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Oil exploration activities have been ongoing in Turkana County, Kenya resulting in discovery of a huge amount of oil deposits. However, very little research has been conducted to determine the effects of oil exploration activities on socio-economic and environmental factors in Turkana County. The purpose of this study was to analyze effects of oil exploration on the social fabrics, the economic variables and the environmental factors in the oil exploration areas in Turkana County. The study applied a cross-sectional survey design. Data was analyzed using both parametric and non-parametric statistical methods. The results of the study showed that despite the ongoing oil exploration in the county, the locals have not adequately benefited from job opportunities created by the exploration companies. In addition, cost of land has also increased. However, the study shows that despite the influx of migrants the trade volumes within oil exploration regions has not substantially increased. Further, oil exploration activities have adverse negative effects on environment, health and education of the locals. The study would greatly help policy makers at national and county levels to develop policies addressing problems associated with oil exploration faced by people living in oil exploration zones of Turkana County.

Publication Estimation of finite population parameters has been an area of concern to statisticians for decades. This paper presents an estimation of the population mean under a model-assisted approach.Dorfman (1992), Breidt and Opsomer (2000) and Ouma et al(2010) carried out theestimation of finite population total on the assumption that the sample size is large and the sampling distribution is approximately normal. Unlike their researches, this paper considered a case when the sample size is small under a model-assisted approach. A model-assisted regression model was considered in a case where the cluster sizes are known only for the sampled clusters in order to predict the unobserved part of the population mean. Under mild assumptions, the proposed estimator is asymptotically unbiased and its conditional error variance tends to zero. Simulation studies show that model assisted estimation performs better than model based estimation of a finite population mean in a case where the sample size is small. Estimation of finite population parameters has been an area of concern to statisticians for decades. This paper presents an estimation of the population mean under a model-assisted approach.Dorfman (1992), Breidt and Opsomer (2000) and Ouma et al(2010) carried out theestimation of finite population total on the assumption that the sample size is large and the sampling distribution is approximately normal. Unlike their researches, this paper considered a case when the sample size is small under a model-assisted approach. A model-assisted regression model was considered in a case where the cluster sizes are known only for the sampled clusters in order to predict the unobserved part of the population mean. Under mild assumptions, the proposed estimator is asymptotically unbiased and its conditional error variance tends to zero. Simulation studies show that model assisted estimation performs better than model based estimation of a finite population mean in a case where the sample size is small.