Change in gender roles as a factor in gender participation and empowerment in the oil mining industry: A case of Lokichar, Turkana county, Kenya
Shikuku, Caroline; Mburugu, Prof. Edward; Kabiru, Dr. Joseph (2022), Change in gender roles as a factor in gender participation and empowerment in the oil mining industry: A case of Lokichar, Turkana county, Kenya, Dryad, Dataset, https://doi.org/10.5061/dryad.ngf1vhhwn
This paper analyses a study done by the authors of this paper on changes on gender role as a factor in gender participation and empowerment in the oil mining industry; a case of Lokichar in Turkana County. The paper centres on three null hypothesis tested. The studies target group was the active labour force aged between 15 to 64 years. Those who retired from work were also targeted. The data was collected from a sample of three hundred (300) respondents selected through systematic random and purposive sampling methods. Focus Group Discussions (FGD) and in-depth interviews were conducted to supplement the questionnaires given to the sampled respondents. Chi-square was used to test the hypotheses. Major findings indicate that there is a relationship between equal hiring and equal opportunity for men and women to work in mining activities; there is relationship between involvement in oil mining activities and change in livelihood and there is no relationship between involvement in oil mining activities and equal opportunity for men and women to work in mining activities. Key recommendation include gender mainstreaming in legal frameworks, policies, Bills and programs.
Two levels of analysis were adopted: Univariate and Bivariate. Tabulation and charts were presented to show a comparison between the various categories. At univariate level one variable at a time was analysed to give out characteristic of the variable under study (Babbie, 201:250). At Bivariate level, two variables are analysed (denoted as X, Y) to assess the empirical relationship between them (Singleton et al, 1988:397). Cross tabulation was done and Chi Square test conducted. Chi Square test is common for non- parametric populations.
Three null hypotheses were tested using chi square of independence and were either rejected or not rejected. We had cases of missing values.