VoxEU Column Macroeconomic policy Monetary Policy

The use and effectiveness of macroprudential policies: New evidence

Macroprudential policies are meant to reduce procyclicality in financial markets and associated systemic risks. However, empirical evidence on which policies are most effective is still preliminary and inconclusive. This column documents the use of macroprudential policies by a large set of countries over an extended period, and covering many instruments. It shows which policies are most effective in reducing the growth rates of overall credit and household and corporate sector credit, and explores differences across countries, degrees of avoidance, and whether policies work better during booms or busts. 

Macroprudential policies  ̶  such as caps on loan-to-value and debt-to-income ratios, limits on credit growth and other balance sheet restrictions, (countercyclical) capital and reserve requirements and surcharges, and Pigouvian levies  ̶  are meant to reduce procyclicality in financial markets and associated systemic risks (Brunnermeier et al. 2009). They have been used by emerging markets for some time and are starting to be used by advanced countries. A growing literature has documented the use of these policies across countries and analysed their effects (IMF 2013, Freixas et al. 2015, Schoenmaker 2014, Claessens 2015). While their use has grown, information on what policies have actually been used across a large set of countries and over a longer period of time is still lacking. As a consequence, relatively few comprehensive studies exist that cover many countries and longer periods and analyse what policies are most effective.

Macroprudential policies are receiving much more attention

In a recent paper (Cerutti et al. 2016), we document the use of macroprudential policies for a large number of countries (119) over an extended period (2000-13) and covering many instruments.1 We review which countries have used which policies most frequently and at what time. Using these data, we analyse which policies have been most effective in terms of reducing the growth of credit, covering both household and corporate sector credit. We also explore differences among types of countries – advanced versus emerging, and financially more open and more closed economies – as well as whether usage comes with greater cross-border borrowing, and if policies work better in different phases of the financial cycle.

The (evolving) toolkit

Many macroprudential tools have been proposed, and some were in use even before the Global Crisis. The toolkit available includes existing microprudential and other regulatory tools, taxes and levies, and new instruments. Most tools considered to date apply to the banking system, mainly given the presence of microprudential tools more easily adaptable to macroprudential objectives and the related more extensive theory and knowledge of these tools. But there are also macroprudential tools applicable to nonbanks and capital markets.

The macroprudential data here come from a recent and comprehensive survey, called Global Macroprudential Policy Instruments (GMPI), carried out by the IMF’s Monetary and Capital Department during 2013-2014 with responses received directly from country authorities, which was cross-checked with other surveys and material published to ensure a high quality dataset.2 The 12 specific tools covered are: general countercyclical capital buffer/requirement (CTC); leverage ratio for banks (LEV); time-varying/dynamic loan-loss provisioning (DP); loan-to-value ratio (LTV); debt-to-income ratio (DTI); limits on domestic currency loans (CG); limits on foreign currency loans (FC); reserve requirement ratios (RR); and levy/tax on financial institutions (TAX); capital surcharges on SIFIs (SIFI); limits on interbank exposures (INTER); and concentration limits (CONC).

Instruments are each coded for the period actually in place, i.e. as simple binary measures for whether or not they are in place.3 An overall macroprudential index (MPI) is the simple sum of the scores on all 12 policies. While tools can be grouped in many ways, one commonly used two-way classification is borrower-oriented tools (LTV and DTI ratios) and financial institution-oriented tools (DP, CTC, LEV, SIFI, INTER, CONC, FC, RR, CG, and TAX). Similarly to the overall macroprudential index, indexes are created for these two groupings. In the final sample, 119 countries – of which 31 are advanced, 64 emerging, and 24 developing – are analysed.

Actual use of policies

Over the period 2000-2013, countries generally increased their usage of macroprudential measures. As depicted in Figure 1, starting with an average macroprudential index of just above 1 in 2000 and ending at almost 2½ in 2013. In terms of tools, most countries have used concentration limits (CONC): in about 75% of the country-year combinations and evenly across country groups (Figure 2). This is followed by INTER (29%), RR_REV (21%), LTV_CAP (21%), DTI (15%), LEV (15%), TAX (14%), FC (14%), CG (12%), DP (9%), CTC (2%), and SIFI (1%).

There are large differences across countries. Usage is most frequent among emerging markets (see Figure 1), consistent with their higher exposure to external shocks, including from volatile capital flows. Developing countries come in second and advanced countries last, despite their recent increase in usage. CONC, INTER, and LEV, however, are consistently used by all countries alike. In terms of relative use (see Figure 2), LTVs are used relatively more by advanced countries, maybe due to their concerns about housing sector-related vulnerabilities, which are typically larger as mortgage markets are more developed. RR and FC are used more by emerging countries, maybe due to their concerns with large and volatile capital flows and related systemic risks; and DP and CG are used more by developing countries, which also rely relatively more on RR and FC.

Figure 1. The Macroprudential Policy Index by income level

Figure 2. The relative use of macroprudential policies over time by income group

Effects of macroprudential policies

Empirical cross-country panel analysis shows that, overall, macroprudential usage has significant mitigating effects on credit developments: a one standard deviation change in the macroprudential index – a change of 1.5, which is large relative to the mean of 1.8 – reduces credit growth by some 11 percentage points. This effect is strongest for developing and emerging markets, where a one standard deviation change in the macroprudential index reduces credit growth by 9 and 8 percentage points, respectively, equivalent to two thirds and one half its standard deviations. For advanced economies, effects are less; a one standard deviation change in the macroprudential index reduces credit growth by some 2 percentage points, equivalent to about one quarter of its standard deviation. Although still significant in open economies, policies are more effective for relatively closed economies, with a coefficient twice as large. As policies are consistently less effective in advanced and relatively open countries, avoidance appears a problem as financial systems become more sophisticated.

Type of macroprudential policies

Borrower-based measures are generally negatively related to credit growth, with effects highest for credit to households and in emerging markets. Financial institution-based policies are also associated with lower credit growth, especially in emerging and closed economies. While various borrower-based measures are negatively related to house prices, these effects are not statistically significant, consistent with other findings that house prices are difficult to moderate using macroprudential policies. Rather, borrower-based macroprudential policies can more usefully dampen household indebtedness, especially in advanced countries. This is important since as analyses have shown, house price booms associated with increased leverage are the most destructive.

Of the individual policies, caps on loan-to-value ratios are strongly associated in developing countries with lower overall credit growth, and with less household credit in all countries. Debt-to-income limits help as well, especially for household credit in both advanced and emerging markets, and corporate credit in emerging markets. Overall and confirming earlier results, direct limits appear very effective, especially for household credit. Foreign currency limits are negatively related to credit growth, especially in emerging markets and developing countries, to corporate credit growth, again especially in emerging markets, and to household credit in advanced countries. And for emerging markets (reserve requirement ratios are not used in advanced economies), reserve requirements affect strongly any type of credit, but especially corporate credit growth.

In terms of other policies, dynamic provisioning, almost exclusively used in emerging markets, has a negative relation with overall credit growth. Leverage and counter-cyclical requirements have negative effects in developing countries. Interconnection and concentration limits are negatively related to credit growth in all markets, with effects for interconnection driven by emerging markets and developing countries. Tax measures dampen growth in overall credit in developing countries and house prices in emerging markets. Otherwise, most other policies used are not significantly negatively related to credit and house prices’ growth. We do find that the greater use of policies is associated with more reliance on cross-border claims for open economies, with a one standard deviation increase in macroprudential index increasing the cross-border ratio by 6 percentage points, about one third of its standard deviation.

Taken together, our results suggest borrower-based measures have an impact for most countries, while foreign currency-related measures are more effective for emerging markets. This suggests some scope for targeted policies such as loan-to-value and debt-to-income ratios in advanced economies and foreign currency related policies in emerging markets. These are important findings given the adverse effects on overall financial and economic stability of real estate developments in advanced countries and of international capital flows for emerging markets. Findings suggesting evasion, however, do point to the need to consider countries’ circumstances, and to possibly adopt macroprudential and capital flow management policies simultaneously and in an integrated manner (see also Ostry et al. 2012).

Variations by country and phase of cycle

Further exploring whether effects of macroprudential policies vary by type of country, we find limited support for the view that (institutionally) more developed countries have greater ability to enforce policies and make them more effective. There is some evidence that open economies, having more flexible exchange rates, have greater difficulty to control overall credit, maybe as exchange rate appreciations (depreciations) related to capital inflows (outflows) further exacerbate domestic boom and bust financial cycles.

It can be expected that the effects of macroprudential policies vary by the intensity and phase of the financial cycle. For one, policies may be more effective when the financial cycle is more intense, i.e. if credit (or house prices) increases (or decreases) are greater. And, importantly, policies are meant to be mostly ex ante tools, that is, they should help reduce booms. To the extent that they are operative in busts, they are meant to limit declines in credit and asset prices. We investigate this by considering cases of exceptionally high (top 10% of the country-specific observations) or low (bottom 10%) credit growth. We find some support that policies have additional effects when credit growth is high, especially in more developed and financially open economies. There is also support for asymmetry in effects. Specifically, for the top 10% of credit growth, policies reduce credit, while for the bottom 10% they support growth, with these patterns existing for almost all groups of countries. This suggests that the effects of policies depend on the intensity and phase of the financial cycle.

Disclaimer: The views expressed here are those of the authors and do not necessarily represent those of the IMF, IMF policy, the Board of Governors of the Federal Reserve System, the ECB or the ECB Board.


Brunnermeier, M, A Crockett, C Goodhart, A Persaud and H Song Shin (2009) “The Fundamental Principles of Financial Regulation,” 11th Geneva Papers on the World Economy.

Cerutti, E, S Claessens, and L Laeven (2016), “The Use and Effectiveness of Macroprudential Policies: New Evidence,” Forthcoming, Journal of Financial Stability (also IMF WP 15/61).

Cerutti, E, R Correa, E Fiorentino, and E Segalla (2015), “Changes in Prudential Policy Instruments–A New Cross-Country Database,” Manuscript, International Banking Research Network.

Claessens, S (2015), “An Overview of Macroprudential Policy Tools,” Annual Review of Financial Economics (also IMF WP 14/214).

Freixas, X, L Laeven, and J-L Peydró (2015), Systemic Risk, Crises, and Macroprudential Regulation, MIT Press, Boston, Massachusetts.

International Monetary Fund, 2013, “Key Aspects of Macroprudential Policy,” IMF Policy Paper, June.

Ostry, J, A Ghosh, M Chamon, and M Qureshi (2012), “Tools for Managing Financial-Stability Risks from Capital Inflows,” Journal of International Economics, 88(2): 407-421.

Schoenmaker, D (2014), “Macroprudentialism – A new Vox eBook”, VoxEU.org, 15 December.


1 The data used in Cerutti et al. (2016) are available from http://www.imf.org/external/pubs/ft/wp/2015/Data/wp1561.zip.

2 The survey includes detailed information on the timing and use of different policies and to the best of our knowledge, is the most comprehensive cross-country database on policies to date.

3 We do not attempt to capture the intensity of the measures and any changes in intensity over time, nor whether and when instruments are actually binding. At a cost of a reduced coverage of countries (64 countries) and instruments (nine macroprudential indices), see Cerutti et al. (2015) for a dataset covering changes in the usage intensity overtime. 

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