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Journal of Financial Services Research

Erschienen in:

25.02.2017

Institutions and Deposit Insurance: Empirical Evidence

verfasst von: Kathryn L. Dewenter, Alan C. Hess, Jonathan Brogaard

Erschienen in: Journal of Financial Services Research | Ausgabe 3/2018

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Abstract

Do banks’ responses to changes in deposit insurance vary across countries even if the countries have comparable institutions? If so, by how much? Using data on the financial performance of large banks in 15 financially and economically developed countries, we find that where deposit insurance has an effect, it is large and varies depending on the level of economic freedom, rule of law and corruption in the bank’s home country. As in prior papers, we show that during stable economic periods, increases in deposit insurance are associated with higher bank risk, both problem loans and leverage. In most, but not all, cases stronger institutions temper these effects. The institutions’ effects are substantial. For example, average changes in the rule of law double the impact of a change in deposit insurance on bank leverage. We contribute to the substantial literature in this area by showing that the institutional effects are significant even across a set of countries with comparable institutions; by conducting a careful calibration of the economic significance of the effects; by providing evidence that during stable periods changes in deposit insurance only affect bank risk and not other measures of performance; and finally by showing that the effects of both deposit insurance and institutions vary across stable and crisis economic periods. The stable period results are consistent with the moral hazard effects of deposit insurance, while the crisis period results are consistent with endogeneity concerns that poor bank performance could drive changes in regulations.

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See Merton (1977), Buser et al. (1981), Dewatripont and Tirole (1994), Benston and Kaufman (1996), and Cordella and Yeyati (2002) for theory papers. See Supplementary Appendix Table A1 for a review of empirical papers, such as Cull et al. (2005) and Hovakimian et al. (2003), who provide evidence that institutional factors affect the relation between deposit insurance and bank performance.

The effect of changes in deposit insurance on banks’ actions is a first-order effect. The effects of institutions on how banks respond to changes in deposit insurance is a second-order effect that is equivalent in a regression model to a dependence of the regression coefficients on institutional factors.

In econometric terms, the statistical significance of the estimated coefficients in a regression increases with the dispersion of the regressors, Greene (2003) p. 46. We want to know if the effects of institutions on deposit insurance are still significant in a sample which has a small dispersion of regressors due to the similarity of the institutions.

Estimates are taken from their Table 1.

The EU began setting common minimum standards for deposit insurance in 1994. Saunders (2014) provides an update of the efforts towards banking union with respect to a common supervisor, private deposit insurance system and failure/bailout/restructuring system. In January 2011, the European Banking Authority (EBA) was established with the main task of contributing “to the creation of the European Single Rulebook in banking whose objective is to provide a single set of harmonized prudential rules for financial institutions throughout the EU.” See European Banking Authority website at: http://www.eba.europa.eu/about-us;jsessionid=9CD5011E33485E62B3DF287CC500E620

Market forces may limit risk taking at banks that have substantial amounts of uninsured deposits and funding. Gropp and Vesala (2004) provide evidence from banks in Europe that if deposit insurance credibly leaves out non-deposit creditors, then monitoring by uninsured subordinated debt holders could reduce banks’ risk taking. Even if large, uninsured depositors do not monitor, Hannan and Hanweck (1988) show they require higher returns to compensate them for their greater risks. The Too-Big-To-Fail (TBTF) doctrine may negate the incentives of large, uninsured depositors to monitor and require higher returns. O’Hara and Shaw (1990) report a significant increase in the stock prices of U.S. banks that the Wall Street Journal declared to be TBTF in 1984.

For example, a November 2005 review of the Basel 2 Framework, found at the BIS website http://www.bis.org/publ/bcbs118.htm, says “the Framework also allows for a limited degree of national discretion in the way in which each of these options may be applied, to adapt the standards to different conditions of national markets. These features, however, will necessitate substantial efforts by national authorities to ensure sufficient consistency in application. The Committee intends to monitor and review the application of the Framework in the period ahead with a view to achieving even greater consistency. In particular, its Accord Implementation Group (AIG) was established to promote consistency in the Framework's application by encouraging supervisors to exchange information on implementation approaches.”

See “EU Commission to push tighter trading controls” by M. Dalton in the 9–21-10 Wall Street Journal; “CFTC, EU vow cooperation on swaps oversight” by S.N. Lynch in the 11–2-10 Wall Street Journal; and “Bank group seeks task force for wind-down system” by A. Bradbery in the 5–24-10 Wall Street Journal.

See the EBA website, http://www.eba.europa.eu/about-us/work-programme/current-work-programme .

Romano’s (2012) main argument is that harmonized regulations increase systemic risk by inducing financial institutions to follow similar strategies. Acharya (2003) and Neuberger and Rissi (2012) also argue that institutional context affects the impact of banking regulations. Qian and Strahan (2007) discuss how institutions affect financial contracts (bank loans).

This value is from 2010 data for our sample banks and for country-level total bank assets from the OECD. Note that the OECD numbers include all banks, broadly defined and including foreign owned, while our sample only includes domestic commercial banks.

We use data from bank holding company financial statements. The one exception is Bank of America where we use the BOA commercial bank statements because the holding company statements included a relatively large and changing share of non-commercial bank subsidiary activity. If we exclude Bank of America from the analyses the inferences do not change. Approximately two-thirds of the banks in our sample adopted IFRS accounting standards some time during our sample period. In an analysis reported in the Supplementary Appendix, we show that inferences with respect to the relation between deposit insurance and bank performance for the IFRS subset are no different than for the full sample across the crisis period. Across the stable year comparison, the IFRS sub-set shows a stronger relation between deposit insurance and leverage than the full sample.

Since the financial crisis, regulators have been placing increasing emphasis on leverage as an indicator of risk. The Group of Twenty finance ministers and central bank governors, in their September 2009 Communique, said they “supported the introduction of a leverage ratio as a supplementary measure to the Basel II risk-based framework …” (source: Financial Times 9/25/09). The Basel III framework “introduced a simple, transparent, non-risk based leverage ratio to act as a credible supplementary measure to the risk-based capital requirements” (p. 1 of “Basel III leverage ratio framework and disclosure requirements,” Basel Committee on Banking Supervision, January 2014). We do not use z-score as a measure of risk because we do not have enough time series observations to reliably calculate it for each sub-period in our analysis.

Demirguc-Kunt et al. (2005) provides dates for some regulatory changes.

As discussed in the next paragraph, there are sound econometric reasons to analyze the data with first differences. In addition, the lack of an annual time series for the regulatory indices precludes analyses that rely on annual time series data.

Supplementary Appendix Table A1 presents examples. For example, Gonzalez (2005) examines civil versus common law legal systems; Hovakimian et al. (2003) economic freedom; Distinguin et al. (2011) and Laeven (2002a, b) law and order; Demirguc-Kunt and Detragiache (2002) corruption.

Supplementary Appendix Table A4 provides the correlation matrix for all variables used in the regressions for values over the stable period years and over the crisis period years. Less than 10% of the correlations are above 0.50, with only one pair above 0.90, for RuleofLaw and Corruption. In first differences, this correlation falls to 0.447.

Stilgitz and Weiss (1981) present a model in which a bank’s bad loans increase as it increases the interest rate it charges on loans. This occurs because as the bank increases its lending rate its potential borrowers are only from the pool of risky borrowers (hidden information effect) and its actual borrowers have incentives to substitute high risk for low risk projects (hidden action effect). Both of these cause bad loans to increase with the bank’s lending rate.

We conduct redundant variable tests of the model’s regressors and reject the null hypothesis that their joint effects are zero. Results available from the authors.

Short term T-bill rates are not available for Australia, Austria, Finland, Netherlands and Norway, so we use the shortest term government bond rates available.

Australia’s deposit rate is supplied by the Reserve Bank of Australia.

Rates for Australia, Austria, Canada, France, Germany, Sweden, Switzerland, the UK and the US are from Datastream Corporate bond yield, middle rate. For the other seven countries, we use bank lending rates from the Economist Intelligence Unit.

This measure, based on a model developed by Panzar and Rosse (1987), estimates how much changes in input prices are reflected in revenue earned by banks. We do not have access to enough observations to calculate this measure over our sample period for all of the countries. We use the Bikker and Spierdijk (2008) values as of the end of their sample period, which spans the late 1980s to 2004, because it provides a value as of the middle of our sample period.

The coefficient estimates for the SUR are available in the Supplementary Appendix, Table A5.

The insignificant interest expense result is inconsistent with Demirguc-Kunt and Huizinga (2004) who find explicit deposit insurance decreases interest expense (defined as interest expense/interest paying debt) in a sample of 51 countries. The insignificant interest revenue and interest expense result is consistent with Demirguc-Kunt et al. (2004) who find deposit insurance does not affect the net interest margin when controlling for the macro and regulatory environment in a sample of 72 countries. The overhead result is consistent with Demirguc-Kunt et al. (2004) who find that bank regulations have no explanatory power for overhead costs when controlling for economic freedom or property rights protection in a sample of banks from 72 countries.

Consistent with Keeley (1990), who shows that banks with higher franchise values are less risky so as to protect the value of the franchise, Caprio et al. (2007) and Laeven and Levine (2009), our results show that banks with a large shareholder have smaller increases in leverage when deposit insurance increases. This may be because large shareholders are restricting leverage so as to reduce financial risk.

If we just insert the fitted-Q value into the A/K and NPL specifications, the estimation results in a near singular matrix. To eliminate the near singular matrix, we exclude separate year intercepts and the 4 interest rate variables for the A/K and NPL specifications. The significant relation in the A/K specification is inconsistent with Gonzalez (2005) who finds no relation between fitted values of Tobin’s Q and bank risk taking in countries with strong institutions (total sample is 36 countries).

The p-value for the d(MVE/BVE) F-test statistic of 8.48 equals 0.13.

Anginer et al. (2014) show bank risk and systemic fragility are lower in the crisis period and conclude that the moral hazard effect of deposit insurance dominates in good times while the stabilization effect dominates in turbulent times. Our finding of (insignificantly) lower NPL is consistent with their risk finding.

As of August 2016, the EC website, Europa, lists no further actions towards deposit insurance harmonization after a July 2010 proposal to meet the €100,000 coverage level listed in Directive 2009/14/EC. See “European Union: Publication of Financial Sector Assessment Program Documentation – Technical Note on Deposit Insurance” in IMF Country Report No 13/66 March 2013. Also, “Commission proposes package to boost consumer protection and confidence in financial service,” IP/10/918, Brussels 12 July 2010. Accessed at: http://europa.eu/rapid/press-release_IP-10-918_en.htm.

These values are calculated from Gross Loans, from BIS, and the ratio of nonperforming loans to gross loans from the World Bank.

Total capital values are not available for Switzerland, so calculations of change in NPL relative to capital exclude this country.

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Titel: Institutions and Deposit Insurance: Empirical Evidence
verfasst von: Kathryn L. Dewenter
Alan C. Hess
Jonathan Brogaard
Publikationsdatum: 25.02.2017
Verlag: Springer US
Erschienen in: Journal of Financial Services Research / Ausgabe 3/2018
Print ISSN: 0920-8550
Elektronische ISSN: 1573-0735
DOI: https://doi.org/10.1007/s10693-017-0271-8