Effectiveness of Revenue Collection Strategies at Kenya Revenue Authority in Nairobi

Abstract

Developing effective ways in revenue collection has been an important matter in tax and revenue collection. The advent of new instruments to help businesses work more efficiently affects the way taxes and revenues are collected. KRA embarked on extensive implementation of various revenue collection strategies in its operations. This was to significantly enhance revenue collection in all Departments. Implementation of innovative revenue collection strategies was supposed to improve its organization structures, training, manpower planning, developing teamwork among management and staff, new approaches to reward management and adaptation of total quality management. The influences of various revenue collection strategies on revenue collection have not been investigated. The purpose of this study is to examine the effectiveness of revenue collection strategies at KRA in Nairobi. The descriptive research design will be adopted with focus on quantitative characteristics and status of revenue collection strategies at KRA with regard to enhancements of revenue collection. The target population will be staff of KRA in Nairobi and its branches. Although there are 3,905 staff, the focus will be senior tax officers who are 1,540. Stratified random sampling technique will be used to select a sample of 154 staff. In this study, data will be collected using a questionnaire which will be administered through face to face interviews. Data analysis will be done using a statistical package for social scientists (SPSS). First, data will be collected, cleaned, sorted and collated. All the data will be matched and coded to maintain the temporary employees’ confidentiality. Then, it will be entered in a computer, after which analysis will be done. Descriptive statistics in the form of pie charts, contingency tables and bar graphs will be used to describe the data. Then measures of association will be used to examine the relationship between the independent and dependent variables. This will be followed by analysis using inferential statistics such as Pearson correlation to examine the relationship between variables.