Poverty-based Classification of Households Using Cluster Analysis
Abstract
Sub-Saharan Africa (SSA) rely on agriculture for livelihood. Agri-climatic shocks such as
prolonged droughts, outbreak of animal and human diseases and crop and pest diseases
make rural poor households in SSA vulnerable. Research gaps exist on poverty-based
clusters in Kenya rural areas. The clusters would be fundamental in understanding the
determinants of poverty.
This study uses K-means and K-medoid algorithms to identify poverty-based clusters in
Kenya rural areas. The data used is collected from rural farming households. K-means
and K-medoid algorithms are the most common clustering algorithms used and have been
implemented by researchers.
The results show that rural poor households have low education level, high dependency
ratio, low gender parity ratio, low income and low household diet diversity compared
to rural non-poor households. Rural non-poor households own agricultural productive
assets, seek extension services, are more aware of financial services and products available
to farmers and access financial services more compared to rural poor households.
Knowledge on the determinants of poverty in Kenya rural areas can be used by the government,
institutions and partners, to formulate strategies and policies in an effort to
reduce poverty. In future, research should be conducted on the role of land sizes and land
tenure on poverty in rural Kenya.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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