The relationship between fiscal policy and economic growth: The case of Serbia

Purpose: The main objective of this paper is to examine the potential cointegration relationship and causality between economic growth and fiscal parameters (public expenditures and revenues) in the Republic of Serbia. Additionally, the aim is to analyze the long-term and short-term effects of fiscal parameters on economic growth. Methodology: Methodologically, the paper applies time series analysis. First, stationarity is tested using the Ng-Perron test, followed by cointegration testing using the Johansen and Bayer-Hanck tests. Finally, VEC (Vector Error Correction) modeling enables the examination of long-term effects and short-term causality. Findings: The analyzed variables are integrated of order one, I(1). In addition, cointegration between them has been established. The results of the VEC model show that an increase in government expenditures reduces economic growth in the long run, while an increase in government revenues enhances it. The causality test showed that changes in government expenditures cause changes in economic growth in the short run. Originality/value: To the best of the author's knowledge, this is the first study in Serbia to address such a specifically formulated objective using specific econometric methods. Practical implications - The results obtained carry practical implications. The negative effect of government expenditures on economic growth indicates low government efficiency, a higher level of corruption, and a lack of institutional quality. In this sense, the recommendations point toward increasing the efficiency of government policies, along with designing an appropriate structure of public spending-focusing on essential services such as education, and healthcare. Limitations: The key limitations relate to the selection of only aggregated variables. In this paper, consolidated public revenues and expenditures were used as fiscal indicators. In the context of future research, it would be interesting to observe these indicators in a disaggregated manner, in order to enable a more targeted analysis and to test the robustness of the obtained results.