Abstract:
The implementation of a budget is crucial for ensuring adequate resources allocation to meet work demands, this study was aimed to investigate the Determinants of budget implementation in the case of city government of Addis Ababa, Ethiopia. A systematic sampling procedure was used to take sample of 122 covers the five sectors such as education, health, public service, and roads and transport sector. For the study both primary and secondary sources of data were collected. Primary data was collected through structured questionnaires, key informant interviews and focus group discussions. The collected data was analyzed using both descriptive and Multinomial logistic regression model. The coefficient fitting table showed a positive co-variance between the independent variables and the dependent variable. The regression model showed that the econometric coefficients for budget plan and prepare, budget control, financial regulation, and budget reporting were statistically significant at a 1% level of significance, indicating a positive impact on budget implementation. However, the other econometric coefficient, Participative Budget, was statistically insignificant at every level of significance. The study also found that the effect of participative budgeting on motivation and performance of managers was insignificant, but the importance of budget participation on implementation budget was not significant. The study found a significant relationship between budget control and budget implementation in Addis Ababa's city government. For every 1unit increase in budget control, it contributed to a 0.158% increase in budget implementation. Financial regulation negatively impacted budget implementation, with a 0.115% decrease observed for every 1unit increase in financial regulations. In addition, it assists in identifying cost-cutting areas and saving for potential investments in the future. It is proper for the Addis Ababa city government that a well-functioning budget control system can help businesses avoid financial problems by identifying potential problems early on and making better, data-driven decisions about how to allocate resources.
