DOI: 10.5285/e736e22c-f409-49ee-930d-a415ade89e79
title: Income source and quantity data from communities in the Katakwi District, Uganda, 2018

description: This dataset is a product of the raw HEA (Household Economy Approach) data that were collected in sixteen communities in the Katakwi district, and the raw IHM (Individual Household Method) data that was collected with 42 households in the community of Anyangabella, and 51 households in the community of Kaikamosing. These data were collected in 2018, and consist of multiple aspects of household and individual income sources and expenditure in the Katakwi District. The data were collected to support the analysis of vulnerability levels to further support livelihood impact modelling, and the development of targeted policies to support resilience at household and community level. The data collection team comprised of local, Ugandan partners. All data were collected in the local language and translated into English.
description: There are many resources available that outline the HEA/IHM methodology that was used. In summary the methodology is as follows:

In HEA studies the first step is to demarcate rural livelihood zones, based on land use, climate, rainfall, markets and other economic information. In each livelihood zone a ‘reference year’ (a recent year when conditions were neither exceptionally bad nor exceptionally good) is identified in consultation with local key informants, and a sample of locations is purposively selected to represent the range of variation across the zone. At each sample site, initial village interviews are conducted with knowledgeable village representatives to establish the characteristics of wealth groups that are recognised there.
Using this information, further interviews are conducted with a focus group selected from each wealth group (e.g. ‘very poor’, ‘poor’, ‘middle’, and ‘better-off’) to establish the incomes and expenditures of a ‘typical’ household in that wealth group in the reference year. These interviews are repeated at the selected sample sites across the zone, providing a baseline dataset which is used to simulate the impacts of economic shocks or changes on access to food and basic non-food needs for typical households from each wealth group.

HEA can be supported by the IHM methodology. In summary the methodology is as follows:

The individual household method differs from most other household budget surveys by collecting data through a semi-structured interview rather than a standard questionnaire format, as well as by using specialised software which allows data checking and analysis to be carried out in the field. These innovations reduce the risk of errors in data collection, and allow any errors that do occur to be identified and corrected early in the process. Rapid analysis can also provide up-to-date information needed by decision makers.

The first stage of IHM research is the identification of livelihood zones and selection of survey sites within the zone. After sampling decisions have been made and locations have been selected, contextual information on the local economy is collected from focus groups including women and men involved in different economic activities. This provides interviewers with data that can be used to cross-check responses from individual households.

Selected households are then interviewed, following a structure that is designed to include all relevant income sources and related details without unnecessary questions. The interview covers household demography, assets, crop and livestock production, employment (including day labour, petty trade, self-employment and salaried work undertaken by men, women and children in the household), wild foods and non-market transfers. Other personal or household characteristics relevant to the study (for example, the gender of the household head or the educational level of each member of the household) are also recorded during the interview.

On the day of collection, interview data is entered into open-IHM software and checked for internal consistency, biological adequacy and disparities with observed living conditions. If anomalies are found or if any further information is needed the household is revisited, and where necessary datasets are amended.