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This document is an initial scoping of the theory and evidence linking digital services to women’s rural-to-urban migration. The document contains (1) a survey of the literature on digital financial services to discern how often this body of literature considers gender-disaggregated impacts on migration, (2) a detailed review of 13 hypotheses regarding the effects of digital services on women’s migration to cities, and (3) an illustrative overview of rural-urban migration patterns and digital technology usage in two East African countries (Ethiopia and Tanzania).
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.
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.
Local crop diversity and crop cultivation patterns among smallholder farmers have implications for two important elements of the design of agricultural interventions in developing countries. First, crop cultivation patterns may aid in targeting by helping to identify geographic areas where improved seed and other productivity enhancing technologies will be most easily applicable. Second, these patterns may help to identify potential unintended consequences of crop interventions focused on a single crop (e.g. maize). This report analyzes the distribution of crop diversity and crop cultivation patterns, and factors that can lead to changes in these patterns among smallholder farmers in Tanzania with a focus on regional patterns of crop cultivation and changes in these patterns over time, the factors that affect crop diversity and changes in crop diversity, and the level of substitutability between crops grown by smallholder farmers. All analysis is based on the Tanzania National Panel Survey (TNPS) datasets from 2008 and 2010. The paper is structured as follows. Section I provides a description of regional patterns of crop cultivation and crop diversity between the two years of the panel. Section II presents background on the theoretical factors affecting crop choice, and presents our findings on the results of a multivariate analysis on the factors contributing to crop diversity. Finally, Section 3 provides a preliminary analysis of the level of substitutability between cereal crop of importance in Tanzania (maize, rice and sorghum/millet) and also between these cereal crops and non-cereal crops.
This brief presents a comparative analysis of men and women and of male- and female-headed households in Tanzania using data from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We compare farm activity, productivity, input use, and sales as well as labor allocations by gender of the respondent and of the household head. In households designated “female-headed” a woman was the decision maker in the household, took part in the economy, control and welfare of the household, and was recognized by others in the household as the head. For questions regarding household labor (both non-farm and farm), the gender of the individual laborer is recorded, and we use this to illustrate the responsibilities of male and female household members. An appendix provides the details for our analyses.
Agriculture is a principal source of livelihood for the Tanzanian population. Agriculture provides more than two-thirds of employment and almost half of Tanzania‘s GDP. Women play an essential role in agricultural production. The sector is characterized as female-intensive, meaning that women comprise a majority of the labor force in agriculture (54%). This brief reviews the academic and grey literature on gender and agriculture in Tanzania, providing an overview on the structure of households, the household structure of agricultural production, information on women’s crops, and gender and land rights in Tanzania. We conclude with a summary of challenges to women in agriculture, and of potential implications for women of advancements in production technology and other economic opportunities at the household level.
Contract farming (CF) is an arrangement between farmers and a processing or marketing firm for the production and supply of agricultural products, often at predetermined prices. This literature review builds on EPAR's review of smallholder contract farming in Sub-Saharan Africa (SSA) and South Asia (EPAR Technical Report #60) by specifically examining the evidence on impacts and potential benefits of contract farming for women in SSA. Key takeaways suggest women’s direct participation in contract farming is limited, with limited access to land and control over the allocation of labor and cash resources key constraints hindering women’s ability to benefit from CF. Further, we find that the impact of contract farming on women is often mediated by their relative bargaining power within the household.
In the decades following independence in 1960, Côte d’Ivoire stood out as a shining example of economic growth in Sub-Saharan Africa. GDP increased at an annual average of 8.1 percent from 1960 to 1979, led largely by cocoa and coffee exports. Low export earnings from a fall in world cocoa prices and a heavy public debt burden halted this growth in the 1980s, followed by civil conflict beginning in 1999. Three decades of focus on export crops rather than food crops also left Côte d’Ivoire with a growing food deficit. This literature review examines the state of agriculture in Côte d’Ivoire and the history of government involvement in the agricultural sector. We find that while the country is poised to reemerge from a decade of economic stagnation and civil war after signing the Ouagadougou Political Accord in 2007, the political economy of Côte d’Ivoire is still heavily dependent upon and influenced by the production of cocoa. Cocoa is the top export, and cocoa export taxes provide one of the largest sources of revenue for the Government of Côte d’Ivoire (GoCI). Cocoa is not heavily dependent on fertilizer inputs and growers have increased production by expanding cropland. The small contribution of fertilizer to the production of this essential crop may help explain the GoCI’s low priority on expanding fertilizer production and use. Given that a large part of government revenue comes from the export of cocoa and coffee, the government has chosen to focus resources on crops that increase revenue. Even with the food riots in 2008, the GoCI has not made increasing domestic food production an important focus of agricultural policy.
Agriculture is the most important sector in the Ghanaian economy. In 2008, it accounted for over 32 percent of GDP and employed over half of the labor force. Economic development in Ghana has historically been dependent on the success of agriculture, particularly the main export crop, cocoa. Despite the sector’s importance, Ghanaian farmers have one of the lowest fertilizer application rates in Sub-Saharan Africa. The combination of a dominant agricultural sector, nutrient-poor soils, low fertilizer use among smallholder farmers, and the absence of locally produced inorganic fertilizers has prompted the government of Ghana (GoG) to intervene in the fertilizer market. This literature review examines the state of agriculture in Ghana, the history of the fertilizer market, and the current market structure. We find that the GoG has been a major actor in the inorganic fertilizer market over the past 50 years, from exercising total control of the domestic supply chain in the 1960s and 1970s to more indirect interventions in later years. In recent years, agricultural growth has averaged 5.5 percent as compared to 5.2 percent growth in the rest of the economy. However, most of this growth has been due to land expansion and favorable weather conditions rather than increased productivity. Increased fertilizer use among smallholder farmers has the potential to contribute to future agricultural growth and continued economic success.