Modeling Self Help Groups’ Impact on Livelihoods in Murang’a East Sub-County: A Logistic Regression Approach

Abstract

According to the World Bank (2022), approximately 8.9 million people, or 17% of Kenya’s population, live below the poverty line of 1.9 USD on a daily basis, majority of them in the rural areas. This research aimed to analyze the impact of self-help groups on the livelihoods of rural areas of Kenya, with the goal of promoting sustainable livelihoods and reducing poverty. To achieve this, the study employed machine learning specifically the logistic regression algorithm to model the impact of self-help groups on livelihoods in Murang’a East sub-county. The study used primary data obtained through the issuance of structured questionnaires to SHG members, on their wealth status since joining the self-help groups on areas such as ability to save, access to credit services and acquiring assets, both income generating and household. A total of 969 members of self-help groups were issued with the questionnaire. The study’s findings helped identify the key predictors of members’ livelihoods and provided insights into how self-help groups influence them. The results of logistic regression indicated that 91.33% of the members had seen a significant improvement on their wealth status since joining self-help groups and the significant predictor variables were income generating assets, access to basic commodities and access to loans. The model’s accuracy was 88.04%. The ethical considerations in this study included ensuring no coercion or pressure to participate in the study and confidentiality and privacy of the respondents.