The relationship between central bank independence and systemic fragility: global evidence

Abstract

This study investigates the relationship between central bank independence and financial stability in a global sample covering 56 countries from 1980 to 2012. We find strong and robust evidence that central bank independence and its four dimensions (personnel independence, financial independence, policy independence, and central bank objectives) are negatively associated with bank systemic risk. In addition, the results indicate that the reductive effect of central bank independence on systemic risk is more pronounced during actual episodes of banking crises. Moreover, our results suggest that the democratic environment plays a vital role in moderating the central bank independence − systemic risk nexus.

Keywords:

• central bank independence
• systemic risk
• democracy

JEL:

• G21
• E58

Acknowledgements

We thank Anil V. Mishra, An Nguyen, Tuan Van Nguyen, Tuan Doan, and participants in a DLU research seminar for insightful comments on an early version of this paper. Duc Nguyen greatly appreciates Robert Faff for his invaluable instruction during the “Pitching Research” section at Western Sydney University, Australia. Duc Nguyen dedicates this article to his two beloved sons (Bond and Pi). Any remaining errors are our own responsibility.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Acharya and Richardson (2009) define systemic risk as the joint failure of financial institutions and capital markets that considerably shortens the supply of capital to the real market.

2. For example, see Trump vs. FED [at https://www.nytimes.com/2019/11/18/business/economy/trump-powell-fed.html]; Argentina’s central bank chief, Martin Redrado, and the government in Acharya (2018); a case about the Bank of England in Balls et al. (2018); and some cases discussed in Blinder (2010).

3. We thank an anonymous reviewer for suggesting this point.

4. Danı́elsson (2002) suggests that macroprudential regulations targeting individual risk in financial institutions are insufficient to prevent financial crises.

5. MES is used in various empirical studies targeting the systemic stability of not only banks (e.g, Engle et al., 2014; Karolyi et al., 2018; Nguyen, 2020; Silva-Buston, 2019) but also non-financial corporations (e.g, Dungey et al., 2022).

6. Meanwhile, the ∆CoVaR (Adrian & Brunnermeier, 2016) requires various types of market data such as the Chicago Board Options Exchange volatility index (VIX), three-month Treasury bill rate and repo rate, the credit spread between BAA-rated bonds and the Treasury rate (see, Adrian & Brunnermeier, 2016; Anginer et al., 2018; Laeven et al., 2016). Data limitation prevents us from using ∆CoVaR because market-based data are unavailable for most economies, especially during the 1980s and 1990s.

7. Arnone et al. (2006) provide a review of various measures of CBI.

8. See, Acharya (2018) for some examples of how governments undermine the independence of central banks and the consequences of such interventions.

9. See, Garriga (2016) online appendix for the variables used to construct each component.

10. There is a drawback when gathering financial information from Datastream following this procedure as researchers cannot obtain data of inactive banks.

11. We conduct the Hausman test to determine the appropriate technique. The Hausman test outcomes (Chi-squared statistics = 331.42, p-value = 0.0000) support the utilization of the fixed effects technique instead of random effects.

12. The Global Financial Development Database provides data on Private credit by deposit money banks to GDP. Hence, LOAN_t = Private credit by deposit money banks to GDP_t *GDP_t.

13. We thank an anonymous reviewer for suggesting this control variable. The data to construct this variable is publicly available at https://www.imf.org/external/np/fin/tad/query.aspx

14. This approach is widely applied in banking literature, for example, Phan et al. (2020).

15. See the rule of thumb to calculate the weighted version of CBI in Garriga (2016).

16. We thank an anonymous reviewer for suggesting this test.

17. For example, banking regulations and other legal frameworks at the country level. Unfortunately, large databases on banking regulations such as Barth et al. (2013) do not fully cover our sample, which spans from 1980 onwards.

18. We thank an anonymous reviewer for recommending this approach.

19. To save space, the full estimates of the first stage are not shown, but they are available on request.

20. The coefficients are the linear combination of CBI (and each component) and the interaction term at two values of crisis variable.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.