## Economic Scarring: Channels and Policy Implications

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### I. Introduction and scope
- Study period and sample:
  - panel starting from 1970 covering 192 countries (cross-country analysis)
  - harmonized sectoral data for 37 countries during 1971-2013
  - industry-level analysis using EU KLEMS for 27 EU member countries and 34 industries
- Crisis types analyzed:
  - systemic banking crises, civil wars, currency crises, and health crises (SARS (2003), H1N1 (2009), MERS (2012); Ebola (2014) excluded from some sectoral analyses due to data limits)
- Methodology overview:
  - Aggregate impulse response functions (IRFs) estimated using a univariate autoregressive model of real GDP growth (see equation (1))
  - Sectoral/industry IRFs estimated using annual growth rates of real value added or employment and a panel specification with country-year fixed effects (see equation (2))
  - Robustness checks include an alternative financial crisis dataset and separation of single-crisis vs. multiple-crisis episodes

### II. Main empirical findings — existence and heterogeneity of scarring
- Core finding:
  - Persistent output losses (economic scarring) occur across all crisis types
- Key quantitative comparisons and magnitudes:
  - Output loss at a ten-year horizon from a civil war is, on average, more than 10 percent compared to a country’s pre-crisis trend without the shock and is more than six folds that of a currency crisis
- Sectoral heterogeneity:
  - Construction: appears to have the deepest scarring across all types of crises
  - Financial services: do not statistically generate economic scarring in a civil war but show the deepest scarring in a systemic banking crisis
  - Trade/restaurants/hotels: scarring only occurs in health and systemic banking crises
  - Other services (including education, health, legal, and traditional services): face persistent losses from both health crises and civil wars
  - Government services: impact is not significant in any crisis type (consistent with countercyclical government activity)
  - Manufacturing: exhibits deeper scarring relative to trade/restaurants/hotels in the sectoral sample (noted as counterintuitive and requiring further disaggregation)

### III. Hypothesized channels of scarring (literature and framework)
- Supply-side channels:
  - Labor market hysteresis (including routinization and bargaining effects)
  - Weak capital and R&D investment due to corporate balance-sheet deterioration and heightened uncertainty
  - Human capital deterioration (skill erosion from long unemployment spells; disruptions to education and health)
  - Resource misallocation (survival of larger/connected firms at the expense of young/small potentially productive firms)
- Demand- and policy-related drivers:
  - Weakening household balance sheets and precautionary savings that slow demand-driven recovery
  - Policy responses can mitigate or deepen scarring (e.g., fiscal austerity raising taxes on labor and cutting public investment can worsen medium-term productivity; increased health spending can speed recovery)

### IV. Empirical verification of supply-side channels (industry-level evidence)
- Data and identification choices:
  - EU KLEMS industry-level database used for supply-side channel tests (27 EU countries, 34 industries)
  - Focused crises for this exercise: health crises and systemic banking crises (limited variation in other crisis types in EU KLEMS sample)
  - Country-year fixed effects used to control for aggregate endogenous policy/demand responses
- Sectoral exposures and indicators used:
  - Routinization: occupational-level routinization index aggregated to industries using 2008 employment shares from Eurostat
  - Financial constraint: Rajan-Zingales (1998) style external financial dependence (US Compustat-based) and country pre-crisis non-financial sector debt-to-asset ratio interacted with sector financial dependence
  - Knowledge/skill intensity: sector classified as knowledge intensive if tertiary educated persons represent more than 33% of total employment (Eurostat definition)
  - Young/startup firms: share of young firms (0–5 years) and startups (0–2 years) from OECD SBDS at 2-digit NACE level, using 2008 values to avoid endogeneity
- Principal empirical results (health crises):
  - Industries with a larger share of young firms (age 0-5) or startup firms (age 0-2) recover more slowly in both GVA growth and employment growth
  - Higher sector routinization is associated with lower employment growth and lower value-added growth after a health crisis (routinization channel)
  - Balance-sheet channel: pre-crisis indebtedness interacts with sector external finance dependence to depress value-added growth (investment-sensitive)
  - Knowledge/skill intensity: mixed results — higher skill-intensity sectors suffer more in employment growth but tend to perform better in value-added growth (possible remote-work adaptation), though statistical significance is relatively weak
- Principal empirical results (systemic banking crises):
  - Scarring more likely in sectors with a larger share of young firms or with more routine workers
  - Knowledge intensity not robustly associated with scarring in systemic banking crises in this sample
  - Aggregate balance-sheet indicator could show non-intuitive sign, but in highly leveraged countries, sectors with higher external financial dependence experience significantly larger losses after a systemic banking crisis
- Caveats and limits:
  - Supply-side channel tests limited to mostly advanced economies (27 EU countries); results may not generalize to emerging markets or low-income countries
  - Interactions among supply-side channels are not fully captured
  - Specification cannot control for sector-targeted fiscal policies that may mitigate sectoral differences (estimates likely represent a lower bound of differential responses)

### V. Policy implications and recommendations
- Macroeconomic policy:
  - Because scarring implies persistent output losses, fiscal and monetary policy that influence macro conditions can affect scarring magnitude
  - Early and aggressive macro policy action can minimize scarring; running a “high-pressure” economy may boost productivity and reduce downturn persistence
- Supply-side and structural policies:
  - Health/crisis-specific human capital policies: promote early childhood development, life-long learning, and education-system improvements to mitigate human capital losses
  - Labor market policies: temporarily expand social safety nets and active labor market programs to facilitate worker reallocation
  - Digital and skill policies: foster digital literacy to help adaptation and remote-work transitions
  - Support for young/high-potential firms: targeted support to prevent resource misallocation and to preserve potential future productivity (improve bankruptcy and debt restructuring mechanisms)
  - Corporate balance-sheet repair: reduce debt overhang to promote investment
  - Public investment: boost public investment, particularly in climate-friendly infrastructure, to crowd-in private investment and promote sustainable growth

### VI. Conclusion — synthesis of quantitative evidence
- Robust outcomes:
  - Economic scarring is a robust outcome across diverse crisis types and persists over medium-to-long horizons
- Heterogeneity highlights:
  - Civil war: output loss at a ten-year horizon is, on average, more than 10 percent relative to pre-crisis trend and more than six folds that of a currency crisis
  - Sectoral differences: construction consistently among the most scarred; financial services most affected by systemic banking crises; trade/restaurants/hotels affected primarily by health and systemic banking crises; government services show no significant scarring on average
- Validated supply-side channels in advanced-country industry data:
  - Young/startup firm exposure and routinization are associated with slower recovery and deeper scars
  - Human capital disruptions are important for health crises (high-skilled industries show deeper employment scars)
  - In high-leverage countries, external financial dependence amplifies scarring in systemic banking crises
- Policy takeaway:
  - Combine prompt macro stabilization with targeted supply-side measures (human capital, firm support, balance-sheet repair, public investment) to limit long-term scarring and support sustainable recovery

*Source: IMF staff summary of “Economic Scarring: Channels and Policy Implications.”*

### 1. Impulse Response Functions for Each Type of Crisis ..................................................................

### wpiea2022248-print-pdf - 1. Impulse Response Functions for Each Type of Crisis ..................................................................

### Major sections
- 1. Impulse Response Functions for Each Type of Crisis ...................................................................................... 5
- 2. Crisis Frequency Across Countries and Time ................................................................................................... 6
- 3. Impulse Response Function at the Sectoral Level ............................................................................................ 7

### Tables listed
- TABLES
  - 1 Health Crisis—Sectoral GVA Growth ................................................................................................................. 11
  - 2 Health Crisis—Employment Growth ................................................................................................................. 12
  - 3 Systemic Banking Crisis—Sectoral GVA Growth ............................................................................................... 13
  - 4 Systemic Banking Crisis—Employment Growth ............................................................................................... 14

*Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022248-print-pdf.pdf*

### References .............................................................................................................

### Economic Scarring: Channels and Policy Implications

### I. Introduction and scope
- Study period and sample: panel starting from 1970 covering 192 countries (cross-country analysis); harmonized sectoral data for 37 countries during 1971-2013; industry-level analysis using EU KLEMS for 27 EU member countries and 34 industries.
- Crisis types analyzed: systemic banking crises, civil wars, currency crises, and health crises (SARS (2003), H1N1 (2009), MERS (2012); Ebola (2014) excluded from some sectoral analyses due to data limits).
- Methodology overview:
  - Aggregate impulse response functions (IRFs) estimated using a univariate autoregressive model of real GDP growth (see equation (1)).
  - Sectoral/industry IRFs estimated using annual growth rates of real value added or employment and a panel specification with country-year fixed effects (see equation (2)).
  - Robustness checks include an alternative financial crisis dataset and separation of single-crisis vs. multiple-crisis episodes.

### II. Main empirical findings — existence and heterogeneity of scarring
- Core finding:
  - Persistent output losses (economic scarring) occur across all crisis types.
- Key quantitative comparisons and magnitudes:
  - Output loss at a ten-year horizon from a civil war is, on average, more than 10 percent compared to a country’s pre-crisis trend without the shock and is more than six folds that of a currency crisis.
- Sectoral heterogeneity (sector-level patterns summarized):
  - Construction: appears to have the deepest scarring across all types of crises.
  - Financial services: do not statistically generate economic scarring in a civil war but show the deepest scarring in a systemic banking crisis.
  - Trade/restaurants/hotels: scarring only occurs in health and systemic banking crises.
  - Other services (including education, health, legal, and traditional services): face persistent losses from both health crises and civil wars.
  - Government services: impact is not significant in any crisis type (consistent with countercyclical government activity).
  - Manufacturing: exhibits deeper scarring relative to trade/restaurants/hotels in the sectoral sample (noted as counterintuitive and requiring further disaggregation).

### III. Hypothesized channels of scarring (literature and framework)
- Supply-side channels (mechanisms described):
  - Labor market hysteresis (including routinization and bargaining effects).
  - Weak capital and R&D investment due to corporate balance-sheet deterioration and heightened uncertainty.
  - Human capital deterioration (skill erosion from long unemployment spells; disruptions to education and health).
  - Resource misallocation (survival of larger/connected firms at the expense of young/small potentially productive firms).
- Demand- and policy-related drivers:
  - Weakening household balance sheets and precautionary savings that slow demand-driven recovery.
  - Policy responses can mitigate or deepen scarring (e.g., fiscal austerity raising taxes on labor and cutting public investment can worsen medium-term productivity; increased health spending can speed recovery).

### IV. Empirical verification of supply-side channels (industry-level evidence)
- Data and identification choices:
  - EU KLEMS industry-level database used for supply-side channel tests (27 EU countries, 34 industries).
  - Focused crises for this exercise: health crises and systemic banking crises (limited variation in other crisis types in EU KLEMS sample).
  - Country-year fixed effects used to control for aggregate endogenous policy/demand responses.
- Sectoral exposures and indicators used:
  - Routinization: occupational-level routinization index aggregated to industries using 2008 employment shares from Eurostat.
  - Financial constraint: Rajan-Zingales (1998) style external financial dependence (US Compustat-based) and country pre-crisis non-financial sector debt-to-asset ratio interacted with sector financial dependence.
  - Knowledge/skill intensity: sector classified as knowledge intensive if tertiary educated persons represent more than 33% of total employment (Eurostat definition).
  - Young/startup firms: share of young firms (0–5 years) and startups (0–2 years) from OECD SBDS at 2-digit NACE level, using 2008 values to avoid endogeneity.
- Principal empirical results (health crises):
  - Industries with a larger share of young firms (age 0-5) or startup firms (age 0-2) recover more slowly in both GVA growth and employment growth.
  - Higher sector routinization is associated with lower employment growth and lower value-added growth after a health crisis (routinization channel).
  - Balance-sheet channel: pre-crisis indebtedness interacts with sector external finance dependence to depress value-added growth (investment-sensitive).
  - Knowledge/skill intensity: mixed results — higher skill-intensity sectors suffer more in employment growth but tend to perform better in value-added growth (possible remote-work adaptation), though statistical significance is relatively weak.
- Principal empirical results (systemic banking crises):
  - Scarring more likely in sectors with a larger share of young firms or with more routine workers.
  - Knowledge intensity not robustly associated with scarring in systemic banking crises in this sample.
  - Aggregate balance-sheet indicator could show non-intuitive sign, but in highly leveraged countries, sectors with higher external financial dependence experience significantly larger losses after a systemic banking crisis.
- Caveats and limits:
  - Supply-side channel tests limited to mostly advanced economies (27 EU countries); results may not generalize to emerging markets or low-income countries.
  - Interactions among supply-side channels are not fully captured.
  - Specification cannot control for sector-targeted fiscal policies that may mitigate sectoral differences (estimates likely represent a lower bound of differential responses).

### V. Policy implications and recommendations
- Macroeconomic policy:
  - Because scarring implies persistent output losses, fiscal and monetary policy that influence macro conditions can affect scarring magnitude.
  - Early and aggressive macro policy action can minimize scarring; running a “high-pressure” economy may boost productivity and reduce downturn persistence.
- Supply-side and structural policies (targeted to channels identified):
  - Health/crisis-specific human capital policies: promote early childhood development, life-long learning, and education-system improvements to mitigate human capital losses.
  - Labor market policies: temporarily expand social safety nets and active labor market programs to facilitate worker reallocation.
  - Digital and skill policies: foster digital literacy to help adaptation and remote-work transitions.
  - Support for young/high-potential firms: targeted support to prevent resource misallocation and to preserve potential future productivity (improve bankruptcy and debt restructuring mechanisms).
  - Corporate balance-sheet repair: reduce debt overhang to promote investment.
  - Public investment: boost public investment, particularly in climate-friendly infrastructure, to crowd-in private investment and promote sustainable growth.

### VI. Conclusion — synthesis of quantitative evidence
- Economic scarring is a robust outcome across diverse crisis types and persists over medium-to-long horizons.
- Heterogeneity is large:
  - Civil war: output loss at a ten-year horizon is, on average, more than 10 percent relative to pre-crisis trend and more than six folds that of a currency crisis.
  - Sectoral differences: construction consistently among the most scarred; financial services most affected by systemic banking crises; trade/restaurants/hotels affected primarily by health and systemic banking crises; government services show no significant scarring on average.
- Supply-side channels validated empirically for health crises and systemic banking crises in advanced-country industry data:
  - Young/startup firm exposure and routinization are associated with slower recovery and deeper scars.
  - Human capital disruptions are important for health crises (high-skilled industries show deeper employment scars).
  - In high-leverage countries, external financial dependence amplifies scarring in systemic banking crises.
- Policy takeaway: combine prompt macro stabilization with targeted supply-side measures (human capital, firm support, balance-sheet repair, public investment) to limit long-term scarring and support sustainable recovery.

*Source: IMF staff summary of “Economic Scarring: Channels and Policy Implications.”*

### References

### References

### Long-term damage, hysteresis, and slow recoveries
- Ball, L., 2014, Long-term Damage from the Great Recession in OECD Countries, European Journal of Economics and Economic Policies: Intervention, 11(2), pp.149–160.
- Blanchard, O.J. and Summers, L.H., 1986, Hysteresis and the European Unemployment Problem, NBER Macroeconomics Annual, 1, pp.15–78.
- Blanchard, O., Cerutti, E. and Summers, L., 2015, Inflation and Activity–Two Explorations and their Monetary Policy Implications (No. w21726), National Bureau of Economic Research.
- Cerra, V. and Saxena, S.C., 2008, Growth Dynamics: The Myth of Economic Recovery, American Economic Review, 98(1), pp.439–57.
- Cerra, V., Panizza, U. and Saxena, S.C., 2013, International Evidence on Recovery from Recessions, Contemporary Economic Policy, 31(2), pp.424–439.
- Cerra, M.V. and Saxena, M.S.C., 2017. Booms, Crises, and Recoveries: A New Paradigm of the Business Cycle and its Policy Implications, International Monetary Fund.
- Dixit, A., 1992. Investment and Hysteresis, Journal of Economic Perspectives, 6(1), pp.107–132.
- Engler, P. and Tervala, J., 2018, Hysteresis and Fiscal Policy, Journal of Economic Dynamics and Control, 93, pp.39–53.
- Haltmaier, J., 2013, Do Recessions Affect Potential Output?, FRB International Finance Discussion Paper, (1066).
- Mueller, H., 2012, Growth Dynamics: The Myth of Economic Recovery: Comment, American Economic Review, 102(7), pp.3774–77.
- Queralto, Albert, 2020. A model of slow recoveries from financial crises, Journal of Monetary Economics, Elsevier, 114(C), pp. 1-25.
- Yagan, D., 2019, Employment Hysteresis from the Great Recession, Journal of Political Economy, 127(5), pp.2505–2558.

### Financial frictions, credit, and banking crises
- Dell'Ariccia, M.G., Ebrahimy, M.E., Igan, M.D.O. and Puy, M.D., 2020. Discerning Good from Bad Credit Booms: The Role of Construction, International Monetary Fund.
- Duval, R., Hong, G.H. and Timmer, Y., 2020, Financial Frictions and the Great Productivity Slowdown, The Review of Financial Studies, 33(2), pp.475–503.
- Hadlock, C.J. and Pierce, J.R., 2010, New Evidence on Measuring Financial Constraints: Moving Beyond the KZ Index, The Review of Financial Studies, 23(5), pp.1909–1940.
- Laeven, M.L. and Valencia, M.F., 2018, Systemic Banking Crises Revisited, International Monetary Fund.
- Reinhart, C.M. and Rogoff, K.S., 2009, The Aftermath of Financial Crises, American Economic Review, 99(2), pp.466–72.
- Franco, G., 2018, Misallocation and the Credit Cycle: Evidence from Europe, Available at SSRN 3140282.
- A. Bauer, A., Craig, R., Garrido, J., Kang, K., Kashiwase, K., Kim, S., Liu, Y., and Rafiq S., 2021, Flattening the Insolvency Curve: Promoting Corporate Restructuring in Asia and the Pacific in the Post-C19 Recovery, IMF Working Paper, WP/21/16.
- Bernstein, F., Colonnelli, E., Giroud, X., and Iverson B., 2019, Bankruptcy Spillovers, Journal of Financial Economics, Volume 133, Issue 3, 2019, Pages 608–633.

### Investment, misallocation, and productivity
- IMF, 2015, Private Investment: What's the Hold-up?, World Economic Outlook, April 2015.
- Duval, R., Hong, G.H. and Timmer, Y., 2020, Financial Frictions and the Great Productivity Slowdown, The Review of Financial Studies, 33(2), pp.475–503.
- Furceri, D., Sinem, K.C., Jalles, J.T., and Koloskova, K., 2021, Recessions and Total Factor Productivity: Evidence from Sectoral Data, Economic Modelling, Elsevier, vol. 94(C), pages 130–138.
- Martin, P. and Rogers, C.A., 1997, Stabilization Policy, Learning-by-Doing, and Economic Growth, Oxford Economic Papers, 49(2), pp.152–166.
- Smolny, W., 2000, Post‐War Growth, Productivity Convergence and Reconstruction, Oxford Bulletin of Economics and Statistics, 62(5), pp.589–606.
- Chaney, T., Sraer, D. and Thesmar, D., 2012. The Collateral Channel: How Real Estate Shocks Affect Corporate Investment, American Economic Review, 102(6), pp.2381–2409.
- Franco, G., 2018, Misallocation and the Credit Cycle: Evidence from Europe, Available at SSRN 3140282.

### Labor markets, employment scarring, and human capital
- Davis, S.J. and Von Wachter, T.M., 2011, Recessions and the Cost of Job Loss (No. w17638), National Bureau of Economic Research.
- Oreopoulos, P., Page, M. and Stevens, A.H., 2008, The Intergenerational Effects of Worker Displacement, Journal of Labor Economics, 26(3), pp.455–483.
- Ouyang, M., 2009, The Scarring Effect of Recessions, Journal of Monetary Economics, 56(2), pp.184–199.
- Mian, A. and Sufi, A., 2010, The Great Recession: Lessons from Microeconomic Data, American Economic Review, 100(2), pp.51–56.
- Charles, K.K., Hurst, E. and Notowidigdo, M.J., 2016, The Masking of the Decline in Manufacturing Employment by the Housing Bubble, Journal of Economic Perspectives, 30(2), pp.179–200.
- Biven, J., 2017, A ‘High-pressure Economy’ Can Help Boost Productivity and Provide Even more ‘Room to Run’ for the Recovery, Economic Policy Institute, EPI Report.
- Oreopoulos, P., Page, M. and Stevens, A.H., 2008, The Intergenerational Effects of Worker Displacement, Journal of Labor Economics, 26(3), pp.455–483.

### Pandemics, public health interventions, and recent policy discussions
- Correia, Sergio and Luck, Stephan and Verner, Emil, Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu (June 5, 2020), Available at SSRN: https://ssrn.com/abstract=3561560 or http://dx.doi.org/10.2139/ssrn.3561560.
- Ma, C., Rogers, J., and Zhou, S., 2020, Modern Pandemics: Recession and Recovery, International Finance Discussion Papers 1295, Washington: Board of Governors of the Federal Reserve System.
- IMF, 2020, Global Prospects and Policies, World Economic Outlook, October 2020.
- IMF, 2021, A Fair Shot, Fiscal Monitor, April 2021.
- IMF, 2021, Global Financial Stability Overview: An Asynchronous and Divergent Recovery May Put Financial Stability at Risk, Global Financial Stability Report, April 2021.

### Policy volatility, fiscal consolidation, and macro policy implications
- Fatás, A. and Mihov, I., 2013, Policy Volatility, Institutions, and Economic Growth, Review of Economics and Statistics, 95(2), pp.362–376.
- Fatás, A. and Summers, L.H., 2018, The Permanent Effects of Fiscal Consolidations, Journal of International Economics, 112, pp.238–250.
- Reifschneider, D., Wascher, W. and Wilcox, D., 2015, Aggregate Supply in the United States: Recent Developments and Implications for the Conduct of Monetary Policy, IMF Economic Review, 63(1), pp.71–109.
- Rajan, R. and Zingales, L., 1998, Financial Development and Growth, American Economic Review, 88(3), pp.559–586.
- Garga, V. and Singh, S., 2018, Output Hysteresis and Optimal Monetary Policy, Society for Economic Dynamics.
- Hadlock, C.J. and Pierce, J.R., 2010, New Evidence on Measuring Financial Constraints: Moving Beyond the KZ Index, The Review of Financial Studies, 23(5), pp.1909–1940.

*Economic Scarring: Channels and Policy Implications Working Paper No. WP/2022/248*

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_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022248-print-pdf.pdf_