RDP 2019-05: Cost-benefit Analysis of Leaning against the Wind 6. Criticisms and Doubts

A common criticism of the research on leaning against the wind is that more complicated analysis would generate different conclusions. We have partially addressed this criticism in Section 5, where we showed that some concerns about the research can be quantified and turn out not to change the conclusions.

Other objections are less easy to assess empirically. Some commentators object to the lack of microeconomic foundations. Others worry that parameter estimates relating to a one-off intervention would not apply to an ongoing policy. Others are concerned about interaction with other policies. And so on. For an extension of these criticisms see BIS (2016). For a rebuttal see Svensson (2016, Appendix K). We do not repeat all these debates here. In the following subsections we discuss three criticisms that we regard as especially serious.

6.1 ‘Financial Crises May Permanently Reduce Productivity’

Gourio et al (2017), BIS (2016) and Borio (2016) suggest that financial crises may cause permanent reductions in output, possibly by misallocating resources, reducing trust, severing financial intermediation networks and lowering the level of productivity.

There are examples that both support and challenge this view. Productivity accelerated following the financial crises in Australia in the 1990s^[15] and the United States in the 1930s (Field 2003, 2012). However, it decelerated in many economies following the financial crisis of 2008 (on which, more below).

Formal research provides mixed results. A survey by the BCBS (2010, Annex 1) finds that banking crises are typically associated with huge long-lasting reductions in output. For example, the median estimate of the cumulative output loss is 63 per cent of annual pre-crisis GDP. However, some of the strongest research underlying this result is based on banking crises before the spread of deposit guarantees (e.g. Ramirez 2009) or under fixed exchange rates, and does not seem applicable to current institutional arrangements. Another difficulty is disentangling causation. In many cases a reduction in perceptions of longer-term growth rates causes the banking crisis, though Alfaro and Drehmann (2009) show that crises often arise from other reasons. Perhaps the most careful attempt to control for reverse causation is Cerra and Saxena (2008), who find that banking crises lower the long-run level of GDP by at least 4 per cent. In contrast, more recent research by Papell and Prodan (2012) finds little evidence that financial crises in advanced economies have long-term effects on either the level or the growth rate of GDP. In a detailed study of the recent US recovery, Fernald et al (2017, p 3) conclude that the growth in output has been relatively slow, but the shortfall was ‘largely—if not entirely—unrelated to the financial crisis’. Consistent with this, productivity growth has also slowed substantially in economies that did not suffer financial crises, such as Australia.

If one assumed that financial crises permanently lowered productivity, it would not follow that a longer-term analysis would show benefits of leaning against the wind. If monetary policy is neutral in the long run, leaning against the wind will not have a long-run effect on the level of real credit. The initial fall in real credit growth would eventually be followed by a period where credit growth is above its baseline level (and the probability of a crisis is higher), with the cumulative response summing to zero over the long run. Under standard assumptions, leaning against the wind simply shifts the probability of a crisis between periods, so that the undiscounted sum of benefits in the long run is approximately equal to zero.

Although it is difficult to say whether crises under current institutional arrangements would have permanent effects, evidence that effects are long-lasting is strong. Additional references include Terrones, Scott and Kannan (2009), Abiad et al (2009) and Reinhart and Rogoff (2014). This is reflected in our assumption that a representative crisis lasts for six years. This is in line with recent Australian and US financial crises, but well beyond the length of the average recession.

6.2 ‘Other Financial Variables are Excluded’

Another potential limitation of the cost-benefit comparisons is that the link between interest rates and the probability of a crisis occurring is assumed to be purely through real credit growth. However, interest rates may affect financial instability through different (and at least partly independent) channels. For example, empirical evidence suggests that changes in interest rates have an effect on the leverage of financial firms, risk-taking behaviour, asset prices and credit spreads.

These concerns raise two issues. First, if policy affects crises through other channels, then our focus on credit growth may understate total effects. Second, if other channels are important, then econometric regressions may suffer from omitted variables bias.

The results of Schularick and Taylor (2012, pp 1051–1052) support the assumption that the effect of interest rates on financial stability mainly occurs through credit growth. When they include real or nominal interest rates in addition to credit growth in their models predicting crises, they find that interest rate terms are insignificant or incorrectly signed^[16] – suggesting that channels other than credit are unimportant. Some channels (such as ‘risk-taking’ or commercial property) are difficult to quantify. But were these channels important, their effect might be expected to be evident in the reduced form.

Perhaps the leading alternative channel of transmission is some transformation of the credit-to-GDP ratio, which Pescatori and Laséen (2016) and Drehmann et al (2010) find has significant predictive power for financial crises. However, as discussed in Section 5.1.2, when we include the detrended credit-to-GDP ratio in our model, it does not qualitatively change the cost-benefit comparison. Pescatori and Laséen find similar results with Canadian data (see Figure 4 above).

A difficulty with assigning the credit-to-GDP ratio a direct or important role is that it does not appear to be significantly affected by monetary policy. Although interest rates affect the numerator, credit (as discussed in Section 3.3), they have similar and hence offsetting effects on the denominator, nominal GDP. Empirical estimates of the sign are variable and of the magnitude are small. See Svensson (2013), Alpanda and Zubairy (2014), Robstad (2014), Habermeier et al (2015, p 15), Bank of Canada (2016, Box 7), Bauer and Granziera (2017) and Gelain, Lansing and Natvik (2018).

The absence of a clear or substantial effect of policy on the credit-to-GDP ratio surprises many observers. Interest rates work by changing the price of credit, so one might expect credit-sensitive expenditure to respond more strongly than other expenditure. But that does not mean the stock of credit (the numerator) changes faster than GDP. It takes several years for changes in expenditure flows to have a large effect on the stock.

In addition to raising the probability of a crisis, a large credit-to-GDP gap might increase its severity. However, there is little evidence suggesting this effect is clear or large (Habermeier et al 2015, p 17). Flodén (2014) finds that a 1 percentage point lower debt-to-income ratio results in only a small rise in the unemployment rate associated with a crisis of 0.02 percentage points.

Jordà, Schularick and Taylor (2015) find that ‘housing bubbles’ are highly significant (in addition to the effects of credit growth) in a logit model explaining recessions associated with financial crises.

Similarly, when we add five annual lags of real house price growth to the preferred model of Schularick and Taylor (2012) and estimate on Jordà et al's (2015) dataset, the house price terms are marginally significant with a p-value of 8 per cent. Anundsen et al (2016) present similar results for 20 OECD countries using quarterly data.

These results raise important issues. As we discussed earlier, interest rates have small effects on real credit growth and no clear effect on the credit-to-GDP ratio. However, they have large effects on house prices (Saunders and Tulip 2019). So a significant effect of house prices on financial stability offers the prospect of substantially raising the benefit of leaning against the wind. Despite that, Alpanda and Ueberfeldt (2016) examine ‘leaning against housing bubbles’ in a structural model calibrated to the Canadian economy and conclude that it lowers welfare. A further complication is that it is often thought to be the ‘bubble’ component of house prices that gives rise to financial instability, whereas interest rates determine the ‘fundamental’ component. Exploration of this issue is beyond the scope of this paper, though we consider it to be a leading area for future research.

Apart from the two exceptions discussed above, a wide-ranging search within the literature has not found alternatives to simple models with credit growth that better predict financial crises. This does not necessarily mean that other channels are not important in explaining financial instability. Instead, it could be that these other indicators of financial instability are highly correlated with credit growth, in which case credit growth would be a sufficient indicator for financial stability risks.

6.3 ‘Policy Should Lean against the Financial Cycle’

The BIS argues that the benefits of leaning against the wind increase with financial imbalances. As we show in Section 5.1.2, the net benefits of leaning against the wind are greatest when the detrended credit-to-GDP ratio is high. In this sense, the standard view can be extended so that monetary policy should ‘react to the cycle’.

Filardo and Rungcharoenkitkul (2016) provide alternative estimates in which ‘leaning against the financial cycle’ is worthwhile. An important factor underlying this conclusion is their estimate that a financial boom, as observed in the United States in 2006, gives rise to a probability of a ‘bust’ occurring of 10 to 20 per cent each quarter (their Figures 5 and 6). These estimates translate to a four-quarter-ahead probability of 35 to 60 per cent. A ‘bust’ is defined as a downtrend in the financial cycle indicator sufficient to drive output 4.5 per cent below potential.

These estimated probabilities are problematic for several reasons. They are an order of magnitude higher than other estimates of the probability of a financial crisis, such as Schularick and Taylor (2012), Habermeier et al (2015) or Pescatori and Laséen (2016).^[17] However, that information is assumed to not affect asset prices, monetary policy or prudential supervision. The conclusions appear to be sensitive to these assumptions.

Filardo and Rungcharoenkitkul's (2016) approach is unusual in many ways and it is not clear which feature of their work explains why their probability estimates are so high. One possibility is that they estimate a large number of free parameters over a sample containing only three financial busts, leading to large ex post predictability or ‘overfitting’. Another possibility is that the financial cycle indicator is mean-reverting by construction, which naturally leads to large estimates of the conditional probability of a bust near the peaks of the financial cycle. Kockerols and Kok (2019) include the financial cycle in a more conventional model and find that costs of leaning against the wind are much greater than benefits.

In a closely related argument, Borio and the BIS emphasise the need for leaning against the wind to occur early, before the peak of the cycle. This argument rests on the assumption that changes in the cycle are persistent and predictable. They view this argument as a criticism of the standard approach, but we view it as addressing a separate question: specifically, when should monetary policy lean against the wind or, more precisely, in response to what? In contrast, our focus is on whether it should do so.

Footnotes

GDP per hour worked grew by 1.1 per cent a year from 1980:Q1 to 1989:Q4 compared with 2.2 per cent from 1990:Q1 to 1999:Q4. [15]

Results for real interest rates are from Schularick and Taylor's online documentation, see footnote 7. [16]

For example, based on Schularick and Taylor's logit model (with fixed effects), the annual probability of a crisis starting in the United States in 2007 was 6 per cent. The highest estimated probability of a crisis starting in the United States was in 1957, at 10 per cent. [17]