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.