A Robust Approach for Identifying the Major Components of the Bribery Tolerance Index

Daniel Homocianu, Aurelian-Petruș Plopeanu and Rodica Ianole-Calin
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Daniel Homocianu: Department of Accounting, Business Information Systems and Statistics, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700505 Iaşi, Romania
Aurelian-Petruș Plopeanu: Humanities and Social Sciences Research Department, Institute of Interdisciplinary Research, Alexandru Ioan Cuza University of Iasi, 700107 Iaşi, Romania
Rodica Ianole-Calin: Faculty of Administration and Business, University of Bucharest, 030018 Bucharest, Romania

Mathematics, 2021, vol. 9, issue 13, 1-20

Abstract: The paper aims to emphasize the advantages of several advanced statistical and data mining techniques when applied to the dense literature on corruption measurements and determinants. For this purpose, we used all seven waves of the World Values Survey and we employed the Naive Bayes technique in SQL Server Analysis Services 2016, the LASSO package together with logit and melogit regressions with raw coefficients in Stata 16. We further conducted different types of tests and cross-validations on the wave, country, gender, and age categories. For eliminating multicollinearity, we used predictor correlation matrices. Moreover, we assessed the maximum computed variance inflation factor (VIF) against a maximum acceptable threshold, depending on the model’s R squared in Ordinary Least Square (OLS) regressions. Our main contribution consists of a methodology for exploring and validating the most important predictors of the risk associated with bribery tolerance. We found the significant role of three influences corresponding to questions about attitudes towards the property, authority, and public services, and other people in terms of anti-cheating, anti-evasion, and anti-violence. We used scobit, probit, and logit regressions with average marginal effects to build and test the index based on these attitudes. We successfully tested the index using also risk prediction nomograms and accuracy measurements (AUCROC > 0.9).

Keywords: bribery tolerance index; Naive Bayes; LASSO; maximum acceptable VIF; correlation matrices; cross-validations; minimum accuracy loss; mixed-effects; average marginal effects; risk prediction nomograms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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