SavedAdd your journal

Journals / İstanbul Üniversitesi Sosyoloji Dergisi / 2022 / Cilt 42 • Ek Sayı 1 • Aralık 2022

Panel Data Analysis on the Socio-Economic Determinants of Corruption in the D-8 Countries

Fatih KARASAÇ
Halil KETE
Pages
117–131
Publication Date
2022-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The rate of inflation, GDP, and government spending did not have a significant relationship with corruption.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The phenomenon of corruption is a problem which has high negative externalities at the economic, sociological, and global levels.
📊
Key Findings & Data:Data was obtained from the period between 2003 and 2021 from the D-8 countries (which consist of Indonesia, Bangladesh, Iran, Egypt, Malaysia, Pakistan, Nigeria, and Turkey), before panel data analysis was conducted.
💡
Conclusion & Impact:The rate of inflation, GDP, and government spending did not have a significant relationship with corruption.
🔬Design:Research Article
📍Scope:2003 Verileri
⚙️Variables:Corruption · Determinants of corruption · D-8 countries

Abstract

The phenomenon of corruption is a problem which has high negative externalities at the economic, sociological, and global levels. Throughout history, corruption has expressed itself differently but has been present in nearly every society. It continues to affect many developed and developing societies today. The widespread public externality caused by corruption has been studied in various scientific fields, such as economics, finance, sociology, and psychology. The majority of the literature reveals that corruption negatively affects economic growth and development. Measuring corruption, a socioeconomic problem that is illegal, is difficult because there are challenges in identifying its determinants. The aim of this study is to conduct an empirical analysis of corruption with selected determinants. In the present study, the following the determinants of corruption were used: economic freedom, GDP, human development index, tax burden, and inflation. Data was obtained from the period between 2003 and 2021 from the D-8 countries (which consist of Indonesia, Bangladesh, Iran, Egypt, Malaysia, Pakistan, Nigeria, and Turkey), before panel data analysis was conducted. In the analysis, corruption was used as the dependent variable, while general government expenditure, economic freedom, GDP, human development index, total tax revenue as a percentage of GDP, and inflation were used as explanatory variables. The results of the analysis revealed that economic freedom, human development index, and the governments total tax revenue as a percentage of GDP positively affect the corruption perception index. The rate of inflation, GDP, and government spending did not have a significant relationship with corruption.

🔖

Cite This Article

KARASAÇ, F., KETE, H. (2022). Panel Data Analysis on the Socio-Economic Determinants of Corruption in the D-8 Countries. İstanbul Üniversitesi Sosyoloji Dergisi, Cilt 42 • Ek Sayı 1 • Aralık 2022, 117–131. https://ajindex.com/articles/882759

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (1)

  1. Akça, H., Ata, A. Y., & Karaca, C. (2012). Inflation and corruption relationship: Evidence from panel data in developed and developing countries. International Journal of Economics and Financial Issues, 2(3), 281-295. google scholar Akçay, S. (2000). Yolsuzluk, ekonomik özgürlükler ve demokrasi. Muğla Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (1), 1-15. google scholar Al-Marhubi, F. A. (2000). Corruption and inflation. Economics Letters, 66(2), 199-202. google scholar Alsarhan, A. A. (2019). Determinants of corruption in Middle East countries: Evidence from panel data. International Journal of Economic Behavior and Organization, 7(4), 57-63. http://dx.doi. org/10.11648/j.ijebo.20190704.11 google scholar Baltagi, B. H. (2005). Econometrics analysis of panel data (3th ed.). John Wiley & Sons. google scholar Banerjee, A., Mullainathan, S., & Hanna, R. (2012). Corruption (No. w17968). National Bureau of Economic Research. Working Paper 17968. google scholar Bayar, G. (2010). Corruption in Turkey-an econometric enalysis. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1(28), 105-131. google scholar Bitterhout, S., & Simo-Kengne, B. D. (2020). The effect of corruption on economic growth in the BRICS countries. A panel data analysis. University of Johannesburg, EDWRG Working Paper, 10-2022. google scholar Çelen, M. (2007). Yolsuzluk ekonomisi- kamusal bir kötülük olarak yolsuzluğun ekonomik analizi. İstanbul Serbest Muhasebeci ve Mali Müşavirler Odası Yayınları. google scholar Davies, A., & Lahiri, K. (1995). A new framework for testing rationality and measuring aggregate shocks using panel data. Journal of Econometrics, 68(1), 205-227. http://dx.doi.org/10.1016/0304-4076(94)01649-K. google scholar Farrales, M. J. (2005). What is corruption?: A history of corruption studies and the great definitions debate. (June 2005). http://dx.doi.org/10.2139/ssrn.1739962. google scholar Ghura, D. (1998). Tax revenue in Sub-Saharan Africa: Effects of economic policies and corruption (No. 98/135). International Monetary Fund. google scholar Greene, W. H. (2003). Econometric analysis. Prentice-Hall. google scholar Gründler, K., & Potrafke, N. (2019). Corruption and economic growth: New empirical evidence. European Journal of Political Economy, 60, 101810. google scholar Heritage Foundation. (2022). The heritage foundation. https://www.loc.gov/item/lcwaN0002695/. google scholar Hsiao, C. (2007). Panel data analysis—advantages and challenges. Test, 16(1), 1-22. http: