Costs of Sovereign Defaults: Restructuring Strategies, Bank Distress and the Capital Inflow-Credit Channel

Tamon Asonuma; Marcos d Chamon; Aitor Erce; Akira Sasahara

doi:10.5089/9781498303255.001.A001

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Costs of Sovereign Defaults: Restructuring Strategies, Bank Distress and the Capital Inflow-Credit Channel

Author: Mr. Tamon Asonuma, Mr. Marcos d Chamon, Aitor Erce¹, and Akira Sasahara²

¹ 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund
| ² 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address: TAsonuma@imf.org; MChamon@imf.org; a.erce@esm.europa.eu; sasahara@uidaho.edu

Publication Date:: 25 Mar 2019

eISBN:: 9781498303255

Language:: English

Keywords:: WP; net capital; growth rate; banking sector; inflow decline; capital inflow-credit channel; decline dummy; default episode; AIPW estimate; experience debt restructuring

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Sovereign debt restructurings are associated with declines in GDP, investment, bank credit, and capital flows. The transmission channels and associated output and banking sector costs depend on whether the restructuring takes place preemptively, without missing payments to creditors, or whether it takes place after a default has occurred. Post-default restructurings are associated with larger declines in bank credit, an increase in lending interest rates, and a higher likelihood of triggering a banking crisis than pre-emptive restructurings. Our local projection estimates show large declines in GDP, investment, and credit amplified by severe sudden stops and transmitted through a “capital inflow-credit channel”.

Abstract

I. Introduction

Countries where public debt is restructured tend to experience declines in GDP, investment, and private sector credit (Sturzenegger 2004; Borensztein and Panizza 2009). Though previous studies have provided different measures of output and banking sector costs during restructurings, little is known about the transmission channels of debt restructuring to the financial system and the broader economy. This is a remarkable gap, with real world implications for sovereign debtors facing the problem of how to restructure their debt. As evidenced by the recent sovereign debt workouts in Jamaica (2010, 2013) or Cyprus (2013), understanding the spillovers and feedback effects that a debt restructuring has through the domestic financial system can help design the restructuring so as to minimize the risk that it leads to a full blown banking crisis.

This paper contributes to that goal by shedding light on how the transmission channels and associated output and banking sector costs of debt restructurings differ depending on the restructuring strategy followed. We classify restructuring strategies as in Asonuma and Trebesch (2016), based on whether payments were missed (post-default debt restructuring), were missed but only temporarily and with the consent of the foreign creditors (weakly pre-emptive debt restructuring) or whether payments continued to be made in full and on time during the negotiations (strictly pre-emptive debt restructuring).

We use local projection models—originally proposed by Jordà (2005)¹—to quantify the overall cumulative effect (both direct and indirect) of the restructuring strategy over a long horizon, while controlling for the dynamic feedback from other variables. Our analysis is based on a sample of 70 countries with at least one sovereign debt restructuring over 1978–2010 and covers: (i) 179 restructurings of privately-held external sovereign debt, (ii) 39 banking crises, (iii) 48 severe credit declines, and (iv) 40 severe protracted declines in net capital inflow. Our estimates show that post-default restructurings are associated with the most severe and protracted declines in GDP and investment (in percent of GDP), with cumulative contractions of 4 and 1 percentage points over the first 3 years, respectively. These restructurings are also associated with severe and prolonged declines in bank credit, and sharp increases in lending interest rates. In contrast, pre-emptive restructuring events have much milder and short-lived declines in GDP, investment, private credit, and net capital inflows. The intensity of the effects of weakly preemptive restructurings falls between those of post-default and strictly preemptive restructurings. The sharper contractions in bank credit, and the associated worse lending terms, seem at the root of the severe and prolonged declines in GDP and investment in post-default restructurings. Furthermore, the deterioration of the banking sector is also confirmed by logit estimates that show countries undergoing post-default restructurings are significantly more likely to experience a banking crisis than those following a preemptive restructuring. We confirm the key role of the capital inflow-credit channel by estimating local projections on subsamples of three restructuring strategies with and without subsequent severe sudden stops—allowing the responses to vary depending on the state of the economy as in Auerbach and Gorodnichenko (2016) and Jordà and Taylor (2016).² Severe sudden stops are defined as events where there is a severe contraction in net capital inflows. They are strongly associated with credit crunches. Both of them are more likely to occur, and are more severe when they do, following post-default restructurings.³ Severe sudden stops and credit crunches are associated with the worst contractions in investment (in percent of GDP) and GDP (with cumulative declines of 4 and 5 percentage points of GDP over 2 years after a severe sudden stop occurs). This prominent “capital inflow-credit channel” influences GDP directly and indirectly through private credit and investment and its feedback effects, driving the larger impact that post-default restructurings have on GDP and investment.

A country’s decision of how to go about restructuring its debt is influenced by the economic conditions it faces.⁴ Thus, Ordinary Least Square (OLS) estimation suffers from endogeneity problems. To address this issue, our baseline results follow the convention in the literature and apply the Augmented Inverse Probability Weighted (AIPW) estimator (Jordà et al., 2013) when using the local projections.⁵ This method assigns a greater weight to observations that are less likely to be associated with an event, aiming to replicate a distribution without selection bias.

The complex linkages and feedback channels between the real, financial, and public sectors we document shape the response to a debt restructuring. Our results suggest much of the adverse costs depend on whether a severe sudden stop can be avoided, which is more likely in preemptive restructurings. However, we must bear in mind that countries face several different constraints on the choice of how to restructure. These include not only economic and political constraints, but also the possibility that a large adverse shock suddenly places the country in a “bad equilibrium” (e.g., Cole and Kehoe 2000). That can leave the country no choice but to stop servicing the debt. Even when payments are missed (post-default episodes), output costs of restructurings are to some extent mitigated depending on the negotiation process and speed of settlement.⁶ The output costs of post-default episodes with short duration or small haircuts are lower than those with long duration or high haircuts, but still higher than the ones for weakly preemptive restructurings. This suggests that much of the benefits stem from avoiding missing payments rather than creditor friendly terms per se.

This paper contributes to various literature strands. First and foremost, it adds to the large literature on the output costs of sovereign defaults, e.g., Sturzenegger (2004), Tomz and Wright (2007), Borensztein and Panizza (2009), De Paoli et al. (2009), Levy-Yeyati and Panizza (2011), Asonuma and Trebesch (2016), Trebesch and Zabel (2017), and Kuvshinov and Zimmermann (2016).⁷^,⁸ These papers provide different measures of output costs by applying conventional panel regressions and local projection approaches. Our contribution to that literature is to show that the output costs over the longer horizon depend crucially on the restructuring strategy adopted. Moreover, we highlight the transmission channels through which a debt restructuring generates the output losses, while many of the papers above do not.

Our findings also contribute to the literature on the nexus between sovereign and banking crises in emerging economies.⁹ The existing evidence on the direction of causality between sovereign and banking crises remains mixed. Borensztein and Panizza (2009) show that default episodes seem to cause banking crises, while Reinhart and Rogoff (2011b) find that banking crises tend to precede sovereign debt crises. Balteanu and Erce (2018) reconcile these papers by exploring the twoway determinants of “twin” banking and sovereign crises simultaneously. Our main contribution is to provide new evidence on the linkage across debt restructurings, severe sudden stops, and credit crunches, showing that the likelihood of a banking crisis depends on the nature of the restructuring.

Finally, we also contribute to a growing literature that studies how debt restructurings can be designed to more likely achieve better outcomes. These aspects have been studied by IMF (2014, 2015), Brookings-CIEPR (2013), Fernandez and Martin (2014), and Mariscal et al., (2015) among others.¹⁰ Most papers contrast outcomes of “reprofilings” (restructurings that mostly involve an extension of payment terms) to those of restructurings that involve deeper haircuts on different measures of borrower and creditor welfare (e.g., growth performance, incidence of future debt crises, return to market access, among others). Our paper highlights that much of the adverse costs hinge on whether restructurings take place pre-emptively, and also sheds light on the particular financial sector linkages that are associated with avoiding the worst outcomes. Our empirical results highlight a channel of transmission of sovereign default into the real economy that is not prominent in recent theoretical work on output dynamics, which has focused on how restructuring domestic debt can affect credit through the banks’ exposure to sovereign debt (e.g., Gennaioli et al., 2014; Sosa-Padilla 2018).¹¹ Our analysis shows that even restructurings of external debt—not held by domestic banks—can have deleterious effects amplified through the capital inflow-credit channel (potentially due to loss in banks’ access to external financing following the sovereigns’ loss of market access).¹²

The rest of the paper is structured as follows. In the next section, we explain our dataset and document stylized facts regarding the response of GDP, investment, bank lending, and net capital flows to different debt restructuring strategies. Section III explains the local projection approach and presents our results, including estimations based on sub-sampling our restructuring events to identify the “capital inflow-credit channel”. Section IV introduces our logit model approach and discusses the results. Finally, Section V concludes. Details of various robustness analysis are provided in the Appendix.

II. New Stylized Facts on Sovereign Debt Restructurings, Banking Crises, Severe Credit and Capital Inflow Declines

A. Data on Sovereign Debt Restructurings, Banking Crises, Severe Credit and Capital Inflow Declines

Throughout the paper, we focus only on private external debt restructurings, so neither official external debt nor domestic debt restructurings are considered—as a robustness check, we consider private external debt restructurings with and without official external debt (Paris Club) restructurings or IMF-supported programs, and single external debt restructurings in Appendix E.3 and E.4.¹³

Asonuma and Trebesch (2016) classify debt restructurings on private external debt as follows:¹⁴

Definition i. ‘Strictly preemptive restructurings’ are those which are implemented without missing any payments at all (no legal default).
Definition ii. ‘Weakly preemptive restructurings’ are those in which some payments are missed, but only temporarily and after the start of formal or informal negotiations with creditor representatives (no unilateral default).
Definition iii. ‘Post-default restructurings’ are all other cases, in which payments are missed unilaterally and without the agreement of creditor representatives (unilateral default prior to negotiations).

Our dataset of 179 restructurings over 1978–2010 is coded accordingly and summarized in Panel A of Table 1.¹⁵ The details of countries experiencing restructurings are summarized in Appendix A.1. Average restructuring duration and haircuts vary substantially across strategies: 5.0, 1.1, and 0.9 years on average for duration of post-default, weakly preemptive, and strictly preemptive restructurings, respectively and 48.0, 18.2, and 18.9 percent on average for haircuts of post-default, weakly preemptive, and strictly preemptive episodes, respectively. We code the restructuring strategy dummies at an annual frequency, setting it to 1 if it either starts in the current year or continues from the previous year, and 0 otherwise. For comparison with previous studies, we also use sovereign defaults from Standard and Poor’s (S&P) database for robustness check in Appendix E.2 though its coverage is a subset of the restructuring episodes of Asonuma and Trebesch (2016).

Laeven and Valencia (2013) define a banking crisis as an event that meets two conditions. First, there are significant signs of financial distress in the banking system (as indicated by significant bank runs or losses in the banking system). Second, there must be significant intervention measures in response to losses in the banking system. Over the same period (1978–2010), they document 39 banking crises—last 3.3 years on average—occurring within the 3 years since the start of 127 debt restructurings as reported in the 1st column in Panel B in Table 1.¹⁶ As the data are at an annual frequency, we set our banking crisis dummy as 1 if a banking crisis occurs or continues in that year and 0 otherwise.

Stylized fact 1: Severe declines in credit and net capital inflows occur simultaneously with sovereign debt restructurings.

Table 1.

Dataset, 3-year Horizon

Notes: ^1/ Including episodes in countries without any experience of private external debt restructurings.

^{^2/}Banking crises over three years since the start of debt restructurings, from year t + 1 to t + 3.

^{^3/}Severe credit declines are defined as observations with the cumulative percentage point difference in the credit-to-GDP ratio over three years, $d_{i, t + 3}^{c} = 100 \times (C r e d i t_{i, t + 3} / G D P_{i, t + 3} - C r e d i t_{i, t} / G D P_{i, t})$ , is less than the 25th percentile of the distribution.

^{^4/}Severe protracted net capital inflow declines (sum) are defined as observations where the sum of cumulative percentage point differences in the net capital inflows-to-GDP ratio over three years, $d_{t + 3}^{N C I, S U M} = 100 \times Σ_{s = 1}^{3} ({N C I}_{i, t + s} / G D P_{i, t + s} - {N C I}_{i, t} / G D P_{i, t})$ , is less than the 25th percentile of the distribution.

^{^5/}Severe protracted net capital inflow declines (difference) are defined as observations where the cumulative percentage point difference in the net capital inflows-to-GDP ratio over three years, $d_{t + 3}^{N C I, D I F} = 100 \times ({N C I}_{i, t + 3} / G D P_{i, t + 3} - {N C I}_{i, t} / G D P_{i, t})$ , is less than the 25th percentile of the distribution.

^{^6/}The sample after 2008 is dropped because macroeconomic variables in the period are largely influenced by the Global Financial Crisis. Debt restructuring episodes with more than one strategy within a window of [-2, +3] years around the start year of a debt restructuring t = 0 are coded with the worst strategy only.

Moreover, in line with conventions in the literature (Bernanke and Cara 1999; Calvo et al. 1996; Kaminsky and Reinhart 1999), we define both severe credit declines and protracted net capital inflow declines—the conventional thresholds of 25th percentile applied symmetrically across observations with or without any types of debt restructurings as reported in Tables A2 and A3 in Appendix A.3—as follows:

Definition iv. A ‘ severe credit decline’ is an event where the cumulative percentage point difference in the credit-to-GDP ratio over three or five years, $d_{i, t + s}^{c} = 100 \times (C r e d i t_{i, t + s} / G D P_{i, t + s} - C r e d i t_{i, t} / G D P_{i, t})$ , for s = 3, 5, is less than the 25th percentile of the distribution.¹⁷
Definition v. A ‘ severe protracted net capital inflow decline’ is an event where (a) the sum of cumulative percentage point differences in the net capital inflows-to-GDP ratio over three or five years, $d_{i, t + r}^{N C I, S U M} = 100 \times Σ_{s = 1}^{r} (N C I_{i, t + s} / G D P_{i, t + s} - N C I_{i, t} / G D P_{i, t})$ for r = 3,5, is less than the 25th percentile of the distribution, or (b) the cumulative percentage point difference in the net capital inflows-to-GDP ratio over three or five years, $d_{i, t + r}^{N C I, D I F} = 100 \times (N C I_{i, t + r} / G D P_{i, t + r} - N C I_{i, t} / G D P_{i, t})$ for r = 3,5, is less than the 25th percentile of the distribution, respectively.

Over the same period (1978–2010), we document 48 severe credit declines and 40 (34) severe protracted net capital inflow declines occur simultaneously with (or follow) sequentially debt restructurings as reported in column 1 in Panels C, D, and E in Table 1. In comparison, 49 and 35 (32) episodes are identified, respectively under 5-year criterion as reported in Panels C, D, and E in Table A4 in Appendix A.4. Using the same approach, we set our severe credit decline dummy (severe net capital inflow decline dummy) as 1 if a severe credit decline (protracted net capital inflow decline) occurs or continues in that year and 0 otherwise.

To illustrate occurrence of these events across countries and time, Figure 1 shows debt restructurings and severe declines in credit and net capital inflows over our sample. One pattern of events emerges from Figure 1 is that severe declines in credit and net capital inflows occur simultaneously with or follow sequentially debt restructurings. Previous studies explore these links, but only between two out of three events separately: Reinhart and Rogoff (2009) and Reinhart et al. (2016) document the link between debt restructurings (sovereign defaults) and severe declines in net capital inflows, and Cesa-Bianchi et al. (2018) find the link between severe net capital inflow declines and severe credit declines.

Stylized fact 2: Banking crises, and severe credit and net capital inflow declines occur more frequently following post-default restructurings.

Figure 2 reports both debt restructurings and banking crises for each country in our sample (summary statistics provided in Panel B of Table 1). One pattern of sequential events emerges from Figure 2: the start of debt restructurings precedes or coincides with that of banking crises. Prominent cases include: (i) Argentina 2001–05 debt restructuring and 2001–03 banking crisis, and (ii) Russia 1998–99 debt restructuring and 1998 banking crisis.

As shown in Panel B in Table 1, in 30 cases out of 91 post-default restructurings, banking crises occur in subsequent years (33 percent). The likelihood of banking crises after post-default restructurings is higher than the one for weakly preemptive (27 percent) or strictly preemptive restructurings (20 percent). In contrast, banking crises precede debt restructurings in 16 cases out of 39 weakly preemptive episodes (41 percent) and in 9 cases out of 21 strictly preemptive episodes (42 percent), respectively. The likelihood of weakly and strictly preemptive restructurings after banking crises is higher than the one for post-default restructurings (20 percent, 18 cases out of 91 episodes in total). The link between banking crises and restructurings is in line with Reinhart and Rogoff (2011b) and Balteanu and Erce (2018), though none of the previous studies distinguished the effects of different restructuring strategies.

Panel C in Table 1 reports that severe credit declines occur in around 49 percent of post-default restructurings i.e., 36 cases out of 73 cases in total—where credit data are available. In contrast, severe credit declines occur in 39 and 25 percent of weakly and strictly preemptive restructurings, respectively. Moreover, Panels D and E in Table 1 shows that severe protracted net capital inflow declines are more likely during post-default restructurings. On average, net capital inflow declines in 38 (33) percent of post-default restructurings, while only in 35 and 20 (31 and 10) percent of weakly preemptive and strictly preemptive restructurings, respectively.

Figure 3 contrasts the likelihood of these three events. One common trend emerges from Figure 3—also confirmed with the events under 5-year criterion as reported in Figure A1 in Appendix A.4—: all banking crises, severe credit and net capital inflow declines occur more likely following post-default restructurings than weakly or strictly preemptive restructurings.

B. Stylized Facts on GDP, Investment, Private Credit, and Capital Flows in Sovereign Debt Restructurings

Stylized fact 3: GDP and investment decline substantially in post-default restructurings, less severely in weakly preemptive restructurings, and are not affected in strictly preemptive cases.

Figure 4 reports the dynamics of GDP and investment for our three restructuring strategies:¹⁸ GDP (real) is measured as a log-deviation from a Hodrick-Prescott (H-P) filtered trend and investment (real) is normalized at the pre-crisis (t = 0) level. The time horizon of each panel corresponds to the number of years since the pre-restructuring year, i.e., one year before the announcement or the default event. Both GDP and investment experience a severe decline following a post-default restructuring and they remain below the pre-crisis levels for several years (red lines in Panels A and B). A much smaller and short-lived drop in both GDP and investment occurs in the run-up to a weakly preemptive restructuring, and GDP and investment recover to their pre-crisis levels after only short periods (blue lines in Panels A and B). In contrast, neither GDP nor investment contracts following a strictly preemptive restructuring (green lines in Panels A and B).¹⁹

While we observe qualitatively similar dynamics for GDP and investment, the magnitude of the decline in investment is larger than that of GDP in both post-default and weakly preemptive events. This is consistent with consumption smoothing during a crisis period where investment bears a disproportionate share of the adjustment relative to consumption. The decline in investment has an immediate impact on GDP—because investment is one of the components of GDP from the demand side—and also contributes to lower GDP growth in the following years. Table B1 in Appendix B shows the tight relationship between GDP growth and investment growth.

Stylized fact 4: Both private credit and capital inflows remain below the pre-crisis level and lending interest rates experience a sharp increase in post-default restructurings.

Figure 5 reports the trend of private credit, lending interest rates, and net capital inflows. We follow the same presentation approach as in Figure 4 in terms of both the classification by restructuring strategies and normalization at the pre-crisis level. Private credit suffers a severe and prolonged decline following post-default restructurings and recovers at the pre-crisis level at the third year since the start of restructurings (red line in Panel A). In contrast, private credit remains more resilient before and after both weakly and strictly preemptive (blue and green lines in Panel A). The lending interest rates experience a sharp and temporary increase in post-default restructurings (red line in Panel B) accompanied by a sharp drop in private credit, followed by a return to the pre-crisis level (possibly as demand-side effects of credit dominate supply-side ones). In contrast, the lending interest rates remain stable in both weakly and strictly preemptive restructurings (blue and green lines in Panel B).

Lastly, net capital inflows experience a severe and protracted decline in post-default restructurings (red line in Panel C). While net capital flows are volatile, they remain subdued and below the pre-crisis level over the following five years. Weakly preemptive restructurings are associated with a milder decline in net capital flows (blue line in Panel C). In contrast, net capital inflows are not affected following a strictly preemptive restructuring (green line in Panel C).

C. Data on Macroeconomic Variables

Following conventions in the literature, we use data at an annual frequency to secure the widest possible country coverage. Both real US-dollar GDP, real US-dollar GDP per capita and investment (denominated in US dollar) data are from the Penn World Table 8.0 (Feenstra et al., 2015).²⁰ For banking sector indicators, both private credit and lending interest rates are from the World Bank World Development Indicators (WDI) database. Net capital inflows are from IMF World Economic Outlook (WEO).

Our set of control variables follows closely Jordà et al., (2016). This includes a lagged dependent variable for each regression, respectively (for instance, lagged GDP growth rate for the GDP growth regression) and the cyclical component of log of real GDP per capita. Appendix A.2 summarizes data sources of these explanatory variables.

D. Time Sample

Our sample covers the period 1970–2013. We do not consider the period prior to 1970 because there were few debt restructurings between 1945 and 1970 (Reinhart and Rogoff 2011a; Sturzenegger and Zettelmeyer 2006). The most recent restructuring episodes in Asonuma and Trebesch (2016) are Ecuador 2008–09 and Seychelles 2008–10. Post-restructuring years for these two restructurings are fully covered in our sample.

We follow Jordà and Taylor (2016) and focus on the sample of countries experiencing a specific event. In our benchmark, the sample of 68 countries is set as those which have experienced at least one debt restructuring over the specified horizon. We also apply a full country coverage including non-restructuring countries for a robustness check (Appendix E.1) to contrast our estimates with those in previous studies (Borensztein and Panizza 2009; Kuvshinov and Zimmermann 2016). Following previous studies (Cruces and Trebesch 2013; Asonuma and Trebesch 2016), we consider each restructuring as an independent event—there are overlapping observations included in our sample. This classification is based on the criteria such that both debt instruments subject to exchange and dates of announcement and of settlements in one restructuring differ from those in other restructurings. Appendix E.4 shows the robustness of our results under different approaches for handling overlapping restructurings.

III. Local Projection Approach

A. Endogeneity of Debt Restructuring Decisions

Countries experiencing restructurings are likely to differ from others in many aspects. Moreover, the restructuring strategy (e.g., preemptive vs post-default) is an endogenous choice by the sovereign debtor (Asonuma and Trebesch, 2016). As a result, conventional ordinary least square (OLS) estimation results—reported in Appendix C.1—could be driven by the characteristics of countries experiencing restructurings rather than the effect of debt restructuring itself.

Table 2 shows the difference in key macroeconomic and structural variables, among the different treatment groups—the start year and one year before the start year of debt restructurings—and the control group. The variables considered, drawing on Asonuma et al. (2016) and Kuvshinov and Zimmermann (2016), include: (1) credit ratings from the Institutional Investor magazine; (2) changes in credit ratings; (3) short-term interest payments-to-GDP ratio; (4) total interest payments-to-GDP ratio; (5) GDP growth rate; (6) cyclical component of log of GDP; and (7) an index of political stability.

Table 2.

Characteristics of the Treatment and Control Groups

Notes: The table shows the difference between the treatment groups (the start year and a year before the start of debt restructurings) and the control group (other observations) in terms of seven variables. The difference between treatment and control groups is estimated by regressing each of the variables on two sets of dummies for the three restructuring strategies, one set which takes unity at the start and the other which takes unity on the year before the start. The average value for normal times is taken from the estimated constant term. The average values for treatment groups are calculated as the sum of the estimated constant term and each estimated coefficient on debt restructuring dummy. All regressions include country fixed effects. The sample period is 1970–2013. ***, **, and * indicate statistical significance at 1 percent, 5 percent, and 10 percent level, respectively. Robust standard errors, clustered at the country level, are in parentheses. Credit ratings take a value between 0 and 100, and a greater value implies a smaller default risk. The political stability index takes a value between 0 and 7, and the greater the value the greater the political instability.

Table 2.

Characteristics of the Treatment and Control Groups

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Credit ratings	Change in credit ratings	Interest payments (short-term)/GDP × 100	Interest payments (total)/GDP × 100	GDP growth rate × 100	Cyclical comp. of log of GDP × 100	Political stability (civil liberties)
Post-default	-2.142*	-2.297***	0.224***	1.352***	-3.423***	-1.435*	0.318***
(start year)	(1.246)	(0.415)	(0.076)	(0.301)	(0.823)	(0.849)	(0.118)
Post-default	0.473	-1.368***	0.300***	1.715***	-1.650**	0.473	0.344***
(a year before start)	(1.310)	(0.430)	(0.076)	(0.303)	(0.822)	(0.848)	(0.118)
Weakly preemptive	-6.028***	-3.312***	0.254**	2.030***	-2.742**	-3.558***	0.236
(start year)	(1.642)	(0.520)	(0.115)	(0.458)	(1.197)	(1.152)	(0.182)
Weakly preemptive	-1.818	-3.551***	0.475***	2.150***	-3.290***	-1.071	0.310*
(a year before start)	(1.645)	(0.537)	(0.116)	(0.459)	(1.198)	(1.154)	(0.182)
Strictly preemptive	-9.298***	-2.052***	0.124	1.142*	-0.676	-3.533**	0.117
(start year)	(2.311)	(0.751)	(0.173)	(0.688)	(1.588)	(1.651)	(0.245)
Strictly preemptive	-7.078***	-0.593	0.0536	0.438	-1.444	-2.965*	0.177
(a year before start)	(2.370)	(0.772)	(0.173)	(0.687)	(1.587)	(1.650)	(0.245)
Constant	28.64*** (255)	0.563*** (082)	0.269*** (014)	2.024*** (057)	3.904*** (152)	0.163 (153)	4.091*** (023)
R - squared	0.029	0.089	0.024	0.050	0.016	0.011	0.010
Number of countries	63	63	54	54	64	59	62
Number of observations	1,566	1,503	2,053	2,053	2,373	2,159	2,405

Table 2.

Characteristics of the Treatment and Control Groups

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Credit ratings	Change in credit ratings	Interest payments (short-term)/GDP × 100	Interest payments (total)/GDP × 100	GDP growth rate × 100	Cyclical comp. of log of GDP × 100	Political stability (civil liberties)
Post-default	-2.142*	-2.297***	0.224***	1.352***	-3.423***	-1.435*	0.318***
(start year)	(1.246)	(0.415)	(0.076)	(0.301)	(0.823)	(0.849)	(0.118)
Post-default	0.473	-1.368***	0.300***	1.715***	-1.650**	0.473	0.344***
(a year before start)	(1.310)	(0.430)	(0.076)	(0.303)	(0.822)	(0.848)	(0.118)
Weakly preemptive	-6.028***	-3.312***	0.254**	2.030***	-2.742**	-3.558***	0.236
(start year)	(1.642)	(0.520)	(0.115)	(0.458)	(1.197)	(1.152)	(0.182)
Weakly preemptive	-1.818	-3.551***	0.475***	2.150***	-3.290***	-1.071	0.310*
(a year before start)	(1.645)	(0.537)	(0.116)	(0.459)	(1.198)	(1.154)	(0.182)
Strictly preemptive	-9.298***	-2.052***	0.124	1.142*	-0.676	-3.533**	0.117
(start year)	(2.311)	(0.751)	(0.173)	(0.688)	(1.588)	(1.651)	(0.245)
Strictly preemptive	-7.078***	-0.593	0.0536	0.438	-1.444	-2.965*	0.177
(a year before start)	(2.370)	(0.772)	(0.173)	(0.687)	(1.587)	(1.650)	(0.245)
Constant	28.64*** (255)	0.563*** (082)	0.269*** (014)	2.024*** (057)	3.904*** (152)	0.163 (153)	4.091*** (023)
R - squared	0.029	0.089	0.024	0.050	0.016	0.011	0.010
Number of countries	63	63	54	54	64	59	62
Number of observations	1,566	1,503	2,053	2,053	2,373	2,159	2,405

Columns (1), (2), and (4) suggest that there are significant differences in credit ratings, changes in credit ratings, and total interest payments-to-GDP ratio between observations in the start of three types of restructurings and normal times. In contrast, a significant difference arises in short term interest payments-to-GDP ratio, GDP growth rate, and the index of political stability only between observations in the year before both post-default and weakly preemptive episodes and normal times. It is worth noting that for some variables, preemptive restructurings are not associated with more favorable ex ante outcomes (e.g., preemptive restructurings on average had a worse credit rating than post-default restructurings).

Table 3.

Predicting the Start of Debt Restructurings, Probit

Dep. Var. = Dummy Taking 1 at the Start of Debt Restructuring

Notes: The sample period is 1970–2013. ***, **, and * indicate statistical significance at 1 percent, 5 percent, and 10 percent level, respectively. Standard errors are in parentheses. Total number of past debt restructurings is used to predict post-default and weakly preemptive debt restructurings. Number of past debt restructurings for the last six years is used to predict strictly preemptive cases.

Table 3.

Predicting the Start of Debt Restructurings, Probit

Dep. Var. = Dummy Taking 1 at the Start of Debt Restructuring

	Post-default		Weakly preemptive		Strictly preemptive
	(1)	(2)	(3)	(4)	(5)	(6)
# of past post-default	0.232*** (0.035)	0.479*** (0.079)	-0.560*** (0.137)	-0.674*** (0.206)	0.020 (0.174)	0.078 (0.268)
# of past weakly preemptive	-0.047 (0.064)	0.310*** (0.105)	0.508*** (0.063)	0.499*** (0.135)	0.407*** (0.115)	0.173 (0.208)
# of past strictly preemptive	-0.056 (0.084)	0.0419 (0.088)	-0.276** (0.129)	-0.197 (0.140)	0.693*** (0.127)	0.605*** (0.194)
# of past banking crises		-0.199*** (0.044)		-0.140** (0.066)		-0.110 (0.089)
External debt-to-GDP ratio, lag		-0.626*** (0.220)		-0.149 (0.369)		-0.636 (0.522)
Total interest payments-to-GDP ratio, lag		9.814*** (2.067)		6.417** (2.888)		5.956 (4.454)
Change in credit ratings, lag		-0.054** (0.023)		-0.068*** (0.026)		0.004 (0.047)
Cyclical comp. of log of GDP per capita, lag		1.773 (1.327)		-2.284 (2.295)		-6.675** (3.183)
GDP growth rate, lag		-3.311** (1.389)		-0.026 (2.265)		-0.342 (2.940)
Freedom index, civil liberty, lag		0.147** (0.060)		-0.155* (0.088)		0.036 (0.110)
Likelihood ratio	43.18	74.59	79.82	89.08	37.70	24.11
Likelihood ratio(p -value)	0.000	0.000	0.000	0.000	0.000	0.000
Pseudo R -squared	0.054	0.186	0.194	0.353	0.152	0.007
Number of observations	2,816	1,244	2,816	1,244	2,816	1,244

Table 3.

Predicting the Start of Debt Restructurings, Probit

Dep. Var. = Dummy Taking 1 at the Start of Debt Restructuring

	Post-default		Weakly preemptive		Strictly preemptive
	(1)	(2)	(3)	(4)	(5)	(6)
# of past post-default	0.232*** (0.035)	0.479*** (0.079)	-0.560*** (0.137)	-0.674*** (0.206)	0.020 (0.174)	0.078 (0.268)
# of past weakly preemptive	-0.047 (0.064)	0.310*** (0.105)	0.508*** (0.063)	0.499*** (0.135)	0.407*** (0.115)	0.173 (0.208)
# of past strictly preemptive	-0.056 (0.084)	0.0419 (0.088)	-0.276** (0.129)	-0.197 (0.140)	0.693*** (0.127)	0.605*** (0.194)
# of past banking crises		-0.199*** (0.044)		-0.140** (0.066)		-0.110 (0.089)
External debt-to-GDP ratio, lag		-0.626*** (0.220)		-0.149 (0.369)		-0.636 (0.522)
Total interest payments-to-GDP ratio, lag		9.814*** (2.067)		6.417** (2.888)		5.956 (4.454)
Change in credit ratings, lag		-0.054** (0.023)		-0.068*** (0.026)		0.004 (0.047)
Cyclical comp. of log of GDP per capita, lag		1.773 (1.327)		-2.284 (2.295)		-6.675** (3.183)
GDP growth rate, lag		-3.311** (1.389)		-0.026 (2.265)		-0.342 (2.940)
Freedom index, civil liberty, lag		0.147** (0.060)		-0.155* (0.088)		0.036 (0.110)
Likelihood ratio	43.18	74.59	79.82	89.08	37.70	24.11
Likelihood ratio(p -value)	0.000	0.000	0.000	0.000	0.000	0.000
Pseudo R -squared	0.054	0.186	0.194	0.353	0.152	0.007
Number of observations	2,816	1,244	2,816	1,244	2,816	1,244

As expected, these results show that countries indeed have different characteristics prior to any type of restructurings, raising concerns over the endogeneity on the restructuring decision (and to some extent on the choice of restructuring strategies). To address this issue, we use external debt-to-GDP ratio, total interest payments-to-GDP ratio, change in credit ratings, the cyclical component of log of GDP per capita, GDP growth rate, and freedom index of civil liberties as explanatory variables to predict the start of each type of restructuring.²¹ Countries that experienced default or debt restructuring in the past are more likely to experience another default or restructuring (Reinhart et al. 2003; Asonuma 2016). We control for this feature by including as explanatory variables the number of post-default, weakly, and strictly preemptive restructurings and banking crises that took place in the last six years. We apply a probit model to estimate separate equations for the start of each type of restructurings.

Results of the probit model are reported in Table 3. Based on the results reported in columns (2), (4), and (6), Panel A of Figure 6 plots the area under the Receiver Operating Characteristic (ROC) curve for the three types of debt restructurings, respectively. The ROC area takes a value between 0.5 and 1. A value of 0.5 indicates that regressors have no explanatory power to classify observations (the start of debt restructuring vs normal times)—so that the model predictions are random. On the opposite extreme, a value of 1 indicates perfect classification power. For each type of debt restructurings, three ROC areas are reported based on the three different sets of regressors; x1 denotes the set of regressors: the numbers of past post-default, weakly preemptive, strictly preemptive debt restructurings, and banking crises, external debt-to-GDP ratio, total interest payments-to-GDP ratio, changes in credit ratings, the cyclical component of log of GDP per capita, the GDP growth rate, the freedom index of civil liberty, and country fixed effects; x2 denotes the set of regressors: the numbers of past post-default, weakly preemptive, strictly preemptive debt restructurings, banking crises, and country fixed effects. While there is no established threshold for this statistic, a ROC curve above 0.7 is generally considered to be adequate (Schularick and Taylor 2012). We choose x1 as the set of instruments in the first-stage regression. Appendix E.6 shows the robustness of our baseline set of instruments under a different approach of setting instruments.

Using these explanatory variables, we predict probabilities of three types of restructurings. Panel B of Figure 6 shows kernel density estimates for the predicted likelihood of debt restructuring for each type of strategies. In each panel, we report two kernel density estimates. One is for observations where debt restructurings actually occur (treatment group), and the other is for those that debt restructurings do not occur (control group). A substantial difference in the distribution of predicted likelihoods between the treatment and control groups for all types of debt restructurings clearly indicates that instruments have a high explanatory power to differentiate between these two groups.

B. Augmented Inverse Probability Weighted (AIPW) Estimation

Since the dominant determinants of the restructuring choice differ among the three strategies, we implement the Augmented Inverse Probability Weighted (AIPW) estimator by running separate regressions for each type of restructurings, rather than including these three dummy variables jointly in one regression.

In the first stage, we estimate propensity scores in the sample, which corresponds to the probability that a restructuring event occurs. We use probit models, treating each restructuring strategy separately, as follows:

\begin{matrix} \Pr {(R)}_{i, t + 1} = Φ (Z_{i, t}, X_{i, t}, γ^{R}) f o r R = {P D, W e a k l y, S t r i c t l y} & (1) \end{matrix}

where Pr(R)_i,t+1 denotes the probability that a type Rdebt restructuring event occurs in country i in year t+1; Z_i,t is a vector of lagged instruments (in year t) to predict the restructuring events with three strategies defined in Section III.A. Regressors from the second stage, X_i,t, are also included in the first-stage estimation. γ^R indicates vectors of coefficients to be estimated for each type of debt restructuring strategies R = {PD, Weakly, Strictly}. Φ denotes the normal cumulative distribution function.

In the second stage, we correct for potential bias in our sample by using the inverse of the estimated propensity score obtained in the first stage: $1 / {\hat{\Pr (R)}}_{i, t + 1}$ . Under this weighting, the observations that are less likely associated with restructurings (for instance, those with low total interest payments-to-GDP ratio and no change in the credit ratings) account for a larger weight in the AIPW estimates. Intuitively, this adjustment generates a hypothetical situation where there is no bias between restructuring and non-restructuring events in contrast with the real world where restructurings are triggered by common features (for which we control by assigning asymmetric weights).

With the AIPW estimates obtained through this bias correction process, we interpret the estimated coefficients as the average treatment effect (ATE). This corresponds to a difference in average debt restructuring effects between observations that actually experience debt restructurings and those that do not experience debt restructurings. To acquire the average effect for each of the treatment groups and the control groups, we estimate local projections as follows:

\begin{matrix} g_{i, t + h} = α_{i}^{R, h} + Λ^{R, h} D_{i, t + 1}^{R} + X_{i, t} β^{R, h} + ε_{i, t + h}^{R}, f o r R = {P D, W e a k l y, S t r i c t l y} a n d h = 1, 2, .., 5 & (2) \end{matrix}

where g_i,t+h = 100 × (GDP_i,t+h – GDP_i,t)/GDP_i,t is the cumulative GDP growth from time t to t + h in country i; $D_{i, t + 1}^{R}$ denotes three dummy variables taking unity if there is a post-default, weakly preemptive, and strictly preemptive debt restructuring at year t + 1 in country i, respectively; $α_{i}^{R, h}$ denotes the country fixed effects for each regression; Λ^R,h and β^R.h indicate coefficients to be estimated; and $ϵ_{i, t + h}^{R}$ is an error term for each regression. Following Jordà (2005) and Jordà and Taylor (2016), we include fixed effects which account for variation in the degree of financial liberalization and other macroeconomic differences across countries.

We denote the predicted dependent variables as

\begin{matrix} {\hat{g}}_{i, t + h}^{R} = {\hat{α}}_{i}^{R, h} + {\hat{Λ}}^{R, h} D_{i, t + 1}^{R} + X_{i, t} {\hat{β}}^{R . h}, f o r R = {P D, W e a k l y, S t r i c t l y} a n d h = 1, 2, .., 5 & (3) \end{matrix}

where a hat indicates an estimated coefficient. The average treatment effect of each restructuring strategy on GDP growth for h-year horizon is computed as follows:

\begin{matrix} A T E (Λ^{R, h}) = \frac{1}{N_{R}} Σ i Σ t \frac{{\hat{g}}_{i, t + h}^{R} D_{i, t + 1}^{R}}{{\hat{\Pr (R)}}_{i, t + 1}} - \frac{1}{N_{N o n - R}} Σ i Σ t \frac{{\hat{g}}_{i, t + h}^{R} (1 - D_{i, t + 1}^{R})}{1 - {\hat{\Pr (R)}}_{i, t + 1}}, f o r R = {P D, W e a k l y, S t r i c t l y} a n d h = 1, 2, .., 5 & (4) \end{matrix}

where N_R indicates the number of observations from the start of type R debt restructurings; N_Non-R indicates the number of observations which are complement to the sample of restructurings;²² ${\hat{\Pr (R)}}_{i, t + 1}$ denotes the estimated probability of type R debt restructurings; and ${\hat{g}}_{i, t + h}^{R} f o r R = {P D, W e a k l y, S t r i c t l y}$ is the predicted dependent variable.

Based on the estimation results in Table C.2 in Appendix C.2, Figure 7 reports the cumulative responses for macroeconomic and banking sector variables. Investment, private credit, and net capital inflows are in percentage point difference in GDP from the pre-restructuring level (at t) GDP and lending interest rates are in cumulative rate of change and cumulative percentage point difference in level, respectively from the pre-restructuring year. Due to limited data availability, we obtain AIPW estimates for the lending interest rates under both weakly and strictly preemptive restructurings.²³ The solid lines in red and the gray bands indicate the point estimates and 95 percent confidence intervals for post-default restructurings, respectively. The solid and dotted lines in blue and green indicate the point estimates and 95 percent confidence intervals for weakly and strictly preemptive restructurings, respectively. Post-default restructurings experience more severe and prolonged declines in GDP and investment than weakly preemptive restructurings, with strictly preemptive restructurings experiencing the mildest impacts (Panels A and B). Declines in GDP and investment for weakly preemptive restructurings past the third year are likely driven by continued weakness after the completion of the restructuring—the duration of weakly preemptive restructurings is 1.2 years on average.

Post-default restructurings experience a severe and prolonged decline in private credit (Panel C). In parallel, we observe a large and protracted rise in lending interest rates (Panel D). In contrast, the negative effect on private credit from weakly preemptive restructurings is milder (Panel C). Capital inflows decline severely after post-default episodes and mildly after weakly preemptive episodes (Panel E). Loss of market access by sovereigns is typically accompanied by the loss of market access by both domestic banks and corporates. Moreover, Panels A, B, C, and E in Figure C1 in Appendix C.1 show the OLS local projection estimates for GDP, investment, private credit, and net capital inflows for the three restructuring strategies which confirm the robustness of our baseline AIPW results. The difference between AIPW and OLS estimates is driven by forecast errors of both restructuring and non-restructuring observations: Larger AIPW estimates (than OLS estimates) stem from occurrence of restructuring events which were predicted with low probability at year t. Appendix E.6 also shows the robustness of our baseline AIPW estimation results under a different approach of setting instruments.

Moreover, Panels A, B, C, and E in Figure C1 in Appendix C.1 show the OLS local projection estimates for GDP, investment, private credit, and net capital inflows for the three restructuring strategies which confirm the robustness of our baseline AIPW results. The difference between AIPW and OLS estimates is driven by forecast errors of both restructuring and non-restructuring observations: Larger AIPW estimates (than OLS estimates) stem from occurrence of restructuring events which were predicted with low probability at year t. Appendix E.6 also shows the robustness of our baseline AIPW estimation results under a different approach of setting instruments.

C. Role of Capital Inflow-Credit Channel

Next, we dig further into the link among capital inflows, private credit, investment, and GDP growth, and study the extent to which the linkage among these variables helps understand the different performance after different debt restructuring approaches. Following conventions in the literature (Calvo et al. 1998), we define severe sudden stops for each restructuring strategy as described below.

Definition vi. A ‘severe sudden stop’ is an event where the percentage point difference in net capital inflows-to-GDP ratio from the pre-restructuring year to the start of restructuring (from year 0 to year 1), $d_{i, t + 1}^{N C I} = 100 \times (N C I_{i, t + 1} / G D P_{i, t + 1} - N C I_{i, t} / G D P_{i, t})$ for R = {PD, Weakly, Strictly}, is less than the 25th or 50th percentile among the observations experiencing that type of restructuring.²⁴

Figure 8 reports the dynamics of private credit, investment, and GDP for our restructuring strategies (post-default, weakly preemptive, and strictly preemptive) together with restructurings with/without a severe sudden stop.²⁵ Private credit, investment, and GDP experiences a more severe decline when a severe sudden stop is accompanied with any of the three types of restructuring strategies (purple lines in Panels A, B, and C).²⁶ In contrast, neither private credit investment nor GDP contracts when the restructuring (regardless of its type) is not associated with a severe sudden stop (brown lines in Panels A, B, and C).

We analyze how the dynamics of macroeconomic variables differ between restructuring events which were accompanied by a severe sudden stop and those which were not. For this purpose, we follow Jordà and Taylor (2016) and Auerbach and Gorodnichenko (2016) to estimate the impact of a severe sudden stop and specify the following local projection equation:

\begin{matrix} g_{i, t + h} = α_{i}^{R, h} + Λ^{S S, h} D_{i, t + i}^{S S} D_{i, t + 1}^{R} + Λ^{N o n S S, h} (1 - D_{i, t + i}^{S S}) D_{i, t + 1}^{R} + {\tilde{X}}_{i, t} β^{R, h} + ε_{i, t + h}^{R} f o r R = {P D, W e a k l y, S t r i c t l y}, a n d h = 1, 2, .., 5 & (5) \end{matrix}

where $D_{i, t + 1}^{S S}$ is a dummy variable taking unity if there is a severe sudden stop in year t + 1 and zero otherwise; $D_{i, t + 1}^{R}$ is a dummy variable taking unity if there is a restructuring (for instance, post-default) at year t + 1 and zero otherwise; A^ss’^h and A^NonSS-^h are coefficients to be estimated. In the second term on the right-hand side of equation (5), $D_{i, t + h}^{S}$ is interacted with $D_{i, t + 1}^{R}$ . Therefore, A^ss’^h captures the impact of a severe sudden stop in year t + 1 associated with a restructuring (for instance post-default) that occurred in the same year, t + 1. In the third term on the right-hand side of equation (5), $(1 - D_{i, t + i}^{S S})$ is interacted with $D_{i, t + 1}^{R}$ . Therefore, A^NonSS’^h reflects the impact when no severe sudden stop is associated with the restructuring that occurred in the same year t + 1. ${\tilde{X}}_{i, t}$ is a set of regressors including cyclical component of log of GDP per capita, lagged changes in the investment-to-GDP ratio, the credit-to-GDP ratio, and the GDP growth rates from period t.

In the first stage, we estimate the likelihood of restructurings with and without severe sudden stops, separately for each restructuring strategy, i.e., a “two-type model”. In the second stage, using both the predicted dependent variable from (5) and applying the propensity score matching technique, we correct the bias by assigning different weights corresponding to a difference in the estimated probability of events as explained in equation (4). That allows us to compute the average treatment effects of both severe sudden stop and non-severe sudden stop events, respectively.

Panels A, B, and C in Figure 9 (Table D2 in Appendix D.1) report the AIPW estimates for private credit, investment—both in terms of percent of GDP—, and GDP for the three restructuring types, with and without associated severe sudden stops. The solid lines in brown and purple indicate the point estimates for restructurings with and without severe sudden stops, respectively. Gray bands and dotted lines in brown indicate the 95-percent confidence intervals. The horizon for post-default, weakly and strictly preemptive episodes is set to 5, 3, and 3 years, respectively based on the average duration of each restructuring strategy (5.0, 1.1, and 0.9 years, respectively as reported in Panel A in Table 1). A difference between the lines in brown and purple reflects how private credit, investment or GDP declines due to the associated severe sudden stops for three types of restructurings.

The results for private credit show that the severe sudden stop leads to a significant decline in any type of restructurings (Panel A in Figure 9). Moreover, the decline in private credit due to the severe sudden stop is deeper for post-default than for preemptive restructurings.

The results for investment and GDP largely mirror those for private credit (Panels B and C in Figure 9). Both investment and GDP continue to point a decline when the restructuring is accompanied by a severe sudden stop, across all three strategies. In parallel with private credit, the decline in investment and GDP due to the severe sudden stop is deeper for post-default than for preemptive restructurings.

Digging further, Panels A, B, and C in Figure D1 (Table D1) in Appendix D.1 are analogous to those in Figure 9, but report the OLS local projection estimates for private credit, investment, and GDP for the three restructuring types, with and without a severe sudden stop. The severe sudden stop leads to a significant decline in private credit under any type of restructuring (Panel A in Figure D1). Moreover, this also results in a significant decline in both investment and GDP under any type of restructuring (Panels B and C in Figure D1). The declines in private credit, investment, and GDP accompanied with the severe sudden stop are deeper for post-default than for preemptive restructurings. Furthermore, we show that our AIPW estimation results are robust by applying both (i) a one-type model AIPW estimation in Figure D2 (Table D3) in Appendix D.1 and (ii) conventional panel OLS and instrument variable (IV) estimations in Table D4 in the Appendix D.1.

D. Duration, Haircuts, and Missed Payments

In this sub-section, we consider whether an additional difference in output cost is incurred depending on the process and settlement of the debt renegotiations (duration and haircuts). In particular, we explore the extent to which a longer restructuring process or a process that delivers larger losses to creditors can explain the dynamics we observe when studying post-default events.

Figure 10 reports the dynamics of GDP for two restructuring strategies (post-default and weakly preemptive) together with “short-duration”, “small-haircut” or “short-duration and small haircut” post-default episodes. These correspond to subsamples of post-default cases whose distribution of either duration, haircuts or both duration and haircuts is identical to that of weakly preemptive restructurings. Duration and/or haircuts of episodes drawn from this subsample are on average the same as those drawn from the weakly preemptive sample. We follow the same presentation as in Figure 4. GDP in a short-duration post-default restructuring suffers a similarly sharp decline over the first two years, but then experiences a quick recovery to its pre-crisis level by the third year (brown line in Panel A). The adverse effect on GDP of a short-duration post default restructuring seems to fall in between that of a post-default restructuring (red line in Panel A) and that of a weakly preemptive restructuring (blue line in Panel A). It is very close to post-default restructurings in the first two years, but much closer to weakly preemptive restructurings from the third year onwards.

Similarly, GDP experiences a severe decline over the first two years during small-haircut post default restructurings, and it recovers gradually from the third year since the start (brown line in Panel B). The adverse effect on GDP of a small-haircut post-default restructuring falls in between that of a post-default restructuring (red line in Panel B) and that of a weakly preemptive restructuring (blue line in Panel B). A comparison of Panels A and B suggests that shorter duration is more important for achieving better outcomes among post-default restructurings than smaller haircuts (although the two dimensions may be closely related as documented in Benjamin and Wright 2013).

Finally, Panel C shows the results for a subsample of post-default restructurings with both short duration and low haircuts (brown line). The results for this subsample also lie in between those of the post-default and weakly preemptive restructurings. This pattern suggests that some of the difference in outcomes between post-default and weakly preemptive restructurings stems from the latter’s shorter duration (1.1 vs 5 years) and smaller haircuts (18 vs 48 percent). But there are still sizable differences even when we compare the short-duration small-haircut subsample of post-default restructurings with weakly preemptive restructurings, particularly in the 3–5 year horizon. This supports the view that, all else equal, the choice of restructuring strategy per se can have major implications for the medium-term recovery after a restructuring.

We confirm the stylized patterns in Figure 10 in local projection estimates. The actual duration and haircuts involved in a restructuring is only known after that process has unfolded. But the local projection estimates can only use the information set available prior to the start of the restructuring. Thus, we rely on GDP growth forecasts available one year before the start of debt restructurings to differentiate sub-samples based on their expected growth, duration, and haircuts. Post-default restructurings are classified into three sub-groups based only on the available information one year prior to the start of restructurings:

Definition vii. A ‘post-default restructuring with high (small) GDP growth forecasts’, where expected GDP growth is above (below) the mean in post-default restructurings.
Definition viii. A ‘post-default restructuring with long (short) expected duration’, where expected duration is above (below) the mean in post-default restructurings.
Definition ix. A ‘post-default restructuring with high (small) expected haircuts’, where expected NPV haircuts are above (below) the mean in post-default restructurings.

Our classification approach is supported by findings in the theoretical literature on sovereign debt restructurings (Benjamin and Wright 2013; Bi 2008; Yue 2010): the debtor’s recovery in capacity of repayment influences both length of restructurings and terms at the settlements. First, we estimate the following regressions on duration and haircuts:

\begin{matrix} D u r a t i o n_{i, t + 1} = β_{0}^{D} + β_{1}^{D} g_{i, t}^{F, S h o r t} + β_{2}^{D} g_{i, t}^{F, L o n g} + Z_{i, t} β_{3}^{D} + ϵ_{i, t + 1}^{D}, & (6) \\ {H a i r c u t}_{i, t + 1} = β_{0}^{H} + β_{1}^{H} g_{i, t}^{F, S h o r t} + β_{2}^{H} g_{i, t}^{F, L o n g} + Z_{i, t} β_{3}^{H} + ϵ_{i, t + 1}^{H} \end{matrix}

where Duration_i,t+1 and Haircut_i,t+1 are duration and haircut (percent) of debt restructuring episode in country i started in year t+1, where these take zeros in non-restructuring start years; $g_{i, t}^{F, S h o r t} a n d g_{i, t}^{F, L o n g}$ are short-term and long-term GDP growth forecasts one year before the start year of restructuring episode— $g_{i, t}^{F, S h o r t} = g_{i, t}^{F, t + 1} a n d g_{i, t}^{F, L o n g} = Σ_{S = t + 2}^{t + 6} g_{i, t}^{F, s} / 5 w h e r e g_{i, t}^{F, s}$ denotes the anticipated GDP growth rate in year s forecasted in year t—; Z_i,t is a set of controls including cyclical component of log of GDP per capita—accounting for the current business cycle—and number of past post-default and of preemptive restructurings; and $ϵ_{i, t + 1}^{D} a n d ϵ_{i, t + 1}^{H}$ are error terms. We use GDP growth forecasts from the IMF WEO. In order to avoid a selection bias, we include non-restructuring observations where Duratlon_i,t+1 and Halrcut_i,t+1 are set to zeros. Using the predicted duration and haircuts, $D \hat{u r a t i} o n_{i, t + 1} a n d H \hat{a i r c} u r t_{i, t + 1}$ , we define dummy variables for long expected duration and high expected haircuts, $D_{i, t + 1}^{L o n g D} a n d D_{i, t + 1}^{H i g h H}$ , taking unity if the predicted duration or haircuts is higher than the 50^th percentile and 0 otherwise.

Table 4.

Predicting Duration and Haircuts, Poisson

Notes: ***, **, and * indicate the statistical significance at 1 percent, 5 percent, and 10 percent level, respectively. Short-run GDP growth forecasts are the expected GDP growth rate in year t + 1 forecasted in year t,

g_{i, t}^{F, t + 1}

. Long-run GDP growth forecasts are the average of GDP growth forecasts over the next five years. For exceptional restructuring cases where haircuts take negative values, these negative values are replaced with zeros.

Table 4.

Predicting Duration and Haircuts, Poisson

	Duration (years)			Haircut (percent)
	Poisson		Neg. Bin.	Poisson		Neg. Bin.
	(1)	(2)	(3)	(4)	(5)	(6)
GDP growth forecasts, short-run	-0.030 (0.026)	-0.028 (0.026)	-0.058 (0.102)	-0.034*** (0.006)	-0.039*** (0.006)	0.003 (0.160)
GDP growth forecasts, long-run	-0.484*** (0.056)	-0.547*** (0.064)	-0.474** (0.199)	-0.493*** (0.014)	-0.552*** (0.016)	-0.757** (0.377)
Cyclical component of log of GDP per capita		2.307** (1.176)			0.262 (0.302)
# of past post-default episodes	0.219*** (0.057)	0.166** (0.071)	0.338 (0.303)	0.194*** (0.015)	0.148*** (0.018)	0.312 (0.454)
# of past preemptive episodes	-0.307*** (0.090)	-0.315*** (0.094)	-0.498** (0.215)	-0.072*** (0.016)	-0.075*** (0.016)	-0.0582 (0.237)
Constant	-0.406* (0.221)	-0.0874 (0.255)	-0.391 (0.992)	2.309*** (0.056)	2.644*** (0.064)	3.164* (1.768)
Observations	1,193	1,056	1,193	1,193	1,056	1,193
Pseudo R -sq.	0.125	0.133	0.039	0.127	0.143	0.010

g_{i, t}^{F, t + 1}

Table 4.

Predicting Duration and Haircuts, Poisson

	Duration (years)			Haircut (percent)
	Poisson		Neg. Bin.	Poisson		Neg. Bin.
	(1)	(2)	(3)	(4)	(5)	(6)
GDP growth forecasts, short-run	-0.030 (0.026)	-0.028 (0.026)	-0.058 (0.102)	-0.034*** (0.006)	-0.039*** (0.006)	0.003 (0.160)
GDP growth forecasts, long-run	-0.484*** (0.056)	-0.547*** (0.064)	-0.474** (0.199)	-0.493*** (0.014)	-0.552*** (0.016)	-0.757** (0.377)
Cyclical component of log of GDP per capita		2.307** (1.176)			0.262 (0.302)
# of past post-default episodes	0.219*** (0.057)	0.166** (0.071)	0.338 (0.303)	0.194*** (0.015)	0.148*** (0.018)	0.312 (0.454)
# of past preemptive episodes	-0.307*** (0.090)	-0.315*** (0.094)	-0.498** (0.215)	-0.072*** (0.016)	-0.075*** (0.016)	-0.0582 (0.237)
Constant	-0.406* (0.221)	-0.0874 (0.255)	-0.391 (0.992)	2.309*** (0.056)	2.644*** (0.064)	3.164* (1.768)
Observations	1,193	1,056	1,193	1,193	1,056	1,193
Pseudo R -sq.	0.125	0.133	0.039	0.127	0.143	0.010

g_{i, t}^{F, t + 1}

Table 4 reports regression results for predicting duration and haircuts. Given that our sample includes non-restructuring observations, we follow the convention in the literature to apply both poisson and negative binominal regressions (Asonuma and Joo 2017). The results show that only high GDP growth forecasts over the long run result in short duration of restructurings. In contrast, both high GDP growth forecasts over the short run and the long run lead to small haircuts (high recovery rates). Past post-default episodes significantly increase both duration and haircuts, while past preemptive episodes significantly decrease both duration and haircuts.

Next, we follow the same approach as in the case of severe sudden stops (equation 5 in Section III.C) to estimate the impact of post-default restructurings with long expected duration and high expected haircuts. We specify the following local projection equation:

\begin{matrix} g_{i, t + h} = α_{i}^{h} + Λ^{L o n g, h} D_{i, t + 1}^{L o n g D} D_{i, t + 1}^{D R} + Λ^{S h o r t, h} (1 - D_{i, t + 1}^{L o n g D}) D_{i, t + 1}^{D R} + β^{D R . h} + ϵ_{i, t + h}^{D R} & (7) \\ g_{i, t + h} = α_{i}^{h} + Λ^{H i g h, h} D_{i, t + 1}^{H i g h H} D_{i, t + 1}^{D R} + Λ^{S m a l l, h} (1 - D_{i, t + 1}^{H i g h H}) D_{i, t + 1}^{D R} + {\tilde{X}}_{i, t} β^{D R . h} + ϵ_{i, t + h}^{D R} \\ f o r h = 1, 2, .., 5 \end{matrix}

where Λ^Long,h and Λ^short,h are coefficients capturing GDP dynamics for post-default episodes with long and short expected duration, respectively; Λ^High,h and Λ^Small,h are coefficients for post-default episodes with high and small expected haircuts, respectively. We also apply a two type model to estimate the likelihood of post-default episodes with long/short expected duration (high/small expected haircuts), separately to account for differences in macroeconomic conditions in the pre-restructuring year (t). Then, we calculate the average treatment effects of both post-default episodes with long/short expected duration (high/small expected haircuts), respectively, built on the different estimated probability of these events. Thus, the AIPW estimates correct for potential bias in the sub-samples of post-default restructurings driven by pre-restructuring macroeconomic conditions.

Figure 11 reports the AIPW estimates. We follow the same presentation as in Figure 8. The decline in GDP is more severe and protracted in post-default restructurings with low GDP growth forecasts (Panel A), with long expected duration (Panel B), and with high expected haircuts (Panel C). This last result is in line with findings in Trebesch and Zabel (2017) and Marchesi (2016).

Figure D3 in Appendix D.2 contrasts our baseline AIPW estimation results (Panel A) with results on one-type model AIPW estimation (Panel B) and on OLS local projection estimation (Panel C). The results are qualitatively similar, indicating the robustness of the results across these three approaches.

From a policy perspective, these findings have important implications. Our results suggest that even if a sovereign debtor inevitably needs to default on its debt obligations, i.e., it misses payments, it can still mitigate the adverse effects on GDP, investment, and banking sector by both/either reaching an agreement with its creditors quickly (short duration) and/or inflicting on its creditors the smaller possible losses (small haircuts) needed to restore solvency.

IV. Logit Approach

Finally, we complement our local projection results by exploring whether banking crises, severe credit and net capital inflow declines are more likely to occur following post-default restructurings. To do so, we apply logit models using restructuring strategies in past years. Our benchmark specification follows Reinhart and Rogoff (2011b) and Balteanu and Erce (2018). While they present multinomial models, we use a bivariate logit approach to study the influence of past restructuring strategies on each of the three events separately. Following the definitions of the three events in Section II.A, we set both the start of debt restructurings at year t = 1 and timing of three events as follows: start of banking crisis at year t = 1 and severe credit and net capital inflow declines over 5 years from t = 0 to t = 5. Dummy variables for the three types of restructuring strategies and the three events are set accordingly.

The logit model for banking crises is specified as follows:

\begin{matrix} \Pr {(B a n k C r i s i s)}_{i, t + 1} = G ({\tilde{D}}_{i, t - 3, t + 1}^{P D}, {\tilde{D}}_{i, t - 3, t + 1}^{W e a k}, {\tilde{D}}_{i, t - 3, t + 1}^{S t r i c t}, {\tilde{X}}_{i, t}, α) & (8) \end{matrix}

where Pr(BankCrisis)_i,t+1 is the probability that there is a banking crisis in country i in year t = 1. We explore whether three types of debt restructuring strategies in the previous five years, i.e., from year t = -3 to t = 1 influences the likelihood of banking crisis in the current year. Restructuring dummies ${\tilde{D}}_{i, t - 3, t + 1}^{P D}, {\tilde{D}}_{i, t - 3, t + 1}^{W e a k}, {\tilde{D}}_{i, t - 3, t + 1}^{S t r i c t}$ take unity if there is each type of debt restructuring starting during year t = -3 to current year t = 1.

In a similar vein, the logit models for severe credit declines and severe protracted net capital inflow declines are specified as follows:

\begin{matrix} \Pr {(C r e d i t D e c l i n e)}_{i, t, t + 5} = G (D_{i, t + 1}^{P D}, D_{i, t + 1}^{W e a k l y}, D_{i, t + 1}^{S t r i c t l y}, {\tilde{X}}_{i, t}, α), \Pr {(N C I D e c l i n e)}_{i, t, t + 5} = G (D_{i, t + 1}^{P D}, D_{i, t + 1}^{W e a k l y}, D_{i, t + 1}^{S t r i c t l y}, {\tilde{X}}_{i, t}, α) & (9) \end{matrix}

where as defined in Section II.A, Pr(CreditDecline)_i,t,t+5 and Pr(NCIDecline)_i,t,t+5 are the probability that the cumulative percentage point differences in the credit-to-GDP ratio and net capital inflows-to-GDP ratio over five years, $d_{t + 5}^{C} = 100 * (C r e d i t_{i, t + 5} / G D P_{i, t + 5} - C r e d i t_{i, t} / G D P_{i, t}) a r e d_{t + 5}^{N C I, D I F} = 100 * (N C I_{i, t + 5} / G D P_{i, t + 5} - N C I_{i, t} / G D P_{i, t})$ are less than the 25th percentile of the distribution (-1.65 and -7.03, respectively). $D_{i, t + 1}^{P D}, D_{i, t + 1}^{W e a k l y}, a n d D_{i, t + 1}^{S t r i c t l y}$ are dummy variables taking unity if there is each type of debt restructuring starting at year t = 1. ${\tilde{X}}_{i, t}$ is a vector of control variables at year t = 0. Note that equations (8) and (9) are identical in terms of both timing of events and time intervals between events and past restructuring strategies. As explained in Section 2.2, we treat each restructuring as an independent event-there are overlapping observations included in our sample. Following Gourinchas and Obstfeld (2012), we choose current account-to-GDP ratio, foreign reserve-to-GDP ratio, real exchange rate, and GDP growth rate to be included in the set of control variables. Vector α denotes the coefficients to be estimated and G denotes the logistic distribution.

Table 5.

Banking Crises and Severe Credit and Net Capital Inflow Declines after Debt Restructurings, Logit

Notes: The debt restructuring dummies take unity at the start of restructurings. All regressions include a constant term and country fixed effects. The sample period is 1970–2013. ***, **, and * indicate the statistical significance at 1 percent, 5 percent, and 10 percent level, respectively. Standard errors are in parentheses. The data on debt restructurings come from Asonuma and Trebesch (2016) and the data on banking crises come from Laeven and Valencia (2013).

Table 5.

Banking Crises and Severe Credit and Net Capital Inflow Declines after Debt Restructurings, Logit

Dep. Var. = Dummy Variables Indicated in the Table
	Bankin Crises at t = 1		Severe Credit Declines cum. from t = 0 to t = 5		Severe Net Cap. Inf. Declines cum. from t = 0 to t = 5
	Coef. (Std. err.)	dy/dx (Delta-method std. err.)	Coef. (Std. err.)	dy/dx (Delta-method std. err.)	Coef. (Std. err.)	dy /dx (Delta-method std. err.)
Post-default 1{t = -3, -2, -1, 0, 1}	1.051*** (0.279)	0.204*** (0.075)
Weakly preemptive 1{t = -3, -2, -1, 0, 1}	0.910** (0.368)	0.176** (0.074)
Strictly preemptive 1{t = -3, -2, -1, 0, 1}	0.270 (0.514)	0.052 (0.100)
Post-default 1{t =1}			0.812*** (0.299)	0.179*** (0.072)	0.704** (0.300)	0.143** (0.065)
Weakly preemptive 1{t =1}			0.298 (0.409)	0.066 (0.090)	0.671* (0.389)	0.137* (0.080)
Strictly preemptive 1{t =1}			-0.419 (0.636)	-0.092 (0.142)	-1.267 (1.052)	-0.258 (0.218)
Controls
Current account-to-GDP ratio, lag	-3.277 (2.339)	-0.635 (0.509)	-0.178 (1.244)	-0.039 (0.276)	-6.967*** (1.237)	-1.418*** (0.342)
Foreign reserves-to-GDP ratio, lag	1.522 (1.850)	0.295 (0.363)	-2.513* (1.423)	-0.554* (0.329)	4.506*** (1.568)	0.917*** (0.338)
Real exchange rate, log, lag	-0.337 (0.250)	-0.065 (0.033)	-0.111 (0.141)	-0.025 (0.027)	-0.238* (0.134)	-0.048* (0.020)
GDP growth rate, lag	-1.798 (2.090)	-0.349 (0.410)	-6.453*** (1.226)	-1.423*** (0.330)	2.648** (1.201)	0.538** (0.257)
Country fixed effects	Yes		Yes		Yes
Log-likelihood ratio	27.60		50.49		76.65
(p -value)	0.000		0.000		0.000
Pseudo R -squared	0.058		0.039		0.057
Number of observations	1,129		1,284		1,474
Number of countries	42		44		57

Table 5.

Banking Crises and Severe Credit and Net Capital Inflow Declines after Debt Restructurings, Logit

Dep. Var. = Dummy Variables Indicated in the Table
	Bankin Crises at t = 1		Severe Credit Declines cum. from t = 0 to t = 5		Severe Net Cap. Inf. Declines cum. from t = 0 to t = 5
	Coef. (Std. err.)	dy/dx (Delta-method std. err.)	Coef. (Std. err.)	dy/dx (Delta-method std. err.)	Coef. (Std. err.)	dy /dx (Delta-method std. err.)
Post-default 1{t = -3, -2, -1, 0, 1}	1.051*** (0.279)	0.204*** (0.075)
Weakly preemptive 1{t = -3, -2, -1, 0, 1}	0.910** (0.368)	0.176** (0.074)
Strictly preemptive 1{t = -3, -2, -1, 0, 1}	0.270 (0.514)	0.052 (0.100)
Post-default 1{t =1}			0.812*** (0.299)	0.179*** (0.072)	0.704** (0.300)	0.143** (0.065)
Weakly preemptive 1{t =1}			0.298 (0.409)	0.066 (0.090)	0.671* (0.389)	0.137* (0.080)
Strictly preemptive 1{t =1}			-0.419 (0.636)	-0.092 (0.142)	-1.267 (1.052)	-0.258 (0.218)
Controls
Current account-to-GDP ratio, lag	-3.277 (2.339)	-0.635 (0.509)	-0.178 (1.244)	-0.039 (0.276)	-6.967*** (1.237)	-1.418*** (0.342)
Foreign reserves-to-GDP ratio, lag	1.522 (1.850)	0.295 (0.363)	-2.513* (1.423)	-0.554* (0.329)	4.506*** (1.568)	0.917*** (0.338)
Real exchange rate, log, lag	-0.337 (0.250)	-0.065 (0.033)	-0.111 (0.141)	-0.025 (0.027)	-0.238* (0.134)	-0.048* (0.020)
GDP growth rate, lag	-1.798 (2.090)	-0.349 (0.410)	-6.453*** (1.226)	-1.423*** (0.330)	2.648** (1.201)	0.538** (0.257)
Country fixed effects	Yes		Yes		Yes
Log-likelihood ratio	27.60		50.49		76.65
(p -value)	0.000		0.000		0.000
Pseudo R -squared	0.058		0.039		0.057
Number of observations	1,129		1,284		1,474
Number of countries	42		44		57

Table 5 reports the logit regression results. For countries which have experienced post-default restructurings in the previous years, a banking crisis occur more likely—quantitatively 20 percent in column (1’)—than for those experiencing a preemptive (weakly or strictly) restructuring. Similarly, countries which have experienced post-default restructurings are more likely to suffer a severe credit decline and severe net capital inflow decline in the following years—quantitatively 18 and 14 percent, respectively. The other control variables have the expected signs. Severe net capital inflow declines are associated with large current account deficits and real exchange rate depreciations, and banking crises and severe credit declines are followed by real exchange rate depreciations—both are consistent with the literature (e.g., Reinhart and Rogoff 2009; Kaminsky and Reinhart 1999). Lastly, the GDP growth rate has a negative sign indicating that financial crises occur during a recession.

V. Conclusion

In this paper, we shed new light on the growing empirical literature on the costs of sovereign debt restructurings, by exploring output and banking sector costs, and linking them to the restructuring strategies chosen by the sovereign. We show that the transmission channels and associated output and banking sector costs depend on whether the restructuring takes place preemptively (without missing payments to creditors), or after a default has occurred (and payments are unilaterally missed). Our local projection estimates show a large decline in GDP and investment, amplified by severe sudden stops and transmitted through a “capital flow-credit channel”—supported by both an increase in lending interest rates and declines in credit and capital inflows.

Our key finding suggests that countries that succeed in a restructuring without missing payments to creditors are largely able to avoid, or at least mitigate both output and banking sector costs associated with restructuring. Though that approach can lead to better outcomes, in practice countries can face several constraints on the choice of how to restructure. For instance, they may be hit by the sudden realization of a large shock or they may find themselves in the “bad equilibrium” in a multiple-equilibria context. Depending on their situation, they might have no choice but to stop servicing their debt without some immediate debt relief. But even then, instances where the suspension of payments is decided quickly in collaboration with creditors are still associated with better outcomes than when payments are suspended unilaterally by the debtor. Smaller haircuts i.e., creditor-friendly terms also tend to be associated with better outcomes. But the haircut offered should be large enough to restore solvency (offering a small haircut which eventually requires another restructuring is likely to be counter-productive, and lead to even higher costs).

Our findings have implications for the ongoing discussion on how to best resolve sovereign debt crises. The real economy, the financial sector and the government are all interconnected. When designing a debt restructuring strategy, it is crucial to understand the spillover and feedback channels that the restructuring can have on the domestic financial system. Much of the output costs hinge on whether a severe sudden stop can be avoided, which is more likely in a preemptive restructuring.

Moreover, our results show that, even if sovereign’s debt has fallen into arrears, a sovereign can still minimize the output costs of the default by reaching a restructuring agreement rapidly. Relatedly, our findings also have implications for the design of official financing, suggesting that where feasible, long-run costs can be attenuated if official financing and creditor cooperation allow countries to restructure without missing payments. It also highlights the costs that countries can face for trying to delay adjustment (and requests for official support) until a default becomes inevitable. These should be important considerations in the design of future debt restructuring strategies.

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Panel B: Banking Crisis Sample ^2/
	All debt restructurings	Post-default	Weakly preemptive	Strictly preemptive	All obs. ^1/
# of available episodes	127	91	39	21	1,126
# of banking crises	39	30	7	2	71
Conditional probability	30.7%	33.0%	17.9%	9.5%	6.3%
Representative episodes in 1999–2010		Argentina 2001–05 Russia 1998–2000	Turkey 1981 Niger 1983	Algeria 1990 Ukraine 1998

Panel C: Severe Credit Declines ^3/^6/
	All debt restructurings	Post-default	Weakly preemptive	Strictly preemptive	All obs. ^1/
# of available episodes	107	73	26	8	1,433
# of severe credit declines	48	36	10	2	308
Conditional probability	44.9%	49.3%	38.5%	25.0%	21.5%
Representative episodes		Turkey 1976 Togo 1991	Brazil 1983 Venezuela 1986	Algeria 1990 Uruguay 2003
Panel D: Severe Protracted Net Capital Inflow Declines (Sum) ^4/^6/
	All debt restructurings	Post-default	Weakly preemptive	Strictly preemptive	All obs. ^1/
# of available episodes	113	77	26	10	1,178
# of severe net cap. inf. dec.	40	29	9	2	303
Conditional probability	35.4%	37.7%	34.6%	20.0%	25.7%
Representative episodes		Costa Rica 1981 Moldova 2001	Brazil 1982 Belize 2006	Ukraine 1998
Panel E: Severe Protracted Net Capital Inflow Declines (Difference) ^5/^6/
	All debt restructurings	Post-default	Weakly preemptive	Strictly preemptive	All obs. ^1/
# of available episodes	113	77	26	10	1,178
# of severe net cap. inf. dec.	40	25	8	1	303
Conditional probability	35.4%	32.5%	30.8%	10.0%	25.7%

Panel B: Banking Crisis Sample ^2/
	All debt restructurings	Post-default	Weakly preemptive	Strictly preemptive	All obs. ^1/
# of available episodes	127	91	39	21	1,126
# of banking crises	39	30	7	2	71
Conditional probability	30.7%	33.0%	17.9%	9.5%	6.3%
Representative episodes in 1999–2010		Argentina 2001–05 Russia 1998–2000	Turkey 1981 Niger 1983	Algeria 1990 Ukraine 1998

Panel C: Severe Credit Declines ^3/^6/
	All debt restructurings	Post-default	Weakly preemptive	Strictly preemptive	All obs. ^1/
# of available episodes	107	73	26	8	1,433
# of severe credit declines	48	36	10	2	308
Conditional probability	44.9%	49.3%	38.5%	25.0%	21.5%
Representative episodes		Turkey 1976 Togo 1991	Brazil 1983 Venezuela 1986	Algeria 1990 Uruguay 2003
Panel D: Severe Protracted Net Capital Inflow Declines (Sum) ^4/^6/
	All debt restructurings	Post-default	Weakly preemptive	Strictly preemptive	All obs. ^1/
# of available episodes	113	77	26	10	1,178
# of severe net cap. inf. dec.	40	29	9	2	303
Conditional probability	35.4%	37.7%	34.6%	20.0%	25.7%
Representative episodes		Costa Rica 1981 Moldova 2001	Brazil 1982 Belize 2006	Ukraine 1998
Panel E: Severe Protracted Net Capital Inflow Declines (Difference) ^5/^6/
	All debt restructurings	Post-default	Weakly preemptive	Strictly preemptive	All obs. ^1/
# of available episodes	113	77	26	10	1,178
# of severe net cap. inf. dec.	40	25	8	1	303
Conditional probability	35.4%	32.5%	30.8%	10.0%	25.7%

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Abstract

I. Introduction

II. New Stylized Facts on Sovereign Debt Restructurings, Banking Crises, Severe Credit and Capital Inflow Declines

A. Data on Sovereign Debt Restructurings, Banking Crises, Severe Credit and Capital Inflow Declines

B. Stylized Facts on GDP, Investment, Private Credit, and Capital Flows in Sovereign Debt Restructurings

C. Data on Macroeconomic Variables

D. Time Sample

III. Local Projection Approach

A. Endogeneity of Debt Restructuring Decisions

B. Augmented Inverse Probability Weighted (AIPW) Estimation

C. Role of Capital Inflow-Credit Channel

D. Duration, Haircuts, and Missed Payments

IV. Logit Approach

V. Conclusion

References