Year Published
- (-) Remove 2008 filter 2008
- 2009 (16) Apply 2009 filter
- 2010 (15) Apply 2010 filter
- 2011 (13) Apply 2011 filter
- 2012 (6) Apply 2012 filter
- (-) Remove 2013 filter 2013
- (-) Remove 2014 filter 2014
- 2015 (14) Apply 2015 filter
- 2016 (13) Apply 2016 filter
- 2017 (11) Apply 2017 filter
- 2018 (3) Apply 2018 filter
- (-) Remove 2019 filter 2019
- 2020 (0)
- 2021 (2) Apply 2021 filter
Research Topics
Populations
- Countries/Governments (0)
- Rural Populations (0)
- Smallholder Farmers (1) Apply Smallholder Farmers filter
- Women (1) Apply Women filter
Types of Research
- Data Analysis (5) Apply Data Analysis filter
- Literature Review (15) Apply Literature Review filter
- Portfolio Review (0)
- Research Brief (0)
Geography
- East Africa Region and Selected Countries (5) Apply East Africa Region and Selected Countries filter
- Global (1) Apply Global filter
- South Asia Region and Selected Countries (10) Apply South Asia Region and Selected Countries filter
- Southern Africa Region and Selected Countries (0)
- Sub-Saharan Africa (9) Apply Sub-Saharan Africa filter
- West Africa Region and Selected Countries (0)
Dataset
- ASTI (0)
- FAOSTAT (0)
- Farmer First (0)
- LSMS & LSMS-ISA (4) Apply LSMS & LSMS-ISA filter
- Other Datasets (2) Apply Other Datasets filter
Current search
- (-) Remove 2008 filter 2008
- (-) Remove 2014 filter 2014
- (-) Remove Finance & Investment filter Finance & Investment
- (-) Remove Agricultural Productivity, Yield, & Constraints filter Agricultural Productivity, Yield, & Constraints
- (-) Remove 2013 filter 2013
- (-) Remove Labor & Time Use filter Labor & Time Use
- (-) Remove 2019 filter 2019
- (-) Remove Development Finance & Policy filter Development Finance & Policy
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:
- 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.
Donors and governments are increasingly seeking to implement development projects through self-help groups (SHGs) in the belief that such institutional arrangements will enhance development outcomes, encourage sustainability, and foster capacity in local civil society – all at lower cost to coffers. But little is known about the effectiveness of such institutional arrangements or the potential harm that might be caused by using SHGs as ‘vehicles’ for the delivery of development aid. This report synthesizes available evidence on the effectiveness of Self-Help Groups (SHGs) in promoting health, finance, agriculture, and empowerment objectives in South Asia and Sub-Saharan Africa. Our findings are intended to inform strategic decisions about how to best use scarce resources to leverage existing SHG interventions in various geographies and to better understand how local institutions such as SHGs can serve as platforms to enhance investments.
Suggested Citation:
Anderson, C. L., Gugerty, M. K., Biscaye, P., True, Z., Clark, C., & Harris, K. P. (2014). Self-Help Groups in Development: A Review of Evidence from South Asia and Sub-Saharan Africa. EPAR Technical Report #283. Evans School of Public Policy & Governance, University of Washington. Retrieved <Day Month Year> from https://epar.evans.uw.edu/sites/default/files/epar_283_shg_evidence_review_brief_10.23.20.pdf
This report draws on past and present peer-reviewed articles and published reports by institutions including the World Health Organization (WHO), the UK Department for International Development (DFID), and others to provide a scoping summary of the household-level spillovers and broader impacts of a select group of health initiatives. Rather than focusing on estimates of the direct health impacts of investments (e.g., reductions in mortality from vaccine delivery), we focus on estimates of the less-often reported spillover effects of specific health investments on household welfare or the broader economy. The brief is designed to give a concise overview of major theories linking health improvements to broader social and economic outcomes, followed by more in-depth summaries of available local- and country-level estimates of broader impacts, defined as project spillovers offering local, regional and national social and economic benefits not typically reported in project evaluations.
This report summarizes current trends in the application of Development Finance Institution (DFI)-based returnable capital finance in developing countries, with an emphasis on “pro-poor” development initiatives. We begin by reviewing the financial instruments used by DFIs. We then review the major DFI providers of returnable-capital based finance, drawing on past and present peer-reviewed articles and published reports exploring trends in the uses of different returnable capital instruments over time. Finally, we conclude by further examining recent efforts to use returnable capital to finance development initiatives explicitly targeting the poor.
This brief draws on recent reports by the OECD, the World Bank, the Overseas Development Institute (ODI), the Climate Policy Initiative (CPI) and others to provide an overview of climate finance in developing countries. The brief is divided into three sections: (i) sources of global climate finance; (ii) country-level flows of climate finance; and (iii) applications of climate finance in developing countries. The brief is designed to give a concise overview of financial flows directed at climate change mitigation and adaptation globally and in developing countries, with an introduction to climate finance accounting such that climate financial flow volumes can be compared to aid volumes in other sectors. Total global climate finance flows were approximately USD $364 billion in 2011 (Buchner et al., 2012) and $359 billion in 2012. However the vast majority of these flows - 76%, or $275 billion - was finance generated and spent within a country’s own borders (domestic finance) (Buchner et al., 2013). The “Fast-Start Finance” period from 2010-2012 saw $35 billion in new aid mobilized for climate finance in developing countries. Developed countries have recently committed to mobilize an additional $100 billion per year by 2020.
Cassava (Manihot esculenta Crantz) is a widely-grown staple food in the tropical and subtropical regions of Africa, Asia, and Latin America. In this brief we examine the environmental constraints to, and impacts of, smallholder cassava production systems in Sub-Saharan Africa (SSA) and South Asia (SA), noting where the analysis applies to only one of these regions. We highlight crop-environment interactions at three stages of the cassava value chain: pre-production (e.g., land clearing), production (e.g., soil, water, and input use), and post-production (e.g., crop storage). At each stage we emphasize environmental constraints on production (poor soil quality, water scarcity, crop pests, etc.) and also environmental impacts of crop production (e.g., soil erosion, water depletion and pesticide contamination). We then highlight good practices for overcoming environmental constraints and minimizing environmental impacts in smallholder cassava production systems. Evidence on environmental issues in smallholder cassava production is relatively thin, and unevenly distributed across regions. The literature on cassava in South Asian smallholder systems is limited, reflecting a crop of secondary importance (though it is widely found elsewhere in Asia such as South East Asia), in comparison to cassava in much of SSA. The majority of the research summarized in this brief is from SSA. The last row of Table 1 summarizes good practices currently identified in the literature. However, the appropriate strategy in a given situation will vary widely based on contextual factors, such as local environmental conditions, market access, cultural preferences, production practices and the policy environment.
This overview introduces a series of EPAR briefs in the Agriculture-Environment Series that examine crop-environment interactions for a range of crops in smallholder food production systems in Sub-Saharan Africa (SSA) and South Asia (SA). The briefs cover the following important food crops in those regions; rice (#208), maize (#218), sorghum/millets (#213), sweet potato/yam (#225), and cassava (#228).
Drawing on the academic literature and the field expertise of crop scientists, these briefs highlight crop-environment interactions at three stages of the crop value chain: pre-production (e.g., land clearing and tilling), production (such as water, nutrient and other input use), and post-production (e.g., waste disposal and crop storage). At each stage we emphasize environmental constraints on crop yields (including poor soils, water scarcity, crop pests) and impacts of crop production on the environment (such as soil erosion, water depletion and pest resistance). We then highlight best practices from the literature and from expert experience for minimizing negative environmental impacts in smallholder crop production systems.
This overview (along with the accompanying detailed crop briefs) seeks to provide a framework for stimulating across-crop discussions and informed debates on the full range of crop-environment interactions in agricultural development initiatives.
A paper based on this research series was published in Food Security in August 2015.
The purpose of this analysis is to provide a measure of marketable surplus of maize in Tanzania. We proxy marketable surplus with national-level estimates of total maize sold, presumably the surplus for maize producing and consuming households. We also provide national level estimates of total maize produced and estimate “average prices” for Tanzania which allows this quantity to be expressed as an estimate of the value of marketable surplus. The analysis uses the Tanzanian National Panel Survey (TNPS) LSMS – ISA which is a nationally representative panel survey, for the years 2008/2009 and 2010/2011. A spreadsheet provides our estimates for different subsets of the sample and using different approaches to data cleaning and weighting. The total number of households for Tanzania was estimated with linear extrapolation based on the Tanzanian National Bureau of Statistics for the years 2002 and 2012. The weighted proportions of maize-producing and maize-selling households were multiplied to the national estimate of total households. This estimate of total Tanzanian maize-selling and maize-producing households was then multiplied by the average amount sold and by the average amount produced respectively to obtain national level estimates of total maize sold and total maize produced in 2009 and 2011.
After cereals, root and tuber crops - including sweetpotato and yam (in addition to cassava and aroids), are the second most cultivated crops in tropical countries. This literature review examines the environmental constraints to, and impacts of, sweetpotato and yam production systems in Sub-Saharan Africa (SSA) and South Asia (SA). The review highlights crop-environment interactions at three stages of the sweetpotato/yam value chain: pre-production (e.g., land clearing), production (e.g., soil, water, and input use), and post-production (e.g., waste disposal, crop storage and transport). We find that sweetpotato and yam face similar environmental stressors. In particular, because sweetpotato and yam are vegetatively propagated, the most significant (and avoidable) environmental constraints to crop yields include disease and pest infection transmitted through the use of contaminated planting materials. Published estimates suggest yield gains in the range of 30–60% can be obtained through using healthy planting material. Moreover, reducing pest damage in the field can greatly increase the storage life of root and tuber crops after harvest – currently losses from rot and desiccation can claim up to 100% of stored sweetpotato and yam on smallholder farms.