Types of Research
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- (-) Remove Agricultural Inputs & Farm Management filter Agricultural Inputs & Farm Management
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- (-) Remove Other Datasets filter Other Datasets
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- (-) Remove Global & Regional Public Goods filter Global & Regional Public Goods
- (-) Remove Risk, Preferences, & Decision-Making filter Risk, Preferences, & Decision-Making
- (-) Remove LSMS & LSMS-ISA filter LSMS & LSMS-ISA
- (-) Remove Poverty filter Poverty
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.
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.
Household survey data are a key source of information for policy-makers at all levels. In developing countries, household data are commonly used to target interventions and evaluate progress towards development goals. The World Bank’s Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) are a particularly rich source of nationally-representative panel data for six Sub-Saharan African countries: Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda. To help understand how these data are used, EPAR reviewed the existing literature referencing the LSMS-ISA and identified 415 publications, working papers, reports, and presentations with primary research based on LSMS-ISA data. We find that use of the LSMS-ISA has been increasing each year since the first survey waves were made available in 2009, with several universities, multilateral organizations, government offices, and research groups across the globe using the data to answer questions on agricultural productivity, farm management, poverty and welfare, nutrition, and several other topics.
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.