Types of Research
- (-) Remove East Africa Region and Selected Countries filter East Africa Region and Selected Countries
- (-) Remove Technology filter Technology
- (-) Remove Data Analysis filter Data Analysis
- (-) Remove Poverty filter Poverty
- (-) Remove Southern Africa Region and Selected Countries filter Southern Africa Region and Selected Countries
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 report we analyze three waves nationally-representative household survey data from Kenya, Uganda, Tanzania, Nigeria, Pakistan, Bangladesh, India, and Indonesia to explore sociodemographic and economic factors associated with mobile money adoption, awareness, and use across countries and over time. Our findings indicate that to realize the potential of digital financial services to reach currently unbanked populations and increase financial inclusion, particular attention needs to be paid to barriers faced by women in accessing mobile money. While policies and interventions to promote education, employment, phone ownership, and having a bank account may broadly help to increase mobile money adoption and use, potentially bringing in currently unbanked populations, specific policies targeting women may be needed to close current gender gaps.
Mobile technology is associated with a variety of positive development and social outcomes, and as a result reaching the “final frontier” of uncovered populations is an important policy issue. We use proprietary 2012 data on mobile coverage from Collins Bartholomew to estimate the proportion of the population living in areas without mobile coverage globally and in selected regions and countries, and use spatial analysis to identify where these populations are concentrated. We then compare our coverage estimates to data from previous years and estimates from the most recent literature to provide a picture of recent trends in coverage expansion, considering separately the trends for coverage of urban and rural populations. We find that mobile coverage expansion rates are slowing, as easier to reach urban populations in developing countries are now almost entirely covered and the remaining uncovered populations are more dispersed in rural areas and therefore more difficult and costly to reach. This analysis of mobile coverage trends was the focus of an initial report on mobile coverage estimates. In a follow-up paper prepared for presentation at the 2016 APPAM International Conference, we investigate the assumption that levels of mobile network coverage are related to the degree of market liberalization at the country level.
This brief explores how two datasets – The Tanzania National Panel Survey (TZNPS) and the TNS-Research International Farmer Focus (FF) – predict the determinants of inorganic fertilizer use among smallholder farmers in Tanzania by using regression analysis. The (TZNPS) was implemented by the Tanzania National Bureau of Statistics, with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) team and includes extensive information on crop productivity and input use. The FF survey was funded by the Bill and Melinda Gates Foundation and implemented by TNS Research International and focuses on the on the behaviors and attitudes of smallholder farmers in Tanzania. The two datasets produce relatively comparable results for the primary predictors of inorganic fertilizer use: agricultural extension and whether or not a household grows cash crops. However, other factors influencing input use produce results that vary in magnitude and direction of the effect across the two datasets. Distinct survey instrument designs make it difficult to test the robustness of the models on input use other than inorganic fertilizer. This brief uses data inorganic fertilizer use, rather than adoption per se. The TZNPS did not ask households how recently they began using a certain product and although the FF survey asked respondents how many new inputs were tried in the past four planting seasons, they did not ask specifically about inorganic fertilizer.