Factors Contributing to Youth Involvement in Crime in Bungoma County, 2014 to 2018

Abstract

The Annual Police Report of 2015 ranked Bungoma 6th out of 47 counties in reported crime incidents. Despite the County’s initiatives, crime continues to be committed by youth in Bungoma County. The County has about 467,171 youth (KNBS, 2019), of which approximately 40% left schooling with about 25% of them engaging in informal employment such as boda boda riders. Reduced crime will encourage and attract investor’s confidence in various sector of the economy as no business is willing to loss its investment to crimes. The specific objectives were; to establish how unemployment among the youth influences them to commit crime in Bungoma County, to investigate the influence of poverty on crime in Bungoma County and to examine the influence of population growth among the youth to crime in Bungoma County. Both qualitative and Quantitative were used. Descriptive research design was utilized to present and analyze data. Questionnaires were administered to the sampled youths. Cluster sampling to get locations from each sub County while the youths were randomly sampled. Results from study indicated that more unemployed youths tend to get involved in crime compared to the ones in the informal and formal employment at a frequency of 7.496 for employed youths and 69.62 for unemployed youths. Additionally, based on indicators of poverty; household assets, access to standard infrastructure and level of education, the higher the poverty level, the more likely the youths were involved in crime at 1% significance level of the probit model. High youth population growth also showed high crime prevalence at 1% significance level of the probit model. Youth unemployment, poverty and youth population growth showed high positive association with crime. The study recommended that; to reduce the effect unemployment then, polices should be aimed at creating more employment opportunities besides improving the prospects of workers. Secondly, there should be policies to advocate for reduction of population among the youths in urban areas, family size, residential mobility as well as the numbers of unwanted children. However it was noted that individual behavior cannot be predicted by analysis at the macro level. People’s behavior and attitude is usually unforeseen and difficult to predict any at time.