The fight against corruption at global level. A metric approach

Lucio Laureti, Alberto Costantiello () and Angelo Leogrande

MPRA Paper from University Library of Munich, Germany

Abstract: In this article we estimate the level of Control of Corruption for 193 countries in the period 2011-2020 using data from the ESG World Bank Database. Various econometric techniques are applied i.e.: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS. Results show that “Control of Corruption” is positively associated, among others, to “Government Effectiveness” and “Political Stability and Absence of Violence/Terrorism”, while it is negatively associated among others to “Agriculture, Forestry, and Fishing Value Added as Percentage of GDP” and “GHG Net Emissions/Removals by LUCF”. A cluster analysis implemented with the k-Means algorithm optimized with the Elbow Method shows four clusters. A confrontation among eight Machine Learning algorithms is proposed for the prediction of Control of Corruption. Polynomial Regression is the best predictor for the training data. The level of Control of Corruption is expected to growth by 10.36% on average.

Keywords: D7; D70; D72; D73; D78. (search for similar items in EconPapers)
JEL-codes: D70 D72 D73 D78 (search for similar items in EconPapers)
Date: 2022-12-30
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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