Identification of determinants of corruption in government: a mar-spline approach
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- Category: Content №6 2022
- Last Updated on 25 December 2022
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Authors:
V.V.Bozhenko*, orcid.org/0000-0002-9435-0065, Sumy State University, Sumy, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
S.V.Lyeonov, orcid.org/0000-0001-5639-3008, Sumy State University, Sumy, Ukraine; Silesian University of Technology, Gliwice, the Republic of Poland; The London Academy of Science and Business, London, the United Kingdom of Great Britain and Northern Ireland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Ev.A.Polishchuk, orcid.org/0000-0002-6133-910X, Kyiv National Economic University named after Vadym Hetman, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
A.O.Boyko, orcid.org/0000-0002-1784-9364, Sumy State University, Sumy, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
N.O.Artyukhova, orcid.org/0000-0002-2408-5737, Sumy State University, Sumy, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.. edu.ua
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (6): 176 - 180
https://doi.org/10.33271/nvngu/2022-6/176
Abstract:
Purpose.Development of a scientific and methodological approach to the identification of the most impactful determinants on corruption using multivariate adaptive regression splines.
Methodology. Methodological tools of the research methods are comparison, grouping, bibliometric analysis, and multivariate adaptive regression splines in the form of piecewise linear functions.
Findings. Systematization of the literary sources and approaches for factors influencing corruption indicates that most empirical studies are based on using panel data. Panel data allows you to insert general patterns, but does not consider the patterns of the national economy. For the study on corruption in Ukraine, 15 influencing factors were selected, characterizing the institutional, economic and social environment. Based on the constructed MAR Spline models, three regression equations were obtained that describe the linear dependence of the level of corruption in governance on the selected factors. The paper found that the relevant factors influencing corruption in Ukraine are: tax burden, general government final consumption expenditure, average monthly wage in Governance and rule of law.
Originality.The proposed approach makes it possible to determine the dynamics of the degree of factor influence on the level of corruption in the country. The paper defines the threshold values of statistically significant indicators at which the maximum degree of correlation with the corruption perception index is achieved.
Practical value.The regularities between the level of corruption and economic, institutional and social factors revealed by the research results can be used in the development of tools to fight corruption in Ukraine. The formation of an effective anti-corruption system will strengthen financial stability in the country and increase the level of public trust in society.
Keywords:effective institutions, corruption, tax burden, regression splines, tax burden, living wage
References.
1. Transparency International (2021). Corruption Perceptions Index 2021. Retrieved from https://images.transparencycdn.org/images/CPI2021_Report_EN-web.pdf.
2. Nguedie, Y.H.N. (2018). Corruption, Investment and Economic Growth in Developing Countries: A Panel Smooth Transition Regression Approach. SocioEconomic Challenges, 2(1), 63-68. https://doi.org/10.21272/sec.2(1).63-68.2018.
3. Zolkover, A., & Georgiev, M. (2020). Shadow Investment Activity as a Factor of Macroeconomic Instability. Financial Markets, Institutions and Risks, 4(4), 83-90. https://doi.org/10.21272/fmir.4(4).83-90.2020.
4. Elbahnasawy, N.G., & Revier, C.F. (2012). The Determinants of Corruption: Cross-Country-Panel-Data Analysis.Developing Economies, 50(4), 311-333. https://doi.org/10.1111/j.1746-1049.2012.00177.x.
5. Linhartov, V., & Halskov, M. (2022). Determinants of corruption: a panel data analysis of Visegrad countries.Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51-79. https://doi.org/10.24136/eq.2022.003.
6. Cariolle, J. (2018). Corruption determinants in developing and transition economies: Insights from a multi-level analysis. Retrieved from https://ferdi.fr/dl/df-KeASzsunjnCeoXbcAAs1Szwh/ferdi-p229-corruption-determinants-in-developing-and-transition-economies.pdf.
7. Shabbir, G., & Anwar, M. (2007). Determinants of corruption in developing countries. Pakistan Development Review, 46, 751-764. https://doi.org/10.30541/v46i4iipp.751-764.
8. Yksel, S., Mukhtarov, S., Mahmudlu, C., Mikayilov, J.I., & Iskandarov, A. (2018). Measuring international migration in Azerbaijan. Sustainability, 10(1), 132. https://doi.org/10.3390/su10010132.
9. Yksel, S., Zengin, S., & Kartal, T. M. (2016). Identifying the Macroeconomic Factors Influencing Credit Card Usage in Turkey by Using MARS Method. China-USA Business Review,15(12), 611-615. https://doi.org/10.17265/1537-1514/2016.12.003.
10. Muharremi, O., Sal, M.J., & Hoxhaj, M. (2022). A Mixed-Methods Study of the Influence of Demographic Factors on Albanian Individual Taxpayers Ethical Beliefs Surrounding Tax Compliance.Business Ethics and Leadership, 6(1), 47-66. https://doi.org/10.21272/bel.6(1).47-66.2022.
11. Singh, S.N. (2021). Budgetary Management and Control of Finance and Economic Cooperation Organization in Mettu Woreda of Ilu Ababor Zone: An Assessment.Financial Markets, Institutions and Risks, 5(4), 106-127.https://doi.org/10.21272/fmir.5(4).106-127.2021.
12. Kaya, H.D., & Engkuchik, E.N.S. (2021). The Perception of Corruption Among Retailers in Central Asia and Eastern Europe During and After the 2008 Crisis. SocioEconomic Challenges, 5(2), 70-80.https://doi.org/10.21272/sec.5(2).70-80.2021.
13. Kushnarev, I.V. (2018). Political corruption as a system of illegal actions: variability of manifestations. Political life, 2, 50-54. https://doi.org/10.31558/2519-2949.2018.2.8.
14. Haque, E. (2019). Balancing Freedom of the Press and Reasonable Restrictions in Bangladesh: An Appraisal.Business Ethics and Leadership, 3(1), 80-100. https://doi.org/10.21272/bel.3(1).80-100.2019.
15. Elbahnasawy, Nasr G., & Revier, C.F. (2012). The determinants of corruption: Cross-country-panel-data analysis. The Developing Economies, 50(4), 311-333. https://doi.org/10.1111/j.1746-1049.2012.00177.x.
16. Juarez-Garcia, M.I. (2020). Personal Corruption & Corrupting Laws: Montesquieus Twofold Theory of Corruption. Business Ethics and Leadership, 4(4), 76-84. https://doi.org/10.21272/bel.4(4).76-83.2020.
17. Mujtaba, B.G., McClelland, B., Williamson, P., Khanfar, N., & Cavico, F.J. (2018). An Analysis of the Relationship between Regulatory Control and Corruption based on Product and Market Regulation and Corruption Perceptions Indices.Business Ethics and Leadership, 2(3), 6-20. https://doi.org/10.21272/bel.2(3).6-20.2018.
18. Jafarzadeh, E., & He, Shuquan (2019). The Impact of Income Inequality on the Economic Growth of Iran: An Empirical Analysis.Business Ethics and Leadership, 3(2), 53-62. https://doi.org/10.21272/bel.3(2).53-62.2019.
19. Lyeonov, S.V., Vasylieva, T.A., & Lyulyov, O.V. (2018). Macroeconomic stability evaluation in countries of lower-middle income economies. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (1), 138-146. https://doi.org/10.29202/nvngu/2018-1/4.
20. Kaya, H.D., & Lumpkin-Sowers, N.L. (2020). The Global Crisis And Crime: A Look Into Manufacturing Firms.SocioEconomic Challenges, 4(3), 66-76.https://doi.org/10.21272/sec.4(3).66-76.2020.
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