Agriculture is the foundation of the economy in Uganda. Over three-quarters of the population are involved in some aspect of the agriculture sector. In addition to income earned from their own agricultural activities, smallholders rely on income from casual labor on other farms, trading, and remittances from relatives. Yet despite their active financial and agricultural lives, smallholders in Uganda have few tools to manage their irregular and volatile household cash flows, and thus, it is difficult for them to plan and expand their livelihood activities.
In 2015, CGAP and GIZ, in coordination with the Uganda Bureau of Statistics, conducted a nationally representative survey of smallholder households. Its objective was to explore the financial needs and behaviors of smallholder farmers in Uganda as a basis for guiding financial institutions, mobile network operators, donors, and government partners to design, improve, and scale solutions that address the needs of farmers.
More specifically, the National Survey of Smallholder Households in Uganda was designed to:
- Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, poverty status, and market relationships.
- Segment smallholder households by the variables driving financial inclusion.
- Characterize the demand for financial services in each segment, focusing on customer needs, attitudes, and perceptions related to both agricultural and financial services.
- Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.
The research methodology is detailed in this user guide to the data sets on the following key topics:
- Sample design—Sampling frame, sample allocation and selection, household listing and the listing documents, sampling weights and errors.
- Questionnaire—Background on how the instrument was designed and its three components.
- Fieldwork—Training, deviations in the sample design, response rates, methods of data collection, and quality checks.
- Data sets—Accessing the raw data for your own research.