The transmission of macroprudential policy in the tails

Álvaro Fernández-Gallardo, Simon Lloyd and Ed Manuel

Since the 2007–09 Global Financial Crisis, central banks have developed a range of macroprudential policies (‘macropru’) to address fault lines in the financial system. A key aim of macropru is to reduce ‘left-tail risks‘ – ie, minimise the probability and severity of future economic crises. However, building this resilience could influence other parts of the GDP-growth distribution and so may not always be costless. In our Working Paper, we gauge these potential costs and benefits by estimating the effects of macropru on the entire GDP-growth distribution, and explore its transmission channels. We find that macropru is effective at reducing the variance of GDP growth, and that it does so by reducing the probability and severity of excessive credit booms.

Measuring macroprudential policy changes

To estimate the effects of macropru, we first obtain a summary measure of policy actions. Unlike for monetary policy, there is no single macropru policy tool, or simple measure of the overall change in policy stance. So we construct a macropru policy index using the MacroPrudential Policies Evaluation Database (MaPPED). The database covers 480 policy actions taken between 1990 Q1 and 2017 Q4 for 12 advanced European economies, including the UK. The actions captured include bank-capital requirements, housing tools and risk weights.

Relative to other databases, such as the IMF’s Integrated Macroprudential Policy (iMaPP) database and the International Banking Research Network’s prudential policy database, MaPPED has several advantages for our purposes. In particular, the survey designed for MaPPED ensures that policy tools and actions are reported in the same manner across countries, allowing for cross-country comparability. Furthermore, MaPPED includes a wealth of information on each policy action, including announcement and enforcement dates, stance (loosening, tightening, or ambiguous), and whether it has a countercyclical design – which is crucial for our identification.

To construct our index, we follow the approach prevalent in the existing literature. Using the announcement date of each policy, we assign a value to each action, giving a positive value to tightening actions and a negative value to loosening actions. We assign different weights to different policy actions based on importance. Under this widely used weighting scheme, the first activation of each policy are given the highest weights. Changes to pre-existing polices are given lower weight.

The resulting index can be interpreted as a composite measure of the overall macropru policy in each of the selected advanced economies. We plot our macroprudential policy index at quarterly frequency over time for each country in the sample in Chart 1. The index displays significant heterogeneity across countries, reflecting the fact that different countries have chosen to tighten or loosen macropru to different extents over time.

Chart 1: Macroprudential policy indices by country

Identification: from correlation to causation

Armed with this macropru index in each country, we then address a second key challenge: identifying the causal effect of macropru on macroeconomic variables. In any statistical exercise, it is well-known that correlations between variables in the data do not necessarily capture causal relations: correlation is not causation. This issue is particularly pertinent in our setting, since macropru policy makers may respond to conditions in the macroeconomy.

Consider the following example. Suppose that a ‘tightening’ in macropru is effective at reducing financial-stability risks. But then suppose that policymakers only tighten macropru when they see financial stability risks rising. This could in turn mean that macropru is uncorrelated with measures of financial stability, since tighter macropru simply serves to offset any potential rise in financial stability risks. But this lack of correlation does not imply macropru has no causal effect – rather it would be evidence that macropru is an effective stabilisation tool.

To sidestep this issue, we use a ‘narrative identification’ approach. In particular, we use the fact that our data set includes a rich set of information on each macropru action – including whether policies were implemented specifically in response to changes in macroeconomic conditions. We strip out any policy that is implemented in response to the economic cycle, as this would run into the issue described above – labelling the remaining subset of macropru changes as macropru ‘shocks’.

To ensure our approach is ‘doubly robust’ we also control for a variety of variables that capture the state of the macroeconomy at the time macroprudential policies were implemented. This allows us to compare outcomes for different time periods and countries where macropru was set at different levels, despite underlying macroeconomic conditions being identical. Finally, we show that our results are robust to controlling for anticipation effects.

Three conclusions about the effects and transmission of macropru in the tails

Having dealt with identification issues, we then estimate the relationship between our macropru shocks and the entire distribution of the GDP distribution for all 12 countries in Chart 1 from 1990 to 2017. Like other studies, we rely on ‘quantile regression’, a statistical tool, to estimate this relationship. We regress GDP growth on our narrative macropru shocks as well as a range of macroeconomic control variables.

Our first finding is that tighter macropru significantly boosts the left tail of future GDP growth (reducing the probability and severity of low-GDP outturns, ie 1-in-10 ‘bad’ outcomes), while simultaneously reducing the right tail of GDP growth (reduces the probability of high-GDP outturns, ie 1-in-10 ‘good’ outcomes). Together, these effects serve to reduce the variance of future growth – making future GDP outcomes less extreme. Chart 2 demonstrates this visually, showing the distribution of future GDP growth in ‘normal’ times (blue), compared to a situation where policymakers tighten macropru (red). The effects on median growth (near the centre of the distribution) are muted, and generally insignificant. This suggests that tightenings in macropru to-date have not come at significant costs via restricting (mediN) GDP-growth.

Chart 2: Effect of macropru on GDP-growth distribution

Notes: Blue line shows distribution of 4-year-ahead GDP growth when all controls set to cross-country and cross-time average values, and macropru index is 0. Red line shows the same distribution when macropru index is +2.

We then repeat this exercise to look at the effect of macropru on intermediate outcomes such as credit growth and asset prices, instead of GDP, to unpick the transmission mechanisms. We find limited evidence for some of these channels. According to our results, macropru does not appear to significantly influence the composition of credit: we find macropru is effective at reducing excessive credit growth for both households and businesses. Moreover, we find limited evidence of transmission through asset prices (eg, financial conditions and house prices).

However, we do find an important role for the overall quantity of credit. This leads us to our second finding: that macropru is particularly effective at reducing the right tail of credit growth (reducing the probability of excessive credit ‘booms’, ie 1-in-10 high-credit-growth episodes), as Chart 3 illustrates.

Chart 3: Effect of macropru on credit-growth distribution

Notes: See Chart 2 notes.

We explore this result further, by assessing the extent to which high realisations of credit growth (formally, outturns above the 90th percentile of the credit-growth distribution) weigh on the left tail of GDP growth (formally, the 10th percentile of the GDP-growth distribution). To do so, we extend our quantile-regression framework to assess the extent to which the link between credit growth and the left tail of GDP growth changes when there is a credit boom (defined here as a realisation of credit growth in the top decile) or not.

The results from this exercise are shown in Chart 4, and highlight our third finding: faster credit growth (90th percentile or above) is associated with a significant reduction in the left tail (10th percentile) of annual average GDP growth and this effect is particularly strong when the economy is already experiencing a credit boom. This suggests that credit growth is strongly associated with a deterioration in the growth-at-risk over the medium term particularly in financial booms. Our empirical finding therefore suggests that the prevention and mitigation of credit booms plays a major role in explaining why macroprudential policy can be effective in defusing downside economic risks.

Chart 4: Effect of credit growth on left tail of GDP growth with and without credit booms

Notes: Estimated change in 10th percentile of annual average real GDP growth following a 1 standard deviation increase in credit growth when there is a ‘credit boom’ (two-year credit growth above its historical 90th percentile) and ‘no credit boom’ (two-year credit growth below its 90th percentile).


In this post, we have estimated the effects of macropru on the entire distribution of GDP growth by incorporating a narrative identification strategy within a quantile-regression framework. While macropru has near-zero effects on the centre of the GDP-growth distribution and therefore appears to have limited overall costs, we find that tighter macropru brings benefits. It does so by significantly and robustly boosting the left tail of future GDP growth, while simultaneously reducing the right. Assessing a range of potential channels through which these effects could materialise, we find tighter macropru reduces the probability of excessive credit booms, which, in turn, is important for reducing the probability and severity of future GDP downturns.

Álvaro Fernández-Gallardo is a PhD student at the University of Alicante. Simon Lloyd works in the Bank’s Monetary Policy Outlook Division. This post was written while Ed Manuel was working in the Bank’s Structural Economics Division.

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