Abstract:
Revenue allocation formulae are in use in over 24 federal states in the world today. In Kenya the Commission on Revenue Allocation (CRA) modelled its first generation revenue allocation formula as per the Kenya Constitution 2010. However this formula has been criticized by Kenyans, politicians and professional groups. When the CRA first unveiled its recommendations to the public, it basically released a formula alone. They did not explain to the public why it had opted for a formula nor how the formula was arrived at. The CRA held a number of forums since the formula’s release, to receive public comment on it. But the Commission has never remedied the fatal flaw in its initial approach. This study investigated the influence of parameters on the revenue allocation formula, allocation of parameter weights and determined other parameters that could be used to improve the CRA formula. Kenya’s 2010 Constitution declares equity to be an underlying principle of governance in the country, which is consistent with its provision for devolution. The study adopted a cross sectional survey research design. Specifically, the study adopted the causal survey research because the data gathered was quantitative in nature as well as pre-planned and structured. This helped in attempting to explain the cause and effect relationship between revenue allocation parameters and the revenue allocation formula. In the end this design helped to understand which variables were the cause and which variables were the effect. It also helped to determine the nature of relationship between the causal variables and the effect predicted. The study targeted 596 senior county officials drawn from 9 Counties. Regression models was used to examine the influence of Parameters on revenue allocation formula while Monte Carlo Simulation was used to determine the parameter weights. The study established that the CRA revenue allocation parameters positively influence the revenue allocation formula for the devolved government of Kenya. The study further revealed that other parameters need to be incorporated to make the formula more equitable. The parameters were subjected to Monte Carlo simulations which gave an output for the recommended weights of each parameter.
