Types of Research
- (-) Remove Household Well-Being & Equity filter Household Well-Being & Equity
- (-) Remove Poverty filter Poverty
- (-) Remove Agricultural Productivity, Yield, & Constraints filter Agricultural Productivity, Yield, & Constraints
- (-) Remove Global & Regional Public Goods filter Global & Regional Public Goods
- (-) Remove Other Datasets filter Other Datasets
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.
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.
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.
This brief reviews the various definitions of global public goods (GPGs) and regional public goods (RPGs) found in the literature and provides examples of each in six frequently discussed sectors: environment, health, knowledge, security, governance, and infrastructure. We identify multiple alternative definitions that have gained some traction in the literature, but GPGs are generally agreed to exhibit publicness in consumption, distribution of benefits, and decision-making. Because policy choices determine what is and what is not a GPG, there cannot be a fixed list of such goods; some always have the property of global publicness, while others have over time changed from being local or national to being global in terms of benefits and costs. GPGs are thus redefined as goods that are in the global public domain. GPG and RPG financing mechanisms include payments by users and beneficiaries, taxes, fees, and levies, private funding by non-profit corporations, profit-making firms, and philanthropic individuals and organizations, national and international public resources, and partnerships between several sources of financing. We conclude with an analysis of trends in GPG and RPG financing through Official Development Assistance (ODA) using time series data from the OECD’s Creditor Reporting System and other sources. We find that 14% of ODA in 2014 was allocated to sub-sectors labelled by Reiner et al. as GPGs, while 15% of ODA was allocated to RPGs, and that GPG and RPG spending has steadily increased from 2002-2014.
This report combines analyses from four previous EPAR briefs on the effects of climate change on maize, rice, wheat, sorghum, and millet production in Sub-Saharan Africa (SSA). In addition, this brief presents new analysis of the projected impact of climate changes in SSA. We include comparisons of the importance of each crop, of their vulnerability to climate change, and of the research and policy resources dedicated to each. Especially with respect to climatic susceptibility, these rankings provide a comparative summary based upon the analysis conducted in the four previous EPAR briefs, statistical analyses of historical yield and climate data, and future climate model predictions. According to the indicators analyzed, our research suggests that maize leads the cereal crops in terms of importance within SSA and in terms of research and policy attention. Our analysis of climate conditions and the crop’s physical requirements suggests that many maize-growing areas are likely to move outside the range of ideal temperature and precipitation conditions for maize production. Rice is the third most important crop in terms of consumption dependency, fourth in terms of production, but second only to maize in terms of research funding and FTEs. Sorghum and millet rank second and third in production importance and second and fifth in consumption importance, but rank below maize and rice in terms of FTE researchers. Their role is complicated by the fact that they are often considered inferior goods; SSA consumers often substitute away from sorghum and millet consumption if they are able to do so. Wheat is the least-produced crop of the five, and the second to last in terms of consumption importance. However, it still ranks above millet in terms of FTE researchers.
As part of the Crops & Climate Change series, this brief is presented in three parts: 1) An evaluation of the importance of Sorghum and Millet in SSA, based on production, net exports, and caloric need, 2) A novel analysis of historical and projected climate conditions in Sorghum and Millet growing regions, followed by a summary of the agronomic and physiological vulnerability of Sorghum and Millet crops, 3) A summary of current resources dedicated to sorghum and millet, based on research and development investments and National Adaptation Programmes of Action. Our analysis indicates that sorghum and millets may become increasingly important in those areas of SSA predicted to become hotter and subject to more variable precipitation as a result of climate change. Although sorghum and millet are currently grown on marginal agricultural lands and consumed for subsistence by poorer population segments, climate change could render these drought- and heat-tolerant crops the most viable future cereal production option in some areas where other cereals are currently grown. Fewer international development resources are currently devoted to sorghum and millet than are devoted to other cereal grains, and current resource allocation may not reflect the increased reliance on these grains necessitated by projected climactic changes.