Determinants of Effective Revenue Collection by Embu County, Kenya

Enhancement of revenue collection in counties is core to meeting their financial responsibilities which will lead to recognition of their directive to offer valuable and well-timed services to the residents and the demand for which may possibly surpass the available resources. Counties have sufficient revenue stations to fund the current service levels, but revenue collection levels often do not meet projections. According to reports by the Controller of Budget, revenue collection by 14 counties in Kenya fell below amounts produced by the previous local authorities under their individual jurisdictions in the 2013/2014 fiscal year. Further, the breakdown exposed that all but four counties could not meet their local income collection objectives. Several counties have faced labor strikes and stoppages among their employees because of delayed salaries and/or poor payment of personnel working under the county governments. Numeral studies have been done in the area of revenue gathering which note revenue flop because of poor senior administration, unsuccessful arrangements, wrong organizational strategy, lack of well - defined and surrogate authority and responsibility, an inept system checking, evaluation and supervisory, misuse of incomes, unsuccessful contingency design, narrow team involvement in the carrying out of revenue decisions, unworkable cost estimates and plans, lack of customer assurance to revenues, restricted customer control and scarce management statistics. However, no research assessed how staff requirements, corruption, equipment, government strategies and protocols affected the optimum revenue collection in Embu county all of which shaped this study. The study used the descriptive survey research design. The study targeted county government staff in revenue collection, accounts/finance and administrative departments. The accessible population was 132 respondents. Purposive sampling was used to pick the Chief Officer in Charge of Finance, Sub county Revenue Officers and the county Executive in charge of Revenues while stratified random sampling was used to select 96 departmental staff from the sub counties. Data was collected using self-administered Likert scale guided structured questionnaires. Overall; it was established that government policy, rules and regulations had the greatest effect on the optimal revenue collection, followed by corruption, then employee qualification, skills and training while technology and information systems had the least effect to the optimal revenue collection. All the variables were significant (p