1. An empirical investigation of the relationship between government revenue, expenditure, and economic growth in selected EMEs
- Author
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Suresh, Anoop K, Seth, Bichitrananda, Behera, Samir Ranjan, and Rath, Deba Prasad
- Subjects
C40 ,panel co-integration ,O40 ,panel vector error correction ,General Engineering ,H20 ,economic growth ,Government revenue ,government expenditure ,H50 - Abstract
Purpose ─ This article explores the relationship between government revenue, government expenditure, and economic growth for nine emerging market economies using annual data from 1991-92 to 2019-20. Method ─ This paper distinguishes itself from the existing literature through the application of co-integration tests, vector error correction, DOLS and FMOLS for an empirical investigation of a unique panel data set of select emerging economies across Asia, Africa, Europe and Latin America. A bi-directional causal long-run relationship between economic growth and government expenditure, as well as between government expenditure and government revenue, was found using standard panel co-integration tests. Findings ─ The long-run elasticities computed using VECM were confirmed from DOLS as well as FMOLS estimates. A one per cent increase in expenditure and revenue, in the long run, would result in an increase in GDP by 0.94 and 0.90 per cent, respectively. Similarly, an increase in GDP by one per cent would lead to an increase in government expenditure by 1.1 per cent. On the other hand, an increase in government revenue by one per cent would cause a corresponding increase in government expenditure by nearly one per cent. The findings of this research point to a positive association between government revenue, expenditure, and economic growth, which will be valuable to policymakers. Contribution ─ Our combination of country selection covering economies from different continents is a first of its kind to the best of our knowledge. Another contribution is the application of panel cointegration and panel error correction techniques to fully use the panel data set, while most previous studies utilised the typical time series modelling with individual time series data.
- Published
- 2023