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Costs of sovereign default

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Presentation from ADEMU Fiscal Risk and Public Sector Balance Sheets Conference at the University of Bonn, July 6-7

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Costs of sovereign default

  1. 1. Costs of Sovereign Default: Restructuring Strategies, Bank Distress and the Credit-Investment Channel Work in Progress with T. Asonuma (IMF), M. Chamon (IMF) & A. Sasahara (UC Davis) Fiscal Risk and Public Sector Balance Sheets ADEMU Workshop July 6-7, 2017 Disclaimer: The views in this presentation are the authors and are not to be reported as those of the IMF, the ESM or their Management Boards.
  2. 2. Where we come from and where we try to go Asonuma & Trebesh (2016): The way to restructure debt matters for its growth effects Balteanu & Erce (2011): Sovereign defaults can trigger bank crises and exacerbate the default costs In this project we cross these two ideas and ask ourselves: 1. What are the channels through which debt restructuring affect GDP? 2. Are those channels affected by the restructuring strategies? Short answers: 1. Both direct and indirect effects through the financial sector οƒ˜ Financial crises trigger decline in bank credit to private sector οƒ˜ Decline on investment follows, feeding into growth 2. Yes, the strength of these channels depends on the debt restructuring strategy οƒ˜ Accumulating arrears is not the right plan if your idea is to bring growth back οƒ˜ If a hard default is unavoidable: restructure fast and don’t be harsh 2
  3. 3. Related literature Output costs of defaults Sturzenegger (04), Tomz & Wright (07), Borensztein and Panizza (09), Furceri & Zdzienicka (12), Kuvshinov & Zimmermann (16), Forni et al. (16), Asonuma et al. (16), Cheng et al. (16) Restructuring strategies Sturzenegger & Zettelmeyer (06), Finger & Mecagni (07), Diaz-Cassou et al. (08), Erce (12, 16), IMF (13), Duggar (13), Asonuma & Trebesch (16), Cheng et al. (16) Sovereign and banking crises Reinhart & Rogoff (09, 11), Borensztein & Panizza (09), Gennaioli et al. (14), Bolton & Jeanne (11), Sosa-Padilla (15), Balteanu & Erce (16), Engler & Große Steffen (16) 3
  4. 4. Data 4
  5. 5. Sources and sample size Data sources: οƒ˜ Debt restructuring data: Asonuma and Trebesch (2016) οƒ˜ GDP, Investment, Population: Penn World Table 8.0 οƒ˜ Bank credit to private sector: World Development Indicators (WB) οƒ˜ Lending rates: International Financial Statistics (IMF) οƒ˜ Financial crises: Laeven and Valencia (2013) οƒ˜ Net capital Flows: World Economic Outlook (IMF) Sample: οƒ˜ 1970-2013, annual frequency οƒ˜ 69 countries experienced at least one DR episode οƒ˜ expanded sample for robustness check 5
  6. 6. Restructuring strategies AT (2016) classify private external debt restructurings as follows: 6
  7. 7. Summary of the dataset Summary of Debt Restructuring and Banking Crisis Events Panel A: Private Debt Restructuring Sample Panel B: Banking Crises Sample Panel B: Banking Crisis Sample 7 Post-default Weakly preemptive Strictly preemptive Episodes 111 45 23 Countries 60 26 13 Duration (in years) 5.1 1.0 0.7 Representative Episodes in 1999–2010 Argentina 2001–5, Russia 1998–2000 Ukraine (Global Exch. 2000, Belize 2006–7 Pakistan (Ext. bonds) 1999, Uruguay 2003, Asonuma and Trebesch (2016) Entire Sample Countries with at least one restructuring /1 Episodes 137 64 Countries 111 49 Duration (in years, average) 3.3 3.3 Representative Episodes in 1999–2010 Korea 1997–8, Portugal 2008, Spain 2008 Argentina 2001–3 Ukraine 1998–9 Laeven and Valencia (2013)
  8. 8. Summary of the dataset Debt Restructurings and Banking Crises for Selected Countries 8 No. Country 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1 Argentina 2 Brazil 3 Bulgaria 4 Cameroon 5 Costa Rica 6 Ecuador 7 Guyana 8 Guinea 9 Jordan 10 Kenya 11 Macedonia 12 Niger 13 Nigeria 14 Panama 15 Peru 16 Philippines 17 Russian Federation 18 Morocco Year Notes : indicates the start year of post-default debt restructuring. indicates the start year of weakly preemptive debt restructuring. indicates the start year of strictly preemptive debt restructuring. indicates banking crises. The data on debt restructurings come from Asonuma and Trebesch (2016) and the data on banking crises come from Laeven and Valencia (2013). Countries that experienced both debt resturucting and banking crisis are listed in the figure.
  9. 9. New Stylized Facts 9
  10. 10. Stylized Fact(s) #1 GDP and investment decline substantially in post-default DRs, less severely in weakly preemptive ones, and are unaffected in strictly preemptive cases Private credit falls and lending rates hike sharply during post-default DRs, while no such effect is found for strictly pre-emptive cases Capital flows remain low after any DR, but recover fast after strictly pre-emptive cases 10
  11. 11. Stylized Fact #2 GDP and Investment co-move more strongly in DRs that in normal times Dep. Var. = GDP growth rate *** Significant at 1% level. Robust standard errors, clustered at country-level, in parenthesis  Output and investment co-move, most strongly in DR, when both tank together  This β€œexcess” co-movement around DR appears strongest in pre-emptive cases  What available theories explain this? 11 All countries Countries that experienced at least one debt restructuring event During post- default (the entire period) During post- default (the first half period) During Weakly preemptive During Strictly preemptive (1) (2) (3) (4) (5) (6) Investment growth rate 0.200*** 0.245*** 0.336*** 0.302*** 0.315*** 0.561*** (0.03) (0.06) (0.05) (0.06) (0.03) (0.12) Country fixed effect Yes Yes Yes Yes Yes Yes R-squared 0.128 0.156 0.247 0.338 0.436 0.472 Number of countries 161 58 49 45 20 6 Number of observations 5,153 1,607 398 229 74 15 Observations with debt restructuring Observations without debt restructuring
  12. 12. Stylized Fact #3 Banking crises occur more frequently following post-default DR 12 Post-default Weakly preemptive Strictly preemptive Debt Restructuring Episodes 111 45 23 Countries 60 26 13 Banking Crisis (within 3 years since the start of debt crisis) 15 (15/111 = 14%) 3 (3/45 = 7%) 2 (2/23 = 9%) Representative Episodes Argentina 2001–5, Russia 1998–2000 Turkey, 1981 Niger, 1983 Algeria, 1990 Ukraine, 1998
  13. 13. Local Projections 13
  14. 14. Local projections As in Jorda & Taylor (2012), we estimate models of the following type: 𝑔𝑐,𝑑+β„Ž=π›Όβ„Ž 𝑐 + 𝐷𝑅 𝑐,𝑑 βˆ™ 𝛾 β„Ž +𝑋𝑐,π‘‘βˆ’1 βˆ™ π›½β„Ž βˆ’1 + 𝑋𝑐,π‘‘βˆ’2 βˆ™ π›½β„Ž βˆ’2 + πœ€ 𝑐,𝑑+β„Ž οƒ˜ Subscripts c and t indicate country and year, respectively. οƒ˜ h indicate horizon and we estimate from h = 0 up to h = 9. οƒ˜ 𝑔𝑐,𝑑+β„Ž = 100 βˆ™ (𝐺𝐷𝑃𝑐,𝑑+β„Ž-𝐺𝐷𝑃𝑐,π‘‘βˆ’1)/𝐺𝐷𝑃𝑐,π‘‘βˆ’1 is the cumulative GDP growth rate from time t -1 to t + h in country c. οƒ˜ π›Όβ„Ž 𝑐 are country-fixed effects. οƒ˜ 𝐷𝑅 𝑐,𝑑 is our debt restructuring indicator (in country c in year t). οƒ˜ 𝑋𝑐,𝑑 is a vector of control variables - lagged dependent variables, cyclical component of GDP per capita, openness, and log of population. οƒ˜ πœ€ 𝑐,𝑑+β„Ž denotes the error term. 14
  15. 15. OLS estimation: GDP GDP after Sovereign Debt Restructuring Dep. Var. = 100 βˆ™ (𝐺𝐷𝑃𝑐,𝑑+β„Ž-𝐺𝐷𝑃𝑐,π‘‘βˆ’1)/𝐺𝐷𝑃𝑐,π‘‘βˆ’1 15
  16. 16. OLS estimation: GDP GDP after Sovereign Debt Restructuring Figures show local projections of 100 βˆ™ (𝐺𝐷𝑃𝑐,𝑑+β„Ž-𝐺𝐷𝑃𝑐,π‘‘βˆ’1)/𝐺𝐷𝑃𝑐,π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 16
  17. 17. OLS estimation: Investment Investment after Sovereign Debt Restructuring Figures show local projections of 100 βˆ™ (πΌπ‘›π‘£π‘’π‘ π‘‘π‘šπ‘’π‘› 𝑐,𝑑+β„Ž βˆ’ πΌπ‘›π‘£π‘’π‘ π‘‘π‘šπ‘’π‘›π‘‘ 𝑐,π‘‘βˆ’1)/πΌπ‘›π‘£π‘’π‘ π‘‘π‘šπ‘’π‘›π‘‘ 𝑐,π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 17
  18. 18. OLS estimation: Private Sector Credit Credit to the Private Sector after Sovereign Debt Restructuring Figures show local projection of 100 βˆ™ (πΆπ‘Ÿπ‘’π‘‘π‘–π‘‘ 𝑐,𝑑+β„Ž-πΆπ‘Ÿπ‘’π‘‘π‘–π‘‘ 𝑐,π‘‘βˆ’1)/πΆπ‘Ÿπ‘’π‘‘π‘–π‘‘ 𝑐,π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 18
  19. 19. OLS estimation: Capital Flows Capital Flows after Sovereign Debt Restructuring Figures show local projections of 100 βˆ™ (πΌπ‘›π‘“π‘™π‘œπ‘€π‘ π‘‘+β„Ž βˆ’ πΌπ‘›π‘“π‘™π‘œπ‘€π‘ π‘‘βˆ’1)/πΌπ‘›π‘“π‘™π‘œπ‘€π‘ π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 19
  20. 20. Selection & endogeneity 20
  21. 21. Endogeneity Debt restructurings are not exogenous - policy makers’ decision Define country i output at time t as π‘Œπ‘‘,𝑖. Our OLS estimates deliver 𝐸 π‘Œπ‘–,𝑑+β„Ž 𝐷𝑖,𝑑 = 1 βˆ’ 𝐸 π‘Œπ‘–,𝑑+β„Ž 𝐷𝑖,𝑑 = 0 But, as shown by Angrist and Psichke (2008), this difference has two components: – 𝐸 π‘Œπ‘–,𝑑+β„Ž 𝐷𝑖,𝑑 = 1 - 𝐸 π‘Œπ‘–,𝑑 𝐷𝑖,𝑑 = 1 οƒ  Average Treatment Effect (ATE) – 𝐸 π‘Œπ‘–,𝑑 𝐷𝑖,𝑑 = 1 - 𝐸 π‘Œπ‘–,𝑑 𝐷𝑖,𝑑 = 0 οƒ  Selection Bias OLS might be biased and simply capture other features of countries undergoing DRs: οƒ˜ Higher public debt-to-GDP ratio οƒ˜ Lower private credit-to-GDP ratio οƒ˜ Lower country’s credit ratings … 21
  22. 22. AIPW: Treatment Models and Selection Bias One way to get around this selection bias is to model DRs as policy treatments and use treatment effects models (Jorda et al. 15, 16) to rid of the selection bias. We follow Jorda et al. (15, 16) and complement our local projections with an Augmented Inverse Probability Weighted estimator (hereafter AIPW) – Assess the extent to which treated and non treated units are different – Estimate the likelihood of being treated (propensity score) and use it to weight the observations when performing the OLS estimation 22
  23. 23. Endogeneity – prior characteristics Characteristics of the Treatment and Control Groups Asses differences between treatment (start of DR) and control groups (other observations), by regressing each variable on the DR dummies and using the constant as normal times value. Regressions include country FE. These results show that countries indeed have different characteristics prior to any type of restructurings, raising an issue of endogeneity 23 (1) (2) (3) (4) (5) (6) Credit ratings Change in credit ratings Interest payment (short- term)/GDPΓ— 100 Interest payment (total)/GDP Γ—100 GDP growth rateΓ—100 Political stability (civil liberties) Average value Normal time 28.83 0.53 0.29 2.11 3.69 4.09 A year before the start year of "Post-default" 22.19 -3.83 0.48 3.32 0.72 4.38 A year before the start year of "Weakly preemptive" 18.74 -3.38 0.46 4.08 3.72 4.42 A year before the start year of "Strictly preemptive" 18.94 -0.83 0.56 4.05 4.09 4.18 Difference from the normal time A year before the start year of "Post-default" - Normal time -6.641*** -4.364*** 0.192*** 1.209*** -2.966*** 0.290** (1.216) (0.388) (0.074) (0.296) (0.788) (0.117) A year before the start year of "Weakly preemptive" - Normal time -10.09*** -3.912*** 0.175 1.971*** 0.038 0.326* (1.612) (0.510) (0.114) (0.457) (1.122) (0.180) A year before the start year of "Strictly preemptive" - Normal time -9.893*** -1.360* 0.274 1.937*** 0.404 0.0812 (2.209) (0.718) (0.171) (0.683) (1.719) (0.243) # of countries 63 63 54 54 63 62 # of observations 1,566 1,503 2,068 2,068 2,419 2,467
  24. 24. Augmented Inverse Probability Weighted Estimator (AIPW) Estimation steps: οƒ˜ 1st stage: o Estimate discrete-variable model: 𝑃 𝐷𝑅 𝑐,𝑑 = Ξ¦(𝑍 𝑐,𝑑, . ), where 𝑍 𝑐,𝑑 includes public debt, private credit, rating, and 2nd stage regressors o Calculate weights based on propensity scores: ipw 𝑐,𝑑 = πœ™(𝑍 𝑐,𝑑) Ξ¦(𝑍 𝑐,𝑑) βˆ’ 1βˆ’πœ™(𝑍 𝑐,𝑑) 1βˆ’Ξ¦(𝑍 𝑐,𝑑) οƒ˜ 2nd stage: o Use ipw 𝑐,𝑑 as weights on the Local Projections to obtain ATE One (big?) issue - # endogenous variables (restructuring strategies) >1 οƒ˜ How do we define the 1st stage? o Treat all types of debt restructuring as having identical drivers? o Three different dependent variables? Use binomial or multinomial models? οƒ˜ Currently, we use a binomial, independent, model for each DR strategy 24
  25. 25. Predicting the start year of debt restructurings Predicting Debt Restructuring Events 25 (1) (2) (3) (4) (5) (6) Start year (Post- default) Start year (Weakly preemptive) Start year (Strictly preemptive) Start year (Post-default) Start year (Weakly preemptive) Start year (Strictly preemptive) Change in credit ratings, lag 1 -0.0016 -0.0058*** -0.0004 -0.0172 -0.125** -0.043 (0.002) (0.001) (0.001) (0.047) (0.050) (0.098) Interest payments (total)/GDP, lag 1 0.0732*** 0.0417*** 0.0021 1.103*** 0.845** 0.600 (0.014) (0.011) (0.006) (0.294) (0.381) (1.033) Political stability (civil liberties), lag 1 0.0194*** -0.0051 0.0009 0.557*** -0.488 0.155 (0.006) (0.005) (0.003) (0.186) (0.359) (0.620) Post-default (the last six years) -0.0215* -0.0119 -0.0005 -0.344 -0.167 0.134 (0.011) (0.008) (0.005) (0.273) (0.533) (0.813) Weakly preemptive (the last six years) 0.0375*** 0.0140 0.0020 0.932*** -0.0747 0.278 (0.013) (0.010) (0.006) (0.314) (0.286) (0.637) Strictly preemptive (the last six years) 0.0322 0.0422*** 0.0340*** 0.924* 2.280** 0.463 (0.020) (0.015) (0.009) (0.553) (1.023) (0.452) Country fixed effect Yes Yes Yes Yes Yes Yes R-squared 0.045 0.048 0.014 # of countries 52 52 52 30 14 8 # of observations 1,244 1,244 1,244 854 371 200 F-stat. 9.30 9.92 2.77 p-val. (F-stat.) 0.00 0.00 0.01 LR Chi-sq. 39.01 27.67 3.00 p-val. (LR Chi-sq.) 0.00 0.00 0.81 Linear probability model Logit
  26. 26. Predicting the start year of debt restructurings Classification Power of the First Stage Regressors Panel A: Post-default Panel B: Weakly Preemptive Figures show the area under the ROC curve. ROC area takes values between 0.50 and 1. A value of 0.50 indicates that regressors have no ability to classify observations. The ROC curve is greater than 0.50 (Schularick & Taylor 2012 argue a curve above 0.70 is sufficient) for all types of DRs (0.81, 0.91, and 0.80, respectively). Past DRs, credit rating changes, and interest payments-to-GDP have classification power 26
  27. 27. AIPW estimation: GDP GDP after Sovereign Debt Restructuring Figures show local projections of 100 βˆ™ (𝐺𝐷𝑃𝑐,𝑑+β„Ž-𝐺𝐷𝑃𝑐,π‘‘βˆ’1)/𝐺𝐷𝑃𝑐,π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 27
  28. 28. AIPW estimation: Investment Investment after Sovereign Debt Restructuring Figures show local projections of 100 βˆ™ (πΌπ‘›π‘£π‘’π‘ π‘‘π‘šπ‘’π‘› 𝑐,𝑑+β„Ž βˆ’ πΌπ‘›π‘£π‘’π‘ π‘‘π‘šπ‘’π‘›π‘‘ 𝑐,π‘‘βˆ’1)/πΌπ‘›π‘£π‘’π‘ π‘‘π‘šπ‘’π‘›π‘‘ 𝑐,π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 28
  29. 29. AIPW estimation: Private Sector Credit Credit to the Private Sector after Sovereign Debt Restructuring Figures show local projection of 100 βˆ™ (πΆπ‘Ÿπ‘’π‘‘π‘–π‘‘ 𝑐,𝑑+β„Ž-πΆπ‘Ÿπ‘’π‘‘π‘–π‘‘ 𝑐,π‘‘βˆ’1)/πΆπ‘Ÿπ‘’π‘‘π‘–π‘‘ 𝑐,π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 29
  30. 30. AIPW estimation: Lending Interest Rate Lending Interest Rate after Sovereign Debt Restructuring Figures show local projections of 100 βˆ™ (𝑖 𝑑+β„Ž βˆ’ 𝑖 π‘‘βˆ’1)/𝑖 π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 30
  31. 31. AIPW estimation: Capital Flows Capital Flows after Sovereign Debt Restructuring Figures show local projections of 100 βˆ™ (πΌπ‘›π‘“π‘™π‘œπ‘€π‘ π‘‘+β„Ž βˆ’ πΌπ‘›π‘“π‘™π‘œπ‘€π‘ π‘‘βˆ’1)/πΌπ‘›π‘“π‘™π‘œπ‘€π‘ π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 31
  32. 32. Role of credit-investment channel 32
  33. 33. Debt restructuring and credit crunch Dig further into the link between credit, investment and GDP growth – to what extent the linkage between these variables helps understand differences in performance? We classify DRs in those accompanied by a credit crunch, and those which were not A credit crunch is an event which the cumulative growth rate of private credit from year 0 to year h --- for h = 1, 2,…, 5 --- is negative 𝑔 π‘π‘Ÿπ‘’π‘‘π‘–π‘‘ 𝑖,β„Ž = ln π‘π‘Ÿπ‘’π‘‘π‘–π‘‘π‘–,𝑑+β„Ž βˆ’ ln π‘π‘Ÿπ‘’π‘‘π‘–π‘‘π‘–,𝑑 33 h = 1 h = 2 h = 3 h = 4 h = 5 Sum Post-default 91 39 35 31 30 22 32 42.9% 38.5% 34.1% 33.0% 24.2% 35.2% Weakly preemptive 39 14 13 11 11 10 11 35.9% 33.3% 28.2% 28.2% 25.6% 28.2% Strictly preemptive 21 8 5 3 3 5 2 38.1% 23.8% 14.3% 14.3% 23.8% 9.5% Total # of episodes # of episodes with credit crunch which is defined as episodes with the cumulative growth rate of private credit from year 0 to year h is negative
  34. 34. Debt restructuring and credit crunch GDP and Investment in Debt Restructurings with/without Credit Crunches (average) Panel A: GDP Panel B: Investment 34
  35. 35. Debt restructuring and credit crunch Debt restructurings with/without Credit Crunch, AIPW Panel A: GDP Panel B: Investment Figures show local projections of the variable shown in each panel for h = 1, 2, …, 5, where h indicates horizon. Bold lines are point estimates. Dotted bands are 95% confidence intervals. Red color refers to events with credit crunch and blue to events without credit crunch 35
  36. 36. DR strategies & banking crises 36
  37. 37. Debt restructuring and banking crises Banking Crisis after Sovereign Debt Restructurings Dep. Var. = Dummy Variable Taking 1 during Banking Crises 37 Mean Std. Dev. (1) (2) (3) (4) Post-default (the last six years) 0.399*** 0.318** 0.689*** 0.591** (0.117) (0.131) (0.220) (0.248) Weakly preemptive (the last six years) 0.246 0.244 0.352 0.436 (0.184) (0.190) (0.303) (0.329) Strictly preemptive (the last six years) -0.570 -0.375 -1.100* -0.693 (0.372) (0.369) (0.647) (0.658) Controls Public debt-to-GDP ratio 0.673 0.564 0.498*** 0.792*** (0.153) (0.267) Private credit-to-GDP ratio 0.302 0.268 2.185*** 4.112*** (0.400) (0.705) Current account-to-GDP ratio -0.041 0.097 1.470* 2.906* (0.766) (1.576) Foreign reserve-to-GDP ratio -0.005 0.063 -2.413*** -3.827** (0.839) (1.674) ln(Real exchange rate) 4.073 2.760 -0.119 -0.187 (0.103) (0.164) GDP growth rate 0.037 0.046 -5.779*** -9.678*** (0.968) (1.747) Country fixed effect Yes Yes Yes Yes # of observations 1,611 1,611 1,611 1,611 # of countries 66 66 66 66 Probit Logit
  38. 38. DR strategies – other aspects (Preliminary) 38
  39. 39. Other aspects of restructuring strategies: Haircuts (TZ, 2014) As Trebesch and Zabel (2014), divide post-default events based on whether haircut is above or below median Figures show local projections of 100 βˆ™ (𝐺𝐷𝑃𝑐,𝑑+β„Ž-𝐺𝐷𝑃𝑐,π‘‘βˆ’1)/𝐺𝐷𝑃𝑐,π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. Policy implication: Sovereigns can minimize output costs of hard default if they minimize the losses imposed on creditors 39
  40. 40. Other aspects of restructuring strategies: Duration Divide post-default events based on the duration the debt restructuring Figures show local projections of 100 βˆ™ (𝐺𝐷𝑃𝑐,𝑑+β„Ž-𝐺𝐷𝑃𝑐,π‘‘βˆ’1)/𝐺𝐷𝑃𝑐,π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. Policy implication: Sovereigns could minimize output losses from hard default by settling (reaching a restructuring agreement) with creditors as fast as possible 40
  41. 41. Wrapping up We add to the empirical literature on the costs of sovereign debt restructuring Main findings οƒ˜ New stylized facts on GDP growth, investment and banking sector costs of DR οƒ˜ Show that a self-reinforcing credit/investment/growth effect, helps understanding the output costs of DR οƒ˜ The strength of these effects depends on the restructuring approach οƒ˜ Post-default DRs imply worse output loss and stronger investment-credit effect β€’ Post-default : => 5%-peak GDP decline, lasting 5+ years. Bank crisis β€’ Weakly/strictly preemptive: => 3%-peak GDP decline, short-lived. No bank crisis οƒ˜ Even more so if: οƒ˜ Haircut imposed is large (Trebesch & Zabel 14) or negotiations lasts long 41
  42. 42. Thanks for your time and comments 42
  43. 43. Banking Crisis Dataset Laeven and Valencia (2013) define a banking crisis as an event that meets: 1) Significant signs of financial distress in the banking system (as indicated by bank runs and large losses); 2) Significant policy measures in response to the losses in the banking system. At least 3 out of the following 6 measures have been used: i. Deposit freezes and/or bank holidays; ii. Significant bank nationalizations; iii. Bank restructuring gross costs (at least 3% of GDP); iv. Large liquidity support (5% of deposits and liabilities to foreigners) v. Significant guarantees put in place vi. Significant asset purchases (at least 5% of GDP); 43
  44. 44. OLS estimation: Lending Interest Rate Lending Interest Rate after Sovereign Debt Restructuring Figures show local projections of 100 βˆ™ (𝑖 𝑑+β„Ž βˆ’ 𝑖 π‘‘βˆ’1)/𝑖 π‘‘βˆ’1 for h = 0, 1, …, 9, where h indicates horizon. Solid lines are point estimates. Gray bands are 95% confidence intervals. 44
  45. 45. Endogeneity – sample selection Figure: Kernel Density- Predicted Probabilities of Debt Restructuring οƒ˜ Treatment group = observations with debt restructuring οƒ˜ Control group = observations without debt restructuring 45
  46. 46. Endogeneity… APIW is close to state-of-art to tackle selection biases due to observables… …but unobservables may still generate endogeneity… Any IV strategy in the room?! 46
  47. 47. Summary statistics Table 4: Summary Statistics (At the Start Year of Debt Restructuring) Panel A: Panel B: Panel C: 47 100*/)( 11  ο€­ ttht GDPGDPGDP Obs Mean Std. Dev. Min Max Post-default (start year) 81 -0.45 11.39 -47.99 30.09 Weakly preemptive (start year) 39 1.04 7.09 -24.70 13.40 Strictly preemptive (start year) 18 0.68 5.05 -9.72 11.13 Countries experienced at least one debt restructuring 2279 3.30 9.13 -65.32 139.26 All observations 6183 5.06 84.53 -96.44 5809.40 100*/)( 11 ο€­ο€­ο€­ ttt InvestmentInvestmentInvestment Obs Mean Std. Dev. Min Max Post-default (start year) 61 -6.58 22.35 -58.95 47.99 Weakly preemptive (start year) 30 -2.40 20.67 -39.42 43.64 Strictly preemptive (start year) 16 5.83 16.64 -17.59 52.91 Countries experienced at least one debt restructuring 1769 8.54 78.58 -376.22 2836.96 All observations 4618 6.77 64.51 -2562.39 2836.96 100*/)( 11 ο€­ο€­ο€­ ttt REXREXREX Obs Mean Std. Dev. Min Max Post-default (start year) 77 9.10 30.43 -59.34 121.83 Weakly preemptive (start year) 38 10.75 18.78 -19.58 71.57 Strictly preemptive (start year) 21 3.98 16.78 -17.39 62.26 Countries experienced at least one debt restructuring 2388 225.90 10752.99 -99.90 525426.40 All observations 2490 216.77 10530.45 -99.90 525426.40
  48. 48. Summary statistics Table 4: Summary Statistics (At the Start Year of Debt Restructuring) Panel D: Panel E: Panel F: 48 Obs Mean Std. Dev. Min Max Post-default (start year) 65 -2.65 10.18 -32.75 23.68 Weakly preemptive (start year) 38 -1.43 9.97 -24.16 17.17 Strictly preemptive (start year) 15 0.47 10.55 -13.31 17.78 Countries experienced at least one debt restructuring 2188 4.72 14.04 -77.04 236.73 All observations 5570 4.91 14.22 -77.04 314.97 100*/)( 11 ο€­ο€­ο€­ ttt DepositDepositDeposit Obs Mean Std. Dev. Min Max Post-default (start year) 60 -0.75 17.82 -49.30 46.24 Weakly preemptive (start year) 36 0.48 12.70 -30.55 30.08 Strictly preemptive (start year) 16 7.27 58.36 -38.75 218.41 Countries experienced at least one debt restructuring 1572 3.90 28.71 -86.28 787.90 All observations 3642 4.45 23.99 -86.28 787.90 Obs Mean Std. Dev. Min Max Post-default (start year) 49 16.48 53.00 -64.84 284.00 Weakly preemptive (start year) 26 3.16 20.67 -38.72 59.96 Strictly preemptive (start year) 14 -0.21 22.53 -50.21 35.71 Countries experienced at least one debt restructuring 1556 1.76 36.34 -98.41 769.79 All observations 4131 0.18 26.69 -98.41 769.79 100*/)( 11 ο€­ο€­ο€­ ttt CreditCreditCredit 100*/)( 11 ο€­ο€­ο€­ ttt eLendingRateLendingRateLendingRat
  49. 49. Backup Slide 1: AIPW estimator β€’ Estimation steps: οƒ˜ Step 1: Estimate the Probit model: o takes unity for debt restructuring events o is a vector including public debt-to-GDP ratio, private credit-to-GDP ration, credit rating, # of past debt restructurings, and 2nd stage regressors οƒ˜ Step 2: Estimate the following equation: o is the cumulative GDP growth rate. o , and are post-default, weakly preemptive, and strictly preemptive debt restructuring dummies, respectively. οƒ˜ Step 3: Obtain the predicted value from the regression in Step 2. οƒ˜ Step 4: Use and , find the average treatment effect: for Type = {Post, Weak, and Strict}. 49 ),,(}{ 1,,, Ξ±ZZ  tctctcDRP tcDR , tc,Z hti h tc h tc Strict tc StircthWeak tc WeakhPost tc Posthh chtc DDDg  ο€«ο€«ο€«οŒο€«οŒο€«οŒο€«ο€½ ,22,11,, , , , , , , ο₯ Ξ²XΞ²X 11, /)(*100  ο€­ο€½ tththtc GDPGDPGDPg Weak tc Post tc DD ,, , Strict tcD , h tc h tc Strict tc StircthWeak tc WeakhPost tc Posthh chtc DDDg 22,11,, , , , , , , Λ†Λ†Λ†Λ†Λ†Λ†Λ†  ο€«ο€«οŒο€«οŒο€«οŒο€«ο€½ Ξ²XΞ²X htcg , Λ† }{Λ† ,tcDRP οƒ₯οƒ₯οƒ₯οƒ₯ ο€­ ο€­ ο€­ο€½οŒ  c t tc Type tchtc Type tNonDebtResc t tc Type tchtc Type DebtRest Typehm DebtRestP Dg NDebtRestP Dg N ATE , ,, , ,,, }{Λ†1 )1(Λ†1 }{Λ† Λ†1 )(
  50. 50. Panel VAR Evidence οƒ˜ Estimate the following Panel VAR model, οƒ˜ 𝑔 𝑐,𝑑, 𝑖 𝑐,𝑑, π‘π‘Ÿπ‘,𝑑, π‘’π‘Ÿπ‘,𝑑, 𝑅 𝑐,𝑑 π‘π‘œπ‘ π‘‘ , 𝑅 𝑐,𝑑 π‘Šπ‘’π‘Žπ‘˜ π‘Žπ‘›π‘‘, 𝑅 𝑐,𝑑 π‘†π‘‘π‘Ÿπ‘–π‘π‘‘ denote GDP growth rate, investment growth rate, credit growth rate, exchange rate, post-default, weakly preemptive, and strictly preemptive indicators, respectively. οƒ˜ 𝛼1, 𝛼2 , π‘Žπ‘›π‘‘ 𝛼3 are 7-by-7 matrices of coefficients to be estimated. οƒ˜ , and are the error terms. 50 . e e e e e e e R R R er cr i g Ξ± R R R er cr i g Ξ± R R R er cr i g Ξ± R R R er cr i g Strict Weak Post R tc, R tc, R tc, er tc, cr tc, i tc, g tc, Strict 3tc, Weak 3tc, Post 3tc, 3tc, 3tc, 3tc, 3tc, 3 Strict 2tc, Weak 2tc, Post 2tc, 2tc, 2tc, 2tc, 2tc, 2 Strict 1tc, Weak 1tc, Post 1tc, 1tc, 1tc, 1tc, 1tc, 1 Strict tc, Weak tc, Post tc, tc, tc, tc, tc, οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ  οƒΉ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ    οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ  οƒΉ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ    οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ  οƒΉ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ    οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ  οƒΉ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ   ο€½ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ οƒΊ  οƒΉ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ οƒͺ   ο€­ ο€­ ο€­ ο€½ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ ο€­ WeakPost R tc R tc er tc cr tc i tc g tc eeeeee ,,,,,, ,,,,, Strict R tce ,
  51. 51. Granger Causality Use the Panel VAR to study Granger causality Table: Granger Causality The table reports Granger causalities implied from the panel VAR regression. Chi-squared statistics are reported. Numbers in parentheses are p-values. Null hypothesis is that an excluded variable is not a Granger-cause variable. ***, **, and * indicate significance at 1%, 5%, and 10% level, respectively 51 Outcome Cause GDP Investment Credit to the Private Sector Real exchange rate Post-default dummy Weakly preemptive dummy Strictly preemptive dummy GDP 4.873 7.303* 1.493 1.640 3.753 2.593 (0.18) (0.06) (0.68) (0.65) (0.29) (0.46) Investment 11.192** 0.747 2.373 1.130 9.251** 7.893* (0.01) (0.86) (0.50) (0.77) (0.03) (0.05) Credit to the Private Sector 10.602** 0.835 7.307* 1.890 1.679 1.154 (0.01) (0.84) (0.06) (0.60) (0.64) (0.76) Real exchange rate 0.706 5.97 0.830 5.053 8.748** 5.149 (0.87) (0.11) (0.84) (0.17) (0.03) (0.16) Post-default dummy 7.882* 21.320*** 13.421*** 3.398 5.765 7.220* (0.05) (0.00) (0.00) (0.33) (0.12) (0.07) Weakly preemptive dummy 2.119 2.144 9.222** 3.630 14.126*** 3.903 (0.55) (0.54) (0.03) (0.30) (0.00) (0.27) Strictly preemptive dummy 2.836 0.835 11.461*** 1.245 9.982** 0.258 (0.42) (0.84) (0.01) (0.74) (0.02) (0.97) All 37.381*** 40.429*** 24.001 22.199 23.252 24.482 16.251 (0.01) (0.00) (0.16) (0.22) (0.18) (0.14) (0.51)
  52. 52. Panel VAR: Transmission Channels Post-default restructurings Weakly/strictly preemptive restructurings 52

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