In this chapter, we estimated the value of Government Expenditure on Education (GEE) in the context of Environmental, Social and Governance (ESG) dataset of the World Bank. We used data from 193 countries in the period 2011-2020. We used Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. Our results show that the value of GEE is positively associated among others to “Case of Death, by communicable disease and maternal, prenatal and nutrition conditions”, and “Unemployment”, and negatively associated among others to “Hospital Beds” and “Government Effectiveness”. 

Furthermore, we applied the k-Means algorithm optimized with the Elbow method and we found the presence of four clusters. Finally, we confronted eight machine learning algorithms for the prediction of the future value of GEE. We found that the polynomial regression is the best predictive algorithm. The polynomial regression predicts an increase in GEE of 7.09% on average for the analysed countries.

Keywords: collective decision-making; education; legislatures and voting behaviour; corruption; policy formulation.

JEL Classification: D70; D72; D73; D78. 

Cite this chapter:

Leogrande, A. and Costantiello, A. (2023). The Impact of Government Expenditure on Education in the Environmental, Social and Governance Models at World Level. In L., Nicola-Gavrilă (Ed), Digital Future in Education: Paradoxes, Hopes and Realities (190-209 pp.). ISBN: 978-606-95516-1-5. Book Series Socio-Economics, Research, Innovation and Technologies (SERITHA) ISSN: 3008-4237. https://doi.org/10.57017/SERITHA.2023.DFE.ch9

Chapter’s history: 

Received 11th of May, 2023; Revised 17th of June, 2023; Accepted for publication 20th of July, 2023; Published 30th of September, 2023.