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The blind side of public debt spikes

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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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The blind side of public debt spikes

  1. 1. The Blind Side of Public Debt Spikes Laura Jaramillo, Carlos Mulas-Granados and Elijah Kimani Fiscal Affairs Department International Monetary Fund ADEMU conference-Bonn, June 7th, 2016
  2. 2. Presentation outline 2 I. Motivation for this project II. Characterizing Debt Spikes III. What drives debt spikes? IV. What is behind SFAs? V. What are the consequences of sizable debt spikes? VI. Can more realistic SFAs improve debt forecasts? VII.Concluding remarks
  3. 3. 3 I. Motivation for this project
  4. 4. 4 I. Motivation for this project Contrary to popular belief, large debt increases are not driven by high primary deficits…WHAT IS BEHIND DEBT SPIKES? -40.0 -30.0 -20.0 -10.0 0.0 10.0 20.0 30.0 40.0 1973-1987 1988-2007 2008-2015 Change in debt to GDP Cumulative primary deficits Source: FAD Historical Public Debt Database, Mauro (2013), IMF Fiscal Monitor, and authors’ estimates Note: Each sub-period shows the averages across advanced and developing countries for which data is available
  5. 5. 5 II. Characterizing Debt Spikes
  6. 6. II. What is a debt spike? 6 Criteria for Episode Selection β€’ At least 1%GDP increase in debt/GDP per year β€’ At least 10 percent of GDP increase in debt during the episode [Abbas, 2011; Weber, 2012] Conditions β€’ No time limit on the maximum duration of episodes β€’ At least 2 years difference between the episodes (if <2 years, then considered the same episode) β€’ There is data available to make the calculations for the debt decomposition for the duration of the episode
  7. 7. II. What is a debt spike? 7 We find a total of 179 episodes of multiyear debt accumulation greater than 10 percent of GDP, 80 among advanced economies and 99 among emerging and low income countries. Table 1. Duration of debt accumulation episodes Max 75% 25% Min Median ALL 179 15 7 3 1 5 ADV 80 14 8 4 1 6 EM & LIC 99 15 6 2 1 4 Number of Episodes Episode Duration
  8. 8. II. What is a debt spike? 8 Total debt increase and duration of debt accumulation episodes Median debt spike episode Median debt spike episode
  9. 9. II. What is a debt spike? 9 Debt spikes are not rare events: After 10 years, the probability of falling into a debt spike is 50%; after 20 years the probability is 80%. 0.000.250.500.751.00 0 20 40 60 80 analysis time ADV EME LIC 1-Kaplan-Meier survival estimates 0.000.250.500.751.00 0 20 40 60 80 analysis time 1-Kaplan-Meier survival estimate 10yr= 50% 20yr= 80%
  10. 10. 10 III. What drives debt spikes?
  11. 11. III. What drives debt spikes? 11 𝑑 𝑇 βˆ’ 𝑑0 = 𝑑=1 𝑇 π‘Ÿπ‘‘ βˆ’ 𝐺𝑑 1 + 𝐺𝑑 𝑑 π‘‘βˆ’1 + 𝑑=1 𝑇 𝑝𝑑 + 𝑑=1 𝑇 𝑠𝑑 𝑑 𝑇 βˆ’ 𝑑0 = 𝑑=1 𝑇 π‘Ÿπ‘‘ 1 + 𝐺𝑑 𝑑 π‘‘βˆ’1 βˆ’ 𝑑=1 𝑇 πœ‹ 𝑑 1 + 𝐺𝑑 𝑑 π‘‘βˆ’1 βˆ’ 𝑑=1 𝑇 𝑔𝑑 1 + 𝑔𝑑 𝑑 π‘‘βˆ’1 + 𝑑=1 𝑇 𝑝𝑑 + + 𝑑=1 𝑇 𝑠𝑑 Where: d= debt to GDP; r = nominal effective interest rate; G = nominal GDP growth rate; Ο€ = growth rate of GDP deflator; g = real GDP growth rate; p = primary deficit to GDP; s=stockflow adjustment to GDP Debt decomposition formula: Interest rate Inflation Real GDP growth Primary deficit Stock flow adjustment Interest rate growth differential Primary deficit Stock flow adjustment Total change in Debt Past Debt
  12. 12. III. What drives debt spikes? 12 Stock-flow adjustment (SFA) is the major driver of debt spikes AEs EMEs & LICs For the median episode in AEs, debt increases 25% of GDP; and SFA increases 20% of GDP For the median episode in EMEs & LICs, debt increases 24 % of GDP; and SFA increases 30% of GDP Data on foreign currency denominated debt is only available for 24 out of the 99 developing country episodes. For these 24 episodes, stock-flow adjustments at the median amount to 16 percent of GDP, of which 6 percent of GDP can be attributed to the depreciation of the exchange rate.
  13. 13. 13 III. What drives debt spikes? These findings confirm previous studies. For example: β€’ Campos et al. (2006) used data from 117 countries between 1972-2003 and showed that budget deficits account for a relatively small fraction of debt growth and that stock-flow reconciliation is one of the key determinants of debt dynamics. β€’ Abbas et al. (2011) looked at 60 episodes of debt increases between 1880–2007 and found that key contributors to debt surges during nonrecessionary periods were both primary deficits and stock-flow adjustments. β€’ Weber (2012), using data for 163 countries between 1980 and 2010, shows that stock-flow adjustments were a significant source of debt increases, while they played only a minor role in explaining debt decreases.
  14. 14. 14 IV. What is behind SFAs?
  15. 15. IV. What is behind SFAs? 15 In the EU (2002-2014), the main driver of SFA increases during debt spikes was the net acquisition of financial assets (9% of GDP)
  16. 16. IV. What is behind SFAs? 16 A large portion of the financial assets acquired were relatively illiquid, such as loans, financial derivatives, equity and investment fund units.
  17. 17. IV. What is behind SFAs? 17 In our global sample, the size of SFAs during debt spikes increases with contingent liabilities, currency depreciation and with β€˜weak’ politics.
  18. 18. IV. What is behind SFAs? 18 Better fiscal institutions can help reduce average SFAs
  19. 19. IV. What is behind SFAs? 19 The probability of suffering a debt spike is lower when there are stronger budget institutions (both measured by PIMA and PIME indexes above the median value) Strong Institutions (PIMA>median) Weak Institutions (PIMA<median) 0.000.250.500.751.00 0 20 40 60 analysis time pimascore_high = 0 pimascore_high = 1 1-Kaplan-Meier survival estimates Strong Institutions (PIME>median) Weak Institutions (PIME<median) 0.000.250.500.751.00 0 20 40 60 analysis time pimescore_high = 0 pimescore_high = 1 1-Kaplan-Meier survival estimates
  20. 20. 20 V. What are the Consequences of Sizeable Debt Spikes?
  21. 21. III. What drives debt spikes? 21 Large SFAs are directly associated with a higher probability of suffering flat- debt paths in the aftermath of debt spikes Probit Ex-post Flat-debt path Ex-post Flat-debt path Ex-post Flat-debt path Ex-post Flat-debt path Debt level (end-of-episode) 1.181*** 1.017*** 1.011*** 1.016*** (0.0569) (0.0566) (0.0525) (0.0533) Average SFA (during episode) 0.671*** 0.782*** 0.613*** (0.1) (0.0954) (0.183) Average Primary Balance (during episode) -2.841*** -2.776*** (0.524) (0.537) Average Episode Growth (during episode) -0.214 (0.576) Average Episode Inflation (during episode) 0.312 (0.32) Constant 25.82*** 24.15*** 20.45*** 20.71*** (3.281) (2.935) (2.805) (2.832) Observations 178 177 177 177 R-squared 0.71 0.773 0.806 0.807 If the average drop in debt between years three to five after the end of the debt spike is less than 10 percent of GDP, we consider that this country suffers from a non-declining debt path (taking a value of 1)
  22. 22. 22 VI. Can more realistic SFAs improve debt forecasts?
  23. 23. 23 Many countries are optimistic in their debt forecasts. VI. Can more realistic SFAs improve debt forecasts? Distribution of Actual and Forecast Annual Changes in Debt to GDP Sources: World Economic Outlook (WEO), Eurostat, and authors’ estimates. Note: Forecasts for t+2, t+3, and t+4. Columns for "EU countries" correspond to observations for 27 countries for annual forecast vintages between 1991 and 2014. Columns for "all countries" correspond to observations for 85 countries for Spring WEO vintages between 1995 and 2014. Debt Forecast Error: 1.5% GDP per year; 6% GDP per forecast period
  24. 24. VI. Can more realistic SFAs improve debt forecasts? 24 Forecasts of SFAs are typically biased (they are always higher than expected). Distribution of Annual Forecast Errors of SFAs Average Stock Flow Adjustments, Actual vs Forecast, 1991-2014 Note: Observations for 27 countries between 1991-2014. Forecasts for T+2, T+3, and T+4.
  25. 25. VI. Can more realistic SFAs improve debt forecasts? 25 WEO forecasts that by 2018 debt would be on a declining path in 17 out of 23 European countries. If we instead used historical SFAs for these countries, only 9 of these countries would have debt on a declining path. WEO Forecasted Change in Debt to GDP, 2015-2018 SGP Forecasted Change in Debt to GDP, 2015-2018
  26. 26. 26 VII. Concluding Remarks
  27. 27. 27 β€’ We use the longest historical time series (including the global financial crisis) and show that large debt spikes are driven by high SFAs, not primary deficits β€’ This blind side of debt dynamics is linked to political and institutional weaknesses. β€’ The consequences of large SFAs are long-lasting, because they lead to higher debt accumulation in exchange of illiquid assets. β€’ SFAs should be properly forecasted. This would make debt projections more realistic. β€’ Higher debt buffers would be needed, given the size of SFAs, their lack of forecasting and their relative frequency, VII. Concluding remarks
  28. 28. 28 Thank you!
  29. 29. 29 Background slides
  30. 30. Episodes of debt declines > 10% of GDP (47 ADV & 90 EME/LIC episodes) 30 AEs EMEs & LICs For the median episode in AEs, debt declines of 18% of GDP; SFA increases 4% of GDP; primary surpluses of 18% of GDP For the median episode in EMEs & LICs, debt declines 35% of GDP; SFA increases 3% of GDP; primary surpluses of 5% of GDP
  31. 31. Debt Spikes > 20% of GDP (Total 107: 49 ADV & 58 EME/LIC episodes) 31
  32. 32. Debt Spikes > 10% of GDP Advanced vs. Developing 32
  33. 33. Debt Spikes > 10% of GDP Advanced vs. Developing 33
  34. 34. Contingent Liabilities (from banking sector) and SFA 34 Table 1.3.1. Regression Analysis: Financial Contingent Liabilities and Size of SFA SIMPLE REGRESSIONS Coefficients (simple regressions) Average Size Number of SFA Obs Size of Realized Contingent Liabilities 0.383*** N=51 [2.34] Size of Realized Contingent Liabilities (financial) 0.639*** N=35 [3.30] Coefficients (simple regressions) Average Size Number of SFA Obs Size of Realized Contingent Liabilities 0.393*** [2.06] N=39 Size of Realized Contingent Liabilities (financial) 0.618*** N=26 [2.60] T-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 20% Threshold 10% Threshold

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