The impact of banking sector stability on the real economy

Does banking sector instability damage the real economy? Or the other way round? This column presents data from 18 OECD countries between 1980 and 2008. It finds that banking sector stability appears to be an important driver of GDP growth in subsequent quarters. It argues that monetary policy should therefore pay more attention to banking sector soundness.

At the November 2008 meeting of the G20, just two months after the collapse of Lehman Brothers, the need for regulatory reform had already been clearly established.

“The IMF, the expanded Financial Stability Forum, and other regulators and bodies should develop recommendations to mitigate pro-cyclicality, including the review of how valuation and leverage, bank capital, executive compensation, and provisioning practices may exacerbate cyclical trends.”(G20 2008)

Yet while there may be consensus over the need for a stable banking system, there is far less certainty about whether the banking instability itself is a cause or an effect of economic crises and subsequent slowdowns (Kaminsky and Reinhart 1999, Demirgüç-Kunt and Maksimovic 1998, Rajan and Zingales 1986). In a recent study (Monnin and Jokipii 2010), we made an attempt to disentangle the links between real economy and banking sector.

How to measure banking sector stability?

Our first step is to find a good measure for banking sector stability. Much of the literature to date has focused on binary indicators, comparing crisis versus non crisis periods. It remains unclear, however, whether “normal” reductions in banking sector stability – i.e. a level of instability that can regularly be observed but that does not translate into a banking crisis – have a significant impact on growth. For example, consider a banking sector which suffers credit losses in a business cycle downturn but which is still able to function without external help. The stability of such a banking sector has clearly decreased after credit losses, but since it is still functioning, it is not in a fully fledged crisis either. To take into account such “normal” variations, we develop a continuous index to measure banking sector’s probability of default. We define instability as the probability of the banking sector becoming insolvent within the next quarter. This measure is based on Merton (1974) and we compute it for a sample of 18 OECD countries, over the 1980-2008 period.

Figure 1.

Figure 1 documents our measure of instability for the 18 countries in our sample. The closer the value is to zero, the greater the instability of the banking sector relative to the country historical mean. The horizontal lines represent the first and third quartiles of the distance to defaults of all countries. Figure 2 shows the number of countries with an unstable banking sector in each period. There are four periods in which more than a third of the countries simultaneously experienced instability in their banking sector:

1987Q4 (Black Monday and the following stock market crisis);
1990Q1-1993Q2 (housing crisis in several countries);
1998Q3 (Russian and LTCM crises); and
2008 (subprime crisis).

Figure 2.

What is the impact on the real economy?

Using a panel vector autoregressive (VAR) model (see Monnin and Jokipii 2010 for details), we find that banking sector stability is an important driver of future GDP growth. Periods of instability are generally followed by a decrease in real output growth. We also show that periods of banking sector instability have a greater impact on output growth than stable periods. This finding suggests that the link between banking sector stability and real economy is not smooth and continuous, but rather non-linear. Moreover, our results indicate that banking sector instability is generally followed by higher uncertainty regarding output growth.

Lessons for policymakers

Our finding that banking sector stability appears to be an important driver of GDP growth in subsequent quarters highlights the need for greater attention to be paid to banking sector soundness in the implementation of monetary policy. Some authors have already pointed out that bank related information can be used to improve macro forecasts. For example, several studies have shown that incorporating confidential supervisory information about bank health improves central bank forecasts of both unemployment and inflation (Peek et al. 1999, 2003; Romer and Romer 2000).

The empirical link between banking sector stability and output growth suggests that policymakers can exploit information embedded in our measure of financial sector stability to improve their economic forecasts, and hence their policy decisions. We test this hypothesis with Fed growth forecasts made between 1980 and 2001. We find that information regarding the stability of the banking sector can improve output growth forecasts. Our findings indicate that during periods of stability (instability), growth projections for the subsequent quarters appear to be significantly underestimated (overestimated). Once again, the relationship is driven predominantly by periods of instability.

References

Demirgüç-Kunt, A, and V Maksimovic (1998), “Law, finance, and firm growth”,Mathematical Finance, 53:2107-2137.

G20 (2008), “Washington Action Plan Progress”, g20.org

Kaminsky, G and C Reinhart (1999), “The twin crises: the causes of banking and balance of payments problems”, American Economic Review, 89:473-500.

Merton, R (1974), “On the pricing of corporate debt: the risk structure of interest rates”, Journal of Finance, 29:449-470.

Monnin, P and T Jokipii (2010), “The impact of banking sector stability on the real economy”, SNB Working Paper 2010-5.

Peek, J, ES Rosengren, and GMB Tootell (1999), “Is bank supervision central to central banking?”, Quarterly Journal of Economics, 114:629-655.

Peek, J, ES Rosengren, and GMB Tootell (2003), “Does the Federal Reserve possess an exploitable informational advantage?”, Journal of Monetary Economics,50:817-839.

Rajan, RG and L Zingales (1986): “Financial dependence and growth,” American Economic Review, 88:559-586.

Romer, CD and DH Romer (2000), “Pricing risk-adjusted deposit insurance: an option based model”, American Economic Review, 90:429-457.

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