Types of Research
- (-) Remove Agricultural Productivity, Yield, & Constraints filter Agricultural Productivity, Yield, & Constraints
- (-) Remove Aid & Other Development Finance filter Aid & Other Development Finance
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
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.
Precise agricultural statistics are necessary to track productivity and design sound agricultural policies. Yet, in settings where intercropping is prevalent, even crop yield can be challenging to measure. In a systematic survey of the literature on crop yield in low-income settings, we find that scholars specify how they estimate the yield denominator in under 10% of cases. Using household survey data from Tanzania, we consider four alternative methods of allocating land area on plots that contain multiple crops, and explore the implications of this measurement decision for analyses of maize and rice yield. We find that 64% of cultivated plots contain more than one crop, and average yield estimates vary with different methods of calculating area planted. This pattern is more pronounced for maize, which is more likely than rice to share a plot with other crops. The choice among area methods influences which of these two staple crops is found to be more calorie-productive per ha, as well as the extent to which fertilizer is expected to be profitable for maize production. Given that construction decisions can influence the results of analysis, we conclude that the literature would benefit from greater clarity regarding how yield is measured across studies.
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.
According to AGRA's 2017 Africa Agriculture Status Report, smallholder farmers make up to about 70% of the population in Africa. The report finds that 500 million smallholder farms around the world provide livelihoods for more than 2 billion people and produce about 80% of the food in sub-Saharan Africa and Asia. Many development interventions and policies therefore target smallholder farm households with the goals of increasing their productivity and promoting agricultural transformation. Of particular interest for agricultural transformation is the degree to which smallholder farm households are commercializating their agricultural outputs, and diversifying their income sources away from agriculture. In this project, EPAR uses data from the World Bank's Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) to analyze and compare characteristics of smallholder farm households at different levels of crop commercialization and reliance on farm income, and to evaluate implications of using different criteria for defining "smallholder" households for conclusions on trends in agricultural transformation for those households.
Crop yield is one of the most commonly used partial factor productivity measures. It is used to estimate the ratio of quantity of crop output, generally measured in kilograms or tons, to a sole input, land area. Ongoing EPAR research explores the policy implications of measuring yield by area planted versus area harvested. In this brief, we consider implications for crop yield estimates of other decisions in how to construct yield measures from household survey microdata. Using data from three waves of the Tanzania National Panel Survey (TNPS) and two waves of the Ethiopia Socioeconomic Survey (ESS), both part of the World Bank’s Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), we calculate separate crop yield estimates across survey waves following different decisions on disaggregating yield by gender(s) of the plot decision-maker(s) and for pure-stand and mixed stand (intercropped) plots, on including crop production from multiple growing seasons, and on how to treat outlier observations.
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.
By examining how farmers respond to changes in crop yield, we provide evidence on how farmers are likely to respond to a yield-enhancing intervention that targets a single staple crop such as maize. Two alternate hypotheses we examine are: as yields increase, do farmers maintain output levels but change the output mix to switch into other crops or activities, or do they hold cultivated area constant to increase their total production quantity and therefore their own consumption or marketing of the crop? This exploratory data analysis using three waves of panel data from Tanzania is part of a long-term project examining the pathways between staple crop yield (a proxy for agricultural productivity) and poverty reduction in Sub-Saharan Africa.
This 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. We summarize the public good characteristics of R&D for agriculture in general and for commodity and subsistence crops in particular, as well as R&D for health in general and for neglected diseases in particular, with a focus on Sub-Saharan Africa and South Asia. Finally, we present rationales for which funders are predicted to fund which R&D types based on these funder and R&D characteristics. We then compile available statistics on funding for agricultural and health R&D from private, public and philanthropic sources, and compare trends in funding from these sources against expectations. We find private agricultural R&D spending focuses on commodity crops (as expected). However contrary to expectations we find public and philanthropic spending also goes largely towards these same crops rather than staples not targeted by private funds. For health R&D private funders similarly concentrate on diseases with higher potential financial returns. However unlike in agricultural R&D, in health R&D we observe some specialization across funders – especially for neglected diseases R&D - consistent with funders’ expected relative advantages.