A. Policy Interactions—Conceptual Framework

5. Recent advances in analytical modeling offer a simple conceptual framework for thinking about policy interactions between monetary and macroprudential policies. This literature examines the interaction between macroprudential and monetary policy in theoretical (DSGE) models with borrower collateral constraints and a banking sector. In these models, monetary policy controls the risk free interest rate and macroprudential policy the risk premium, or the spread between lending rates and the risk free rate.¹

6. A basic result is that, in the presence of macroprudential policy, it is optimal for monetary policy to stay focused on price stability. In particular, the optimal calibration of the reaction of monetary policy to output and inflation does not change markedly when macroprudential policy is also used, and instead remains close to that commonly found in traditional models without financial sector distortions or macroprudential policy.

7. In practice, macroprudential policy may not be fully effective in containing systemic risk. The assumption made by the models is that the available macroprudential instrument is perfectly targeted and fully offsets financial shocks. In practice, this is unlikely to be the case. For instance, political economy considerations may limit the deployment of certain, unpopular, macroprudential instruments, in particular when use of the instrument has strong distributional implications. In addition, institutional arrangements may limit the frequency with which macroprudential policy may be used, as when parliamentary or political approval is required to reset an instrument.

8. As a result, monetary policy may still need to respond to financial conditions. Indeed, in models where macroprudential policy is absent or time invariant, but in the presence of financial sector distortions, it is optimal for monetary policy to consider financial shocks.² In such contexts, optimal monetary policy responds to the growth in credit (in addition to the output gap and deviations of inflation from target).³ By extension, when macroprudential policy is imperfectly targeted, it can be desirable for monetary policy to respond to financial conditions.

9. In these models, having two policies to achieve both price and financial stability enhances welfare. Moreover, in practice, especially when capital accounts are fully open, achieving both objectives with one instrument may not be feasible. For example, when capital inflows appreciate the currency and lead to imbalances, increases in policy rates cannot reduce incentives for (foreign exchange) wholesale funding or credit expansion. Conversely, the literature shows that when monetary policy is constrained or absent, using macroprudential policy in the place of monetary policy to control output and inflation is inefficient and costly, as it severely constrains the financial sector and output.⁴

10. More generally, the literature points to synergies, rather than conflicts, even if the optimal policy mix can vary with the type of shock hitting the economy. In the presence of a financial shock, most models imply that only macroprudential policy should be used since it is more targeted at the distortion.⁵ In the presence of aggregate demand (preference) shocks that induce an increase in both credit and inflation, both policies are tightened, complementing each other in responding to the shock. In the presence of productivity shocks, conflicts can arise since a positive supply shock can lead asset prices and credit demand to rise but dampens goods market inflation. The optimal policy mix then depends on the strength of the externality from increases in credit to aggregate financial risks. If this externality is strong, the accommodative monetary policy response to the productivity shock is complemented by targeted macroprudential policy to contain the buildup of leverage that may be induced by the shock.⁶

11. While structural models offer clear insights into policy interactions, their downside is their simplicity. This includes the abstraction in the majority of cases from modeling the side effects of monetary policy on financial stability, described in the next section; and the lack of realistic modeling of the transmission of macroprudential instruments to financial and output stability, which is explored further below. Moreover, the adaptation and calibration of models to country circumstances is often yet to be undertaken and hindered empirically by limited (cross-country) experiences with both policies.

B. Monetary Policy and Side Effects on Financial Stability

12. It has long been understood that monetary policy can affect financial stability. This section offers a taxonomy of these—beneficial or adverse—effects. It also considers the factors that may impact the strength of the effects and explores how well-designed macroprudential policies have the potential to contain the adverse effects of monetary policy on financial stability.

13. Building on the financial markets’ imperfections literature, there are a number of channels by which monetary policy can affect financial stability. It can affect the tightness of borrowing constraints and likelihood of default; the risk seeking incentives of intermediaries; and externalities operating through aggregate price variables, such as asset prices and exchange rates. Table 1 shows for each channel the prediction from theoretical models of the effects of changes in the monetary policy stance on financial stability. It also summarizes related empirical evidence.⁷ An appendix in the main paper reviews the empirical evidence in more detail.

Table 1.

Monetary Policy Effects on Financial Stability^1/

Source: IMF.

^1/↓ r means a decrease of policy rates, î r means an increase of policy rates, ““ means a decline of stability, ““ an improvement, and “X” no statistically significant effect.

Table 1.

Monetary Policy Effects on Financial Stability^1/

Sources of Financial Instability	Channel	Predicted Effect ( improves stability)		Selected Empirical Evidence
		↓ r	↑ r
Borrowing Constraints	Balance Sheet (default) Channel			Sengupta (2010) Jiménez and others (2009) Gertler and Gilchrist (1994) Asea and Blomberg (1998)	↑ r, ↑ r, ↑ r, ↑ r,
Risky Behavior of Financial Institutions	Risk-taking Channel			Jiménez and others (2009) Ioannidou and others (2009) Merrouche and Nier (2010)	↓ r, ↓ r, X
	Risk-shifting Channel			Gan (2004) Landier and others (2011)	↑ r, ↑ r,
Externalities through Aggregate Prices	Asset price Channel			Altunbas and others (2012) Del Negro and Otrok (2007) IMF (2009)	↓ r, ↓ r, X
	Exchange rate Channel			Hahm and others (2012) Merrouche and Nier (2010) Jonsson (2009)	↑ r, ↑ r, ↑ r,

Source: IMF.

^1/↓ r means a decrease of policy rates, î r means an increase of policy rates, ““ means a decline of stability, ““ an improvement, and “X” no statistically significant effect.

View Table

Table 1.

Monetary Policy Effects on Financial Stability^1/

Sources of Financial Instability	Channel	Predicted Effect ( improves stability)		Selected Empirical Evidence
		↓ r	↑ r
Borrowing Constraints	Balance Sheet (default) Channel			Sengupta (2010) Jiménez and others (2009) Gertler and Gilchrist (1994) Asea and Blomberg (1998)	↑ r, ↑ r, ↑ r, ↑ r,
Risky Behavior of Financial Institutions	Risk-taking Channel			Jiménez and others (2009) Ioannidou and others (2009) Merrouche and Nier (2010)	↓ r, ↓ r, X
	Risk-shifting Channel			Gan (2004) Landier and others (2011)	↑ r, ↑ r,
Externalities through Aggregate Prices	Asset price Channel			Altunbas and others (2012) Del Negro and Otrok (2007) IMF (2009)	↓ r, ↓ r, X
	Exchange rate Channel			Hahm and others (2012) Merrouche and Nier (2010) Jonsson (2009)	↑ r, ↑ r, ↑ r,

Source: IMF.

^1/↓ r means a decrease of policy rates, î r means an increase of policy rates, ““ means a decline of stability, ““ an improvement, and “X” no statistically significant effect.

14. Changes in the monetary stance can affect the tightness of borrowing constraints and the likelihood of default. Monetary easing relaxes collateral constraints, mitigating financial distortions both on the demand and supply side of credit. Conversely, a tightening of rates can adversely affect borrowers’ quality, leading to higher default rates and potentially precipitating a crisis (Allen and Gale, 2000; Illing, 2007, Goodhart and others, 2009).

15. Changes in the monetary stance can affect the risk-seeking behavior of financial intermediaries in multiple ways. Two channels may move in opposite directions, as follows:

• Risk-taking. Low monetary policy rates can create incentives for banks to expand their balance sheets and reduce efforts in screening borrowers (Borio and Zhu, 2008). They can also lead other agents to seek more risks in order to achieve higher returns (Rajan 2006). These effects are likely to be worse if monetary policy is (too) accommodative for too long during expansions. If monetary policy is expected to be lowered during recessions to support the financial system, this may create additional incentives to correlate risks (Farhi and Tirole, 2012).

• Risk-shifting. Increases in policy rates can reduce intermediation margins, and lead lenders, especially poorly capitalized intermediaries, to seek more risk (Bhattacharya, 1982). This channel may be stronger just ahead of a crisis, when intermediary leverage is high and competition limits the pass-through of policy rates to lending rates. More generally, a flattening of the yield curve associated with increases in policy rates can lead banks to seek risk in order to maintain profits (Merrouche and Nier, 2010).

16. Monetary policy can affect externalities operating through aggregate financial prices, including asset prices and exchange rates. By affecting asset prices and exchange rates, monetary policy affects the value of collateral, which influences the tightness of borrowing constraints.

• Asset prices. Low interest rate can increase asset prices, which can trigger further increases in leverage and lead to asset price booms, exacerbating the financial cycle (Bernanke and Gertler, 1989). Conversely, a tighter monetary stance can cause collateral constraints to bind, fire sales to follow, with resulting adverse asset price externalities (Shin, 2005).

• Exchange rates. In open economies, interest rate increases can attract capital flows, appreciating the currency, leading to excessive borrowing in foreign currency and laying the ground for exchange rate externalities during the depreciation phase (Bruno and Shin 2012, Hahm and others, 2012).

17. The intensity of these effects can depend on the point in the financial cycle. As financial imbalances build up, low monetary policy rates reduce current defaults, but can induce banks to make riskier loans and increase leverage. When rates are increased close to the peak of the financial cycle, this can induce risk-shifting and borrower defaults. Moreover, incentives to correlate risks due to the expectation of future monetary easing can be stronger in the upswing of the financial cycle.

18. The strength of the effects can also depend on financial structure and capital account openness. For example, securitization generally reduces the strength of the effects of monetary policy on credit extension by banks (e.g., Altunbas, and others 2012). But the importance of risk-taking and risk-shifting channels may not diminish, since they come to work through both banks and non-banks. Moreover, in open and financially-integrated economies, domestic monetary policy has a weaker influence over domestic long-term rates and asset prices, but exchange rate externalities become more important.

• In open economies, high policy rates can encourage capital inflows and foreign exchange borrowing. In a number of countries in emerging Europe, foreign exchange (FX) lending to households increased ahead of the crisis, with tighter monetary policy aggravating the situation, as it provided further incentives for borrowing in FX (case study, Section IV). At the same time, when the central bank lowers rates to support the economy in a downturn, this can lead to a depreciation and worsen exchange rate externalities arising from tightening constraints.

• As international financial integration increased over the past decades, domestic monetary control has weakened for both advanced countries and emerging markets. Bernanke (2005) argued that a global saving glut reduced long-term rates in advanced economies, and that as a result the relationship between short rates and long rates had become weak (Greenspan conundrum), thereby reducing the pass-through of policy rates to asset prices.

• For emerging economies, similarly, the correlation between domestic short and long rates has weakened (Moreno, 2008) and the importance of foreign factors strengthened. Increased cross-border banking contributes to these effects since for cross-border banks, global monetary conditions seem to matter more than local conditions (Cetorelli and Goldberg, 2012; Shin, 2011).

19. First principles suggest that well-targeted macroprudential policies have the potential to contain the undesirable effects of monetary policy. Where the side effects of monetary policy on financial stability are expected to be undesirable, this can create conflicts between financial and price stability objectives. Appropriate macroprudential policies can attenuate these side effects, thereby reducing policy dilemmas and creating additional “room for maneuver” for monetary policy. For most of the channels discussed above, a range of specific macroprudential instruments may reduce the effect when brought in ex ante.

• The impact on defaults from a tightening of monetary policy can be contained by macroprudential tools, such as a limit on DTI. A conservative DTI ratio may reduce the effect of increases in policy rates on debt affordability, thereby lessening an unwanted transmission of increased policy rates to household default rates (Igan and Kang, 2011). This in turn can help protect bank balance sheets and reduce the force of fire-sale dynamics for asset-backed securities.

• A range of macroprudential measures can affect the risk-taking channel. Increases in capital requirements or a tight leverage ratio can help contain increases in bank leverage in response to low policy rates and reduce the incentives to take risk (Farhi and Tirole, 2012). Such measures also create additional buffers to absorb risks from an erosion of lending standards. However, where shadow banks are important providers of credit, macroprudential tools are needed that control leverage both inside and outside of the banking system (United States case study, section IV). The regulation of margin in securities lending is an example (1 and others, 2010).

• Risk-shifting incentives associated with increases in policy rates could also be addressed through appropriate macroprudential tools. Liquidity measures, such as the Basel Net Stable Funding ratio, encourage banks to seek stable and longer-term funding. Where funding is longer term, this can reduce the impact of a monetary policy tightening on lending margins and profits and attenuate the incentive for intermediaries (banks and non-banks) to seek further risk exposure in response to increases in policy rates.⁸ Capital buffers can also reduce risk-shifting incentives from a compression of margins (Bhattacharya 1982, Hellmann and others, 2000).

• When accommodative monetary policy drives up asset prices, macroprudential measures, such as limits on LTV ratios, can tame house price boom-busts. When low policy rates encourage borrowing and greater credit in turn drives up asset prices, a lower LTV ratio can counter this effect. Some studies have found that a conservative LTV ratio can contain the feedback loops between credit and house prices (IMF, 2011b). Moreover, studies have found that a tightening of LTV ratios can slow the rate of house price appreciation, thereby reducing the potential for a housing bubble to emerge (Crowe and others, 2011; Igan and Kang, 2011; Wong and others 2011).

• The policy dilemma that may arise from interactions between domestic monetary policy and capital flows can be addressed by macroprudential measures. Macroprudential measures can affect gross flows and help change the composition of flows away from short-term and FX denominated liabilities issued by banks, thereby reducing the systemic risk associated with capital flows (Hahm and others, 2012).⁹ Examples are FX reserve requirements (RRs) implemented in Romania and the levy on FX denominated non-core liabilities introduced in Korea. In addition, where high domestic rates encourage corporations or households to borrow in FX, macroprudential measures can reduce heightened default risks, including higher risk weights and tighter LTV ratios, as well as limits on FX lending, as applied in a number of countries in emerging Europe (case study, section IV).

20. In sum, in its transmission, monetary policy can interact with financial distortions in several ways, with the net effect on financial stability often ambiguous. Several channels may be at work, operating simultaneously with their strength varying with the stage of the financial cycle, financial structure, and other country characteristics. Where the side effects are expected to be undesirably strong, well-designed macroprudential policies that are brought in ex ante can attenuate these effects.

C. Macroprudential Transmission and Effects on Real Economic Outcomes

21. This section presents a closer examination of existing literature on the transmission of macroprudential policies and traces out implications for monetary policy. This analysis focuses on tools that have traditionally been used most frequently in emerging markets and those that are likely to be used most actively in future, in both advanced and emerging economies, such as dynamic capital buffers, limits on loan-to-value ratios and RRs. ¹⁰ For each of these tools, the paper traces out the transmission to reduced systemic risk, as well as real economic outcomes, and draws out implications for monetary policy.¹¹

Capital buffers

22. The main objective of an increase in the dynamic capital buffer is to increase the resilience of the banking system. The idea is that when credit grows strongly, the quality of the credit portfolio is likely to deteriorate, increasing the likelihood of future losses. When high credit growth triggers an increase in the dynamic capital buffer in good times, the buffer can cushion the effect of losses on bank balance sheets and thus help maintain the flow of credit when losses materialize.

23. The effect of increases in the dynamic capital buffers on aggregate credit is likely to be weak, in principle. When credit growth is strong, banks have ample profits that can be used to build up the buffer through retained earnings. In addition, theory suggests that asymmetric information and the resulting adverse signaling effects are among the main reasons for banks’ reluctance to issue new equity (Myers and Majluf, 1984; Kashyap and others, 2010) or to cut dividend payouts (Bhattacharya, 1979). These effects are likely to be weak in good times. Moreover, since the increase in capital is mandated, adverse signaling effects from a decrease in the dividend payout ratio or from issuing new equity are likely to be small. On the other hand, since banks must fear losing profitable business if they increase lending rates or cut exposures outright, they may be less likely to pursue these strategies to meet the capital buffer.

24. In practice, increases in the buffer may still reduce credit and output for a number of reasons, which may need to be offset by monetary policy. First, if the increase in the buffer is brought in fast, banks will not be able to accumulate it through retained earnings alone. Second, further distortions, such as the tax benefits of equity may make banks reluctant to issue new equity. In some countries, in addition, banks may not have easy access to capital markets, or are privately or cooperatively held, making it difficult for these banks to issue new equity. In the presence of such distortions, some effect on aggregate credit and output is to be expected. Existing evidence suggests, however, that the effects on output of increases in capital requirements may be relatively modest (BIS 2010, Jimenez and others 2012). This implies that any dampening effect exerted by an increase in the buffer can be countered by more accommodative monetary policy, if necessary, as long as monetary policy is effective.¹²

25. On balance, stronger effects are likely in bad times when the accumulated buffer helps sustain the provision of credit to the economy. Existing evidence points to stronger effects of capital buffers on credit in bad times. Nier and Zicchino (2008) find that a larger capital buffer mitigates the adverse effect of loan losses on loan growth and that this effect is stronger in crisis times. Jimenez and others (2012) show that the effects of varying dynamic provisions on credit in Spain were much stronger in crisis times than they were ahead of the crisis. New results presented in Section III are also consistent with stronger effects on credit in bad times.

26. Where a dynamic capital buffer is in place, this will therefore reduce the need for monetary policy makers to offset the effects of tighter credit conditions on output. In response to a tightening of the availability of credit from October 2008, many advanced country central banks cut interest rates aggressively in an effort to support the financial system. When capital buffers are built up ahead of the downturn, the buffers can help sustain the provision of credit to the economy and reduce the depth of the downturn. The presence of a dynamic capital buffer may then lessen the risk that monetary policy runs into the constraints posed by the lower bound on nominal rates and complement monetary policy in bad times, resulting in a smoother path of monetary policy through the cycle.

27. In addition, when capital buffers have been built up in the upswing of the financial cycle, the buffers may help keep open the transmission of monetary policy. In the absence of sufficient buffers, the erosion of capital may lead banks to reduce the supply of credit to the economy. Even where policy rates are lowered aggressively, this may not be enough to counter banks’ reluctance to lend. A bigger capital buffer that banks are allowed to run down can help unblock the transmission of monetary policy to the provision of credit (Turner, 2012).

Loan-to-value ratios

28. Limits on loan-to-value (LTV) and debt-to-income (DTI) ratios are increasingly being viewed as useful to contain potentially damaging boom-bust cycles in residential housing markets (Igan and Kang, 2011; IMF, 2011b; Crowe and others, 2011). An LTV ratio imposes a cap on the size of the loan relative to the value of the property, thereby imposing a minimum down payment. In principle, even a static, but conservatively calibrated LTV ratio can strongly affect house-price dynamics. Its effect can be enhanced when the calibration is varied with cyclical conditions in the housing market (as in Korea and Hong Kong SAR), or when it is complemented with a DTI ratio (as in Poland and many other countries). A DTI ratio caps total loans to a fixed multiple of household income and may help contain unsustainable increases in household debt more broadly.

29. These measures have been found successful in containing house price accelerations in the upswing. Limits on LTV ratios can reduce financial accelerator mechanisms that otherwise lead to a positive two-way feedback between credit and house prices. A number of cross-country studies have found that positive shocks to household income or the size of the population translates into larger house price increases where prevailing leverage ratios are higher (Almeida and others 2005; IMF, 2011b). Moreover, a number of studies have found that a tightening of LTV ratios can slow the rate of house price appreciation, thereby reducing the potential for a housing bubble to emerge (Igan and Kang, 2011; Wong and others, 2011; Crowe and others, 2011). For example, Crowe and others (2011) find that a 10 percentage point tightening of the LTV ratio leads to a decline in the rate of house price appreciation of between 8 and 13 percentage points.

30. Where these measures limit house price acceleration and household indebtedness, they may also dampen the associated increases in aggregate demand, in turn modifying optimal monetary policy. LTV and DTI measures may reduce the response of residential investment and household consumption to positive financial shocks. When they reduce the strength of financial accelerator mechanisms in the upturn, this may allow monetary policy to be somewhat looser than in the absence of these measures (IMF, 2008).

31. A growing body of evidence also points to the benefit of LTV and DTI ratios in containing the severity of the property bust when the housing market turns.

• In theory, where leverage is high, even a relatively small fall in house prices may lead borrowers to become underwater. This creates incentives to default strategically, which in turn imparts further downward pressure on prices. Consistent with this, IMF (2011b) show that across OECD countries over the 1980 to 2010 period, conditional on a housing bust occurring, the fall in property prices is less steep where LTV ratios are tight.

• A housing bust can put stress on financial intermediaries’ engaged in mortgage credit, and tight LTV ratios can reduce these impacts. High rates of default can reduce profitability and deplete banks’ capital cushions. Wong and others (2011) document that, for a given fall in house prices, the incidence of mortgage default is higher for countries without a LTV ratio limit than it is for countries with such a tool. They also show that losses sustained by lenders for a given fall in house prices are lower.

• Stress on financial intermediaries can lead to a contraction of mortgage credit and credit more broadly, adversely affecting both household consumption and business investment. Based on 1960-2007 cross-country data, Claessens, Kose, and Terrones (2008) show that output losses in recessions accompanied by housing busts are two to three times larger than otherwise. Moreover, housing busts tend to prolong recessions, as falling house prices act as a further drag on household consumption and residential investment, while putting financial intermediary balance sheets under stress.

32. By reducing the depth and duration of the downturn, limits on LTV and DTI ratios can also lessen the risk that monetary policy will run into its lower bound. When tight LTV and DTI ratios contain mortgage defaults and losses sustained by financial intermediaries in the wake of a fall in house prices, this can also reduce output losses from the property bust. A milder downturn can in turn reduce the need for monetary easing that would otherwise be necessary to counter the financial headwinds from the property bust. IMF (2008) shows formally that offsetting the deflationary impact of a negative financial shock requires a larger accommodative monetary policy response in an economy with a high LTV ratio and a smaller response where LTV ratios are tight.

33. These complementarities are further strengthened by the effects of LTV and DTI ratios on the transmission of monetary policy. As seen in many advanced countries since the crisis broke, when a large fraction of borrowers have high LTV mortgages, this can clog up the transmission of lower policy rates on conditions in mortgage markets after the bust. After a fall in house prices, high LTV borrowers will find themselves unable to refinance their loans since the principal exceeds the value of their property. These borrowers will then not be able to take advantage of lower mortgage rates that an easing of monetary policy may help bring about. A tighter LTV constraint going into the property bust can mitigate this and help strengthen the transmission of monetary policy after a house price falls (Geanakoplos, 2010).

34. Limits on DTI and LTV ratios can also affect developments in the composition of output that are not easily controlled by monetary policy. First, a tightening of these measures can, by slowing housing transactions and dampening house price growth, reduce the share of residential construction in GDP (see Section III for empirical evidence). Second, to the extent that these measures contain increases in household leverage, they can help control a rise in debt-financed consumption spending that worsens the current account. Along with other policy measures (such as structural and fiscal policies), LTV and DTI policies may thus contribute to a reduction of external imbalances.

Reserve requirements

35. Central banks can use variations in the level of RRs to affect broader credit conditions. When RRs are remunerated below the policy rate or are unremunerated, a variation in the level of the requirement imposes a tax on lending. This tax is expected to increase the spread between lending and deposit rates as banks pass on increased costs to their customers (Gray, 2011; Tovar and others 2012; Glocker and Towbin, 2012a). Independent of the incidence, since the tax will lead to a fall in deposit supply, or a contraction of loan demand, or both, the amount of credit provided to the economy is expected to fall. By increasing the spread between lending and deposit rates, RRs will then lower the amount of credit provided to the economy, acting as a “speed limit.”

36. Inflation targeting central banks will typically offset the impact on banking system liquidity and interbank interest rates of a change in RRs. The volume of open market operations can be adjusted to offset the impact on banking system liquidity and to keep interbank rates close to the target rate. But even if the monetary effect of changes in RRs is sterilized, there can be a macroprudential effect, which works through an increase in the spread between lending and deposit rates. See further the case studies for Brazil and Turkey in Section IV of this paper.

37. Empirical studies tend to find evidence in support of an effect on credit. Vargas and others (2011) study the experience in Colombia and find that RRs have a strong and lasting effect on lending rates charged on business loans. Glocker and Towbin (2012b) estimate a structural vector autoregressive (VAR) model for the Brazilian economy. They find that a one percentage point increase in the RRs leads to a peak increase in the spread between lending and deposit rates of 80 basis points. Moreover, domestic credit falls on impact by about 2.5 percent and remains below trend for close to two years. The empirical exercise reported in Section III is also consistent with a significant effect on credit.

38. However, increases in RRs do not increase resilience and can have unintended side effects. Unlike an increase in capital requirements, an increase in RRs has no impact on the resilience of the banking system to loan losses. In addition, an increase in RRs can exacerbate risk-shifting incentives. When RRs squeeze profitability this can lead banks to shift into higher margin, but higher risk segments, in an effort to restore return on equity. In Turkey, for instance, relatively aggressive increases in RRs in early 2011 may have further spurred banks’ consumer lending, which was ultimately addressed by increases in regulatory risk weights on such lending (Turkey FSAP and case study). ¹³

39. An increase in RRs can lead to nominal depreciation and affect capital inflows. In small open economies, increases in the monetary policy rate will tend to attract capital inflows and lead to an appreciation of the currency. Increases in RRs tend to have the opposite effects, since they will tend to decrease returns on domestic and FX deposits (Glocker and Towbin, 2012a). ¹⁴ Using data from Brazil, Glocker and Towbin (2012b) find that an increase in the reserves requirement by one percentage point leads to a 2 percent depreciation of the domestic currency. Evidence presented in Tovar and others (2012) confirms the effects of RRs on exchange rates, even if their results point to a more transitory depreciation. New evidence presented in Section III suggests that RRs have the potential to affect the composition of capital inflows, away from bank portfolio flows.

40. The effects of increases in RRs on output are ambiguous in theory. The rise in bank lending rates should tighten credit and lead to a decline in investment spending. However, the fall in deposit rates may decrease domestic savings and increase consumption. Moreover, any depreciation resulting from the increase in RRs would lead to an increase in net exports that boosts aggregate demand. This implies that while an increase in RRs unambiguously lowers aggregate credit, its net effect on output may be relatively small (Glocker and Towbin, 2012a).¹⁵ Empirical results presented in Section III suggest that there is no measurable effect of an increase in RRs on output.

41. RRs provide a potential way to curb excessively strong credit growth, while effects on other economic variables are quite different from that of monetary policy. In contrast to increases in policy rates, an increase in RRs can reduce excessive credit growth without attracting net capital inflows and appreciating the exchange rate. Moreover, when increases in RRs dampen capital inflows, this can give greater room for maneuver for monetary policy to increase interest rates, as has been the experience in Peru (Tovar and others 2012).

42. Equally, in economic downturns, a relaxation of RRs can stimulate credit growth without this leading to a depreciation of the exchange rate or capital outflows. This contrasts again with the effects of an easing of the monetary policy rate, which is likely to contribute to a fall in the currency and capital outflows, especially in bad times (Federico, Vegh, and Vuletin, 2012).

43. In sum, the transmission of macroprudential policy tools and the implication for the conduct of monetary policy may differ with the tools considered.

• Some tools, including the dynamic capital buffer and limits on LTV ratios, increase the resilience of the economy against aggregate shocks, mitigating the effects on output of a credit crunch and housing bust. This can reduce the need to for accommodative policy in such scenarios and makes it less likely that monetary policy will hit the constraint imposed by the lower bound. It can also help keep open monetary transmission channels in a downturn scenario.

• When capital requirements and LTV ratios are tightened in upturns, they may have effects on credit and asset prices, and hence, potentially on aggregate output. Where these effects are sizable, they can be offset, as necessary, by more accommodative monetary policy.

• RRs may be a useful complement to monetary policy, especially in open economies, since use of this tool can control credit growth. This can give greater room for maneuver for monetary policy in the face of potentially destabilizing capital flows. The effect of an increase in RRs on output is ambiguous, though, and empirically found to be small.

A. Macroprudential Policies—Effects on Credit, House Prices, and Output

44. This section describes empirical analysis of the effects of macroprudential policy measures. Several empirical studies show that macroprudential policy instruments can be effective in addressing systemic risk externalities, if used appropriately. Lim and others (2011) find that macroprudential instruments may reduce the correlation between credit growth and GDP growth, and several studies show that limits on LTV and DTI ratios can curb the feedback loop between mortgage credit availability and house price appreciation. However, few studies consider differential effects across macroprudential tools. Moreover, there is to date only a very limited analysis of any macroeconomic effects of the use of macroprudential tools.

45. Our analysis expands on the existing literature in several ways.

• First, the direct effects of macroprudential measures on financial outcomes—credit growth and housing price appreciation—are tested.

• Second, we perform analysis of “side effects” of the macroprudential tools on economic growth, residential investment, and capital inflows.

• Third, we investigate whether the strength of these effects differs with measures of the economic and financial cycle.

Model specification and data

46. We focus on (varying) capital requirements (CR), limits on LTV ratios, caps on DTI ratios, and RRs. For each macroprudential instrument, an index variable is constructed. This index increases by 1 whenever an instrument was introduced or tightened and decreases by 1 whenever the instrument is loosened, resulting in a series that looks like a step function. The index variable captures both semi-quantitative effects and the average treatment effects of the instrument. Countries and periods in which instruments are not used are included as counterfactuals and help in identifying the effects of key control variables. The information required on the use of the instruments is obtained and extended from the 2010 IMF survey (Lim and others, 2011).¹⁶

47. A fixed-effect dynamic panel regression is used with the following specification:

For each country i, Y_it and MaPP_it represent changes of outcome variables and a time-series index of one of the four macroprudential measures respectively, where the coefficient ß captures the effects of macroprudential measures on the outcome. X_it denotes a set of control variables and (MaPP · X)_it is included to capture the interaction between the control variables and the macroprudential instruments. Throughout, we include time-fixed effects, to account for common variation across countries, as well as country-fixed effects, to account for time-invariant country characteristics. Our regressions also control for monetary policy rates and dummy variables denoting phases of credit and economic cycles.^17,18

48. The measurement of the effects of policy changes on both financial and aggregate variables is subject to well-known endogeneity problems. This issue is shared by most existing studies on the effects of macroprudential policy study (e.g., Lim and others, 2011). When macroprudential policy responds to credit and asset prices, rather than output, this bias should in principle be stronger when measuring the effect on credit and asset prices—as does much of the existing literature—and weaker when investigating aggregate effects—which is the focus here. Moreover, as long as the problem does not differ across tools considered it may not affect comparisons across tools in their relative effects. Throughout, we lag all policy variables by one quarter in an attempt to address endogeneity biases.¹⁹ Nonetheless, we take the results as only suggestive of the relative strength of the effects across tools, rather than as a reliable measure of the size of each effect.²⁰

49. Quarterly data from 2000 to 2011 were used for 36 countries,²¹ including 21 emerging market economies (EMEs) and 15 advanced economies (AEs).²² Most of the data are collected from official and commercial sources, such as IFS, central banks, national statistical offices, Haver Analytics, and CEIC database, being specified along the results. Detailed information on countries which have used macroprudential policies can be found in Table 2.

Table 2.

Use of Macroprudential Measures Across Countries

Sources: Lim and others (2011) and IMF staff.

Table 2.

Use of Macroprudential Measures Across Countries

	Advanced Economies			Emerging Market Economies
	(Free) Floating	Fixed	Other Managed	(Free) Floating	Fixed	Stabilized or Other Managed
Capital requirement	Estonia, Israel, Korea	Ireland, Spain	—	Argentina, Brazil, Mexico, India, Thailand, Turkey	Bulgaria	China, Malaysia, Croatia
Limits on LTV ratio	Canada, Norway, Korea, Sweden	Hong Kong SAR, Netherlands	Singapore	India, Thailand, Hungary, Romania, Turkey	Bulgaria, Latvia	China, Malaysia
Caps on DTI ratio	Canada, Korea, Norway	Hong Kong SAR	—	Thailand, Poland, Romania, Hungary, Serbia	—	—
Reserve requirements	Korea	—	—	Argentina, Brazil, Chile, Colombia, Peru, Uruguay, India, Indonesia, Romania, Serbia	Bulgaria	China, Croatia, Russia

Sources: Lim and others (2011) and IMF staff.

View Table

Table 2.

Use of Macroprudential Measures Across Countries

	Advanced Economies			Emerging Market Economies
	(Free) Floating	Fixed	Other Managed	(Free) Floating	Fixed	Stabilized or Other Managed
Capital requirement	Estonia, Israel, Korea	Ireland, Spain	—	Argentina, Brazil, Mexico, India, Thailand, Turkey	Bulgaria	China, Malaysia, Croatia
Limits on LTV ratio	Canada, Norway, Korea, Sweden	Hong Kong SAR, Netherlands	Singapore	India, Thailand, Hungary, Romania, Turkey	Bulgaria, Latvia	China, Malaysia
Caps on DTI ratio	Canada, Korea, Norway	Hong Kong SAR	—	Thailand, Poland, Romania, Hungary, Serbia	—	—
Reserve requirements	Korea	—	—	Argentina, Brazil, Chile, Colombia, Peru, Uruguay, India, Indonesia, Romania, Serbia	Bulgaria	China, Croatia, Russia

Sources: Lim and others (2011) and IMF staff.

Results—effect on financial variables

50. Investigating the effects on credit growth, we find statistically significant effects for both (varying) capital requirements and RRs (Table 3). For capital requirements in particular, we find that the effect on credit growth is stronger during credit busts. For a subsample containing EMEs only, we find that limits on LTV and DTI ratios are also associated with lower credit growth. In this subsample, the effect of RRs is little changed relative to the full sample. The effect of capital requirements on credit growth weakens, but remains stronger during credit busts.

Table 3.

Effects of Macroprudential Measures on Credit Growth^1/

Source: IMF staff estimates.

^1/1/ Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

Table 3.

Effects of Macroprudential Measures on Credit Growth^1/

All: 35 Countries	Credit Growth Rate (%, q-o-q)
Credit Growth Rate (-1)	0.40	0.39	0.40	0.40	0.40	0.39	0.39	0.39	0.40	0.39	0.40	0.39	0.40
GDP Growth Rate	0.23	0.22	0.22	0.23	0.23	0.23	0.22	0.23	0.23	0.21	0.22	0.22	0.22
Interest Rate (-1)	-0.04	-0.04	-0.04	-0.04	-0.04	-0.05	-0.05	-0.05	-0.04	-0.04	-0.04	-0.04	-0.04
Credit Bust						0.88	0.44	0.56	0.48
Recession										-0.61	-0.53	-0.56	-0.60
Capital Requirement (-1)		-0.51				-0.37				-0.59
Limits on DTI Ratio (-1)			-0.21				-0.32				-0.22
Limits on LTV Ratio (-1)				-0 13				-0 18				-0 15
Reserve Requirements (-1)					-0.32				-0.36				-0.38
CR(-1)*Credit Bust						-1.38
DTI(-1)*Credit Bust							1.60
LTV(-1)*Credit Bust								0.16
RR(-1)*Credit Bust									0.36
CR(-1)*Re cession										0.16
DTI(-1)*Recession											-0.04
LTV(-1)*Recession												0.14
RR(-1)*Re cession													0.16
EME: 21 Countries	Cred it Growth Rate (%, q-o-q)
Credit Growth Rate (-1)	0.42	0.42	0.42	0.41	0.41	0.40	0.40	0.39	0.40	0.41	0.41	0.40	0.41
GDP Growth Rate	0.28	0.28	0.26	0.26	0.28	0.24	0.21	0.22	0.24	0.26	0.25	0.24	0.26
Interest Rate (-1)	-0.04	-0.04	-0.04	-0.04	-0.04	-0.05	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04
Credit Bust						1.24	0.49	0.77	0.69
Recession									-0.97	-0.92	-1.08	-1.12
Capital Requirement (-1)		-0.23				-0 19				-0.34
Limits on DTI Ratio (-1)			-0.63				-0.80				-0.67
Limits on LTV Ratio (-1)				-0.79				-0.73				-1.01
Reserve Requirements (-1)					-0.40				-0.51				-0.51
CR(-1)*Credit Bust						-1.44
DTI(-1)*Credit Bust							2.69
LTV(-1)*Credit Bust								0.27
RR(-1)*Credit Bust									0.21
CR(-1)*Re cession										0.17
DTI(-1)*Recession											-0.01
LTV(-1)*Recession												0.68
RR(-1)*Re cession													0.46

Source: IMF staff estimates.

^1/1/ Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

View Table

Table 3.

Effects of Macroprudential Measures on Credit Growth^1/

All: 35 Countries	Credit Growth Rate (%, q-o-q)
Credit Growth Rate (-1)	0.40	0.39	0.40	0.40	0.40	0.39	0.39	0.39	0.40	0.39	0.40	0.39	0.40
GDP Growth Rate	0.23	0.22	0.22	0.23	0.23	0.23	0.22	0.23	0.23	0.21	0.22	0.22	0.22
Interest Rate (-1)	-0.04	-0.04	-0.04	-0.04	-0.04	-0.05	-0.05	-0.05	-0.04	-0.04	-0.04	-0.04	-0.04
Credit Bust						0.88	0.44	0.56	0.48
Recession										-0.61	-0.53	-0.56	-0.60
Capital Requirement (-1)		-0.51				-0.37				-0.59
Limits on DTI Ratio (-1)			-0.21				-0.32				-0.22
Limits on LTV Ratio (-1)				-0 13				-0 18				-0 15
Reserve Requirements (-1)					-0.32				-0.36				-0.38
CR(-1)*Credit Bust						-1.38
DTI(-1)*Credit Bust							1.60
LTV(-1)*Credit Bust								0.16
RR(-1)*Credit Bust									0.36
CR(-1)*Re cession										0.16
DTI(-1)*Recession											-0.04
LTV(-1)*Recession												0.14
RR(-1)*Re cession													0.16
EME: 21 Countries	Cred it Growth Rate (%, q-o-q)
Credit Growth Rate (-1)	0.42	0.42	0.42	0.41	0.41	0.40	0.40	0.39	0.40	0.41	0.41	0.40	0.41
GDP Growth Rate	0.28	0.28	0.26	0.26	0.28	0.24	0.21	0.22	0.24	0.26	0.25	0.24	0.26
Interest Rate (-1)	-0.04	-0.04	-0.04	-0.04	-0.04	-0.05	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04
Credit Bust						1.24	0.49	0.77	0.69
Recession									-0.97	-0.92	-1.08	-1.12
Capital Requirement (-1)		-0.23				-0 19				-0.34
Limits on DTI Ratio (-1)			-0.63				-0.80				-0.67
Limits on LTV Ratio (-1)				-0.79				-0.73				-1.01
Reserve Requirements (-1)					-0.40				-0.51				-0.51
CR(-1)*Credit Bust						-1.44
DTI(-1)*Credit Bust							2.69
LTV(-1)*Credit Bust								0.27
RR(-1)*Credit Bust									0.21
CR(-1)*Re cession										0.17
DTI(-1)*Recession											-0.01
LTV(-1)*Recession												0.68
RR(-1)*Re cession													0.46

Source: IMF staff estimates.

^1/1/ Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

51. When investigating effects on house price appreciation rates we find statistically strong effects for limits on LTV ratios and capital requirements, but not for RRs. This suggests that in our sample, variation in capital requirements might have been specifically targeted at housing credit in a number of countries, such as higher risk weights on mortgage loans, while RRs by construction do not differentiate between asset classes, and are therefore less likely to have an effect on a particular asset price. Interestingly, the effects of macroprudential tightening (or loosening) on house prices is estimated stronger during recessions across most measures, while for house prices the stage of the credit cycle appears to play less of a role. Throughout, the evidence for direct effects of variation in the DTI ratio on house prices is in general not as strong (Table 4).

Table 4.

Effects of Macroprudential Measures on House Price Appreciation^1/

Source: IMF staff estimates.

^1/Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

Table 4.

Effects of Macroprudential Measures on House Price Appreciation^1/

All: 3B Countries	House Price Appreciation Rate (%, q-o-q)
House Price (-1)	0.39	0.39	0.39	0.39	0.39	0.39	0.39	0.39	0.39	0.38	0.39	0.39	0.39
GDP Growth Rate	0.48	0.48	0.49	0.48	0.48	0.47	0.49	0.47	0.48	0.48	0.49	0.48	0.50
Interest Rate (-1)	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02
Credit Bust						-0.26	-0.03	-0.18	-0.19
Recession										0.20	0.08	0.33	0.43
Capital Requirement (-1)		-0.57				-0.64				-0.33
Limits on DTI Ratio (-1)			0.30				0.33				0.33
Limits on LTV Ratio (-1)				-0.34				-0.42				-0.22
Reserve Requirements (-1)					-0.09				-0 13				0.10
CR(-1)*Credit Bust						0.72
DTl(-1)*Credit Bust							-0.38
LTV(-1)*Credit Bust								0 37
RR(-1)*Credit Bust									0.42
CR(-1)*Recession										-0.77
DTI(-1)*Recession											-0.19
LTV(-1)*Recession												-0.86
RR(-1)*Recession													-1.19
EME: 21 Countries	House Price Appreciation Rate (%, q-o-q)
House Price (-1)	0.39	0.39	0.39	0.38	0.39	0.31	0.32	0.31	0.38	0.38	0.39	0.37	0.38
GDP Growth Rate	0 40	0.40	0.41	0.37	0.40	0.25	0.26	0.22	0.51	0.42	0.41	0.38	0.43
Interest Rate (-1)	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.02	-0.03	-0.03	-0.03	-0.03
Credit Bust						-0.50	-0.22	-0.37	-0.08
Recession										0.37	0.13	0.54	0.85
Capital Requirement (-1)		-0.21				-0.48				0.08
Limits on DTI Ratio (-1)			0.53				0.40				0.52
Limits on LTV Ratio (-1)				-1.05				-0.99				-0.66
Reserve Requirements (-1)					0.05				-0 19				0.25
CR(-1)*Cnedit Bust						0.93
DTI(-1)*Credit Bust							0.11
LTV(-1)*Credit Bust								0.67
RR(-1)*Credit Bust									0.42
CR(-1)*Recession										-0.85
DTI(-1)*Recession											0.05
LTV(-1)*Recesslon												-1.21
RR(-1)*Recession													-1.26

Source: IMF staff estimates.

^1/Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

View Table

Table 4.

Effects of Macroprudential Measures on House Price Appreciation^1/

All: 3B Countries	House Price Appreciation Rate (%, q-o-q)
House Price (-1)	0.39	0.39	0.39	0.39	0.39	0.39	0.39	0.39	0.39	0.38	0.39	0.39	0.39
GDP Growth Rate	0.48	0.48	0.49	0.48	0.48	0.47	0.49	0.47	0.48	0.48	0.49	0.48	0.50
Interest Rate (-1)	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02
Credit Bust						-0.26	-0.03	-0.18	-0.19
Recession										0.20	0.08	0.33	0.43
Capital Requirement (-1)		-0.57				-0.64				-0.33
Limits on DTI Ratio (-1)			0.30				0.33				0.33
Limits on LTV Ratio (-1)				-0.34				-0.42				-0.22
Reserve Requirements (-1)					-0.09				-0 13				0.10
CR(-1)*Credit Bust						0.72
DTl(-1)*Credit Bust							-0.38
LTV(-1)*Credit Bust								0 37
RR(-1)*Credit Bust									0.42
CR(-1)*Recession										-0.77
DTI(-1)*Recession											-0.19
LTV(-1)*Recession												-0.86
RR(-1)*Recession													-1.19
EME: 21 Countries	House Price Appreciation Rate (%, q-o-q)
House Price (-1)	0.39	0.39	0.39	0.38	0.39	0.31	0.32	0.31	0.38	0.38	0.39	0.37	0.38
GDP Growth Rate	0 40	0.40	0.41	0.37	0.40	0.25	0.26	0.22	0.51	0.42	0.41	0.38	0.43
Interest Rate (-1)	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.02	-0.03	-0.03	-0.03	-0.03
Credit Bust						-0.50	-0.22	-0.37	-0.08
Recession										0.37	0.13	0.54	0.85
Capital Requirement (-1)		-0.21				-0.48				0.08
Limits on DTI Ratio (-1)			0.53				0.40				0.52
Limits on LTV Ratio (-1)				-1.05				-0.99				-0.66
Reserve Requirements (-1)					0.05				-0 19				0.25
CR(-1)*Cnedit Bust						0.93
DTI(-1)*Credit Bust							0.11
LTV(-1)*Credit Bust								0.67
RR(-1)*Credit Bust									0.42
CR(-1)*Recession										-0.85
DTI(-1)*Recession											0.05
LTV(-1)*Recesslon												-1.21
RR(-1)*Recession													-1.26

Source: IMF staff estimates.

^1/Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

Results—effect on macro variables

52. Turning to the effects on output, the results of the main panel regressions suggest that limits on LTV ratio have an impact on output growth, and that this may work through a negative impact on investments in construction (Table 5). After controlling for monetary policy rates and foreign exchange rates, the coefficient on limits on LTV ratio across different regression equations is statistically significant in the whole sample. Especially for EMEs, a higher LTV ratio is associated with smaller investments in construction.

Table 5.

Effects of Macroprudential Measures on Output Growth and Residential Investment^1/

Source: IMF staff estimates.

^1/Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

Table 5.

Effects of Macroprudential Measures on Output Growth and Residential Investment^1/

All: 36 Countries	Growth Rate (% q-o-q)
GDP Growth Rate (-1)	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.06	0.06	0.06	0.06
Exchange Rate	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Interest Rate (-1)	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01
Credit Bust						-0.05	0.01	-0.03	-0.05
Recession										-0.26	-0.27	-0.29	-0.27
Capital Requirement [-1)		-0.08				-0.12				-0.12
Limits on DTI Ratio [-1)			-0.11				-0.12				-0.15
Limits on LTV Ratio (-1)				-0.12				-0.17				-0.15
Reserve Requirements (-1 )					0.16				0.12				0.12
CR(-1)*CreditBust						0.30
DTI(-1)*CreditBust							0.02
LTV(-1)*Credit Bust								0.23
RR(-1)*Credit Bust									0.30
CR(-1)*Recession										0.06
DTI(-1)*Recession											0.21
LTV(-1)*Recession												0.17
RR(-1)*Recession													0.13
EMEs with De Facto Floating Exchange Rate Regime: 14 Countries	Residential Investment (Construction GDP) (%, as a percent of GDP)
Residential Investment (-1)	0.99		0.99		0.99		0.98		0.99		0.98		0.99
Industrial Production	0.01		0.01		0.01		0.01		0.01		0.01		0.01
Housing Price	0.00		0.00		0.00		0.00		0.00		0.00		0.00
Interest Rates (-1)	-0.03		-0.02		-0.02		-0 02		-0.02		-0.02		-0.02
Credit Bust							-0.17		-0 16
Recession											0.00		0.05
Limits on DTI Ratio (-1)			-0.06				-0.06				-0.05
Limits on LTV Ratio (-1)					-0.09				-0.09				-0.08
DTI(-1)*Credit Bust							0.03
LTV(-1)*Credit Bust									0.05
DTI(-1)*Recession											-0.03
LTV(-1)*Recession													-0.15

Source: IMF staff estimates.

^1/Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

View Table

Table 5.

Effects of Macroprudential Measures on Output Growth and Residential Investment^1/

All: 36 Countries	Growth Rate (% q-o-q)
GDP Growth Rate (-1)	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.06	0.06	0.06	0.06
Exchange Rate	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Interest Rate (-1)	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01
Credit Bust						-0.05	0.01	-0.03	-0.05
Recession										-0.26	-0.27	-0.29	-0.27
Capital Requirement [-1)		-0.08				-0.12				-0.12
Limits on DTI Ratio [-1)			-0.11				-0.12				-0.15
Limits on LTV Ratio (-1)				-0.12				-0.17				-0.15
Reserve Requirements (-1 )					0.16				0.12				0.12
CR(-1)*CreditBust						0.30
DTI(-1)*CreditBust							0.02
LTV(-1)*Credit Bust								0.23
RR(-1)*Credit Bust									0.30
CR(-1)*Recession										0.06
DTI(-1)*Recession											0.21
LTV(-1)*Recession												0.17
RR(-1)*Recession													0.13
EMEs with De Facto Floating Exchange Rate Regime: 14 Countries	Residential Investment (Construction GDP) (%, as a percent of GDP)
Residential Investment (-1)	0.99		0.99		0.99		0.98		0.99		0.98		0.99
Industrial Production	0.01		0.01		0.01		0.01		0.01		0.01		0.01
Housing Price	0.00		0.00		0.00		0.00		0.00		0.00		0.00
Interest Rates (-1)	-0.03		-0.02		-0.02		-0 02		-0.02		-0.02		-0.02
Credit Bust							-0.17		-0 16
Recession											0.00		0.05
Limits on DTI Ratio (-1)			-0.06				-0.06				-0.05
Limits on LTV Ratio (-1)					-0.09				-0.09				-0.08
DTI(-1)*Credit Bust							0.03
LTV(-1)*Credit Bust									0.05
DTI(-1)*Recession											-0.03
LTV(-1)*Recession													-0.15

Source: IMF staff estimates.

^1/Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

53. For other macroprudential tools, we find little evidence of a direct effect on output. The coefficients on capital requirements, caps on DTI ratio and RRs are not statistically significant, indicating weaker effects on output than found for the LTV ratio. It is possible that for capital and RRs statistically significant effects could be picked up in larger samples or using a different study design. However, the results also confirm existing studies that tend to find modest effects of these tools on output. See BIS (2010) and Glocker and Towbin (2012b), respectively.

54. By contrast, throughout, we find that variation in the policy rate has a statistically strong effect on output growth. These findings suggest that some macroprudential policy tools may be able to separately target a specific component of domestic demand, unlike monetary policy, but that the effect of these macroprudential tools on aggregate output is more limited. Conversely, the policy rate affects all economic activity regardless of which sector is vulnerable to systemic risks, and may then have stronger effect on output growth.

55. It is possible that the effects are too small to show up as statistically significant in our panel analysis. An indirect way of gauging the effects on output is to extrapolate from the effect on credit growth. A number of empirical studies show the effects of credit supply shocks on output growth, ranging from 0 to 0.34, that is, a one percent decline in credit supply induces a drop in the GDP growth rate of up to 34 basis points. Thus, combining with results in Table 5, some macroprudential policy instruments may still affect output growth to a meaningful degree.

Results—effects on capital flows

56. We finally turn to an investigation of the effect of macroprudential measures on capital inflows. Monetary policy is often said to be constrained in open economies since policy rate hikes to contain financial exuberance are likely to trigger more capital inflows. Some macroprudential tools, on the other hand have been found in the literature to be useful to affect gross flows and the composition of capital inflows.

57. We specifically investigate the determinants of portfolio investments. The specification is similar to those employed before. However, we now account for the effect of the spread between the domestic rate and the federal funds rate. In particular, we create a dummy variable that indicates whether this spread is unusually high, relative to the average in the country concerned.

58. The results of this exercise are contained in Table 6. We find that where the interest spread is high, this stimulates portfolio inflows. Moreover we do not find that capital requirements, LTV and DTI ratios have any effect on the strength of portfolio inflows. By contrast, we find statistically strong evidence that increases in RRs reduce portfolio inflows in emerging economies with floating exchange rates. These results chime with those found elsewhere in the literature: increases in RRs lead to a depreciation of the currency. They can also, more mechanically, arise when RRs apply to FX debt securities issued by banks.

Table 6.

Effects of Macroprudential Measures on Capital Inflows ^1/

Source: IMF staff estimates

^1/Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

Table 6.

Effects of Macroprudential Measures on Capital Inflows ^1/

EMEs with De Facto Floating Exchange Rate Regime: 14 Countries	Portfolio Investment Inflows (%, as a percent of GDP)
Portfolio Inflows (-1)	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.05	0.05	0.04
Growth Rate Differential	0.16	0.17	0.15	0.15	0.16	0.17	0.10	0.15	0.16	0.16	0.14	0.13	0.15
Cu ment Account	-0.12	-0.12	-0.12	-0.12	-0.13	-0.11	-0.12	-0.12	-0.13	-0.12	-0.12	-0.13	-0.14
Interest Rate Spread	0.46	0.50	0.48	0.50	0.51	0.51	-0.01	0.52	0.54	0.52	0.46	0.45	0.53
Credit Bust						-0 31	-0.59	-0.45	-0.53
Recession										-0.31	-0.38	-0.46	-0.41
Capital Requirement (-1)		0.87				0.94				0.61
Limits on DTI Ratio (-1)			-0.06				0.16				-0.16
Limits on LTV Ratio (-1)				-0.24				-0.29				-0.30
Reserve Requirements (-1)					-0.37				-0.50				-0.53
CR(-1)*Credit Bust						-0.50
DTI(-1)*Credit Bust							0.58
LTV(-1)*Credit Bust								0.42
RR(-1)*Credit Bust									0.73
CR(-1)*Recession										2.10
DTI(-1)*Recession											0.70
LTV(-1)*Recession												1.54
RR(-1)*Recession													1.04

Source: IMF staff estimates

^1/Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

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Table 6.

Effects of Macroprudential Measures on Capital Inflows ^1/

EMEs with De Facto Floating Exchange Rate Regime: 14 Countries	Portfolio Investment Inflows (%, as a percent of GDP)
Portfolio Inflows (-1)	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.05	0.05	0.04
Growth Rate Differential	0.16	0.17	0.15	0.15	0.16	0.17	0.10	0.15	0.16	0.16	0.14	0.13	0.15
Cu ment Account	-0.12	-0.12	-0.12	-0.12	-0.13	-0.11	-0.12	-0.12	-0.13	-0.12	-0.12	-0.13	-0.14
Interest Rate Spread	0.46	0.50	0.48	0.50	0.51	0.51	-0.01	0.52	0.54	0.52	0.46	0.45	0.53
Credit Bust						-0 31	-0.59	-0.45	-0.53
Recession										-0.31	-0.38	-0.46	-0.41
Capital Requirement (-1)		0.87				0.94				0.61
Limits on DTI Ratio (-1)			-0.06				0.16				-0.16
Limits on LTV Ratio (-1)				-0.24				-0.29				-0.30
Reserve Requirements (-1)					-0.37				-0.50				-0.53
CR(-1)*Credit Bust						-0.50
DTI(-1)*Credit Bust							0.58
LTV(-1)*Credit Bust								0.42
RR(-1)*Credit Bust									0.73
CR(-1)*Recession										2.10
DTI(-1)*Recession											0.70
LTV(-1)*Recession												1.54
RR(-1)*Recession													1.04

Source: IMF staff estimates

^1/Green, orange, and yellow color in each cell indicate significance at 1, 5, and 10 percent level, respectively.

B. Effects of Macroprudential Policy Measures—Symmetric or Asymmetric?

59. This section describes additional empirical tests to investigate potential asymmetries in the effectiveness of macroprudential measures. From a policy perspective, it is important to ascertain whether the size of the effect of a tightening of a macroprudential policy tool differs from that of a loosening of the tool, or whether the effectiveness of macroprudential measures is symmetric.

60. Table 7 documents the number of instances, for each macroprudential tool, in which the measures were tightened and loosened. This suggests that for most measures, the number of tightening events is far greater than that of loosening events. This is a key limitation for the empirical analysis, since if there are few instances of loosening this will reduce the power of any test of differential effects. Inspection of the table suggests that this is a major issue in particular for capital requirements and DTI ratios. For RRs in particular, the situation is somewhat better with a ratio of tightening to loosening events roughly 3 to 1.

Table 7.

Number of Macroprudential Measures—Tightening or Loosening

Sources: Lim and others (2011) and author’s extension.

Table 7.

Number of Macroprudential Measures—Tightening or Loosening

	Total Sample	Tightening	Loosening
Capital Requirements	1728	21	3
Limits on DTI Ratio	1728	17	2
Limits on LTV Ratio	1728	34	5
Reserve Requirements	1728	28	9

Sources: Lim and others (2011) and author’s extension.

View Table

Table 7.

Number of Macroprudential Measures—Tightening or Loosening

	Total Sample	Tightening	Loosening
Capital Requirements	1728	21	3
Limits on DTI Ratio	1728	17	2
Limits on LTV Ratio	1728	34	5
Reserve Requirements	1728	28	9

Sources: Lim and others (2011) and author’s extension.

61. The potential for asymmetric effects is gauged by interaction exercises. The basic strategy followed is to interact the measure of the tightness of the macroprudential tool (the step function used previously) with dummy variables that indicate, for each change in the step function, whether the change is an increase (tightening) or a decrease (loosening). We examine the issue of symmetry for the key financial variables investigated before. That is, our dependent variables are credit growth as well as the growth of asset prices.

62. Figure 1 suggests that loosening events tend to occur from 2008, when in many countries there would have been financial stress as a result of the global financial crisis. This suggests that countries are more likely to loosen macroprudential policy tools when the financial system is in need of support. Our empirical analysis takes account of the resulting potential measurement bias by including dummy variables that indicate financial stress, such as credit bust, asset price busts and recession, as well as interactions with these variables that capture differential effects of macroprudential policy in times of stress.

Number of Macroprudential Measures—Tightening or Loosening

Citation: Policy Papers 2012, 106; 10.5089/9781498339513.007.A001

Source: IMF staff calculations.Source: IMF staff calculations.Source: IMF staff calculations.Source: IMF staff calculations.

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Number of Macroprudential Measures—Tightening or Loosening

Citation: Policy Papers 2012, 106; 10.5089/9781498339513.007.A001

Source: IMF staff calculations.Source: IMF staff calculations.Source: IMF staff calculations.Source: IMF staff calculations.

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Number of Macroprudential Measures—Tightening or Loosening

Citation: Policy Papers 2012, 106; 10.5089/9781498339513.007.A001

Source: IMF staff calculations.Source: IMF staff calculations.Source: IMF staff calculations.Source: IMF staff calculations.

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