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The origin of bias in sovereign credit ratings: reconciling agency views with institutional quality

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  • Huseyin Ozturk*

    (University of Leicester, UK)

Abstract

The 2008 financial crisis has served as a catalyst for re-examining the forces of systemic financial crisis, the leading of which was perceived to be the inaccurate credit ratings. Although sovereign credit ratings constitute a small part of the credit rating industry, the impact of unexpected downgrades or upgrades has a huge potential to distort a well-functioning financial system. There appeared plenty of researches in the literature that discussed the inaccuracy of sovereign credit ratings and argued that these measurements are negative biased toward developing countries. By using ordered response models, this paper investigates whether this bias stems partially from cross country variations in the quality of institutions. Measured by six different governance indicators namely "Control of Corruption", "Voice and Accountability", "Political Stability and No Violence", "Government Effectiveness", "Regulatory Quality", and "Rule of Law", institutional quality was found to have positive impact on the rating decisions. Although the simulation of the estimation results revealed that all the governance indicators reduce prediction errors significantly, "Government Effectiveness" and "Regulatory Quality" were predominantly responsible for low sovereign credit ratings.

Suggested Citation

  • Huseyin Ozturk*, 2014. "The origin of bias in sovereign credit ratings: reconciling agency views with institutional quality," Journal of Developing Areas, Tennessee State University, College of Business, vol. 48(4), pages 161-188, October-D.
  • Handle: RePEc:jda:journl:vol.48:year:2014:issue4:pp:161-188
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    1. Ozturk, Huseyin & Namli, Ersin & Erdal, Halil Ibrahim, 2016. "Modelling sovereign credit ratings: The accuracy of models in a heterogeneous sample," Economic Modelling, Elsevier, vol. 54(C), pages 469-478.
    2. Luitel, Prabesh & Vanpée, Rosanne & De Moor, Lieven, 2016. "Pernicious effects: How the credit rating agencies disadvantage emerging markets," Research in International Business and Finance, Elsevier, vol. 38(C), pages 286-298.
    3. Bart H. L. Overes & Michel Wel, 2023. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1273-1303, March.
    4. Erdem, Orhan & Varli, Yusuf, 2014. "Understanding the sovereign credit ratings of emerging markets," Emerging Markets Review, Elsevier, vol. 20(C), pages 42-57.
    5. Ben Hmiden, Oussama & Tatoutchoup, Didier & Nguimkeu, Pierre & Avelé, Donatien, 2024. "Discrepancy and cross-regional bias in sovereign credit ratings: Analyzing the role of public debt," Economic Modelling, Elsevier, vol. 131(C).
    6. Yalta, A. Talha & Yalta, A. Yasemin, 2018. "Are credit rating agencies regionally biased?," Economic Systems, Elsevier, vol. 42(4), pages 682-694.
    7. Tennant, David F. & Tracey, Marlon R. & King, Damien W., 2020. "Sovereign credit rating: Evidence of bias against poor countries," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    8. De Moor, Lieven & Luitel, Prabesh & Sercu, Piet & Vanpée, Rosanne, 2018. "Subjectivity in sovereign credit ratings," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 366-392.
    9. Bart H. L. Overes & Michel van der Wel, 2021. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Papers 2101.12684, arXiv.org, revised Jul 2021.
    10. Carlos Uribe-Teran & Santiago Mosquera, 2019. "Structural factors, global shocks and sovereign debt credit ratings," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(1), pages 104-126, January.
    11. A. Talha Yalta & Yasemin Yalta, 2018. "Are the Credit Rating Agencies Biased Against MENA Countries?," Working Papers 1274, Economic Research Forum, revised 19 Dec 2018.

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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