available comparing planned or budgeted vs. actual spending, government vs. donor spending, soending by activity or funding area, and spending by commodity or value chain activity. Our goals are to facilitate further analysis of trends in agricultural public expenditures across countries and over time, and to highlight gaps and differences in data sources.