EFFECTIVENESS OF DECONGESTION PROGRAMS IN SELECTED PRISONS IN KENYAN

Abstract

The incarcerated population has drastically increased in Kenyan prisons. As of 2018, the total number of inmates in Kenyan prisons was 55,000, far exceeding the official holding capacity of prisons which is 27,000. This has adverse effects on prisons, especially by overstretching the available facilities and economic resources. To address the problem of congestion in prisons, the government designed programs with the view to decongesting prisons. This study, therefore, sought to assess the effectiveness of prison decongestion programs. The study objectives were to assess the extent to which the prison decongestion programs have helped to reduce the number of inmates in prisons in Kenya, investigate the effects of the constraints of implementation of prison decongestion programs, and examine the effect of the prison reforms on prison decongestion programs in the reduction of the prison population. The study significantly provides the basis for either expansion of the existing prisons or establishment of new prisons. The study used a descriptive survey design. GK prisons and probation departments in Embu, Kakamega, and Eldoret were study locations. A purposive sampling design was used to select the three prisons. The target population was 830 prison staff. Proportionate, stratified, and simple random sampling selected the sample. This resulted in a sample size of 282 subjects comprising 266 prison officers and 16 probation officers. Statistical Package for Social Sciences (SPSS) version 21.0 was used for data analysis. The data collection instrument was the questionnaire which was pilot-tested in Kiambu GK Prison to determine its reliability before the actual study. The reliability coefficient of the instruments was approximated using the Cronbach’s Alpha coefficient of 0.7. Data analysis methods used for descriptive statistics included frequencies, percentages, and means to summarize raw data. Results of data analysis were presented using frequency distribution tables. The study carried out diagnostic tests before developing the multiple linear regression model. The multiple linear regression model was established to determine how the three independent variables influenced the prison population. The study found that individually, all the three independent variables have a positive and statistically significant relationship with the prison population. Since decongestion programs reduce the prison population, a recommendation is given that the judicial services should focus on applying community-based sentences to curb populating prisons with petty crime offenders.