## Macroprudential Policy Effects

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### Executive Summary — Introduction and Key Lessons
- The global financial crisis (GFC) demonstrated the need for additional policy tools to safeguard financial stability and macroeconomic stability.
- Macroprudential policy measures emerged to target specific financial vulnerabilities and build buffers to cushion adverse aggregate shocks.
- Use of macroprudential tools expanded after the GFC, aided by databases such as the integrated macroprudential policy database (Alam and others 2019) and the IMF’s annual Macroprudential Policy (MP) Survey (IMF 2018).
- The COVID-19 shock tested postcrisis reforms and the ability to relax macroprudential tools to support credit provision; post-GFC reforms helped banking systems remain resilient though new vulnerabilities emerged (for example, sharp increases in real estate prices and rising inflation).

### What We Have Learned About Containing Vulnerabilities
- Meta-analysis and empirical research support effectiveness in containing growth of credit and residential real estate prices.
- Micro-level and borrower/sectoral tools (for example, housing) show relatively stronger effects — macroprudential policy can act as a “surgical” tool to tackle specific macro-financial vulnerabilities.

### Effects on Economic Activity and Resilience
- Near-term costs to economic activity are small:
  - Average effect: 0.004 standard deviations of output from tightening macroprudential policy measured by broad composite indices.
- Resilience-building:
  - Macroprudential policy strengthens resilience to shocks, lowering risks of output declines and output volatility over the medium term.
  - Resilience effects persist over time, especially for borrower-based tools.
  - Releasing bank capital buffers in stress periods supported credit provision during COVID-19, supporting the case for accumulating positive neutral buffers in normal times.

### Nonlinear, Dynamic Effects, and Leakages
- Nonlinearities and diminishing marginal benefits:
  - Tightening beyond certain thresholds becomes less effective and more costly.
  - Evidence of diminishing marginal net benefits: increasing capital buffers yields high initial benefits but declining returns once risk-weighted capital ratios reach the 15–23 percent level.
  - Borrower-based thresholds: mortgage default probability rises only when DSTI exceeds an estimated threshold of 50 percent (Romanian credit registry data).
- Tightening vs loosening:
  - Early evidence indicates stronger effects for tightening than for loosening measures (pre-COVID-19 sample).
- Leakages and asset substitution:
  - Credit substitution by nonbanks and cross-border substitution are documented.
  - Leakage stronger in advanced economies with developed financial markets and more pronounced in response to quantitative (quantity) constraints than price-based tools.
  - Effective regulation of nonbanks and coordinated capital flow management measures (CFM) can help reduce leakages.

### Interactions with Other Policies
- In EMDEs, monetary policy (MP), foreign exchange intervention (FXI), and macroprudential policies (MaPP) appear mutually reinforcing in moderating credit growth—particularly when credit growth is already high.
  - Triple interaction MaPP*MP*FXI: a one standard deviation tightening in each of the three policies in EMDEs is associated with a decrease in credit of 0.9 percent over one quarter, cumulative decrease of 1.8 percent up to four quarters.
- In AEs, little evidence that the marginal effect on credit of one policy is strongly affected by settings of another; some limited indications but not robust.
- New analysis highlights the interaction of MaPP with MP, FXI, and CFMs, contributing to an integrated policy framework.

### Conceptual Framework — Objectives, Tools, and Channels
- Objective: manage tail risks to output by containing systemic financial risk (IMF 2013, IMF-FSB-BIS 2016).
- Dual goals:
  - Leaning (ex ante): contain buildup of vulnerabilities (examples: LTV, LTI caps).
  - Resilience (ex post): strengthen ability to absorb shocks (examples: higher bank buffers, structural measures).
- Macroprudential tools (examples):
  - Broad-based institutional tools: capital and provisioning requirements; countercyclical capital buffer (CCyB).
  - Sectoral/borrower-based tools: limits to LTV, LTI, DSTI.
  - Liquidity and currency risk tools: LCRs, limits to FX positions, reserve requirements.
  - Structural tools: capital surcharges for systemically important institutions; O-SII rules; designation as global systemically important bank.
- Transmission channels: leaning reduces procyclical amplification; resilience reduces amplification after shocks; borrower resilience channel reduces probability of sharp consumption cuts and defaults.

### Empirical Evidence — Scope and Quantitative Results
- Meta-analysis: Araujo and others (2020) covers 6000+ estimates and about 60 studies.
- Main empirical patterns:
  - Broad-based tools and liquidity tools tend to have relatively strong effects on total credit growth.
  - Borrower-based tools tend to have stronger effects on household credit flows.
  - Micro-level studies generally find larger effects than macro-level studies.
- Meta-analysis average effects of tightening on credit (standard deviations), Araujo and others (2020):
  - Broad based: All −0.056*** (0.007); Micro Data −0.045** (0.018); Macro Data −0.032* (0.015).
  - Housing: All −0.045*** (0.011); Micro Data −0.192*** (0.009); Macro Data −0.039*** (0.009).
  - Liquidity & Other: All −0.129*** (0.009); Micro Data −0.130*** (0.007); Macro Data −0.030*** (0.009).
- Structural tools and SIFIs:
  - Evidence scarce; some studies find slower balance-sheet expansion after O-SII rules or G-SIB designation.
  - Too-big-to-fail reforms since 2012 contributed to reduced funding cost advantages though advantages remain higher than pre-GFC (FSB 2021).

### Empirical Challenges and Identification Methods
- Endogeneity issues:
  - Tightening may occur when vulnerabilities are rising, inducing attenuation bias.
  - Actions at cycle peaks may overstate effects.
- Identification methods used:
  - Lagged indicators, GMM, event studies, cross-sectional microdata, confidential bank-specific rule changes, credit-registry matched microdata, surveys, propensity score matching, narrative approaches, “policy surprises” methods.
- Limitations: microdata approaches may not generalize economywide; clean identification remains challenging.

### Costs and Benefits: Near-Term Costs, Resilience, and Intertemporal Trade-Offs
- Near-term costs:
  - Overall near-term negative impact on activity appears small and statistically significant in some studies.
  - Example: tightening LTV by 10 percentage points yields decline in output of 1.1 percent after four years (Richter and others 2019).
  - Effect of a 10 percentage points change in LTV limit on consumption growth is around 1 percentage points (Alam and others 2019).
  - Effects on credit and asset prices are about six times stronger in percentage terms than effects on output and consumption.
- Resilience benefits:
  - Countercyclical capital buffer of 4.7 percent could have enabled US banks to continue balance-sheet growth in 2008 scenario (Aikman and others 2019).
  - Spain dynamic provisioning: 1 percentage point higher buffer increased credit to firms by 9 percentage points, firm employment by 6 percentage points, firm survival by 1 percentage point (Jiménez and others 2017).
- Intertemporal trade-offs:
  - Tightening can entail short-term output costs but reduce medium-term tail risks.
  - Tightening macroprudential policies in response to a loosening of financial conditions reduces losses over the policy horizon by about 9 percent (Brandao-Marques and others 2020).
  - Macroprudential policy effects often larger than those from monetary policy, FX intervention, or capital controls in these studies.

### Nonlinear and Dynamic Effects; Leakages — Detailed Findings
- Diminishing marginal benefits:
  - Benefits of tightening decline as settings become more stringent; side-effects strengthen with aggressive tightening.
  - Example thresholds: bank capital benefits decline once risk-weighted capital ratios reach the 15–23 percent level.
- Effects over the financial cycle:
  - Tools are more potent when vulnerabilities are building; credit and asset price effects peak typically at a one- to two-year horizon.
  - Borrower-based tools yield greater net benefits when credit-to-GDP is already high; lender-based tools yield larger benefits where credit is low relative to GDP.
- Tightening versus loosening:
  - Pre-COVID evidence suggests tightening effects are stronger and more often significant than loosening effects; findings are preliminary and tool-specific heterogeneity exists.
  - COVID-19: releasing capital buffers helped support credit, but stigma and overlapping requirements can hinder buffer use.
- Leakages and substitution:
  - Bank-to-nonbank substitution documented: bank credit declines relative to counterfactual while nonbank credit expands (Cizel and others 2019).
  - Cross-border leakages: borrower-based limits can lead to increases in foreign inflows and FX debt issuance by corporates when domestic bank FX borrowing falls.
  - Policy implication: domestic measures may need extension (reciprocity, CFMs, MPMs) to capture nonbank and cross-border channels.

### COVID-19 Experience and Macroprudential Buffers (Box 2 highlights)
- Banks more constrained cut lending by "1.4 percent more (quarterly)" and were "4 percent more likely to end pre-existing lending relationships" during the pandemic.
- Banks reluctant to breach regulatory buffers even when supervisors signaled capital should be used; fear of increased funding costs a driver.
- Evidence:
  - BCBS 2021: CDS spread increases from end-2019 to March/April 2020 were strongly conditioned by banks’ capital strength; effect driven by “headroom” above combined buffer requirement.
  - Banks with less capital space experienced larger funding-cost increases and lent less.
  - Release of countercyclical capital buffers and systemic risk buffers led to credit expansion, especially benefiting small- and medium-sized enterprises.
- Other concurrent policy measures (fiscal guarantees, payment moratoria, dividend restrictions) also affected outcomes.

### Interactions with Other Policies — Empirical Model Results
- Sample: 23 AEs and 16 EMDEs, 2001:Q1–2018:Q4; MaPP indices from iMaPP.
- EMDEs:
  - Triple interaction MaPP*MP*FXI negative and significant.
  - Numerical results (EMDEs, coefficient lag 1, horizon h = 0; standard errors in parenthesis):
    - MaPP −0.091 (0.09)
    - MP −0.133 (0.35)
    - CFM 0.011 (0.10)
    - Fiscal −0.169 (0.18)
    - FXI −0.139 (0.09)
    - MaPP*MP −0.260 (0.23)
    - MaPP*MP*FXI −0.899** (0.36)  [** denotes significance at 5 percent]
  - A one standard deviation tightening in MaPP, MP and FXI associated with:
    - decrease in credit of 0.9 percent over one quarter
    - cumulative decrease of 1.8 percent up to four quarters (equals one third of average cumulative credit growth over the same period).
- AEs:
  - Little evidence that policy interactions materially change marginal effects.
  - Selected AE coefficients (lag 1, horizon h = 0):
    - MaPP −0.200** (0.08)
    - MP 0.013 (0.06)
    - CFM −0.071 (0.05)
    - Fiscal 0.094** (0.04)
    - MaPP*MP*CFM −0.068** (0.03)
- Mechanisms and robustness:
  - In EMDEs, interaction primarily driven by lender-based (supply) measures and liquidity measures.
  - Lasso machine-learning (cross-fit partial-out estimator) confirms negative significance of MaPP*MP*FXI.
  - Interactions more sizeable when credit growth is high (significant at the 75th percentile).

### Regression and Machine-Learning Findings (selected supply/demand MaPP results)
- Demand MaPP −0.349 (0.52) with subsequent lags:
  - −1.283 (0.96); −1.361 (1.16); −1.544 (1.32)
- Supply MaPP −0.673* (0.32) with subsequent lags:
  - −1.215** (0.45); −1.246** (0.58); −1.632** (0.72)
- Supply component coefficients (clustered standard errors in parentheses; significance markers preserved):
  - a. Capital measures 0.140 (0.19)
  - b. Limits on credit 0.210 (0.32)
  - c. FX measures −0.279 (0.44)
  - d. Liquidity measures −0.423* (0.24)
  - e. SIIs measures 0.330* (0.18)
  - f. Other 0.109 (0.36)
- MaPP (excluding tax measures) −1.141*** (0.26) with lags −1.122** (0.46); −0.985 (0.86); −0.382 (0.98)
- Machine learning (Lasso) estimates:
  - −0.857*** (0.16); −1.531*** (0.30); −1.865*** (0.46); −1.965*** (0.56)
- Quantile regression highlights state dependence:
  - 75th percentile includes −1.021** (0.516) and other larger negative coefficients.

### Time Profile, Horizons, and Sample Facts
- Typical empirical horizons:
  - Short-term studies: up to four quarters.
  - Longer-run impulse-response analyses: "14 to 16 quarters."
  - Credit effects tend to be hump-shaped, peaking at a "one- to two-year horizon."
  - Macro variables (GDP, consumption, prices) tend to peak after "two to three years."
- Examples:
  - Richter, Schularick, and Shim (2019): real household credit reduced by "almost 6 percent after two years" and mortgage credit by "more than 5 percent"; house prices show "a highly significant eight-percent decline after four years."
  - Resilience benefits peak "around after 10 quarters" in one study; for financial-institution-based tools about "half the initial reduction in losses is reversed after 14 quarters."
- Sample fact: "39 countries during the period 2001:Q1–2018:Q4." In that sample, MaPP tightened in "more than 40 percent" of observations, but tightenings occurred without another policy change in only "approximately 3 percent" of the time.

### Limits, Research Needs, and Policy-Relevant Conclusions
- Limits and caveats:
  - Decreasing marginal returns and leakages require broadening the policy approach.
  - Most evidence focuses on banks and their borrowers; nonbank intermediation, crypto assets, and digital money are new frontiers.
- Research needs and operational guidance:
  - More granular, tool-specific analysis to quantify effects and improve calibration.
  - Better data on the size of policy changes (intensive margin) and enhanced microdata to address endogeneity.
  - Further evaluation of how macroprudential policy reduces tail risks to output and distributional implications.
- Policy-relevant conclusions (verbatim summaries preserved):
  - Empirical evidence supports effectiveness of MaPP; micro-level evidence points to larger effects than aggregate data, especially for liquidity and housing related tools.
  - Output sacrifice tends to be modest when MaPP is used outside periods of stress; preemptive use can reduce vulnerabilities and increase resilience.
  - Evidence points to diminishing marginal returns; tightening may have stronger effects than loosening, though evidence on relaxation is limited.
  - COVID-19 experience suggests releasing capital buffers helped support credit; there may be a case for positive neutral buffers going forward.
  - Interaction results: in EMDEs, FXI, monetary, and macroprudential policies reinforce each other; in AEs, interactions are not robust.
  - Nonbank financial intermediation, crypto assets, and digital money pose new challenges for macroprudential policy design.

*Source: Executive Summary and selected chapters/excerpts from IMF DEPARTMENTAL PAPERS • Macroprudential Policy Effects (mpeeoqea).*

### Executive Summary ..............................................................................................

### Executive Summary

### Introduction
- The global financial crisis (GFC) demonstrated the need for additional policy tools to safeguard financial stability and macroeconomic stability, as systemic financial vulnerabilities developed under low inflation and small output gaps.
- Macroprudential policy measures emerged to target specific financial vulnerabilities and build buffers to cushion adverse aggregate shocks, allowing traditional policy levers (monetary and microprudential policies) to focus on their traditional roles.
- Use of macroprudential tools expanded after the GFC, aided by databases such as the integrated macroprudential policy database (Alam and others 2019) and the IMF’s annual Macroprudential Policy (MP) Survey (IMF 2018).
- The COVID-19 shock tested postcrisis reforms and the ability to relax macroprudential tools to support credit provision through downturn conditions; overall post-GFC reforms helped banking systems remain resilient to pandemic stresses, though new vulnerabilities emerged (for example, sharp increases in real estate prices and rising inflation).

### What we have learned about containing vulnerabilities
- Meta-analysis and empirical research provide strong support for the effectiveness of macroprudential policy in containing the growth of credit and residential real estate prices.
- Micro-level data and sectoral and borrower-based tools (for example, housing) show relatively stronger effects, underscoring the usefulness of targeted measures.
- Findings underscore that macroprudential policy can act as a “surgical” tool to tackle specific macro-financial vulnerabilities.

### Effects on economic activity and resilience
- Evidence points to small adverse effects on economic activity in the near term; these modest near-term costs should lessen policymakers’ concerns about immediate tradeoffs between preserving financial stability and growth.
- Macroprudential policy can strengthen resilience to external and domestic financial shocks, lowering risks of output declines and output volatility over the medium term.
- Resilience-building effects appear to persist rather than wane over time, especially for borrower-based tools.
- The COVID-19 experience indicates that releasing bank capital buffers in periods of stress can help cushion adverse shocks on the supply of credit, supporting the case for accumulating positive neutral buffers in normal times.

### Nonlinear, dynamic effects, and leakages
- Policy effects often involve non-linearities, with evidence of diminishing marginal benefits: tightening beyond certain thresholds becomes costly.
- Early evidence (not differentiating across tools) indicates stronger effects for tightening than for loosening measures, and stronger effects during the buildup phase of the financial cycle.
- Macroprudential tools are subject to domestic and cross-border leakages, including credit substitution by nonbanks and cross-border substitution.
- Effective regulation of nonbanks is needed to complement bank-focused macroprudential actions; effective capital flow management measures (CFM) used in tandem can help reduce cross-border leakages.
- Time-profile evidence: resilience effects persist, and the ability to relax buffers in stress periods has been valuable.

### Interactions with other policies
- In emerging markets, monetary policy, foreign exchange intervention (FXI), and macroprudential policies appear to have mutually reinforcing effects in moderating credit growth—particularly when credit growth is already high.
- By contrast, evidence suggests a lack of such reinforcing effects in advanced economies—the marginal effect on credit of one policy is not much affected by the policy settings of another.
- The paper presents new analysis on policy interactions, contributing to the IMF’s agenda on an integrated policy framework.

### Limits and caveats
- Macroprudential policies have limits: decreasing marginal returns and evidence of leakages require broadening the policy approach.
- Asymmetries are important: tightening may have stronger effects on credit than loosening, according to early evidence that does not differentiate across tools.
- Most existing evidence focuses on measures imposed on banks and their borrowers; systemic risks from nonbank financial intermediation, crypto assets, and digital money represent new frontiers.

### Research needs and operational guidance
- Further research is needed on:
  - The role of macroprudential policy in strengthening financial system resilience.
  - Interactions of macroprudential measures with other policies.
  - More granular, tool-specific analysis to quantify effects and improve calibration.
- Continued efforts to explore new methods and to enhance data quality and granularity on macroprudential policy changes are needed to better quantify effects and calibrate the range of tools.

*Source: Executive Summary, IMF Departmental Paper "Macroprudential Policy Effects".*

### 1. Conceptual Framework

### 1. Conceptual Framework

### Objective and Economic Rationale
- Objective: manage tail risks to output by containing systemic financial risk.  
- Definition: systemic financial risk is the risk of widespread disruption to the provision of financial services that is caused by an impairment of all or parts of the financial system, which can lead to serious negative consequences for the real economy (IMF 2013, IMF-FSB-BIS 2016).
- Underlying market failures and externalities motivating macroprudential policy:
  - Asymmetric information, limited enforcement of contracts, and market incompleteness encourage excessive private risk-taking and the buildup of systemic vulnerabilities.
  - Strategic complementarities (mutually reinforcing private agents’ decisions) and interconnectedness among financial institutions can further exacerbate risk-taking incentives and financial vulnerabilities (examples cited: Farhi and Tirole 2012, Acemoglu 2015, Farhi and Werning 2016, Davila and Korinek 2018, Mendoza 2018).
- Aggregate effect: these distortions and externalities give rise to systemic externalities, providing a rationale for policy intervention through macroprudential policies (IMF 2013).

### Dual Goals: “Leaning” and “Resilience”
- Macroprudential policy pursues two interrelated objectives:
  - Leaning (ex ante): contain the buildup of systemic financial vulnerabilities before negative shocks materialize by reducing the force of macro-financial feedback mechanisms as vulnerabilities are building.
    - Example channel: procyclical feedback between asset prices and credit—rising asset prices inflate collateral values and relax borrowing constraints; stronger credit flows impart momentum to asset prices.
    - Typical tools for leaning: borrower-based limits such as loan-to-value (LTV) and loan-to-income (LTI) ratios to avoid stretching of balance sheets.
  - Resilience (ex post): strengthen the ability of the financial system to absorb adverse shocks and reduce amplification after shocks occur.
    - If shocks erode bank capital or create funding pressures, macroprudential intervention (e.g., higher bank buffers) can help absorb losses and contain defaults; structural measures can limit interconnectedness.
    - Borrower-based tools (caps on LTV, LTI, DSTI) can increase borrower resilience and blunt feedback effects from borrower defaults or cuts in consumption/investment.
- Policy calibration: because some measures entail costs and side effects, macroprudential policy settings are ideally adjusted according to financial and economic conditions to smooth credit provision while avoiding unnecessary burden on the economy.

### Macroprudential Tools (examples)
- Broad-based institutional tools: capital and provisioning requirements; countercyclical capital buffer (CCyB).
- Sectoral/borrower-based tools: limits to LTV, LTI, DSTI (often housing-related).
- Liquidity and currency risk tools: liquidity coverage ratios (LCRs), limits to open positions in foreign exchange, reserve requirements.
- Structural tools: capital surcharges for systemically important institutions; O-SII rules; designation as global systemically important bank.

### Transmission and Channels of Effect
- Leaning reduces procyclical amplification (e.g., credit–asset price loop) by constraining balance-sheet stretching.
- Resilience reduces the force of macro-financial feedbacks after shocks via higher buffers and reduced interconnectedness.
- Borrower resilience channel: highly indebted households are more likely to cut spending sharply in recessions; borrower-based tools can dampen leverage buildup and limit defaults that amplify downturns (example: Bank of England evidence).

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### 2. Empirical Evidence on Effects of Macroprudential Policy

### Scope of the Evidence
- Araujo and others (2020) meta-analysis covers 6000+ estimates and about 60 studies.
- Literature focus: primarily on intermediate outcome variables related to the ex ante “leaning” role—most studied are credit growth, household leverage, and residential real estate prices.

### Main Findings
- On average, a wide range of macroprudential tools have been found to be statistically significant in containing growth of (total and household) credit and residential real estate prices, though there is significant heterogeneity across studies.
- Tool-specific patterns:
  - Broad-based tools on financial institutions (capital and provisioning requirements) and liquidity tools (reserve requirements) tend to have relatively strong effects on total credit growth.
  - Borrower-based tools (often housing-related) tend to have stronger effects on household credit flows.
- Micro-level studies generally find larger effects than macro-level studies:
  - Economic explanation: aggregate estimates average impacts across constrained and unconstrained agents; micro studies often focus on constrained or near-constrained agents where effects are larger.
  - Statistical explanation: micro studies have greater statistical power and may be less subject to reverse causality bias.
- Many empirical results are statistically insignificant in the literature, reflecting challenges such as imprecise measurement of policy actions, endogeneity, and limited statistical power with aggregate data.

### Quantitative Results (meta-analysis averages)
- Average effects of tightening macroprudential tools on credit (in standard deviations), from Araujo and others (2020):
  - Broad based: All −0.056*** (0.007); Micro Data −0.045** (0.018); Macro Data −0.032* (0.015).
  - Housing: All −0.045*** (0.011); Micro Data −0.192*** (0.009); Macro Data −0.039*** (0.009).
  - Liquidity & Other: All −0.129*** (0.009); Micro Data −0.130*** (0.007); Macro Data −0.030*** (0.009).
- Interpretation: tightening is associated with declines in credit growth measured in standard deviations; effects are larger in micro-data studies, especially for housing-related tools.

### Structural Tools and Systemic-Important Institutions
- Limited isolation of structural tools in studies; evidence is scarce but indicative:
  - Some studies find slower balance-sheet expansion for systemically important institutions after prudential rule announcements (for example, O-SII rules) or designation as global systemically important bank.
  - Too-big-to-fail reforms since 2012 appear to have contributed to a reduction in funding cost advantages of systemically important banks, although these advantages remain higher than before the GFC (FSB 2021).

### Empirical Challenges: Endogeneity (Box 1)
- Endogeneity problem manifests in multiple ways:
  - If macroprudential tightening occurs when vulnerabilities are rising (e.g., credit booms), this induces a positive association between tightening and vulnerability growth, generating attenuation bias that underestimates true effects.
  - If actions are taken at cycle peaks (due to inertia), estimates can overstate effects.
- Econometric approaches used to address endogeneity:
  - Lagged macroprudential indicators and GMM techniques (rely on strong assumptions).
  - Event study analyses (clean events are rare).
  - Exploiting cross-sectional heterogeneity in micro data (bank-, firm-, or loan-level studies).
  - Use of confidential bank-specific rule changes unrelated to macro conditions.
  - Credit-registry matched borrower-lender microdata to disentangle loan demand from loan supply shocks.
  - Surveys designed to measure household willingness to pay and behavioral responses.
  - Propensity score matching comparing countries with and without policy changes.
- Limitations of approaches:
  - Microdata approaches may yield results particular to specific segments and limit inference about economywide effects.
  - Clean identification remains challenging; study design choices affect estimated magnitudes and statistical significance.

---

*Source: IMF 2021.*

### Box 1. Empirical Challenges—Endogeneity of Macroprudential Policy (continued)

### Box 1. Empirical Challenges—Endogeneity of Macroprudential Policy (continued)

### Identification approaches and empirical challenges
- Inverse propensity-score weighted estimators have been used to assess impacts of LTV ratios on household credit growth and consumption (Alam and others 2019) and to investigate leakage to nonbank credit (Cizel and others 2019). A challenge is identifying good control groups due to data limitations.
- Narrative approaches study contemporary primary sources (policymakers’ stated intentions) to identify macroprudential actions exogenous to current and lagged real variables (examples: Friedman and Schwartz 1963; Romer and Romer 1989, 2007). Applications include Richter, Schularick, and Shim (2019) and Rojas, Vegh, and Vuletin (2020). Limitation: distinguishing motivations for policy adoption is often difficult and this method does not identify unanticipated measures.
- “Policy surprises” methods measure deviations from estimated policy rules (drawing on fiscal policy literature, Auerbach and Gorodnichenko 2013). Examples: Cizel and others (2019), Brandao-Marques and others (2020), Nier, Olafsson, and Rollinson (2020), Ahnert and others (2021), Gelos and others (2022). Advantages: transparency and ease of implementation across many measures. Challenge: obtaining a good first-stage fit while retaining sufficient variation in “policy surprises” for precise effect estimation.

### 3. What We Are Learning About the Costs and Benefits of Macroprudential Policy for Economic Activity

#### A. Near-Term Costs to Output
- Overall near-term negative impact on economic activity appears small, supporting the view of macroprudential policies as an efficient “surgical” tool.
- Evidence points to statistically significant negative effects on economic activity in the near term (Araujo and others 2020).
- Average effect: 0.004 standard deviations of output from tightening macroprudential policy measured by broad composite indices comprising tools across all types.
- Costs of additional capital and liquidity buffers are modest in the short term and might yield output benefits in the long term (examples: Gambacorta and Shin 2016, Bahaj and Malherbe 2020). Procyclical short-term effects are stronger if buffer implementation does not allow gradual phase-in (BIS 2010; IMF 2012; Imbierowicz, Kragh, and Rangvid 2018; Fang and others 2022).
- LTV caps:
  - A tightening by 10 percentage points of the LTV ratio cap yields a decline in output of 1.1 percent after four years, roughly corresponding to the effect of a tightening of monetary policy by 25 basis points (Richter and others 2019).
  - The effect of a 10 percentage points change in the LTV limit on consumption growth is around 1 percentage points (Alam and others 2019).
  - Effects on financial variables (credit and asset prices) are measured as substantially—about six times—stronger in percentage terms than effects on output and consumption, indicating a favorable trade-off.

#### B. Effects on Resilience
- Literature examines whether macroprudential tools reduce amplification of adverse shocks and strengthen system resilience (building on capital tools literature: Damar and Molico 2016; Cournède, Sakha, and Ziemann 2019).
- Estimated resilience effects tend to be sizeable across different tools:
  - For the US subprime crisis of 2008, estimates suggest a countercyclical capital buffer of 4.7 percent would have been sufficient to enable banks to continue growing their balance sheets in line with the long-term average growth rate (Aikman and others 2019).
  - Dynamic provisioning in Spain: a 1 percentage point higher buffer increased credit to firms by 9 percentage points, firm employment by 6 percentage points, and firm survival by 1 percentage point (Jiménez and others 2017).
- Borrower-based tools reduce borrowers’ probability of default and lenders’ loss given default in stress (Nier and others 2019, Ampudia and others 2021).
- Some lender-based tools (e.g., liquidity requirements) can reduce structural risks from interlinkages that magnify bank-failure impacts (Meulemann and Vander Vennet 2020).
- Macroprudential policies can mitigate impacts of external financial shocks on emerging market economies:
  - More stringent macroprudential regulation dampens the fall in GDP growth in response to adverse global financial shocks (Bergant and others 2020).
  - Tools that boost bank capital and liquidity, limit foreign exchange exposures, and contain overly risky credit drive these results.
  - Prudential policies can dampen the effect of capital inflows on economic growth (Ouyang and Guo 2019, Brandao-Marques and others 2020, Forbes 2020).

#### C. Intertemporal Trade-Offs
- Macroprudential tightening may entail short-term output costs but strengthen medium-term resilience.
- Empirical regularity: loosening financial conditions leads to better short-term growth but increases downside risks over the medium term (Growth-at-Risk literature; Adrian and others 2019, Adrian and others 2018).
- Interaction of looser financial conditions with macroprudential tightening can be examined to see if policy flattens the growth trade-off by pulling in the tail of the GDP distribution (examples: Brandao-Marques and others 2020, Galan 2020).
- Empirical findings:
  - Tightening macroprudential policies in response to a loosening of financial conditions reduces losses incurred over the entire policy horizon by about 9 percent (Brandao-Marques and others 2020).
  - The estimated impact from a sample of 37 countries combines effects through “leaning” and resilience strengthening.
  - Macroprudential policy effects are far larger than those from monetary policy, foreign exchange intervention, or capital controls. Responding to easing financial conditions by tightening monetary policy is found to be counterproductive, exacerbating output volatility over the entire horizon.
  - Countries that more frequently use macroprudential tools experience stronger and less volatile GDP growth (Boar and others 2017).
- Calls for further empirical progress:
  - Better operationalization of systemic risk and financial stability concepts.
  - Empirical advances linking effects back to the ultimate objective: reducing tail risks to economic activity.
  - Further work to clarify resilience channels and distributional implications of macroprudential tools, with consequences for long-term growth.

*Source: Box 1. Empirical Challenges—Endogeneity of Macroprudential Policy (continued), IMF Departmental Papers • Macroprudential Policy Effects.*

### 4. What We Are Learning About Nonlinear and

### 4. What We Are Learning About Nonlinear and Dynamic Effects, and the Potential for Leakages

### A. Diminishing Marginal Benefits
- Macroprudential tools introduce nonlinearity because regulatory constraints can be either binding or slack; costs and benefits depend on size and direction of policy change and financial cycle position.
- Empirical findings point to declining marginal net benefits as macroprudential settings are progressively tightened:
  - Tighter macroprudential policy settings have decreasing marginal returns in emerging economies’ GDP sensitivity to global financial shocks (Bergant and others 2020): a tightening from the lowest reading of the aggregate indicator toward the sample median sharply reduces adverse GDP effects, while tightening from higher levels has weaker effects.
  - For bank capital buffers in advanced economies, marginal benefits of increases in capital are high initially but decline rapidly once banks’ risk-weighted capital ratios reach the 15–23 percent level; once within this range, larger buffers would not have been desirable when capital also comes with costs (Dagher and others 2016).
  - Similar nonlinear effects of capital on bank lending are evidenced in Indonesia (Catalán, Hoffmaister, and Anggadewi Harun 2020).
- Borrower-based tools (e.g., DSTI, LTV) show threshold effects in resilience benefits:
  - Mortgage default probability rises only when DSTI exceeds an estimated threshold of 50 percent based on Romanian credit registry data (Nier and others 2019).
  - For consumer loans, the threshold above which higher DSTI ratios increase default probability is lower than for mortgages.
  - When regulatory limits are set just below identified thresholds, further tightening cannot achieve additional decreases in borrower default probability.
- Side-effects strengthen with aggressive tightening:
  - Studies (Alam and others 2019) find diminishing effect on household credit per unit of LTV tightening as overall size of change increases, consistent with leakage toward nonbanks.
  - Complementary use of other macroprudential tools in countries with tight LTV limits could improve overall policy efficiency.
- Overall conclusion: evidence points to declining marginal net benefits once stringency exceeds certain levels for both lender- and borrower-based tools, while side-effects increase; further research across settings is needed to improve calibration.

### B. Effects over the Financial Cycle
- Macroprudential policy effects on credit and asset prices are more potent when vulnerabilities are growing:
  - Multiple studies (Lim and others 2011; Claessen, Ghosh, and Mihet 2013; McDonald 2015; Alam and others 2019; De Schryder and Opitz 2021) indicate stronger effects when financial vulnerabilities are building, as tightening then more readily develops “bite” on credit and asset price growth (Araujo and others 2020).
- Resilience benefits depend on existing degree of vulnerabilities:
  - Net benefits of borrower-based tools (LTV and LTI caps) appear greater than lender-based tools when credit-to-GDP is already high.
  - Conversely, lender-based tools (capital and reserves requirements) yield larger benefits where credit is low relative to GDP and may help support credit in downturns.
- Table 2 (Reduction in Loss from Macroprudential Measures) — selected entries preserved exactly:
  - Columns labeled Low Credit / High Credit with ωy and ωp variants.
  - MPM All: −0.089**  −0.086**  −0.084**  −0.099**  −0.094**  −0.090**
  - MPM Borrower-Based: −0.033  −0.032  −0.031  −0.083***  −0.078***  −0.075***
  - MPM FI-Based: −0.076**  −0.072**  −0.070**  −0.028  −0.027  −0.026
  - MP: 0.137***  0.132***  0.129***  0.126***  0.120***  0.115***
  - Note: Vulnerabilities measured by level of credit to GDP—high (low) vulnerabilities mean credit to GDP at 75th (25th) percentile.

### C. Effects of Tightening versus Loosening
- Some evidence suggests tightening actions have stronger effects than loosening actions (pre-COVID-19 sample):
  - Tightening effects are more often statistically significant than loosening effects; tightening coefficients in absolute terms are often larger than easing coefficients (Araujo and others 2020).
  - This may reflect that tightening constrains borrowing decisions, while relaxation does not guarantee agents will use the additional borrowing space.
- Caveats and heterogeneity:
  - Findings are preliminary and may reflect higher incidence and estimation power for tightening actions pre-COVID-19; loosening actions are often bunched around the GFC, raising endogeneity concerns.
  - The result does not hold for all tools; for example, dynamic provisioning in Spain had a smaller negative effect on firm credit but provided benefits during stress when capital was scarce (Jiménez and others 2017).
- COVID-19 experience emphasizes value of usable macroprudential buffers:
  - Relaxation of capital buffer requirements supported credit for banks near regulatory requirements.
  - Stigma and overlapping prudential requirements may hinder buffer use unless explicitly relaxed.
  - Evidence from the pandemic suggests a case for positive neutral buffers that could be drawn down in times of stress (see BCBS 2022).
- Regulatory forbearance used extensively during the pandemic (relaxation of accounting standards for nonperforming loans), indicating trade-offs; greater use of explicit macroprudential buffers could reduce reliance on forbearance.

### D. Leakages and Asset Substitution Effects
- Macroprudential policy faces a “boundary problem”: activation of binding measures tends to shift activity toward less regulated sectors or across borders.
  - Leakages include nonbank leakage (credit provision by nonbanks) and cross-border leakage (credit from across the border) when domestic banking system is subject to measures (IMF 2014a).
  - Asset substitution (risk-shifting) occurs when banks attempt to maintain pre-policy risk exposures; documented in empirical studies (Aiyar, Calomiris, and Wieladek 2014; Reinhardt and Sowerbutts 2015; Avdjiev and others 2016).
- Growing evidence of leakage from banks to nonbanks:
  - Upon introduction of macroprudential measures, bank credit growth shrinks relative to a counterfactual while nonbank credit expands relative to the counterfactual, even if aggregate total credit declines (Cizel and others 2019).
  - Leakage to nonbanks is stronger in advanced economies with more developed financial markets and is more pronounced in response to quantitative constraints (bank- or borrower-based limits) than price-based tools, consistent with earlier IMF (2014b) evidence.
- Leakage can also occur across banks with different regulatory statuses (e.g., unregulated banks or resident foreign branches increasing lending in response to tighter capital requirements).

### Box 2. Effects of Macroprudential Buffers in Times of Stress: The COVID-19 Experience
- Preliminary evidence indicates macroprudential buffers and the ability to relax them have value in stress periods, though identifying effects is complex due to concurrent policy measures.
- Key empirical findings:
  - BCBS 2021: Increase in CDS spreads from end-2019 to March/April 2020 was strongly conditioned by banks’ capital strength; banks with larger CET1 ratios experienced far smaller CDS spread increases.
    - Effect driven largely by “headroom” above combined buffer requirement rather than CET1 ratio level per se.
  - Banks with less capital space experienced larger funding-cost increases and lent less during the pandemic than banks with more headroom.
  - Berrospide (2021) and other granular studies for the United States and the euro area find banks reluctant to dip into regulatory-constrained regions of capital structure; banks close to buffer regions reduced loan commitments to small- and medium-sized enterprises.
- Implications:
  - Ample capital “headroom” supported loan growth during the pandemic.
  - Usable explicit macroprudential buffers can help sustain credit in stress periods and reduce reliance on regulatory forbearance.

*IMF DEPARTMENTAL PAPERS • Macroprudential Policy Effects (Chapter 4).*

### 1.4 percent more (quarterly) and were 4 percent more likely to end pre-existing lending relationships

### 1.4 percent more (quarterly) and were 4 percent more likely to end pre-existing lending relationships

### Banks' behavior and regulatory buffers during the COVID-19 pandemic
- Banks that were more constrained cut lending by "1.4 percent more (quarterly)" and were "4 percent more likely to end pre-existing lending relationships" during the pandemic than less-constrained banks.
- Banks were reluctant to “breach” regulatory buffers (including the Basel III capital conservation buffer and the stress capital buffer in the United States) even when supervisors signaled capital should be used.
- Fear of an increase in funding costs from breaching buffers is a likely driver of banks' reluctance to use buffers.
- Losses incurred or expected amplify cut-backs in lending; larger capital buffers can cushion this effect.

### Evidence on the effects of releasing capital buffers
- Explicit release of countercyclical capital buffer requirements and systemic risk buffers leads to an expansion in credit, especially for banks close to combined regulatory thresholds.
- The expansion in credit from the release of countercyclical buffers especially benefits small- and medium-sized enterprises.
- While the relaxation of macroprudential buffers appears to have worked in line with desired policy impacts during COVID-19, the effect is observed in a context where other supporting policies were also deployed.

### Other policies that affected financial amplification during COVID-19
- Fiscal support included government guarantees for new lending and direct fiscal support to households and firms.
- Payment moratoriums and regulatory forbearance likely reduced near-term banking system losses on existing loans.
- Policies to constrain banks’ dividend payouts led to increasing capital resources through the COVID-19 episode in many jurisdictions.
- A release of buffers in the absence of other supporting policies has not been fully tested.

### Cross-border leakages and asset substitution
- Cross-border leakage: “Other flows” from the balance of payments (cross-border loans and deposits received by financial institutions and nonfinancial corporate sector flows) tend to move in tandem with domestic credit.
- Tightening macroprudential policy in response to increases in domestic credit can lead to further rises in foreign inflows; leakage effects are strongest for borrower-based limits.
- When FX regulations are applied to the banking system, bank FX borrowing and lending fall, but corporate issuance of FX debt securities in international markets increases.
- Asset substitution: Banks subject to borrower-focused constraints (LTV, LTI, DSTI) may expand credit to corporate borrowers or higher-income borrowers, increase exposure to high-yield securities, or increase potentially riskier corporate lending.
- Domestic and cross-border leakages are present across broad-based, housing, and liquidity tools, with larger effects detected for broad-based and liquidity measures.
- Policy implication: approaches may need to extend beyond domestic banks’ credit to capture nonbank credit provision and credit from abroad (for example, through reciprocity agreements or CFM/MPMs), though more evidence is needed on effectiveness.

### Time profile of macroprudential policy effects
- Most studies focus on short-term effects—often up to four quarters; less is known about longer horizons.
- Impulse-response studies typically use horizons of "14 to 16 quarters."
- Credit effects tend to be hump-shaped, peaking at a "one- to two-year horizon" for most studies.
- Macro variables (GDP, consumption, prices) tend to peak after "two to three years."
- Evidence on borrower-based tools:
  - Richter, Schularick, and Shim (2019): after tightening maximum LTV ratios, real household credit is reduced by "almost 6 percent after two years" and mortgage credit by "more than 5 percent"; coefficients remain stable for longer horizons but confidence intervals widen and effects are no longer statistically significant after four years.
  - Significant and persistent effects on house prices: the Richter, Schularick, and Shim (2019) study finds a "highly significant eight-percent decline after four years" in real house prices.
- Resilience effects:
  - Net benefits of macroprudential policy continue accruing in the short term and are fully realized in the medium term, with benefits peaking "around after 10 quarters" in one study.
  - Borrower-based macroprudential tools show more persistent beneficial effects than financial-institution-based tools (capital and reserves requirements); for financial-institution-based tools, about "half the initial reduction in losses is reversed after 14 quarters."
  - Tightening capital measures during expansions may take up to "two years" to show benefits on growth-at-risk, while borrower-based measures have more immediate positive impacts; in downturns the benefits of loosening capital measures materialize more quickly.

### Key empirical magnitudes and sample facts
- Study sample example: "39 countries during the period 2001:Q1–2018:Q4."
- In that sample, macroprudential policies were tightened in "more than 40 percent" of observations, but those tightenings occurred without another policy change in only "approximately 3 percent" of the time.
- Typical empirical horizons: "up to four quarters" for many short-term studies; "14 to 16 quarters" for longer-run impulse-response analyses.

*IMF DEPARTMENTAL PAPERS • Macroprudential Policy Effects*

### Chapter 3) have analyzed them on their own, or primarily considered their interaction with monetary policy

### Chapter 3) have analyzed them on their own, or primarily considered their interaction with monetary policy

### Objective and scope
- Evaluates whether the response of bank credit to macroprudential policies varies with:
  - (1) monetary policy (MP)
  - (2) capital flow management measures (CFMs)
  - (3) foreign exchange market interventions (FXIs)
  - (4) fiscal policy
- Sample: 23 advanced economies (AEs) and 16 emerging markets and developing economies (EMDEs) over the period 2001:Q1–2018:Q4.
- Macroprudential policy (MaPP) data source: Integrated Macroprudential Policy database (iMaPP).
- Three MaPP indices used:
  - overall index encompassing all 17 instruments
  - borrower-based (demand) instruments (limits on the loan-to-value ratio and limits on the debt-service-to-income ratio)
  - lender-based (supply) instruments (the other 15 instruments)
- CFM index constructed from net inflows and net outflows actions.
- FXI defined as all transactions that change the central bank’s foreign currency position (positive value indicates an increase in the position).
- Credit data: lending by banks to the nonfinancial private sector (BIS and CEIC), deflated by national consumer CPI (WEO).
- Fiscal data: government debt (BIS and CEIC) and GDP (WEO) used to construct debt-to-GDP ratios.
- Policy shocks: authors extract the shock component for each policy to mitigate endogeneity concerns.

### Theoretical channels (summary)
- Monetary policy can affect credit via:
  - bank lending channel (affects loan supply)
  - balance sheet channel (affects borrowers’ net worth and collateral values)
  - open-economy channel (affects exchange rate and capital flows)
- CFMs and FXIs affect credit through capital flows and exchange rate movements; CFMs can curb inflows and FXIs can slow appreciation.
- Fiscal policy effects on credit are ambiguous:
  - tax incentives (mortgage interest deductions, interest deductibility for corporates) can increase leverage and credit demand
  - government spending may either reduce or increase credit demand depending on mechanisms (interest rate effects, liquidity, riskiness of borrowers, borrowing constraints)

### Main empirical findings
- In an empirical model controlling for all policies and interactions, evidence of interactions between MaPP, MP, and FXI is found among EMDEs; little evidence of important interactions for AEs.
- EMDEs:
  - The triple interaction MaPP*MP*FXI is negative and significant.
  - A one standard deviation tightening in each of the three policies among EMDEs is associated with:
    - a decrease in credit of 0.9 percent over a period of one quarter
    - the effect remains significant up to four quarters with a cumulative decrease of 1.8 percent
  - Interpretation: the three policies reinforce each other in reducing credit; the cumulative 1.8 percent equals one third of the average cumulative credit growth over the same period.
- AEs:
  - Little evidence that the marginal effect on credit of any one policy is affected by the policy settings of another.
  - Some indication that tightening CFMs and an increase in the FX position amplify the negative impact of MaPP on credit, but results are not robust over time.

### Key numerical estimates from table results (EMDEs, coefficient of lag 1 for horizon h = 0)
- Selected coefficients (standard errors in parenthesis):
  - MaPP −0.091 (0.09)
  - MP −0.133 (0.35)
  - CFM 0.011 (0.10)
  - Fiscal −0.169 (0.18)
  - FXI −0.139 (0.09)
  - MaPP*MP −0.260 (0.23)
  - MaPP*CFM 0.045 (0.08)
  - MaPP*Fiscal 0.126* (0.07)  [* denotes significance at 10 percent]
  - MaPP*FXI −0.062 (0.06)
  - MaPP*MP*FXI −0.899** (0.36)  [** denotes significance at 5 percent]
  - MaPP*MP*Fiscal*FXI −0.184 (0.21)
  - MaPP*MP*Fiscal*CFM −0.165 (0.24)
  - MaPP*MP*CFM*FXI −0.090 (0.20)
  - MaPP*Fiscal*CFM*FXI 0.075 (0.10)
  - MaPP*MP*Fiscal*CFM*FXI −0.393 (0.28)
- Number of observations: 687

### Key numerical estimates from table results (AEs, coefficient of lag 1 for horizon h = 0)
- Selected coefficients (standard errors in parenthesis):
  - MaPP −0.200** (0.08)
  - MP 0.013 (0.06)
  - CFM −0.071 (0.05)
  - Fiscal 0.094** (0.04)
  - FXI 0.054 (0.04)
  - MaPP*MP 0.031 (0.05)
  - MaPP*CFM −0.037 (0.05)
  - MaPP*Fiscal 0.019 (0.04)
  - MaPP*FXI 0.017 (0.05)
  - MaPP*MP*CFM −0.068** (0.03)
  - MaPP*MP*FXI −0.062 (0.04)
  - MaPP*MP*CFM*FXI 0.027 (0.05)
- Number of observations: 1397

### Robustness and mechanism checks (EMDEs)
- Separation of MaPP into borrower-based (demand) and lender-based (supply) shocks:
  - The interactive effect of MaPP with MP and FXI on credit is primarily driven by lender-based (supply) measures.
- Excluding tax-related measures from MaPP indicator:
  - Main EMDE results continue to hold, though they are less persistent.
- Decomposition of lender-based measures into categories examined:
  1. FX measures (limits on foreign exchange positions and limits on foreign currency)
  2. Measures to mitigate risks from systemically important institutions (capital and liquidity surcharges)
  3. Capital related measures (countercyclical buffers, conservation buffers, capital requirements, leverage limits, loan loss provisions)
  4. Liquidity related measures (liquidity and reserve requirements)
  5. Direct measures to curb credit limits (limits on credit growth, loan restrictions, tax measures, limits on loan-to-deposit ratios)
  6. Other measures
- Findings: the triple interaction in EMDEs is largely driven by liquidity measures.
  - Mechanism highlighted: tightening MP combined with FX purchases (which work against inflows and hold down the exchange rate) and tightening of reserve requirements (sterilization) reduces credit growth.
- Lasso-based machine learning (cross-fit partially out estimator) confirms negative significance of MaPP*MP*FXI, consistent with baseline linear regressions.

### Nonlinearities and state dependence
- Macroprudential policy interactions with other policies are more sizeable when credit growth is high.
  - Quantile regressions show MaPP*MP*FXI is significant at the 75th percentile (high credit growth states).
- Implication: policy complementarities are more effective during booms or extreme events.

### High-level policy implications
- For EMDEs:
  - FXI, MP, and MaPP are complementary tools.
  - Simultaneous tightening of these policies when credit growth is high leads to a larger reduction in credit growth than when each policy acts in isolation.
  - Emerging markets and developing economies may need a combination of tools to achieve policy goals given constraints (for example, reserve levels, exchange rate considerations, institutional capacity).
- For AEs:
  - Limited evidence that policy interactions materially change the marginal effect of a single policy on credit; policy tools appear more independent in their effects on credit in the sample period.

*Source: IMF Departmental Paper — Chapter content provided in the source PDF.*

### 1. Demand MaPP −0.349

### mpeeoqea - 1. Demand MaPP −0.349

### Regression estimates and policy-shock coefficients
- Demand MaPP −0.349 (0.52)
  - −1. 2 8 3 (0.96)
  - −1. 3 61 (1.16)
  - −1. 5 4 4 (1.32)

- Supply MaPP −0.673* (0.32)
  - −1. 215* * (0.45)
  - −1. 24 6* * (0.58)
  - −1. 6 3 2 * * (0.72)

- Supply component estimates (coefficients with clustered—by country—standard errors in parentheses)
  - a. Capital measures 0.140 (0.19)
    - 0.059 (0.23)
    - −0.091 (0.28)
    - −0.583 (0.36)
  - b. Limits on credit 0.210 (0.32)
    - −0.468 (0.61)
    - −0.450 (0.81)
    - −0.901 (0.92)
  - c. FX measures −0.279 (0.44)
    - 0.050 (0.69)
    - 0.303 (0.74)
    - 1.135 (0.94)
  - d. Liquidity measures −0.423* (0.24)
    - −0.691* (0.38)
    - −0.758 (0.43)
    - −1. 2 6 6* * (0.54)
  - e. SIIs measures 0.330* (0.18)
    - 0.148 (0.29)
    - 0.127 (0.43)
    - −0.510 (0.48)
  - f. Other 0.109 (0.36)
    - −0.240 (0.52)
    - −0.492 (0.52)
    - −1. 417 * * (0.65)

- MaPP (excluding tax measures) −1.141* * * (0.26)
  - −1.12 2 * * (0.46)
  - −0.985 (0.86)
  - −0.382 (0.98)

- Machine learning (Lasso)
  - −0.857*** (0.16)
  - −1.531*** (0.30)
  - −1.865*** (0.46)
  - −1.965*** (0.56)

### Quantile regressions (coefficients with standard errors)
- 25th percentile
  - −0.610 (0.599)
  - −0.843 (0.782)
  - 0.080 (1.026)
  - −1. 0 94 (1.136)
- 50th percentile
  - −0.217 (0.430)
  - −0.758 (0.827)
  - −1. 876 (1. 298)
  - −2.412* (1. 248)
- 75th percentile
  - −1. 0 21* * (0.516)
  - −1. 2 8 9 (1.322)
  - −2.673* * (1.347)
  - −2.940 (2.330)

### Notes on measures, estimation, and definitions (as presented)
- Supply MaPP and Demand MaPP denote the macroprudential policy shocks estimated using indexes that include 15 lender-based and 2 borrower-based instruments, respectively.
- MP and FXI refer to the estimated shocks for monetary policy and FXI, respectively.
- Supply capital includes countercyclical buffers, conservation buffers, capital requirements, leverage limits and loan loss provisions.
- Supply direct includes limits on credit growth, loan restrictions, tax measures (for example, stamp duties and capital gain taxes), limits on loan to deposit ratios.
- Supply FX includes limits on foreign exchange positions and limits on foreign currency.
- Supply liquidity includes liquidity measures and reserve requirements.
- Supply SI includes measures to mitigate risks from global and domestic systemically important financial institutions (for example, capital and liquidity surcharges).
- Supply other includes all other supply related macroprudential measures.
- All regressions control for quarterly time and country fixed effects. Clustered—by country—standard errors are included in parenthesis.  *, **, *** denote significance at 10, 5 and 1 percent, respectively.

### Key empirical conclusions and policy-relevant findings (verbatim summaries)
- Empirical evidence offers consistent support for the effectiveness of macroprudential policy. A meta-analysis of the literature on how macroprudential tools affect asset prices and credit confirms this finding, with micro-level evidence pointing to larger effects than those found in aggregate data, especially for liquidity and housing related tools.
- The sacrifice in terms of output foregone tends to be modest when macroprudential policy is used to achieve financial stability goals. Dampening effects on output are found to be modest when tools are tightened outside of periods of stress. Such use can then reduce the build-up of vulnerabilities and increase resilience to shocks, thereby reducing tail-risks to future output, in line with the objectives of the policy.
- Effects of macroprudential policy seem to depend on both the direction and the size of the policy change. There is evidence for diminishing marginal returns: the net benefits of tighter policies tend to decline and eventually be exhausted. Some evidence also points to stronger effects of tightening than loosening actions, although evidence on the effectiveness of relaxation measures is still more limited.
- The experience during the COVID-19 pandemic suggests that releasing capital buffers, as part of the policy response, helped support the provision of credit to the economy and suggests there might be a case for positive neutral buffers going forward. Additional work should examine how dividend payment restrictions complemented these relaxation measures and enhanced their ultimate effectiveness by helping preserve capital.
- The emerging evidence also suggest that the resilience-building effects of macroprudential policy hold-up through time, especially for borrower-based tools, suggesting that preemptive use of such tools can reap important benefits.
- Further progress evaluating these issues is essential for policymaking. Policymakers need to know not just whether macroprudential intervention is useful, but also “how much” of it is needed to achieve the ultimate policy objectives effectively and efficiently. Some research is already available to help guide the calibration of macroprudential policy but further research and operational guidance are needed to ensure a more effective design and use of tools to mitigate systemic risks. To make progress, better data on the size of policy changes—the intensive margin of the change—is critical.
- Additional work is needed to evaluate the role of macroprudential policy in strengthening the resilience of the financial system. The challenge to understanding its ultimate effectiveness has been the lack of consensus on how to best operationalize concepts such as financial stability and systemic risk, including on whether their dimensionality can be reduced to a single or at least a few indicators. Recent empirical progress in estimating the effects of macroprudential policy on risks to output is useful in providing a summary assessment that can be tied back to the ultimate objectives of macroprudential policy, thereby providing an important anchor for policy discussions.
- New analysis presented in this paper highlights the interaction of macroprudential measures with other policies, especially in EMDEs. Among EMDEs, FXI, monetary, and macroprudential policies reinforce each other and tightening these policies simultaneously can have a larger impact on credit. In contrast, there is no robust evidence of interactions with macroprudential policies for AEs.
- Nonbank financial intermediation (including fintech), crypto assets, and digital money are the new frontier of macroprudential policy. This paper has largely focused on the evidence for traditional bank-centered measures. However, given the rapid recent growth of nonbank financial institutions, including mutual funds and fintech firms, and the growing evidence on leakage to nonbanks, macroprudential regulation in this area will gain further prominence in coming years. Similarly, the emergence of crypto assets and forms of digital money pose new challenges to macroprudential policies that will need to be tackled.

*Source: Authors’ calculations; IMF DEPARTMENTAL PAPERS • Macroprudential Policy Effects (excerpt as provided).*

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### Theoretical, methodological, and complementary policy literature
- Acemoglu, Daron, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. 2015. “Systemic Risk and Stability in Financial Networks.” American Economic Review 105 (2): 564–608.
- Aoki, Kosuke, Gianluca Benigno, and Nobuhiro Kiyotaki. 2018. “Monetary and Financial Policies in Emerging Markets.” Unpublished, Princeton University.
- Auerbach, Alan J., and Yuriy Gorodnichenko. 2013. “Output Spillovers from Fiscal Policy.” American Economic Review 103 (3): 141–46.
- Auerbach, Alan J., Yuriy Gorodnichenko, and Daniel Murphy. 2020. “Effects of Fiscal Policy on Credit Markets.” AEA Papers and Proceedings 110: 119–24.
- Avdjiev, Stefan, Catherine Casanova, Patrick McGuire, and Goetz von Peter. 2016. “International Prudential Policy Spillovers: A Global Perspective.” BIS Working Papers 589, Bank for International Settlements, Basel.
- Bailey, ... [Note: content continues in the source; the list above follows the source ordering and preserves titles, authors, and publication details exactly as provided.]

*Source: mpeeoqea - References (mpeeoqea - References) — https://www.imf.org/-/media/files/publications/dp/2023/english/mpeeoqea.pdf*

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_Source: https://www.imf.org/-/media/files/publications/dp/2023/english/mpeeoqea.pdf_