Abstract:
This research reflects on the small area estimation (SAE) with the principal objective of
presenting the status of the poverty and extreme poverty at sector level. To accomplish this
objective, we first present the theory of the small area estimation (SAE) technique. The
SAE is concerned on the generation of believable estimations of characteristic of interest for
small demesnes, starting on small or non samples coming from these demesnes; and the
assessment of the estimate or error preduction. To improve direct estimations for a small
demesne, SAE technique tries to ”borrow strength” (covariates) from other related data
sets, either from similar areas, or relevant/auxiliary information obtained from a recent
census or some other administrative records. The covariates used that are related to
poverty indicators were the rates of household with no electricity, production, roof with
local tiles, roof with other materials, and unimproved sanitation facilities. These five
covariates were chosen as they were found to be of great impact on poverty status as
published in the fourth integrated household living conditions (EICV4) report 2013-14 and
in Poverty mapping report 2013-14.
This study showed that, there is inequality in sectors of Karongi District. Bwishyura and
Rubengera sectors were less suffering from poverty, with poverty incidence of 43.047% and
49.9902% respectively and with the extreme poverty incidence 27.7159% and 32.3044%
respectively. Mutuntu sector was the most suffering from the poverty in Karongi district
with the poverty incidence of 73.2498% and the extreme poverty of 49.2456% followed by
Rwankuba sector with the poverty incidence of 70.2774% and the extreme poverty
incidence of 46.6706%.
