Research Topics

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 #363
Publication Date: 02/10/2019
Type: Data Analysis
Abstract

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.

EPAR TECHNICAL REPORT #362
Publication Date: 01/16/2019
Type: Data Analysis
Abstract

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:
    • Ethiopia Socioeconomic Survey (ESS), Wave 3 (2015-16)
  • Kenya:
    • Kenya FinScope, Wave 4 (2015)
    • Kenya FII, Wave 4 (2016)
  • Nigeria
    • Nigeria FII, Wave 4 (2016)
  • Rwanda:
    • Rwanda FII, Wave 4 (2016)
  • Tanzania:
    • Tanzania National Panel Survey (TNPS), Wave 4 (2014-15)
    • Tanzania FinScope, Wave 4 (2017)
    • Tanzania FII, Wave 4 (2016)
  • Uganda:
    • 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:

Sub-Populations

  • 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

Characteristics
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.

EPAR Technical Report #335
Publication Date: 11/21/2017
Type: Data Analysis
Abstract
EPAR has developed Stata do.files for the construction of a set of agricultural development indicators using data from the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA). We are sharing our code and documenting our construction decisions both to facilitate analyses of these rich datasets and to make estimates of relevant indicators available to a broader audience of potential users. 
Code, Code, Code, Code
EPAR Technical Report #356
Publication Date: 10/31/2017
Type: Data Analysis
Abstract

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.

 

Code
EPAR Technical Report #358
Publication Date: 09/30/2017
Type: Data Analysis
Abstract

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.

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 #326
Publication Date: 06/01/2017
Type: Data Analysis
Abstract

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. 

Code
EPAR Technical Report #240
Publication Date: 07/28/2016
Type: Data Analysis
Abstract

There is a wide gap between realized and potential yields for many crops in Sub-Saharan Africa (SSA). Experts identify poor soil quality as a primary constraint to increased agricultural productivity. Therefore, increasing agricultural productivity by improving soil quality is seen as a viable strategy to enhance food security. Yet adoption rates of programs focused on improving soil quality have generally been lower than expected. We explore a seldom considered factor that may limit farmers’ demand for improved soil quality, namely, whether farmers’ self-assessments of their soil quality match soil scientists’ assessments. In this paper, using Tanzania National Panel Survey (TZNPS) data, part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA), we compare farmers’ own assessments of soil quality with scientific measurements of soil quality from the Harmonized World Soil Database (HWSD). We find a considerable “mismatch” and most notably, that 11.5 percent of survey households that reported having “good” soil quality are measured by scientific standards to have severely constrained nutrient availability. Mismatches between scientific measurements and farmer assessments of soil quality may highlight a potential barrier for programs seeking to encourage farmers to adopt soil quality improvement activities. 

EPAR Technical Report #337
Publication Date: 06/20/2016
Type: Data Analysis
Abstract

Relative to chronic hunger, seasonal hunger in rural and urban areas of Africa is poorly understood. No estimates are compiled, and limited evidence exists on prevalence, causes, and impacts. This paper contributes to the body of evidence by examining the extent and potential drivers of seasonal hunger using panel data from the Malawi Integrated Household Panel Survey (IHPS). Farmers are commonly thought to use various strategies to smooth consumption, including planting “off-season” crops, investing in post-harvest storage technologies, or generally diversifying farm portfolios including livestock products and/or wild crops. Similarly, when markets are available, farmers may diversify through off-farm income sources in order to purchase food in lean seasons. We investigate whether seasonal hunger – distinct from chronic hunger – exists in Malawi, drawing on two waves of panel data from the LSMS-ISA series. We examine the extent of seasonal hunger, factors associated with variation in seasonal hunger, and how recurring and longer-term seasonal hunger might be associated with various household welfare measures. We find that both urban and rural households report experiencing seasonal hunger in the pre-harvest months, with descriptive evidence suggesting male gender, age, and education of household head, livestock ownership, and storage of crops are associated with lower levels of seasonal hunger. In addition, we find that Malawian households with seasonal hunger harvest crops earlier than average – a short-term coping mechanism that can reduce the crop’s yield and nutritional value, possibly perpetuating hunger.

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