An Evaluation Of Rural Electrification Adoption Dynamics In Meru-South Sub-County, Tharaka-Nithi County, Kenya

ABSTRACT

Electricity services are crucial for human well-being and to a country‘s socio-economic development. Despite its importance, low levels of electricity adoption and use continue to prevail in most rural areas in SSA. Low socio-economic development has been attributed among others factors, to lack of modern energy sources especially electricity among households, which has been identified as a major setback in propelling empowerment and development at household and community level. There is minimal or no research conducted to understand the socio-economic dynamics of electricity adoption among households in Meru-South Sub-County. The objective of this study was therefore to analyse and identify determinants of electricity adoption, assess the socio-economic benefits and challenges of electricity adoption, assess the effect of rural electrification on development of public facilities and examine spatial distribution of electricity adopters, non-adopters and transformers in Meru-South Sub-County. To achieve these, household interviews were conducted from 150 randomly selected households using closed and opened ended questionnaire. In-depth interview guide was used to collect information from two Rural Electrification officials, two from Kenya Power and two local administration officers. A GPS set was used to geo-code adopter, non-adopter and transformer points. Data collected was statistically analyzed using descriptive statistics. Chi-square and t-test were used to test the magnitude of the association between dependent and independent variables. Logistic regression was used to predict the socio-economic factors influencing electricity adoption in households using statistical package for social sciences (SPSS) programme version 19.0. Data from GPS sets was organized into a compatible file and imported into ArCGIS 10.2 to generate maps. Results showed that 36% and 64% of the respondents in the study area were electricity adopters and non-adopters respectively. Possible predictor factors that significantly influenced adoption were found to be distance from the transformer (p=0.000), education status (p=0.020), gender (p=0.045), household size (p=0.009), and income (p=0.011). Besides low electricity adoption, electricity benefits and potentials among the adopters including improved quality of life through lighting (100%) and businesses (38.8%) among others were revealed. Results revealed a significant difference (p