Costs of sovereign defaults : restructuring strategies, bank distress and the capital inflow-credit channel.
Date: 2019-07-25
Institutions: European Stability Mechanism
Subjects: international finance, investment, macroeconomics, monetary crisis, public debt, rescheduling of public debt

Working Paper Series  | 37 |  2019

Costs of sovereign defaults: Restructuring 
strategies, bank distress and the capital 
inflow-credit channel 

This paper shows how the output costs of sovereign defaults are 
intimately linked to the credit crunch and capital flight that such 
processes tend to generate

Tamon Asonuma
 International Monetary Fund 

Marcos Chamon 
 International Monetary Fund
Aitor Erce
 European Stability Mechanism 
Akira Sasahara
 University of Idaho

Disclaimer
The views expressed herein are those of the authors and should not be 
attributed to the European Stability Mechanism or IMF, their Executive 
Boards, or their managements.

Working Paper Series  |  37  |  2019

Costs of sovereign defaults: Restructuring 
strategies, bank distress and the capital 
inflow-credit channel

Tamon Asonuma 1 International Monetary Fund
Marcos Chamon 2 International Monetary Fund
Aitor Erce 3 European Stability Mechanism
Akira Sasahara 4 University of Idaho

Abstract
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  pre-emptively,  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 
inflow-credit  channel”. 
by  severe  sudden  stops  and  transmitted  through  a  “capital 

Keywords: sovereign debt; sovereign defaults; sovereign debt restructurings; GDP growth;
investment; banking crisis; local projection 

JEL codes: F34, F41, H63

1 TAsonuma@imf.org
2 MChamon@imf.org
3 a.erce@esm.europa.eu
4 sasahara@uidaho.edu

Disclaimer
The  views  expressed  herein  are  those  of  the  authors  and  should  not  be  attributed  to  the 
European  Stability  Mechanism  or  IMF,  their  Executive  Boards,  or  their  managements. 
No  responsibility  or  liability  is  accepted  by  the  ESM  in  relation  to  the  accuracy  or 
completeness  of  the  information,  including  any  data  sets,  presented  in  this  Working  Paper.

© European Stability Mechanism, 2019 All rights reserved. Any reproduction, publication and reprint in the form of a 
different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit 
written authorisation of the European Stability Mechanism.

ISSN 2443-5503 
ISBN 978-92-95085-68-8

doi:10.2852/65954
EU catalog number DW-AB-19-003-EN-N

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

Tamon Asonuma†
IMF

Marcos Chamon‡
IMF

Aitor Erce§
ESM

Akira Sasahara¶
University of Idaho

March 25, 2018

Abstract

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 trig-
gering 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”.

Key Words: Sovereign Debt; Sovereign Defaults; Sovereign Debt Restructurings; GDP Growth;
Investment; Banking Crisis; Local Projection
JEL codes: F34; F41; H63

∗The views expressed herein are those of the authors and should not be attributed to the European Stability Mechanism
or IMF, their Executive Boards, or their managements. The authors thank Kosuke Aoki, Suman Sambha Basu, Tamim
Bayoumi, Francesca Gaia Caselli, Anusha Chari, Flavia Corneli, Xavier Debrun, Giovanni Dell’Ariccia, Raphael Andre
Espinoza, Michal Fabinger, Johannes Fleck (discussant), Victor Gaspar, Grey Gordon, Nils Gornemann, Yuriy Gorod-
nichenko, Yuko Hashimoto, Graciela Kaminsky, Şebnem Kalemli-Özcan, Leonard Martinez, Carlos Mulas-Granado,
Tomoyuki Nakajima, Maurice Obstfeld, Óscar Jordá, Jonathan D. Ostry, Mahvash Saeed Qureshi, Alan M. Taylor,
Dominike Thaler, Christoph Trebesch, Juergen Von Hagen, Alejandro M. Werner, and Jeromin Zettlemeyer (discus-
sant) as well as seminar participants at ADEMU “Fiscal Risk and Public Sector Balance Sheets” (Bonn), ESM, IMF
(FAD, Surveillance Meetings, WHD), IMF RES SID “Too Many Objectives or Too Few Instruments? Economic Policy
Challenges Ten Years After the Crisis” Workshop, Kyoto University, LACEA-LAMES (Guayaquil), Spanish Economic
Association (Barcelona), Summer Workshop on Economic Theory (Hokkaido University), and University of Tokyo for
comments and suggestions. We would also like to thank Manzoor Ahmed Gill for research assistance and Chifundo Moya
for editorial assistance.

†International Monetary Fund, 700 19th Street, N.W., Washington, D.C. 20431 USA. E-mail: TAsonuma@imf.org
‡International Monetary Fund, 700 19th Street, N.W., Washington, D.C. 20431 USA. E-mail: MChamon@imf.org

(corresponding author)

§European Stability Mechanism,

6a Circuit de

la Foire

Internationale L-1347 Luxembourg.

E-mail:

a.erce@esm.europa.eu

¶College of Business and Economics, University of Idaho, 875 Perimeter Drive MS 3161, Moscow, ID, 83844, E-mail:

sasahara@uidaho.edu

1

1 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 restruc-

turings 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 sub-

samples 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 con-
traction 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

1See also Jordà and Taylor (2016), Angrist et al. (2016), and Auerbach and Gorodnichenko (2016).
2We use the information set available at the time of the restructuring to predict whether a sudden stop will take place

(so as to avoid using ex post information in the local projections).

3See Broner et al. (2013), Reinhart et al. (2016), Kaminsky and Vega-García (2016), and Arteta and Hale (2008) for

capital ﬂows and sovereign debt crises.

2

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 condi-
tions 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 restructur-

ings. 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).78 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 bank-
ing crises remains mixed. Borensztein and Panizza (2009) show that default episodes seem to cause

4See Asonuma and Trebesch (2016) for empirical and theoretical ﬁndings on the choice of restructuring strategies.
5Our AIPW estimator falls into the broad class of ‘doubly robust’ estimators (Robins et al. 1994). The ‘doubly
robust’ property means that consistency of the estimated average treatment eﬀect (ATE) can be proved in the special
cases where either the propensity score model and/or the conditional mean is correctly speciﬁed; Monte Carlo evidence
also suggests that the estimator performs better than alternatives even in more general cases too.

6Our ﬁnding on smaller output costs in post-default restructurings with small haircuts is in line with Trebesch and
Zabel (2017)—“soft defaults” classiﬁed by both government’s behavior (coerciveness) and haircuts—and Marchesi (2016).

7See also Forni et al. (2016) and Furceri and Zdzienicka (2012).
8Theoretical literature explores endogenous output costs through trade channel (Mendoza and Yue 2012) and through

investment channel (Gordon and Guerron-Quintana 2018; Park 2017; Gornemann 2015).

9For further empirical studies, see Gennaioli et al. (2018), Jordà et al. (2016), Acharya et al. (2015), and Sandleris
and Wright (2014). For recent studies on speciﬁc debt restructuring episodes using micro data, see Hébert and Schreger
(2017), Chari et al. (2018), and Fakos et al. (2018).

3

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 two-way 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 ex-
tension 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 3 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 4 introduces our logit model approach and discusses

the results. Finally, Section 5 concludes. Details of various robustness analysis are provided in the

Appendix.

2 New Stylized Facts on Sovereign Debt Restructurings, Banking

Crises, Severe Credit and Capital Inﬂow Declines

2.1 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) restructur-
ings or IMF-supported programs, and single external debt restructurings in Appendix E.3 and E.4.13

10For restructuring strategies, see Sturzenegger and Zettelmeyer (2006), Diaz-Casssou et al. (2008), Panizza et al.

(2009), Das et al. (2012), Erce (2014), Asonuma and Trebesch (2016), and Asonuma et al. (2016).

11See also Bocola (2016), Arellano et al. (2017), Engler and Große Steﬀen (2016), Bolton and Jeanne (2011), Perez

(2016), Pei (2016), and Mallucci (2015).

12Sandleris (2014) theoretically shows that even if domestic agents do not hold government bonds, a sovereign default

can create a credit crunch in domestic credit markets and a contraction in foreign lending to the private sector.

13See Das et al. (2012), Cheng et al. (2018), and Marchesi (2016) for work on oﬃcial external debt restructurings,

Reinhart and Rogoﬀ (2011a) and Erce and Mallucci (2018) for work on restructurings involving domestic debt.

4

Table 1: Dataset, 3-year Horizon

Panel A: Debt Restructuring Sample

# of episodes
# of countries
Average duration (years)
Average haircuts (percent)

Post-default
111
60
5.0
48.0

Representative episodes Argentina 2001-05,
Russia 1998-2000

in 1999-2010

Weakly preemptive
45
26
1.1
18.2
Ukraine
(Global Exch.) 2000,
Belize 2006-07

Strictly preemptive
23
13
0.9
18.9
Pakistan
(Ext. bonds) 1999,
Uruguay 2003

# of available episodes
# of banking crises
Conditional probability
Representative episodes
in 1999–2010

# of available episodes
# of severe credit declines
Conditional probability
Representative episodes

Panel B: Banking Crisis Sample 2/

All debt
restructurings
127
39
30.7%

Post-
default
91
30
33.0%

Weakly
preemptive
39
7
26.9%

Strictly
preemptive
21
2
20.0%

All
obs.1/
1,126
71
6.3%

Argentina 2001–05, Turkey 1981 Algeria 1990
Ukraine 1998
Russia 1998–2000

Niger 1983

Panel C: Severe Credit Declines 3/ 6/

All debt
restructurings
107
48
44.9%

Post-
default
73
36
49.3%
Turkey 1976
Togo 1991

Weakly
preemptive
26
10
38.5%
Brazil 1983

Strictly
preemptive
8
2
25.0%
Algeria 1990
Venezuela 1986 Uruguay 2003

All
obs.1/
1,433
308
21.5%

Panel D: Severe Protracted Net Capital Inﬂow Declines (Sum) 4/ 6/

# of available episodes
# of severe net cap. inf. dec.
Conditional probability
Representative episodes

All debt
restructurings
113
40
35.4%

Post-
default
77
29
37.7%

Weakly
preemptive
26
9
34.6%

Strictly
preemptive
10
2
20.0%

All
obs.1/
1,178
303
25.7%

Costa Rica 1981 Brazil 1982 Ukraine 1998

Moldova 2001

Belize 2006

Panel E: Severe Protracted Net Capital Inﬂow Declines (Diﬀerence) 5/ 6/

# of available episodes
# of severe net cap. inf. dec.
Conditional probability

All debt
restructurings
113
34
30.1%

Post-
default
77
25
32.5%

Weakly
preemptive
26
8
30.8%

Strictly
preemptive
10
1
10.0%

All
obs.1/
1,178
303
25.7%

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 where the cumulative percentage point diﬀerence in the credit-to-
GDP ratio over three years, dC
i,t+3 = 100 × (Crediti,t+3/GDPi,t+3 − Crediti,t/GDPi,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, dN CI,SUM
s=1(N CIi,t+s/GDPi,t+s −
N CIi,t/GDPi,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, dN CI,DIF
= 100 × (N CIi,t+3/GDPi,t+3 −
N CIi,t/GDPi,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

= 100×P3

i,t+3

i,t+3

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.

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

14Their classiﬁcation of the nature of the restructuring strategy is only made after its completion.

5

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 unilat-

erally 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.

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 in-

ﬂow 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 diﬀer-
ence in the credit-to-GDP ratio over three or ﬁve years, dC
i,t+s = 100 × (Crediti,t+s/GDPi,t+s −

15Asonuma and Trebesch (2016) deﬁne the start of a restructuring process as the default month and/or month in
which a distressed restructuring is announced, and the end of a restructuring as the month of the ﬁnal agreement and/or
the implementation of the debt exchange. They also provide the duration of all episodes at a monthly frequency.

16Leaven and Valencia (2013) also code the duration of banking crisis at an annual frequency.

6

Crediti,t/GDPi,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 cu-

= 100 × Pr

mulative percentage point diﬀerences in the net capital inﬂows-to-GDP ratio over three or ﬁve years,
dN CI,SU M
s=1(N CIi,t+s/GDPi,t+s − N CIi,t/GDPi,t) for r=3 or 5 is less than the 25th
i,t+r
percentile of the distribution, or (b) the cumulative percentage point diﬀerence in the net capital
inﬂows-to-GDP ratio over three or ﬁve years, dN CI,DIF
= 100×(N CIi,t+r/GDPi,t+r −N CIi,t/GDPi,t)
for r = 3, 5 is less than the 25th percentile of the distribution, respectively.

i,t+r

Figure 1: Debt Restructurings, Severe Credit Declines and Protracted Net Capital Inﬂow
Declines, 3-year Horizon

Notes: Debt restructuring episodes (start) come from Asonuma and Trebesch (2016). Severe credit declines are deﬁned as
observations where the cumulative percentage point diﬀerence in the credit-to-GDP ratio over three years, dC
i,t+3 = 100 ×
(Crediti,t+3/GDPi,t+3 − Crediti,t/GDPi,t), is less than the 25th percentile of the of the distribution. 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, dN CI,DIF
= 100 × (N CIi,t+3/GDPi,t+3 − N CIi,t/GDPi,t), is less
than the 25th percentile of the distribution.

i,t+3

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 restruc-

turings 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 restruc-

17Bernanke and Lown (1991) deﬁne a bank credit crunch as a signiﬁcant leftward shift in the supply curve for bank
loans, holding both the safe real interest rate and the quality of potential borrowers. Clair and Tucker (1993) further
deﬁne it as a curtailment of the credit supply in response to both (i) a decline in the value of bank capital and (ii)
conditions imposed by regulators, bank supervisors, or banks themselves that require banks to hold more capital than
they previously would have held.

7

# of severe credit declines# of debt restructurings# of severe net capital inflow declines05101520# of debt restructurings0204060# of severe credit/net capital inflow declines 1976198119861991199620012006Figure 2: Debt Restructurings and Banking Crises

turings 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

8

 No.Country19761977197819791980198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120121Argentina2Albania3Algeria4Bolivia5Bosnia and Herzegovina6Brazil7Bulgaria8Cameroon9Chile10Croatia11Costa Rica12Congo, Rep.13Cote d'Ivoire14Dominican Republic15Ecuador16Guyana17Guinea18Jamaica19Jordan20Kenya21Liberia22Madagascar23Macedonia24Mexico25Nicaragua26Niger27Nigeria28Panama29Paraguay30Peru31Philippines32Poland33Russian Federation34Romania35Morocco36Mozambique37Senegal38Sierra Leone39Slovenia40Tanzania41Togo42Turkey43Uganda44Ukraine45Uruguay46Venezuela, RB47ZambiaYearNotes:         indicates the start year of post-default debt restructuring.                  indicates the start year of weakly preemptive debt restructuring.                  indicates the start year of strictly preemptive debt restructuring.                  indicates the start year of domestic debt restructuring.                      indicates banking crises.       The data on private external and domestic debt restructurings come from Asonuma and Trebesch (2016) and Asonuma and Papaioannou (2016), respectively. The data on banking crises come from Laeven and Valencia (2013). Countries that experienced both debt resturucting and banking crisis are listed in the figure. 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.

Figure 3: Debt Restructurings, Severe Credit and Net Capital Inﬂow Declines, 3-year
Horizon

Notes: Panel charts show the probability of banking crises (Panel A), severe credit declines (Panel B), and severe pro-

tracted net capital inﬂow declines (Panels C and D), respectively.

Panel B: Severe credit declines are deﬁned as observations where the cumulative percentage point diﬀerence in the credit-
to-GDP ratio over three years, dC
i,t+3 = 100 × (Crediti,t+3/GDPi,t+3 − Crediti,t/GDPi,t), is less than the 25th percentile
of the distribution.

Panel C: Severe protracted net capital inﬂow declines (sum) are deﬁned as observations where the sum of cumu-

lative percentage point diﬀerences in the net capital
P3

s=1(N CIi,t+s/GDPi,t+s − N CIi,t/GDPi,t), is less than the 25th percentile of the distribution.

inﬂows-to-GDP ratio over three years, dN CI,SUM

i,t+3

= 100 ×

Panel D: Severe protracted net capital inﬂow declines (diﬀerence) are deﬁned as observations where the cumulative per-
centage point diﬀerence in the net capital inﬂows-to-GDP ratio over three years, dN CI,DIF
= 100×(N CIi,t+3/GDPi,t+3−
N CIi,t/GDPi,t), is less than the 25th percentile of the distribution.
The sample after 2008 is dropped because macroeconomic variables in the period are largely inﬂuenced by the Global

i,t+3

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.

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 restructur-

ings 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).

9

0.1.2.3.4.5ProbabilityPanel A: Banking Crises0.1.2.3.4.5Panel B: Severe Credit DeclinesPost−defaultWeakly preemptiveStrictly preemptiveNon restructuring time0.1.2.3.4.5Inflow Declines (Sum)Panel C: Severe Net Capital0.1.2.3.4.5Inflow Declines (Difference)Panel D: Severe Net Capital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.

2.2 New 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).

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 dis-

proportionate 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.

18See Benjamin and Wright (2009), Sturzenegger and Zettelmeyer (2006, 2008), Reinhart and Rogoﬀ (2011a), Cruces
and Trebesch (2013), Bai and Zhang (2012), Das et al. (2011, 2012), and Asonuma and Joo (2017) for stylized facts
around sovereign debt restructurings.

10

Figure 4: GDP and Investment around Debt Restructurings, Average

Sources: Asonuma and Trebesch (2016) for restructurings, Penn World Table 8.0 (Feenstra et al. 2015) for GDP and

investment. GDP (real) is measured as a deviation from a Hodrick-Prescott ﬁltered trend and investment (real, ﬂow) is

normalized at the pre-crisis (t = 0) level. Black vertical lines correspond to the start of restructurings (at t =1).

Figure 5: Private Credit, Lending Interest Rates, and Capital Flows around Debt Re-
structurings, Average

Sources: Asonuma and Trebesch (2016) for restructurings, WB WDI for private credit and lending interest rates, and

IMF WEO for net capital inﬂows. Private credits-to-GDP ratio, lending interest rates (nominal), and net capital inﬂows-

to-GDP ratio are measured as a percentage point diﬀerence from the pre-crisis level (t = 0) respectively. Black vertical

lines correspond to the start of restructurings (t = 1).

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

11

Post−defaultWeakly preemptiveStrictly preemptive−3−2−1012log deviation from year 0 times 100012345YearsPanel A: GDPPost−defaultWeakly preemptiveStrictly preemptive−20−100102030log deviation from year 0 times 100012345YearsPanel B: InvestmentPost−defaultWeakly preemptiveStrictly preemptive−2−1012Percentage point dif. from year 0012345YearsPanel A: Private Credit Post−defaultWeakly preemptiveStrictly preemptive−505101520Percentage point dif. from year 0012345YearsPanel B: Lending Interest RatesPost−defaultWeakly preemptiveStrictly preemptive−50510Percentage point dif. from year 0012345YearsPanel C: Net Capital Inflows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).

2.3 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).19 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). Summary statistics of our key macroeconomic and banking indicators are reported

in Appendix A.3.

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.

2.4 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 restruc-

turing 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.

19Debtors’ GDP, GDP per capita, and investment are measured in constant US dollar price.

12

3 Local Projection Approach

3.1 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) estima-

tion results—reported in Appendix C.1—could be driven by the characteristics of countries experi-

encing 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

(1)

(2)

Credit
ratings

-2.142*
(1.246)
0.473
(1.310)

Change
in credit
ratings

-2.297***
(0.415)
-1.368***
(0.430)

Post-default

(start year)

Post-default

(a year before start)

Weakly preemptive
(start year)
Weakly preemptive
(a year before start)

-6.028***
(1.642)
-1.818
(1.645)

-3.312***
(0.520)
-3.551***
(0.537)

Strictly preemptive
(start year)
Strictly preemptive
(a year before start)

-9.298***
(2.311)
-7.078***
(2.370)

-2.052***
(0.751)
-0.593
(0.772)

Constant

R-squared
# of countries
# of observations

28.64***
(0.255)
0.029
63
1,566

0.563***
(0.082)
0.089
63
1,503

(3)
Interest
payments
(short-term)
/GDP×100

(4)
Interest
payments
(total)
/GDP×100

0.224***
(0.076)
0.300***
(0.076)

0.254**
(0.115)
0.475***
(0.116)

0.124
(0.173)
0.0536
(0.173)

0.269***
(0.014)
0.024
54
2,053

1.352***
(0.301)
1.715***
(0.303)

2.030***
(0.458)
2.150***
(0.459)

1.142*
(0.688)
0.438
(0.687)

2.024***
(0.057)
0.050
54
2,053

(5)

GDP
growth

(6)
Cyclical
comp. of
log of

rate×100 GDP×100

-3.423***
(0.823)
-1.650**
(0.822)

-1.435*
(0.849)
0.473
(0.848)

(7)
Political
stability
(civil
liberties)

0.318***
(0.118)
0.344***
(0.118)

-2.742**
(1.197)
-3.290***
(1.198)

-3.558***
(1.152)
-1.071
(1.154)

-0.676
(1.588)
-1.444
(1.587)

-3.533**
(1.651)
-2.965*
(1.650)

0.236
(0.182)
0.310*
(0.182)

0.117
(0.245)
0.177
(0.245)

3.904***
(0.152)
0.016
64
2,373

0.163
(0.153)
0.011
59
2,159

4.091***
(0.0233)
0.010
62
2,405

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%, 5%, and 10% 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.

13

Table 3: Predicting the Start of Debt Restructurings, Probit

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

Post-default

Strictly preemptive

(1)

(2)

(3)

(4)

(5)

(6)

# of past post-default

# of past weakly preemptive

# of past strictly preemptive

# of past banking crises

0.232***
(0.035)
-0.047
(0.064)
-0.056
(0.084)

External debt-to-GDP ratio, lag

Total interest payments-to-GDP ratio, lag

Change in credit ratings, lag

Cyc. comp. of log of GDP per capita, lag

GDP growth rate, lag

Freedom index, civil liberty, lag

Constant

Likelihood ratio
(p-val.)
Pseudo R-squared
# of observations

-2.094***
(0.071)
43.18
(0.000)
0.054
2,816

0.479***
(0.079)
0.310***
(0.105)
0.042
(0.088)
-0.199***
(0.044)
-0.626***
(0.220)
9.814***
(2.067)
-0.054**
(0.0225)
1.773
(1.327)
-3.311**
(1.389)
0.147**
(0.060)
-2.642***
(0.323)
74.59
(0.000)
0.186
1,244

-0.560***
(0.137)
0.508***
(0.063)
-0.276**
(0.129)

-2.276***
(0.102)
79.82
(0.000)
0.194
2,816

-0.674***
(0.206)
0.499***
(0.135)
-0.197
(0.140)
-0.140**
(0.066)
-0.149
(0.369)
6.417**
(2.888)
-0.068***
(0.0263)
-2.284
(2.295)
-0.026
(2.265)
-0.155*
(0.088)
-1.326***
(0.416)
89.08
(0.000)
0.353
1,244

0.020
(0.174)
0.407***
(0.115)
0.693***
(0.127)

-2.658***
(0.109)
37.70
(0.000)
0.152
2,816

0.078
(0.268)
0.173
(0.208)
0.605***
(0.194)
-0.110
(0.089)
-0.636
(0.522)
5.956
(4.454)
0.004
(0.0470)
-6.675**
(3.183)
-0.342
(2.940)
0.0355
(0.110)
-2.486***
(0.531)
24.11
(0.000)
0.007
1,244

Notes: The sample period is 1970–2013. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and 10% 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.

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).

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.20 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

20The GDP growth rate is not included in the set of explanatory variables because cyclical component of GDP growth
is included as a control variable in the second stage, estimating the eﬀects of restructurings on macroeconomic and
banking sector variables.

14

Figure 6: Classiﬁcation Power of the First Stage Regressors

Panel A: ROC Curves

Panel B: Kernel Density Estimates for Treatment and Control Groups

Notes: Panel A shows the area under the ROC curve. The ROC area takes a value between 0.50 and 1. Taking a

value of 0.50 indicates that regressors have no classiﬁcation power when diﬀerentiating observations at the start of debt

restructurings from other observations. Taking a value of 1 indicates that regressors have a perfect classiﬁcation power.

See the main text for the deﬁnition of x1 and x2. Panel B shows kernel density estimates for observations of the start

of debt restructurings—we call the treatment group—, and other observations—we call the control group.

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:

15

0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.84x2 ROC area: 0.80Post−default0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.85x2 ROC area: 0.82Weakly preemptive0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.78x2 ROC area: 0.63Strictly preemptiveTreatment groupControl group0510Kernel density0.2.4.6.8Predicted probabilitiesPost−defaultTreatment groupControl group02460.2.4.6.81Predicted probabilitiesWeakly preemptiveTreatment groupControl group01020300.1.2.3Predicted probabilitiesStrictly preemptive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.

3.2 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 proba-

bility that a restructuring event occurs. We use probit models, treating each restructuring strategy

separately, as follows:

P r(R)i,t+1 = Φ(Zi,t, Xi,t, γγγR) for R = {P D, W eakly, Strictly}

(1)

where P r(R)i,t+1 denotes the probability that a type R debt restructuring event occurs in country i
in year t + 1; Zi,t is a vector of lagged instruments (in year t) to predict the restructuring events with
three strategies deﬁned in Section 3.1. Regressors from the second stage, Xi,t, are also included in
the ﬁrst-stage estimation. γγγR indicates vectors of coeﬃcients to be estimated for each type of debt
restructuring strategies R = {P D, W eakly, 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/ (cid:92)P r(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 restruc-

turing 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

16

and the control groups, we estimate local projections as follows:

gi,t+h = αR,h
for R = {P D, W eakly, Strictly} and h = 1, 2, .., 5

i,t+1 + Xi,tβββR,h + (cid:15)R

i + ΛR,hDR

i,t+h,

(2)

where gi,t+h = 100 × (GDPi,t+h − GDPi,t)/GDPi,t is the cumulative GDP growth from time t to t + h
in country i; DR
i,t+1 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; αR,h
denotes the country ﬁxed eﬀects for each regression; ΛR,h and βββR,h indicate coeﬃcients to be estimated;
and (cid:15)R
i,t+h 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

i

macroeconomic diﬀerences across countries.

We denote the predicted dependent variables as

i,t+h = ˆαR,h
ˆgR
for R = {P D, W eakly, Strictly} and h = 1, 2, .., 5

i + ˆΛR,hDR

i,t+1 + Xi,t

ˆβββ

,

R,h

(3)

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:

AT E(ΛR,h) =

X

i,t+hDR
ˆgR
(cid:92)P r(R)i,t+1
for R = {P D, W eakly, Strictly} and h = 1, 2, .., 5

1
NN on−R

1
NR

i,t+1

X

X

X

−

t

t

i

i

i,t+h(1 − DR
ˆgR
i,t+1)
1 − (cid:92)P r(R)i,t+1

,

(4)

where NR indicates the number of observations from the start of type R debt restructurings; NN on−R
indicates the number of observations which are complement to the sample of restructurings;21 (cid:92)P r(R)i,t+1
denotes the estimated probability of type R debt restructurings; and ˆgR
is the predicted dependent variable.

i,t+h for R = {P D, W eakly, Strictly}

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.22
The solid lines in red and the gray bands indicate the point estimates and 95% conﬁdence intervals for

post-default restructurings, respectively. The solid and dotted lines in blue and green indicate the point

estimates and 95% 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

21Since sovereigns make the restructuring strategy choice sequentially, i.e., at diﬀerent points of time (Asonuma and
Trebesch 2016), the complement to one type of restructurings includes other restructuring strategies. Excluding these
sample from the complement does not change our AIPW estimation results given the large size of non-restructuring
observations (repayment choice).

22We merge lending interest rate observations for weakly and strictly preemptive restructurings together due to limited
observations of both lending interest rates and a set of controls (changes in credit ratings and the total interest payments-
to-GDP ratio) for each restructuring strategy.

17

Figure 7: Local Projections, AIPW

Notes: The ﬁgure shows local projections of the variables shown in each panel for h = 1, 2,.., 5, where h indicates horizon.

Solid lines in red, blue, green, and brown are point estimates. Gray bands and dotted lines in blue, green, and brown

are 95% conﬁdence intervals. The sample period is 1970–2013.

1/ Estimates for the lending interest rates merge observations for weakly and strictly preemptive restructurings together

due to limited observations of both lending interest rates and a set of controls (changes in credit ratings and the total

interest payments-to-GDP ratio) for each restructuring strategy.

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 esti-

mates 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

18

Post−defaultWeakly preemptiveStrictly preemptive−6−3036Cumulative rate of change from year 0012345YearsPanel A: GDPPost−defaultWeakly preemptiveStrictly preemptive−4−2024Cumulative percentage point diff. from year 0012345YearsPanel B: InvestmentPost−defaultWeakly preemptiveStrictly preemptive−4−202Cumulative percentage point dif. from year 0012345YearsPanel C: Private CreditPost−defaultWeakly & strictly preemptive−5051015Cumulative percentage point dif. from year 0012345YearsPanel D: Lending Interest Rates 1/Post−defaultWeakly preemptiveStrictly preemptive−10−50510Cumulative percentage point dif. from year 0012345YearsPanel E: Net Capital Inflows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 pre-

dicted 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.

3.3 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 diﬀer-

ent 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 capi-

tal inﬂows-to-GDP ratio from the pre-restructuring year to the start of restructuring (from year 0
to year 1), dN CI
i,t+1 = 100 × (N CIi,t+1/GDPi,t+1 − N CIi,t/GDPi,t) for R = {P D, W eakly, Strictly}, is
less than the 25th or 50th percentile among the observations experiencing that type of restructuring.23

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.24 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 strate-
gies (purple lines in Panels A, B, and C).25 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:

gi,t+h = αR,h
for R = {P D, W eakly, Strictly} , and h = 1, 2, .., 5

i,t+1 + ΛN onSS,h(1 − DSS

i + ΛSS,hDSS

i,t+1DR

i,t+1)DR

i,t+1 + ˜Xi,tβββR,h + (cid:15)R

i,t+h,

(5)

where DSS
i,t+1 is a dummy variable taking unity if there is a severe sudden stop in year t + 1 and
zero otherwise; DR
i,t+1 is a dummy variable taking unity if there is a restructuring (for instance, post-
default) at year t + 1 and zero otherwise; ΛSS,h and ΛN onSS,h are coeﬃcients to be estimated. In
the second term on the right-hand side of equation (5), DSS
i,t+1. Therefore,
ΛSS,h captures the impact of a severe sudden stop in year t + 1 associated with a restructuring (for

i,t+1 is interacted with DR

23Calvo et al. (1998) deﬁne a sudden stop as a sharp slowdown in net capital inﬂows. Recent papers broaden this
original deﬁnition by adding criteria such as (i) the requirement that the stop occurred at the same time as an output
contraction in order to exclude positive terms of trade shocks (Calvo et al., 2004), or (ii) the requirement that the stop
had to occur in conjunction with a sharp rise in interest rate spreads in order to capture a global component and qualify
as a “systemic sudden stop” (Calvo et al., 2008). Focusing on gross ﬂows instead of net ﬂows, Forbes and Warnock
(2012) deﬁne it a “sudden stop” as a sharp decrease in gross capital inﬂows.

24The role of banks’ direct exposure channel, i.e., banks’ holding of external debt is limited in external debt restruc-
turings. This is shown by both limited changes in banks’ holding of sovereign debt and the composition of creditor
committee members in external debt restructurungs (Asonuma and Joo 2017).

25In strictly preemptive restructurings with a severe sudden stop, investment declines in parallel with a decline in GDP

leaving investment-to-GDP ratio steady as shown by the purple line in Panel B (Right) in Figure 8.

19

Figure 8: Private Credit,
with/without Severe Sudden Stops, Average

Investment, and GDP around Debt Restructurings,

Panel A: Private Credit

Panel B: Investment

Panel C: GDP

Sources: Severe sudden stops are deﬁned as observations where the percentage point diﬀerence in the net capital inﬂows-
to-GDP ratio from year 0 to year 1, dN CI
i,t+1 = 100×(N CIi,t+1/GDPi,t+1 −N CIi,t/GDPi,t), is less than the 25th percentile
of the distribution. The 50th percentile cutoﬀ is employed for the post-default cases in Panel C. Black vertical lines

correspond to the start of restructurings (at t =1). GDP (real) and private credit (real) are detrended using a Hodrick-

Prescott ﬁlter. Investment is measured as the investment-to-GDP ratio and its percentage point diﬀerence from year 0

is shown.

20

All episodeswith Sudden Stopswithout Sudden Stops−15−10−505Cumulative percentage diviation from year 0012345YearsPost−defaultAll episodeswith Sudden Stopswithout Sudden Stops−15−10−505012345YearsWeakly preemptiveAll episodeswith Sudden Stopswithout Sudden Stops−15−10−505012345YearsStrictly preemptiveAll episodeswith Sudden Stopswithout Sudden Stops−6−4−2024Cumulative percentage point diff. from year 0012345YearsPost−defaultAll episodeswith Sudden Stopswithout Sudden Stops−6−4−2024012345YearsWeakly preemptiveAll episodeswith Sudden Stopswithout Sudden Stops−6−4−2024012345YearsStrictly preemptiveAll episodeswith Sudden Stopswithout Sudden Stops−8−6−4−2024Cumulative percentage deviation from year 0012345YearsPost−defaultAll episodeswith Sudden Stopswithout Sudden Stops−8−6−4−2024012345YearsWeakly preemptiveAll episodeswith Sudden Stopswithout Sudden Stops−8−6−4−2024012345YearsStrictly preemptiveinstance post-default) that occurred in the same year, t + 1. In the third term on the right-hand side
of equation (5), (1 − DSS
i,t+1. Therefore, ΛN onSS,h reﬂects the impact when
no severe sudden stop is associated with the restructuring that occurred in the same year t + 1. ˜Xi,t
is a set of regressors including cyclical component of log of GDP per capita, lagged changes in the

i,t+1) is interacted with DR

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 accompa-

nied 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.

3.4 Duration, Haircuts, and Missed Payments

In this sub-section, we consider whether an additional diﬀerence in output cost is incurred depend-

ing 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.

21

Figure 9: Restructurings with/without Severe Sudden Stops, AIPW

Panel A: Private Credit

Panel B: Investment

Panel C: GDP

Notes: Severe sudden stops are deﬁned as observations where the percentage point diﬀerence in the net capital inﬂows-to-
GDP ratio from year 0 to year 1, dN CI
i,t+1 = 100 × (N CIi,t+1/GDPi,t+1 − N CIi,t/GDPi,t), is less than the 25th percentile
among the observations experiencing that type of restructurings for Panel A. The 50th percentile cutoﬀ for Panels B and

C in order to have enough number of observations for both groups. The ﬁgure shows AIPW estimates of the variable

shown in each panel for h = 1, 2, .., 5, where h indicates horizon. Solid lines in brown and purple are point estimates.

Gray bands and dotted lines in brown are 95% conﬁdence intervals. The sample period is 1970–2013.

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”

22

with Sudden Stopswithout Sudden Stops−30−20−100Cumulative rate of change from year 0012345YearsPost−defaultwith Sudden Stopswithout Sudden Stops−30−20−1000123YearsWeakly preemptivewith Sudden Stopswithout Sudden Stops−30−20−1000123YearsWeakly & Strictly preemptivewith Sudden Stopswithout Sudden Stops−8−404Cumulative percentage point diff. from year 0012345YearsPost−defaultwith Sudden Stopswithout Sudden Stops−8−4040123YearsWeakly preemptivewith Sudden Stopswithout Sudden Stops−8−4040123YearsWeakly & Strictly preemptivewith Sudden Stopswithout Sudden Stops−8−6−4−20Cumulative rate of change from year 0012345YearsPost−defaultwith Sudden Stopswithout Sudden Stops−8−6−4−200123YearsWeakly preemptivewith Sudden Stopswithout Sudden Stops−8−6−4−200123YearsWeakly & Strictly preemptive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.

Figure 10: GDP around Debt Restructurings with Short Duration and/or Small Hair-
cuts, Average

Sources: Asonuma and Trebesch (2016, restructurings, duration), Cruces and Trebesch (2013, haircuts), Penn World

Table 8.0 (Feenstra et al. 2015) (GDP). GDP (real) is measured as a deviation from a Hodrick-Prescott ﬁltered trend.

Black vertical lines correspond to the start of restructurings (at t =1).

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.

23

Post−default (all episodes)Weakly preemptiveShort duration post−default−4−3−2−1012Cumulative percentage deviation from year 0012345YearsPanel A: DurationPost−default (all episodes)Weakly preemptiveSmall haircut post−default−4−3−2−1012012345YearsPanel B: HaircutsPost−default (all episodes)Weakly preemptiveShort duration & small haircut post−default−4−3−2−1012012345YearsPanel C: Duration and Haircuts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 re-

structurings 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 du-

ration 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:

Durationi,t+1 = βD
Haircuti,t+1 = βH

0 + βD
0 + βH

1 gF,Short
1 gF,Short

i,t

i,t

i,t

+ βD
+ βH

2 gF,Long
2 gF,Long

i,t

+ Zi,tβββD
+ Zi,tβββH

3 + (cid:15)D
3 + (cid:15)H

i,t+1,
i,t+1,

(6)

i,t

= gF,t+1
i,t

and gF,Long
i,t

where Durationi,t+1 and Haircuti,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; gF,Short
and
gF,Long
are short-term and long-term GDP growth forecasts one year before the start year of restruc-
i,t
turing episode—gF,Short
= Pt+6
i,t denotes the anticipated
GDP growth rate in year s forecasted in year t—; Zi,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 (cid:15)D
i,t+1 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 Durationi,t+1 and Haircuti,t+1 are set to zeros. Using the predicted duration and haircuts,
(cid:92)Durationi,t+1 and (cid:92)Haircuti,t+1, we deﬁne dummy variables for long expected duration and high ex-
pected haircuts, DLongD
and DHighH
, taking unity if the predicted duration or haircuts is higher than
i,t+1
the 50th percentile and 0 otherwise.

i,t /5 where gF,s

i,t+1 and (cid:15)H

s=t+2 gF,s

i,t+1

i,t

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.

24

Next, we follow the same approach as in the case of severe sudden stops (equation 5 in Section 3.3)

to estimate the impact of post-default restructurings with long expected duration and high expected

haircuts. We specify the following local projection equation:

gi,t+h = αh
gi,t+h = αh

i + ΛLong,hDLongD
i + ΛHigh,hDHighH
h = 1, 2, .., 5
for

i,t+1 DDR
i,t+1 DDR

i,t+1 + ΛShort,h(1 − DLongD
i,t+1 + ΛSmall,h(1 − DHighH

i,t+1 )DDR
)DDR

i,t+1 + Xi,tβββDR,h + (cid:15)DR
i,t+h,
i,t+1 + Xi,tβββDR,h + (cid:15)DR
i,t+h,

i,t+1

(7)

where ΛLong,h and ΛShort,h are coeﬃcients capturing GDP dynamics for post-default episodes with
long and short expected duration, respectively; ΛHigh,h and ΛSmall,h are coeﬃcients for post-default
episodes with high and small expected haircuts, respectively. We also apply a two-type model to esti-

mate 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 prob-

ability 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.

Table 4: Predicting Duration and Haircuts, Poisson

GDP growth forecasts, short-run

GDP growth forecasts, long-run

Cyclical component of log of GDP per capita

# of past post-default episodes

# of past preemptive episodes

Constant

# of obs.
Pseudo R-squared

Duration (years)

Haircut (percent)

Poisson

(1)

(2)

-0.030
(0.026)
-0.484***
(0.056)

0.219***
(0.057)
-0.307***
(0.090)
-0.406*
(0.221)
1,193
0.125

-0.025
(0.026)
-0.558***
(0.064)
2.642**
(1.183)

0.177**
(0.071)
-0.242**
(0.104)
-0.111
(0.256)
1,037
0.132

Neg. Bin.
(3)

-0.058
(0.102)
-0.474**
(0.199)

0.338
(0.303)
-0.498**
(0.215)
-0.391
(0.992)
1,193
0.039

Poisson

(4)

(5)

-0.034***
(0.006)
-0.493***
(0.014)

0.194***
(0.015)
-0.072***
(0.016)
2.309***
(0.056)
1,193
0.127

-0.038***
(0.006)
-0.585***
(0.016)
0.723**
(0.316)

0.164***
(0.018)
-0.006
(0.021)
2.675***
(0.063)
1,037
0.155

Neg. Bin.
(6)

0.003
(0.160)
-0.757**
(0.377)

0.312
(0.454)
-0.058
(0.237)
3.164*
(1.768)
1,193
0.010

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

i,t

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

25

Figure 11: GDP after Post-default Restructurings, AIPW

Notes: The ﬁgure shows dynamics of GDP after post-default debt restructurings, classifying the sample into those high

and low GDP growth forecasts (Panel A), those with short and long expected duration (Panel B), and those with small

and high expected haircuts (Panel C). Panel A classiﬁes post-default restructurings into two groups based on the long-

run GDP growth forecasts. Panel B and C classify post-default restructurings into two groups based on the estimated

duration and haircuts obtained in columns (2) and (5) in Table 4, respectively. Solid lines in red and purple are point

estimates. Gray bands and dotted lines in purple are 95% conﬁdence intervals.

an agreement with its creditors quickly (short duration) and/or inﬂicting on its creditors the smaller

possible losses (small haircuts) needed to restore solvency.

4 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 2.1, 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:

P r(BankCrisis)i,t+1 = G( ˜DP D

i,t−3,t+1, ˜DW eak

i,t−3,t+1, ˜DStrict

i,t−3,t+1, Xi,t, ααα).

(8)

where P r(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
i,t−3,t+1, and ˜DStrict
dummies ˜DP D
i,t−3,t+1 take unity if there is each type of debt restructuring
starting during the period between year t = −3 and year t = −1.

i,t−3,t+1, ˜DW eak

In a similar vein, the logit models for severe credit declines and severe protracted net capital inﬂow

26

Low GDP forecastsHigh GDP forecasts−10−505Cumulative rate of change x 100012345YearsPanel A: GDP ForecastsLong expected durationShort expected duration−10−505012345YearsPanel B: DurationHigh expected haircutsSmall expected haircuts−10−505012345YearsPanel C: Haircutsdeclines are speciﬁed as follows:

P r(CreditDecline)i,t,t+5 = G(DP D
P r(N CIDecline)i,t,t+5 = G(DP D

i,t+1, DW eak
i,t+1, DW eak

i,t+1 , DStrict
i,t+1 , DStrict

i,t+1 , Xi,t, ααα),
i,t+1 , Xi,t, ααα).

(9)

where as deﬁned in Section 2.1, P r(CreditDecline)i,t,t+5 and P r(N CIDecline)i,t,t+5 are the prob-
ability that the cumulative percentage point diﬀerences in the credit-to-GDP ratio and net capital
inﬂows-to-GDP ratio over ﬁve years, dC
i,t+5 = 100 × (Crediti,t+5/GDPi,t+5 − Crediti,t/GDPi,t) and
dN CI,DIF
= 100 × (N CIi,t+5/GDPi,t+5 − N CIi,t/GDPi,t), are less than the 25th percentile of the
i,t+5
corresponding distribution (-1.65 and -7.05, respectively, as reported in Table A3 in Appendix A.3).
DP D
i,t+1 are dummy variables taking unity if there is each type of debt restructuring
starting at year t = 1. Xi,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

i,t+1 , and DStrict

i,t+1, DW eak

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 ααα denotes the coeﬃcients
to be estimated and G denotes the logistic distribution.

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.

5 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-

27

Table 5: Banking Crises and Severe Credit and Net Capital Inﬂow Declines after Debt
Restructurings, Logit

Dep. Var. = Dummy Taking 1 if There Is a Banking Crisis, Severe Credit Decline, or Severe Net Capital Inﬂow Decline

Banking Crises
at t = 1

Severe Credit Declines
Cum. from t = 0 to t = 5

Coef.
(Std. err.)

(1)

1.051***
(0.279)
0.910**
(0.368)
0.270
(0.514)

dy/dx
(Delta-method
std. err.)
(1’)

Coef.
(Std. err.)

(2)

dy/dx
(Delta-method
std. err.)
(2’)

0.204***
(0.075)
0.176**
(0.074)
0.052
(0.100)

0.812***
(0.299)
0.288
(0.409)
-0.419
(0.636)

-0.178
(1.244)
-2.513*
(1.423)
-0.111
(0.141)
-6.453***
(1.226)

0.179***
(0.072)
0.063
(0.090)
-0.092
(0.142)

-0.039
(0.276)
-0.554*
(0.329)
-0.025
(0.027)
-1.423***
(0.330)

Yes
50.49
(0.000)
0.039
1,284
44

-3.277
(2.339)
1.522
(1.850)
-0.337
(0.250)
-1.798
(2.090)

-0.635
(0.509)
0.295
(0.363)
-0.065
(0.033)
-0.349
(0.410)

Yes
27.60
(0.000)
0.058
1,229
42

Post-default, 1t=1,0,−1,−2,−3

Weakly pre., 1t=1,0,−1,−2,−3

Strictly pre., 1t=1,0,−1,−2,−3

Post-default, 1t=1

Weakly pre., 1t=1

Strictly pre., 1t=1

Controls

CA-to-GDP ratio, lag

Reserves-to-GDP ratio, lag

Real exchange rate, log, lag

GDP growth rate, lag

Country ﬁxed eﬀects
Log-likelihood ratio
(p-value)
Pseudo R-squared
# of observations
# of countries

Sev. Net Cap. Inf. Declines
Cum. from t = 0 to t = 5
Coef.
(Std. err.)

dy/dx
(Delta-method
std. err.)
(3’)

(3)

0.704**
(0.300)
0.653*
(0.389)
-1.267
(1.052)

-6.967***
(1.237)
4.506***
(1.568)
-0.238*
(0.134)
2.648**
(1.201)

0.143**
(0.065)
0.133*
(0.080)
-0.258
(0.218)

-1.418***
(0.342)
0.917***
(0.338)
-0.048*
(0.020)
0.538**
(0.257)

Yes
76.65
(0.000)
0.057
1,474
57

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%, 5%,
and 10% 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).

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.

28

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.

References

[1] Acharya, V. V., T. Eisert, C. Euﬁnger, and C. Hirsch, 2015, “Real Eﬀects of the Sovereign Debt

Crisis in Europe: Evidence from Syndicated Loans,” CEPR Discussion Paper 10108.

[2] Angrist, J. D., Ó. Jordá, and G. M. Kuersteiner, 2016, “Semiparametric Estimates of Mone-

tary Policy Eﬀects: String Theory Revisited,” forthcoming in Journal of Business & Economic

Statistics.

[3] Arellano, C., Y. Bai and L. Bocola, 2017, “Sovereign Default Risk and Firm Heterogeneity,”

Federal Reserve Bank of Minneapolis Research Department Staﬀ Report 547.

[4] Arteta, C., and G. Hale, 2008, “Sovereign Debt Crises and Credit to the Private Sector,” Journal

of International Economics, Vol.74(1), pp.53–69.

[5] Asonuma, T., 2016, “Serial Sovereign Defaults and Debt Restructurings,” IMF Working Paper

16/66.

[6] Asonuma, T., M. Chamon and A. Sasahara, 2016, “Trade Costs of Sovereign Debt Restructurings:

Does a Market-Friendly Approach Improve the Outcome?” IMF Working Paper 16/222.

[7] Asonuma, T., and H. Joo, 2017, “Sovereign Debt Restructurings: Delays in Renegotiations and

Risk Averse Creditors,” Manuscript, IMF.

[8] Asonuma, T., and M. G. Papaioannou, 2016, “Domestic Sovereign Debt Restructurings: Pro-

cesses, Outcomes, and Challenges,” Manuscript, IMF.

[9] Asonuma, T., and C. Trebesch, 2016, “Sovereign Debt Restructurings: Preemptive or Post-

default,” Journal of the European Economic Association, Vol.14(1), pp.175–214.

[10] Auerbach, A., and Y. Gorodnichenko, 2016, “Eﬀects of Fiscal Shocks in a Globalized World,”

IMF Economic Review, Vol.64(1), pp.177–215

[11] Bai, Y., and J. Zhang, 2012, “Duration of Sovereign Debt Renegotiation,” Journal of International

Economics, Vol.86(2), pp.252–268.

[12] Balteanu, I., and A. Erce, 2018, “Linking Bank Crises and Sovereign Defaults: Evidence from

Emerging Markets,” IMF Economic Review, Vol.66(4), pp.617–664.

[13] Benjamin, D., and M. L. J. Wright, 2013, “Recovery Before Redemption? A Theory of De-

lays in Sovereign Debt Renegotiations.” Manuscript, Federal Reserve Bank of Chicago and State

University of New York at Buﬀalo.

[14] Bernanke, B. S., and C. S. Lown, 1991, “The Credit Crunch,” Brookings Papers on Economic

Activity, Vol.22(2), pp.205–248.

29

[15] Bi, R., 2008, “‘Beneﬁcial’ Delays in Debt Restructuring Negotiations,” IMF Working Paper 08/38.

[16] Bianchi, J., S. Lizarazo, and H. Sapriza, 2013, “Bailout Guarantees, Banking Crises and Sovereign

Debt Crises,” Manuscript, Board of Governors of the Federal Reserve System, Federal Reserve

Bank of Philadelphia, and IMF.

[17] Bocola, L., 2015, “The Pass-Through of Sovereign Risk,” Federal Reserve Bank of Minneapolis

Working Paper 722.

[18] Bolton, P., and O. Jeanne, 2011, “Sovereign Default Risk and Bank Fragility in Financially

Integrated Economies,” IMF Economic Review, Vol.59(2), pp.162–194.

[19] Borensztein, E., and U. Panizza, 2009, “The Costs of Sovereign Default,” IMF Staﬀ Papers,

Vol.56(4), pp.683–741.

[20] Boz, E., P. D’Erasmo, and B. Durdu, 2016, “Sovereign Risk and Bank Balance Sheets: The Role

of Macroprudential Policies,” Manuscript, Board of Governors of the Federal Reserve System,

Federal Reserve Bank of Philadelphia, and IMF.

[21] Broner, F., T. Didier, A. Erce, and S. L. Schmukler, 2013, “Gross Capital Flows: Dynamics and

Crises,” Journal of Monetary Economics, Vol.60(1), pp.113–133.

[22] Brookings Committee on International Economic Policy and Reform (CEIPR), 2013, “Revisiting

Sovereign Bankruptcy,” Brookings Institution, October.

[23] Bulow, J., and K. Rogoﬀ, 1989, “Sovereign Debt: Is to Forgive or to Forget?” American Economic

Review, Vol.79(1), pp.43–50.

[24] Calvo, G., 1998, “Capital Flows and Capital-market Crises: the Simple Economics of Sudden

Stops,” Journal of Applied Economics, Vol.1(1), pp.35–54.

[25] Calvo, G., A. Izquierdo, and L. F. Mejía, 2004, “On the Empirics of Sudden Stops: the Relevance

of Balance-sheet Eﬀects,” NBER Working Paper 10520.

[26] Calvo, G., A. Izquierdo, and L. F. Mejía, 2008. Systemic Sudden Stops: the Relevance of Balance-

sheet Eﬀects and Financial Integration. NBER Working Paper 14026.

[27] Calvo, G., L. Leiderman, and C. M. Reinhart, 1996, “Inﬂows of Capital to Developing Countries

in the 1990s,” Journal of Economic Perspectives, Vol.10(2), pp.123–139.

[28] Chari, A., R. Leary, and T. Phan, 2018, “The Costs of (sub)Sovereign Default Risk: Evidence

from Puerto Rico,” Federal Reserve Bank of Richmond WP 18-03.

[29] Cesa-Bianchi, A., A. Ferrero, and A. Rebucchi, 2017, “International Credit Supply Shocks,”

Journal of International Economics, Vol.112(C), pp.219–237.

[30] Cheng G., J. Diaz-Cassou, and A. Erce, 2018, “The Macroeconomic Eﬀects of Oﬃcial Debt

Restructuring: Evidence from the Paris Club,” Oxford Economic Papers, pp.1–20.

[31] Clair, R. T., and P. Tucker, 1993, “Six Causes of the Credit Crunch (Or, Why Is It So Hard to

Get a Loan?),” Federal Reserve Bank of Dallas Economic Review, Third Quarter 1993, pp.1–19.

30

[32] Cole, H., and P. Kehoe, 2000, “Self-Fulﬁlling Debt Crises,” Review of Economic Studies, Vol.67,

pp.91–116.

[33] Cruces, J., and C. Trebesch, 2013, “Sovereign Defaults: The Price of Haircuts,” American Eco-

nomic Journal: Macroeconomics, Vol.5(3), pp.85–117.

[34] Das, U., M. Papaioannou, and C. Trebesch, 2011, “Spillovers of Sovereign Default Risk: How

Much is the Private Sector Aﬀected?” In Sovereign Debt: From Safety to Default, edited by

R.W. Kolb, pp.33–41, John Wiley & Sons, Inc.

[35] Das, U., M. Papaioannou, and C. Trebesch, 2012, “Sovereign Debt Restructurings 1950–2010:

Literature Survey, Data, and Stylized Facts,” IMF Working Paper 12/203.

[36] De Paoli, B., G. Hoggarth and V. Saporta, 2009, “Output Costs of Sovereign Crises: Some

Empirical Estimates,” Bank of England Working Paper 362.

[37] Diaz-Cassou, J., A. Erce, and J. Vazquez-Zamora, 2008, “Recent Episodes of Sovereign Debt

Restructurings. A Case-study Approach,” Banco de España Occasional Paper 0804.

[38] Enderlein, H., C. Trebesch, and L. von Daniels, 2012, “Sovereign Debt Disputes: A Database

on Government Coerciveness during Debt Crises,” Journal of International Money and Finance,

Vol.31(2), pp.250–266.

[39] Engler, P., and C. Große Steﬀen, 2016, “Sovereign Risk, Interbank Freezes, and Aggregate Fluc-

tuations,” European Economic Review, Vol.87, pp.34–61.

[40] Erce, A. 2014, “Sovereign Debt Restructurings and the IMF: Implications for Future Oﬃcial

Interventions”, Federal Reserve of Dallas, Globalization and Monetary Policy Institute, Working

Paper 143.

[41] Erce, A., and E. Mallucci, 2018, “Selective Sovereign Defaults,” Manuscript, Board of Governors

of the Federal Reserve System and European Stability Mechanism.

[42] Fakos, A., P. Sakellaris and T. Tavares, 2018, “Investment Slumps during Financial Crises: The

Role of Credit Constraints,” Manuscript, Athens University of Economics and Business and

ITAM.

[43] Feenstra, R. C., R. Inklaar, and M. P. Timmer, 2015, “The Next Generation of the Penn World

Table,” American Economic Review, Vol.105(10), pp.3150–3182.

[44] Fernandez, R., and A. Martin, 2014, “The Long and the Short of It: Sovereign Debt Crises and

Debt Maturity,” NBER Working Paper 20786.

[45] Forbes, K., and F. E. Warnock, 2012, “Capital Flow Waves: Surges, Stops, Flight, and Retrench-

ment,” Journal of International Economics, Vol.88(2), pp.235–251.

[46] Forni, L., G. Palomba, J. Pereira and C. Richmond, 2016, “Sovereign Debt Restructuring and

Growth,” IMF Working Paper 16/147.

[47] Furceri, D., and A. Zdzienicka, 2012, “How Costly Are Debt Crises?” Journal of International

Money and Finance, Vol.31(4), pp.726–742.

31

[48] Gennaioli, N., A. Martin, and S. Rossi, 2014, “Sovereign Default, Domestic Banks, and Financial

Institutions,” The Journal of Finance, Vol.69(2), pp.819–866.

[49] Gennaioli, N., A. Martin, and S. Rossi, 2018, “Banks, Government Bonds and Default: What do

the Data Say?” Journal of Monetary Economics, Vol.98, pp.98–113.

[50] Gordon, G., and P. Guerron-Quintana, 2018, “Dynamics of Investment, Debt and Default,” Re-

view of Economic Dynamics, Vol.28, pp.71–85.

[51] Gornemann, N., 2014, “Sovereign Default, Private Investment, and Economic Growth,”

Manuscript, Board of Governors of the Federal Reserve System.

[52] Gourinchas, P. O., and M. Obstfeld, 2012, “Stories of the Twentieth Century for the Twenty-

First,” American Economic Journal: Macroeconomics, Vol.4(1), pp.226–265.

[53] Hahm, J. H., H. S. Shin, and K. Shin, 2013, “Noncore Bank Liabilities and Financial Vulnerabil-

ity,” Journal of Money, Credit and Banking, Vol.45(1), pp.3–36.

[54] Hébert, B., and J. Schreger, 2017, “The Costs of Sovereign Default: Evidence from Argentina,”

American Economic Review, Vol.107(10), pp.3119–3145.

[55] Ilzetzki, E. O, C. M. Reinhart, and K. Rogoﬀ, 2015, “Exchange Rate Arrangements into the 21st

Century: Will the Anchor Currency Hold?” Manuscript, Harvard University.

[56] International Monetary Fund (IMF), 2014, “The Fund’s Lending Framework and Sovereign

Debt—Preliminary Considerations,” Policy Paper, June (Washington: International Monetary

Fund).

[57] International Monetary Fund (IMF), 2015, “The Fund’s Lending Framework and Sovereign

Debt—Further Considerations,” Policy Paper, April (Washington: International Monetary Fund).

[58] International Monetary Fund (IMF), 2016, International Financial Statistics, 1948–2016.

[59] International Monetary Fund (IMF), 2017, World Economic Outlook. April.

[60] Jordá, Ó., 2005, “Estimation and Inference of Impulse Responses by Local Projections,” American

Economic Review, Vol.95(1), pp.161–182.

[61] Jordá, Ó., M. Schularick, and A. M. Taylor, 2013, “When Credit Bites Back,” Journal of Money,

Credit and Banking, Vol.45(2), pp.3–28.

[62] Jordá, Ó., M. Schularick, and A. M. Taylor, 2016, “Sovereigns versus Banks: Credit, Crises, and

Consequences,” Journal of the European Economic Association, Vol.14(1), pp.45–79.

[63] Jordá, Ó. and A. M. Taylor, 2016, “The Time for Austerity: Estimating the Average Treatment

Eﬀect of Fiscal Policy,” The Economic Journal, Vol.126(590), pp.219–255.

[64] Kaminsky, G. L. and C. M. Reinhart, 1999, “The Twin Crises: The Causes of Banking and

Balance-of-Payments Problems,” American Economic Review, Vol 89(3), pp.473–500.

[65] Kaminsky, G. L., and P. Vega-García, 2016, “Systemic and Idiosyncratic Sovereign Debt Crises,”

Journal of the European Economic Association, Vol.14(1), pp.80–114.

32

[66] Kuvshinov, D., and K. Zimmermann, 2016, “Sovereign Going Bust: Estimating the Cost of

Default,” University of Bonn Discussion Paper 01/2016.

[67] Laeven, L., and F. Valencia, 2013, “Systemic Banking Crises Database” IMF Economic Review,

Vol.61(2), pp.225–270.

[68] Levy-Yeyati, E., and U. Panizza, 2011, “The Elusive Costs of Sovereign Defaults,” Journal of

Development Economics, Vol.94(1), pp.95–105.

[69] Mallucci, E., 2015, “Domestic Debt and Sovereign Defaults,” Board of Governors of the Federal

Reserve System International Finance Discussion Paper 1153.

[70] Marchesi, S., 2016, “Life After Default? Private vs. Oﬃcial Sovereign Debt Restructurings,

Development Working Papers 398, Centro Studi Luca d’Agliano, University of Milano.

[71] Mariscal, R., A. Powell, G. Sandleris, and P. Tavella, 2015, “Sovereign Defaults: Has the Current

System Resulted in Last (Re)solutions?” Universidad Torcuato Di Tella Business School Working

Papers 2015-03.

[72] Mendoza, E. G., and V. Z. Yue, 2012, “A General Equilibrium Model of Sovereign Default and

Business Cycles.” Quarterly Journal of Economics, Vol.127(2), pp.889–946.

[73] Panizza, U., F. Sturzenegger, and J. Zettelmeyer, 2009, “The Economics and Law of Sovereign

Debt and Default,” Journal of Economic Literature, Vol.47(3), pp.651–698.

[74] Park, J., 2017, “Sovereign Default and Capital Accumulation,” Journal of International Eco-

nomics, Vol.106, pp.119–133.

[75] Pei, Y., 2016, “Optimal Sovereign Defaults in the Presence of Financial Frictions,” Manuscript,

State University of New York at Buﬀalo.

[76] Perez, D. J., 2015, “Sovereign Debt, Domestic Banks and the Provision of Public Liquidity,”

Manuscript, NYU.

[77] Reinhart, C. M., V. Reinhart, and C. Trebesch, 2016, “Global Cycles: Capital Flows, Commodi-

ties, and Sovereign Default, 1815–2015,” American Economic Review: Papers & Proceedings,

Vol.106(5), pp.574–580.

[78] Reinhart, M. C., and K. Rogoﬀ, 2009, This Time is Diﬀerent: Eight Centuries of Financial Folly,

Princeton University Press.

[79] Reinhart, M. C., and K. Rogoﬀ, 2011a, “The Forgotten History of Domestic Debt,” Economic

Journal, Vol.121(552), pp.319–350.

[80] Reinhart, M. C., and K. Rogoﬀ, 2011b, “From Financial Crash to Debt Crisis” American Eco-

nomic Review, Vol.101(5), pp.1676–1706.

[81] Reinhart, M. C., K. Rogoﬀ, and M. A. Savastano, 2003, “Debt Intolerance,” Brookings Papers

on Economic Activity, Vol.34(1), pp.1–74.

[82] Robins, J. M., A. Rotnitzky, and L. P. Zhao, 1994, “Estimation of Regression Coeﬃcients When

Some Regressors Are Not Always Observed,” Journal of the American Statistical Association,

Vol. 89(427), pp. 846–866.

33

[83] Roldan-Pena, J., 2012, “Default Risk and Economic Activity: A Small Open Economy Model

with Sovereign Debt and Default,” Banco Central de Mexico Working Paper 2012-16.

[84] Sandleris, G., 2014, “Sovereign Defaults, Credit to the Private Sector, and Domestic Credit

Market Institutions,” Journal of Money, Credit and Banking, Vol.46(2–3), pp.321–345.

[85] Sandleris, G., and M. L. J. Wright, 2014, “The Costs of Financial Crises: Resource Misallocation,

Productivity, and Welfare in the 2001 Argentine Crisis,” The Scandinavian Journal of Economics,

Vol.116(1), pp.87–127.

[86] Schularick, M., and A. M. Taylor, 2012, “Credit Booms Gone Bust: Monetary Policy, Leverage

Cycles and Financial Crises, 1870–2008,” American Economic Review, Vol.102(2), pp.1029–1061.

[87] Sosa-Padilla, C., 2018, “Sovereign Defaults and Banking Crises,” Journal of Monetary Economics,

Vol.99, pp.88–105.

[88] Standard and Poor’s, 2006, “Default Study: Sovereign Defaults At 26-Year Low, To Show Little

Change In 2007,” September 18, 2006.

[89] Sturzenegger, F., 2004, “Tools for the Analysis of Debt Problems,” Journal of Restructuring

Finance, Vol.1(1), pp.201–223.

[90] Sturzenegger, F., and J. Zettelmeyer, 2006, Debt Defaults and Lessons from a Decade of Crises,

MIT Press.

[91] Sturzenegger, F., and J. Zettelmeyer, 2008, “Haircuts: Estimating Investors Losses in Sovereign

Debt Restructurings, 1998–2005,” Journal of International Money and Finance, Vol.27(5),

pp.780–805.

[92] Tomz, M., and M. L. J. Wright, 2007, “Do Countries Default in “Bad” Times?” Journal of the

European Economic Association, Vol.5(2–3), pp.352–360.

[93] Trebesch, C., and M. Zabel, 2017, “The Output Costs of Hard and Soft Sovereign Default,”

European Economic Review, Vol.92, pp.416–432.

[94] World Bank (WB), 2016a, Global Financial Development Database.

[95] World Bank (WB), 2016b. World Development Indicators.

[96] Yue, V., 2010, “Sovereign Default and Debt Renegotiation,” Journal of International Economics,

Vol.80(2), pp.176–187.

34

Appendices

A Data

A.1 Sample countries

The dataset includes only countries that experienced debt restructurings. Among them, 60 countries experienced

111 episodes of post-default debt restructuring between 1978 and 2010:

Albania, Algeria, Argentina, Bolivia, Bosnia and Herzegovina, Brazil, Bulgaria, Cameroon, Congo Rep.,
Costa Rica, Cote d’Ivoire, Croatia, Cuba, Dominican Republic, Ecuador, Ethiopia, Gabon, the Gam-
bia, Guinea, Guyana, Honduras, Iraq, Jamaica, Jordan, Kenya, Liberia, Macedonia (FYR), Madagas-
car, Malawi, Mauritania, Moldova, Morocco, Mozambique, Nicaragua, Niger, Nigeria, Pakistan, Panama,
Paraguay, Peru, the Philippines, Poland, Romania, Russian Federation, Sao Tome and Principe, Sene-
gal, Serbia and Montenegro, Seychelles, Sierra Leone, Slovenia, Sudan, Tanzania, Togo, Turkey, Uganda,
Venezuela, Vietnam, Yemen, Rep. of, Zaire, and Zambia

26 countries experienced 46 episodes of weakly preemptive debt restructuring:

Argentina, Belize, Brazil, Chile, Dominica, Dominican Republic, Ecuador, Grenada, Jamaica, Nigeria,
Malawi, Mexico, Morocco, Niger, Panama, Peru, the Philippines, Romania, Senegal, South Africa, Trinidad
and Tobago, Turkey, Ukraine, Uruguay, Venezuela, and Yugoslavia

13 countries experienced 23 episodes of strictly preemptive debt restructuring:

Algeria, Chile, Dominican Republic, Jamaica, Mexico, Moldova, Nicaragua, Pakistan, Peru, South Africa,
Ukraine, Uruguay, and Yugoslavia

A.2 Data sources

Variable
• Private external debt restructuring data
• Banking crisis data
• Terms of trade
• Population
• Openness
• Investment
• Real exchange rates
• Real GDP, US-dollar denominated
• Real GDP per capita
• Real GDP growth rates
• Cyclical component of real GDP per capita
• Private credit (real)
• Lending interest rates
• Net capital inﬂows
• Exchange rate regime classiﬁcation
• S&P sovereign default data
• Oﬃcial external (Paris Club) debt restructuring data
• Public debt-to-GDP ratio
• Countries’ credit ratings
• Political stability index (civil liberties)
• Total interest payments-to-GDP ratio
• Short-term interest payments-to-GDP ratio
• Haircuts on private external debt restructurings
• Coerciveness index on private external debt restructurings Enderlein et al. (2012)
• Legislative and executive indices of

Data source
Asonuma and Trebesch (2016)
Laeven and Valencia (2013)
PWT 8.0
WDI (World Bank, 2016a)
Authors’ calculation based on PWT 8.0 data
WDI (World Bank, 2016a)
IMF International Financial Statistics
WDI (World Bank, 2016a)
WDI (World Bank, 2016a)
Authors’ calculation based on WDI (World Bank, 2016a)
Authors’ calculation based on WDI (World Bank, 2016a)
WDI (World Bank, 2016a)
IMF International Financial Statistics
IMF World Economic Outlook
Ilzetzki, Reinhart and Rogoﬀ (2015)
Standard and Poor’s
Das et al. (2012) and Paris Club
World Bank’s Global Financial Development Database
Institutional Investor magazine
FreedomHouse
World Bank’s Global Financial Development Database
World Bank’s Global Financial Development Database
Cruces and Trebesch (2013)

Inter-American Development Bank’s Database
of Political Institutions 2015 (DIP2015)

electoral competitiveness (LIEC and EIEC)

Notes: PWT and WDI stand for the Penn World Table and the World Development Indicators, respectively.

A1

Min
0.16
-5.74
1.66
3.00
-1.15
-0.65
1.03
-248.13
-0.65
-1.84
-63.38

Max
2400
73.49
105.92
95.08
1.24
0.52
316.08
49.16
2.61
10.53
107.41

Min Max
6
4
5
10
18.47
0.64
10.48
7
38
7
7
9
30.03
55.56

0
0
0
0
0.00
0.00
-22.28
1
0
1
1
0
-1.82
-6.66

A.3 Data Statistics

Table A1 presents summary statistics of variables.

Table A1: Summary Statistics

Panel A: Macroeconomic Variables

GDP (billion USD, contant 2010 prices)
Investment-to-GDP ratio (percent)
Private credit-to-GDP ratio (percent)
Lending interest rates (percent)
Net capital inﬂows-to-GDP ratio (percent)
Cyclical component of log of GDP per capita
Public debt-to-GDP ratio (percent)
Current account-to-GDP ratio (percent)
Foreign reserves-to-GDP ratio (percent)
ln(Real exchange rates)
GDP growth rate (percent)

Obs Mean Std. Dev.
251.01
8.39
18.27
13.50
0.15
0.07
47.47
17.60
0.10
2.56
7.08

98.47
22.17
27.47
19.27
0.08
0.00
73.49
-6.62
0.00
3.71
3.66

2,501
2,205
2,345
1,361
2,069
2,159
1,535
1,986
1,949
2,525
2,437

Panel B: Instruments and Other Variables

# of past post-defaults
# of past weakly preemptive
# of past strictly preemptive
# of past banking crises
Total external debt-to-GDP ratio (percent)
Interest payments-to-GDP ratio (percent)
Credit rantings, change
Freedom house index, civil liberty
# of years from the last debt restructurings
LIEC (Political stability index)
EIEC (Political stability index)
Coerciveness index

2,816
2,816
2,816
2,816
2,107
2,107
1,503
2,467
1,791
2,296
2,296
713
Forecastted GDP growth rates, long-run 1,472
Forecastted GDP growth rates, short-run 1,481

Obs Mean Std. Dev.
1.09
0.81
0.68
2.36
1.09
0.03
3.15
1.64
9.20
2.03
2.13
1.98
1.98
2.72

0.87
0.37
0.19
1.56
0.76
0.02
0.24
4.11
14.42
5.54
5.28
1.10
4.71
4.58

A2

Table A2: Debt Restructurings, Private Credit, and Net Capital Inﬂows, 3-Year Horizon

Panel A: Private Credit, 3-Year Cumulative Percentage Point Diﬀerence from Year 0
dC
i,t+3 = 100 × (Crediti,t+3/GDPi,t+3 − Crediti,t/GDPi,t)

Post-default
Weakly preemptive
Strictly preemptive
All obs. from restructuring countries

Obs. Mean
-1.63
0.47
0.31
1.56

73
26
8
1,895

p(5)
-13.67
-8.51
-9.54
-8.13

p(25)
-4.94
-2.62
-2.00
-1.22

p(50)
-1.21
-1.02
-0.28
1.53

p(75)
0.80
4.52
2.52
4.65

p(95)
6.56
8.73
11.51
12.17

Panel B: Net Capital Inﬂows, Sum of Cumulative Percentage Point Diﬀerences from Year 0 to Year 3

dN CI,SUM
i,t+3

= 100 × P3

Post-default
Weakly preemptive
Strictly preemptive
All obs. from restructuring countries

s=1(N CIi,t+s/GDPi,t+s − N CIi,t/GDPi,t)
p(50)
p(5)
-4.35
-77.06
-0.94
-53.12
6.49
-31.33
0.83
-51.47

Obs. Mean
-10.18
-4.80
11.02
1.48

p(25)
-31.54
-16.71
1.90
-13.40

77
26
10
1,625

p(75)
9.78
11.42
17.14
15.69

p(95)
41.41
29.59
91.91
53.76

Panel C: Net Capital Inﬂows, 3-Year Cumulative Percentage Point Diﬀerence from Year 0

dN CI,DIF
i,t+3

= 100 × (N CIi,t+3/GDPi,t+3 − N CIi,t/GDPi,t)

Post-default
Weakly preemptive
Strictly preemptive
All obs. from restructuring countries

Obs. Mean
-2.42
0.10
6.04
0.53

77
26
10
1,625

p(5)
-31.50
-23.76
-13.95
-22.00

p(25)
-8.77
-8.53
-0.01
-5.90

p(50)
-1.88
1.35
5.49
0.48

p(75)
3.78
6.94
8.12
6.81

p(95)
16.83
26.25
38.65
23.33

Notes: The table shows the number of observations (Obs.), mean, 5th percentile p(5), 25th percentile p(25), 50th
percentile p(50), 75th percentile p(75), and 95th percentile p(95) of the 3-year cumulative percentage point diﬀerences
in the credit-to-GDP ratio (Panel A) and in the net capital inﬂows-to-GDP ratio from year 0 (Panel C), and the sum of
cumulative percentage point diﬀerences in the net capital inﬂows-to-GDP ratio over 3 years from year 0 (Panel B). All
observations from restructuring countries reported in the bottom of the tables are from 1970–2007 where the data after
the 2008–09 Global Financial Crisis are dropped.

Table A3: Debt Restructurings, Private Credit, and Net Capital Inﬂows, 5-Year Horizon

Panel A: Private Credit, 5-Year Cumulative Percentage Point Diﬀerence from Year 0
dC
i,t+5 = 100 × (Crediti,t+5/GDPi,t+5 − Crediti,t/GDPi,t)

Post-default
Weakly preemptive
Strictly preemptive
All obs. from restructuring countries

Obs. Mean
-1.72
2.75
1.11
2.74

73
26
9
1,888

p(5)
-14.79
-10.04
-13.72
-9.78

p(25)
-6.63
-4.15
-1.48
-1.65

p(50)
-2.35
0.23
2.00
2.54

p(75)
1.99
10.86
2.63
7.21

p(95)
10.15
19.82
12.92
17.19

Panel B: Net Capital Inﬂows, Sum of Cumulative Percentage Point Diﬀerences from Year 0 to Year 5

dN CI,SUM
i,t+5

= 100 × P5

Post-default
Weakly preemptive
Strictly preemptive
All obs. from restructuring countries

s=1(N CIi,t+s/GDPi,t+s − N CIi,t/GDPi,t)
p(50)
p(5)
Obs. Mean
-8.22
-123.09
-16.72
-0.33
-88.06
-4.34
24.34
-45.85
31.56
1.70
-83.49
2.34

p(25)
-42.56
-25.93
7.65
-22.48

73
26
10
1,599

p(75)
14.77
32.13
39.67
27.13

p(95)
70.11
62.27
185.32
94.16

Panel C: Net Capital Inﬂows, 5-Year Cumulative Percentage Point Diﬀerence from Year 0

dN CI,DIF
i,t+5

= 100 × (N CIi,t+5/GDPi,t+5 − N CIi,t/GDPi,t)

Post-default
Weakly preemptive
Strictly preemptive
All obs. from restructuring countries

Obs. Mean
-3.97
0.98
11.54
0.49

73
26
10
1,599

p(5)
-31.03
-22.05
-5.32
-24.13

p(25)
-12.26
-7.00
2.18
-7.05

p(50)
-3.21
0.98
8.59
0.57

p(75)
5.83
9.02
18.12
7.77

p(95)
19.00
18.84
44.88
25.96

Notes: The table shows the same variables as Table A2 for 5-year horizon. See the notes for Table A2.

A3

A.4 Severe Credit Declines and Severe Protracted Net Capital Inﬂow Declines

Table A4: Dataset, 5-year Horizon

Panel A: Debt Restructuring Sample

# of episodes
# of countries
Average duration (years)
Average haircuts (percent)

Post-default
111
60
5.0
48.0

Representative episodes Argentina 2001-05,
Russia 1998-2000

in 1999-2010

Weakly preemptive
45
26
1.1
18.2
Ukraine
(Global Exch.) 2000,
Belize 2006-07

Strictly preemptive
23
13
0.9
18.9
Pakistan
(Ext. bonds) 1999,
Uruguay 2003

# of available episodes
# of banking crises
Conditional probability
Representative episodes
in 1999–2010

# of available episodes
# of severe credit declines
Conditional probability
Representative episodes

Panel B: Banking Crisis Sample 2/

All debt
restructurings
127
39
30.7%

Post-
default
91
30
33.0%

Weakly
preemptive
39
7
26.9%

Strictly
preemptive
21
2
20.0%

All
obs.1/
1,126
71
6.3%

Argentina 2001–05, Turkey 1981 Algeria 1990
Ukraine 1998
Russia 1998–2000

Niger 1983

Panel C: Severe Credit Declines 3/ 6/

All debt
restructurings
108
49
45.4%

Post-
default
73
39
53.4%
Turkey 1976
Togo 1991

Weakly
preemptive
26
8
30.8%
Brazil 1983

Strictly
preemptive
9
2
25.0%
Algeria 1990
Venezuela 1986 Uruguay 2003

All
obs.1/
1,427
314
22.0%

Panel D: Severe Protracted Net Capital Inﬂow Declines (Sum) 4/ 6/

# of available episodes
# of severe net cap. inf. dec.
Conditional probability
Representative episodes

All debt
restructurings
109
35
32.1%

Post-
default
73
25
34.2%

Weakly
preemptive
26
9
34.6%

Strictly
preemptive
10
1
10.0%

All
obs.1/
1,161
302
26.1%

Costa Rica 1981 Brazil 1982 Ukraine 1998

Moldova 2001

Belize 2006

Panel E: Severe Protracted Net Capital Inﬂow Declines (Diﬀerence) 5/ 6/

# of available episodes
# of severe net cap. inf. dec.
Conditional probability

All debt
restructurings
109
32
29.4%

Post-
default
73
26
35.6%

Weakly
preemptive
26
6
23.1%

Strictly
preemptive
10
0
0%

All
obs.1/
1,161
303
26.1%

Notes: 1/ Including episodes in countries without any experience of private external debt restructurings.
2/ Banking crises over ﬁve years since the start of debt restructurings, year from t + 1 to t + 5.
3/ Severe credit declines are deﬁned as observations where the cumulative percentage point diﬀerence in the credit-to-
GDP ratio over ﬁve years, dC
i,t+5 = 100 × (Crediti,t+5/GDPi,t+5 − Crediti,t/GDPi,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 ﬁve years, dN CI,SUM
s=1(N CIi,t+s/GDPi,t+s −
N CIi,t/GDPi,t), is less than the 25th percentile of the distribution.
5/ Severe protracted net capital inﬂow declines (diﬀerence) are deﬁned as observations where the sum of cumulative per-
centage point diﬀerences in the net capital inﬂows-to-GDP ratio over ﬁve years, dN CI,DIF
= 100×(N CIi,t+5/GDPi,t+3 −
N CIi,t/GDPi,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

= 100 × P5

i,t+5

i,t+5

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.

A4

Figure A1: Debt Restructurings, Severe Credit and Net Capital Inﬂow Declines, 5-year
Horizon

Notes: Panel charts show the probability of banking crises (Panel A), severe credit declines (Panel B), and severe pro-

tracted net capital inﬂow declines (Panels C and D), respectively.

Panel B: Severe credit declines are deﬁned as observations where the cumulative percentage point diﬀerence in the credit-
to-GDP ratio over ﬁve years, dC
i,t+5 = 100 × (Crediti,t+5/GDPi,t+5 − Crediti,t/GDPi,t), is less than the 25th percentile
of the distribution.

Panel C: Severe protracted net capital
mulative percentage point diﬀerences in the net capital inﬂows-to-GDP ratio over ﬁve years, dN CI,SUM
P5

s=1(N CIi,t+s/GDPi,t+s − N CIi,t/GDPi,t), is less than the 25th percentile of the distribution.

inﬂow declines (sum) are deﬁned as observations where the sum of cu-

= 100 ×

i,t+5

Panel D: Severe protracted net capital inﬂow declines (diﬀerence) are deﬁned as observations where the cumulative per-
centage point diﬀerence in the net capital inﬂows-to-GDP ratio over ﬁve years, dN CI,DIF
= 100 × (N CIi,t+5/GDPi,t+5 −
N CIi,t/GDPi,t), is less than the 25th percentile of the distribution.
The sample after 2008 is dropped because macroeconomic variables in the period are largely inﬂuenced by the Global

i,t+5

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.

A5

0.2.4.6ProbabilityPanel A: Banking Crises0.2.4.6Panel B: Severe Credit DeclinesPost−defaultWeakly preemptiveStrictly preemptiveNon restructuring time0.2.4.6Inflow Declines (Sum)Panel C: Severe Net Capital0.2.4.6Inflow Declines (Difference)Panel D: Severe Net CapitalB Further Empirical Analysis — Support for Stylized Facts

We explore whether GDP and investment are tightly associated during debt crisis periods i.e.,
debt restructuring periods than non-restructuring periods (normal times). For this purpose, we apply
an ordinary least square (OLS) estimator on GDP growth rates with contemporaneous investment
growth rates in diﬀerent samples: non-restructuring samples (both all countries and countries that
experienced at least one debt restructuring), samples of post-default, weakly preemptive, and strictly
preemptive restructurings.

Table B1 shows that both estimated coeﬃcients and explanatory power of investment growth
rates (expressed by R2) are higher in debt restructuring periods (post-default, weakly preemptive and
strictly preemptive) than those in non-restructuring periods. Moreover, these are higher than those
in the sample of all countries. This clearly shows that GDP growth is tightly linked with investment
growth (severely inﬂuenced by a decline in investment growth) during debt restructuring periods.

Table B1: Correlation between GDP and Investment Growth Rates

Dep. Var. = 100 × (GDPi,t+1 − GDPi,t)/GDPi,t
Observations without
debt restructurings

Observations with debt restructurings

Countries that During post- During post-
default
experienced at
(the entire
least one debt
period)
restructuring

default
(the ﬁrst half
period)

During
Weakly
preemptive

During
Strictly
preemptive

0.377***
(0.137)
3.459***
(0.180)
Yes
0.189
1,407

0.736***
(0.0940)
1.963***
(0.272)
Yes
0.324
297

0.768***
(0.0762)
1.340***
(0.378)
Yes
0.353
187

0.763***
(0.176)
2.384***
(0.348)
Yes
0.410
65

0.486**
(0.182)
2.286***
(0.624)
Yes
0.321
18

All
countries

0.431***
(0.074)
3.483***
(0.097)
Yes
0.264
4,230

Investment growth rates 1/

Constant

Country ﬁxed eﬀects
R-squared
# of obs.

Notes: Standard errors are in parentheses. ***, **, and * indicate the statistical signiﬁcance at 1%, 5%, and 10% level,
respectively.
1/ 100 × (Investmenti,t+1 − Investmenti,t)/GDPi,t.

A6

C Estimation Results from Local Projections

C.1 Ordinary Least Square (OLS) Estimator

Figure C1: Local Projections, OLS

Notes: The ﬁgure shows local projections of the variables shown in each panel for h = 1, 2,.., 5, where h in-
dicates horizon. Dependent variables are as follows. Panel A: 100 × (GDPi,t+h − GDPi,t)/GDPi,t, Panel B:
100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t), Panel C: 100 × (Crediti,t+h/GDPi,t+h − Crediti,t/GDPi,t),
Panel D: 100 × (LendingRatei,t+h − LendingRatei,t), and Panel E: 100 × (N CIi,t+h/GDPi,t+h − N CIi,t/GDPi,t). Solid
lines in red, blue, and green are point estimates. Gray bands and dotted lines in blue and green are 95% conﬁdence

intervals. The sample period is 1970–2013.

A7

Post−defaultWeakly preemptiveStrictly preemptive−6−3036Cumulative rate of change from year 0012345YearsPanel A: GDPPost−defaultWeakly preemptiveStrictly preemptive−6−3036Cumulative percentage point diff. from year 0012345YearsPanel B: InvestmentPost−defaultWeakly preemptiveStrictly preemptive−8−404Cumulative percentage point diff. from year 0012345YearsPanel C: Private CreditPost−defaultWeakly preemptiveStrictly preemptive−200204060Cumulative percentage point diff. from year 0012345YearsPanel D: Lending Interest RatesPost−defaultWeakly preemptiveStrictly preemptive−10−5051015Cumulative percentage point diff. from year 0012345YearsPanel E: Net Capital InflowsTable C1: Estimation Results, OLS

Panel A: Dep. Var. = 100 × (GDPi,t+h − GDPi,t)/GDPi,t

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of obs.
# of countries
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-1.930***
(0.707)
-1.741**
(0.665)
-1.790***
(0.594)
0.412
2,044
59
65
31
11

-3.047***
(0.777)
-1.909**
(0.895)
-1.273
(1.089)
0.622
2,044
59
65
31
11

-3.477***
(0.672)
-1.322
(1.218)
-0.773
(1.469)
0.701
1,985
59
65
31
11

-3.579***
(0.916)
-1.375
(1.683)
-0.923
(1.539)
0.702
1,926
59
65
31
11

-3.605**
(1.495)
-3.036
(1.853)
-1.359
(2.221)
0.664
1,867
59
65
31
11

-15.10***
(2.874)
-9.197*
(4.914)
-5.862
(4.002)
0.768
1,867
59
65
31
11

Panel B: Dep. Var. = 100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t)

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of countries
# of obs.
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-2.196**
(0.847)
-1.726**
(0.690)
-0.639
(0.412)
0.054
55
1,757
56
28
11

-3.669***
(1.035)
-2.557**
(1.064)
0.702
(0.992)
0.138
55
1,757
56
28
11

-2.314**
(1.013)
-2.045**
(0.949)
2.086*
(1.096)
0.179
55
1,702
56
28
11

-1.856*
(0.933)
-1.950**
(0.927)
2.166*
(1.173)
0.191
55
1,647
56
28
11

-2.618**
(1.024)
-2.620**
(1.290)
1.822*
(0.984)
0.199
55
1,592
56
28
11

-12.35***
(4.064)
-11.03**
(4.201)
6.055
(3.783)
0.235
55
1,592
56
28
11

Panel C: Dep. Var. = 100 × (Crediti,t+h/GDPi,t+h − Crediti,t/GDPi,t)
h = 5

h = 1

h = 4

h = 3

h = 2

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of obs.
# of countries
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

-0.0660
(0.370)
-0.151
(0.460)
0.287
(0.341)
0.073
1,855
59
57
31
10

-1.956***
(0.527)
-1.506**
(0.670)
-0.865**
(0.425)
0.049
1,848
59
56
31
10

-2.800***
(0.653)
-2.083**
(1.005)
-0.906
(1.578)
0.038
1,787
59
56
31
9

-3.252***
(0.857)
-2.472*
(1.409)
-0.829
(2.344)
0.029
1,728
59
56
31
10

-3.832***
(0.805)
-2.959*
(1.579)
-1.225
(3.447)
0.026
1,669
58
56
31
10

Sum

-12.06***
(2.949)
-9.340**
(4.558)
-3.327
(8.296)
0.046
1,646
58
56
31
9

Notes: The table shows local projections (OLS) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1 and lag 2), cyclical component of log
of GDP obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust
standard errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%,
5%, and 10% level, respectively.

A8

Panel D: Dep. Var. = 100 × (LendingRatei,t+h − LendingRatei,t)

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of obs.
# of countries
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

h = 1

4.469
(3.450)
12.78
(9.036)
5.681
(4.024)
0.120
1,031
52
24
15
9

h = 2

10.09
(7.149)
19.90
(17.52)
10.86
(9.990)
0.226
1,018
51
24
15
9

h = 3

9.905
(5.966)
17.81
(14.21)
6.273
(6.427)
0.498
965
51
23
15
9

h = 4

9.449
(6.348)
20.87
(15.67)
1.970
(5.410)
0.519
914
51
23
14
9

h = 5

11.12*
(6.571)
24.74
(16.58)
0.401
(6.446)
0.524
864
51
23
14
9

Panel E: Dep. Var. = 100 × (N CIi,t+h/GDPi,t+h − N CIi,t/GDPi,t)
h = 5
h = 2

h = 4

h = 1

h = 3

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of obs.
# of countries
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

-1.934
(1.225)
-6.394***
(1.597)
1.412
(1.326)
0.165
1,662
58
62
31
10

-6.094***
(2.220)
-5.142**
(2.234)
3.215
(3.094)
0.234
1,651
58
62
31
10

-0.387
(2.435)
-3.443
(2.387)
5.149**
(2.194)
0.214
1,588
58
62
31
10

-1.197
(1.781)
-4.671*
(2.537)
4.438*
(2.382)
0.187
1,527
58
62
31
10

-3.898***
(1.307)
-4.427
(3.254)
9.384***
(1.842)
0.192
1,466
58
60
31
10

Sum

44.56
(29.61)
117.8
(87.96)
29.90
(31.73)
0.419
847
51
23
14
9

Sum

-13.78***
(5.108)
-22.87**
(9.941)
23.07***
(3.756)
0.328
1,454
58
60
31
10

Notes: The table shows local projections (OLS) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1 and lag 2), cyclical component of log
of GDP obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust
standard errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%,
5%, and 10% level, respectively.

A9

C.2 Augmented Inverse Probability Weighted Estimator

Table C2: Estimation Results, AIPW

Panel A: Dep. Var. = 100 × (GDPi,t+h − GDPi,t)/GDPi,t

Post-default

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive

# of observations
# of countries
# of debt restructuring obs.
Strictly preemptive

# of observations
# of countries
# of debt restructuring obs.

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-2.486***
(0.314)
825
29
44
-2.270***
(0.340)
371
14
21
-1.908***
(0.276)
287
7
11

-3.141***
(0.461)
825
29
44
-2.602***
(0.453)
371
14
21
-1.069**
(0.434)
287
7
11

-3.777***
(0.479)
825
29
44
-1.557***
(0.455)
371
14
21
1.237
(0.887)
280
7
11

-2.422***
(0.607)
825
29
44
-2.591***
(0.632)
371
14
21
2.197*
(1.218)
273
7
11

-5.178***
(0.966)
796
29
44
-4.147***
(0.841)
357
14
21
3.124*
(1.870)
266
7
11

-16.21***
(2.237)
796
29
44
-12.81***
(2.116)
357
14
21
3.921
(4.028)
266
7
11

Panel B: Dep. Var. = 100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t)

Post-default

# of observations
# of countries
# of debt restructuring obs.
Weakly preemptive

# of observations
# of countries
# of debt restructuring obs.
Strictly preemptive

# of observations
# of countries
# of debt restructuring obs.

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-1.930***
(0.396)
737
26
40
-0.211
(0.338)
343
13
20
-0.230
(0.470)
287
7
11

-2.317***
(0.433)
737
26
40
-0.386
(0.447)
343
13
20
0.097
(0.530)
287
7
11

-0.798*
(0.430)
737
26
40
0.180
(0.387)
343
13
20
1.810***
(0.488)
280
7
11

-0.186
(0.578)
737
26
40
-0.664
(0.420)
343
13
20
2.333***
(0.528)
273
7
11

-0.679
(0.686)
711
26
40
-1.931***
(0.482)
330
13
20
1.425***
(0.470)
266
7
11

-5.714**
(2.301)
711
26
40
-2.726
(1.814)
330
13
20
5.628***
(1.837)
266
7
11

Notes: The table shows local projections (AIPW) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively.

A10

Panel C: Dep. Var. = 100 × (Crediti,t+h/GDPi,t+h − Crediti,t/GDPi,t)
h = 5

h = 1

h = 3

h = 2

h = 4

Post-default

# of observations
# of countries
# of debt restructuring obs.
Weakly preemptive

# of observations
# of countries
# of debt restructuring obs.
Strictly preemptive

# of observations
# of countries
# of debt restructuring obs.

-0.230
(0.217)
752
27
41
0.136
(0.192)
371
14
21
0.881***
(0.331)
284
7
10

-2.038***
(0.347)
750
27
40
-1.157***
(0.276)
371
14
21
0.433
(0.505)
284
7
10

-2.420***
(0.437)
748
27
40
-1.617***
(0.358)
371
14
21
0.275
(0.508)
277
7
9

-2.381***
(0.516)
747
27
40
-1.824***
(0.471)
371
14
21
-0.452
(0.611)
270
7
10

-2.563***
(0.588)
720
27
40
-2.418***
(0.550)
357
14
21
-1.733**
(0.736)
263
7
10

Sum

-9.883***
(1.897)
716
27
40
-7.375***
(1.590)
357
14
21
0.715
(2.131)
259
7
9

Panel D: Dep. Var. = 100 × (LendingRatei,t+h − LendingRatei,t)

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

Post-default

# of observations
# of countries
# of debt restructuring obs.
Weakly & strictly pre.

# of observations
# of countries
# of debt restructuring obs.

2.633***
(0.410)
356
15
22
1.507
(1.357)
135
7
2

7.469***
(0.692)
350
15
22
0.997
(1.671)
133
7
2

9.323***
(0.893)
345
14
21
5.140***
(1.914)
132
7
2

7.412***
(0.906)
340
14
21
5.829***
(1.985)
130
7
2

10.59***
(1.137)
325
14
21
5.233**
(2.100)
122
7
2

Panel E: Dep. Var. = 100 × (N CIi,t+h/GDPi,t+h − N CIi,t/GDPi,t)
h = 5
h = 2

h = 3

h = 1

h = 4

Post-default

# of observations
# of countries
# of debt restructuring obs.
Weakly preemptive

# of observations
# of countries
# of debt restructuring obs.
Strictly preemptive

# of observations
# of countries
# of debt restructuring obs.

-1.365
(1.056)
792
29
44
-3.753***
(0.782)
360
14
21
0.572
(1.152)
251
7
10

-4.904***
(1.326)
787
29
44
-1.112
(0.925)
358
14
21
0.169
(1.297)
251
7
10

0.034
(1.218)
783
29
44
0.627
(0.933)
357
14
21
2.041
(1.594)
244
7
10

-1.926*
(1.024)
778
29
44
-2.060*
(1.069)
357
14
21
2.597**
(1.281)
237
7
10

-2.972***
(1.017)
747
29
43
-2.024*
(1.151)
343
14
21
7.287***
(1.607)
230
7
10

38.20***
(3.475)
320
14
21
19.20**
(9.185)
121
7
2

Sum

-10.93***
(4.078)
744
29
43
-7.759**
(3.950)
338
14
21
12.71**
(5.784)
230
7
10

Notes: The table shows local projections (AIPW) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively.

A11

D Further Regression Results - Support for Main Results

D.1 Capital Inﬂow-Credit Channel

Figure D1: Restructurings with/without Severe Sudden Stops, OLS

Panel A: Private Credit

Panel B: Investment

Panel C: GDP

Notes: Severe sudden stops are deﬁned as observations where the percentage point diﬀerence in the net capital inﬂows-to-
GDP ratio from year 0 to year 1, dN CI
i,t+1 = 100 × (N CIi,t+1/GDPi,t+1 − N CIi,t/GDPi,t), is less than the 25th percentile
among the observations experiencing that type of restructurings for Panel A. The 50th percentile cutoﬀ for Panels B and

C in order to have enough number of observations for both groups. The ﬁgure shows AIPW estimates of the variable

shown in each panel for h = 1, 2, .., 5, where h indicates horizon. Solid lines in brown and purple are point estimates.

Gray bands and dotted lines in brown are 95% conﬁdence intervals. The sample period is 1970–2013.

A12

with Sudden Stopswithout Sudden Stops−70−60−50−40−30−20−10010Cumulative rate of change from year 0012345YearsPost−defaultwith Sudden Stopswithout Sudden Stops−70−60−50−40−30−20−100100123YearsWeakly preemptivewith Sudden Stopswithout Sudden Stops−70−60−50−40−30−20−100100123YearsWeakly & Strictly preemptivewith Sudden Stopswithout Sudden Stops−8−6−4−202Cumulative percentage point diff. from year 0012345YearsPost−defaultwith Sudden Stopswithout Sudden Stops−8−6−4−2020123YearsWeakly preemptivewith Sudden Stopswithout Sudden Stops−8−6−4−2020123YearsWeakly & Strictly preemptivewith Sudden Stopswithout Sudden Stops−12−8−40Cumulative rate of change from year 0012345YearsPost−defaultwith Sudden Stopswithout Sudden Stops−12−8−400123YearsWeakly preemptivewith Sudden Stopswithout Sudden Stops−12−8−400123YearsWeakly & Strictly preemptiveTable D1: Estimation Results, Restructurings with Severe Sudden Stops, OLS

Panel A: Private Credit
Dep. Var. = 100 × (Crediti,t+h − Crediti,t)/Crediti,t

Post-default, with sudden stops

Post-default, without sudden stops

Weakly preemptive, with sudden stops

Weakly preemptive, without sudden stops

R-squared
# of observations
# of countries
# of post-default rest. with sudden stops
# of post-default rest. without sudden stops
# of weakly pre. rest. with sudden stops
# of weakly pre. rest. without sudden stops

Weakly & strictly pre., with sudden stops

Weakly & strictly pre., without sudden stops

R-squared
# of observations
# of countries
# of preemptive rest. with sudden stops
# of preemptive rest. without sudden stops

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-4.027
(2.477)
-2.879
(3.286)
-8.771**
(4.152)
-3.769
(2.511)
0.079
1,904
59
14
43
8
23

-8.285**
(1.984)
-3.563*
(0.272)
0.079
1,904
59
10
31

-18.19***
(5.333)
-14.39***
(4.270)
-16.11**
(6.244)
-6.556
(5.070)
0.039
1,898
59
14
42
8
23

-16.50***
(3.840)
-6.581*
(0.520)
0.039
1,898
59
10
31

-27.21***
(6.966)
-20.10***
(5.469)
-18.13**
(8.629)
-8.080
(7.187)
0.028
1,836
59
14
42
8
23

-19.77**
(5.719)
-7.624
(0.871)
0.028
1,836
59
10
30

-31.52***
(10.05)
-21.39***
(7.879)
-13.78
(11.83)
-10.81
(10.44)
0.029
1,775
59
14
42
8
23

-20.68*
(8.046)
-8.991
(1.261)
0.030
1,775
59
10
31

-37.39**
(14.87)
-26.45***
(8.881)
-23.03*
(12.01)
-14.29
(15.20)
0.039
1,716
59
14
42
8
23

-34.59**
(11.59)
-11.05
(1.554)
0.040
1,716
59
10
31

-122.7***
(34.32)
-85.97***
(28.12)
-77.32*
(38.82)
-49.37
(35.83)
0.049
1,692
59
14
42
8
23

-100.3**
(28.55)
-42.37
(4.156)
0.050
1,692
59
10
30

Panel B: Investment
Dep. Var. = 100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t)

Post-default, with sudden stops

Post-default, without sudden stops

Weakly preemptive, with sudden stops

Weakly preemptive, without sudden stops

R-squared
# of observations
# of countries
# of post-default rest. with sudden stops
# of post-default rest. without sudden stops
# of weakly pre. rest. with sudden stops
# of weakly pre. rest. without sudden stops

Weakly & strictly pre., with sudden stops

Weakly & strictly pre., without sudden stops

R-squared
# of observations
# of countries
# of preemptive rest. with sudden stops
# of preemptive rest. without sudden stops

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-4.376***
(1.455)
-0.190
(0.899)
-3.284***
(0.752)
0.193
(0.822)
0.051
1,812
56
25
33
14
14

-2.897***
(0.530)
0.097
(0.578)
0.051
1,812
56
18
21

-5.180***
(1.406)
-1.989
(1.240)
-4.714***
(1.417)
0.0661
(0.892)
0.119
1,812
56
25
33
14
14

-3.649***
(1.355)
0.356
(0.740)
0.118
1,812
56
18
21

-4.458***
(1.066)
-0.489
(1.440)
-4.001***
(1.038)
0.162
(0.953)
0.168
1,756
55
25
33
14
14

-2.781**
(1.135)
0.933
(0.846)
0.167
1,756
55
18
21

-1.913
(1.261)
-1.515
(1.130)
-3.276***
(1.034)
-0.346
(1.165)
0.178
1,701
55
25
33
14
14

-2.568***
(0.905)
0.941
(0.995)
0.178
1,701
55
18
21

-2.327
(1.455)
-2.469*
(1.235)
-3.721***
(1.249)
-0.982
(1.910)
0.177
1,646
55
25
33
14
14

-2.435*
(1.334)
-0.0167
(1.308)
0.176
1,646
55
18
21

-18.43***
(5.403)
-5.937
(5.170)
-19.18***
(4.253)
-0.907
(4.556)
0.211
1,646
55
25
33
14
14

-14.53***
(4.389)
2.335
(3.540)
0.210
1,646
55
18
21

Notes: The table shows local projections (OLS) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively.

A13

Panel C: GDP
Dep. Var. = 100 × (GDPi,t+h − GDPi,t)/GDPi,t

Post-default, with sudden stops

Post-default, without sudden stops

Weakly preemptive, with sudden stops

Weakly preemptive, without sudden stops

R-squared
# of observations
# of countries
# of post-default rest. with sudden stops
# of post-default rest. without sudden stops
# of weakly pre. rest. with sudden stops
# of weakly pre. rest. without sudden stops

Weakly & strictly pre., with sudden stops

Weakly & strictly pre., without sudden stops

R-squared
# of observations
# of countries
# of preemptive rest. with sudden stops
# of preemptive rest. without sudden stops

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-3.496***
(1.016)
-1.382
(1.066)
-3.024***
(0.850)
-1.572*
(0.925)
0.324
2,102
59
29
38
16
15

-2.259***
(0.829)
-2.286***
(0.681)
0.324
2,102
59
21
21

-4.940***
(1.128)
-2.804**
(1.344)
-3.809**
(1.479)
-2.064**
(0.959)
0.497
2,102
59
29
38
16
15

-3.152**
(1.302)
-2.109**
(0.818)
0.497
2,102
59
21
21

-5.669***
(1.110)
-3.261**
(1.307)
-1.787
(1.389)
-3.519**
(1.577)
0.597
2,043
59
29
38
16
15

-2.286*
(1.172)
-2.148
(1.466)
0.597
2,043
59
21
21

-5.061***
(1.594)
-4.393***
(1.575)
-0.337
(1.957)
-5.762***
(1.843)
0.620
1,984
59
29
38
16
15

-0.996
(1.658)
-4.052**
(1.778)
0.620
1,984
59
21
21

-5.156**
(2.192)
-4.509**
(2.110)
-1.676
(2.180)
-7.853***
(2.269)
0.601
1,925
59
29
38
16
15

-1.523
(1.823)
-6.187***
(2.295)
0.601
1,925
59
21
21

-23.63***
(4.670)
-15.76***
(5.754)
-10.66
(6.471)
-20.54***
(5.807)
0.662
1,925
59
29
38
16
15

-10.31*
(5.245)
-16.44***
(5.565)
0.662
1,925
59
21
21

Notes: The table shows local projections (OLS) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively.

A14

Table D2: Estimation Results, Restructurings with Severe Sudden Stops, AIPW, Two-
type Model

Panel A: Private Credit
Dep. Var. = 100 × (Crediti,t+h − Crediti,t)/Crediti,t

Post-default, with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Post-default, without sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive, with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive, without sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly pre., with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly pre., without sudden stops

# of obs.
# of countries
# of debt restructuring obs.

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-4.012***
(0.721)
752
27
10
-3.403***
(0.863)
752
27
31
0.081
(0.840)
371
14
5
-3.918***
(0.946)
371
14
16
-8.986***
(0.640)
894
23
10
-5.330***
(0.960)
894
23
31

-22.70***
(1.172)
750
27
10
-11.27***
(1.460)
750
27
30
-4.583***
(1.514)
371
14
5
-4.471***
(1.608)
371
14
16
-14.06***
(1.044)
893
23
10
-7.823***
(1.353)
893
23
31

-29.11***
(1.506)
748
27
10
-12.70***
(2.041)
748
27
30
-7.017***
(2.025)
371
14
5
-1.784
(2.230)
371
14
16
-12.96***
(1.516)
870
23
10
-9.510***
(1.980)
870
23
30

-32.33***
(1.812)
747
27
10
-6.577**
(3.192)
747
27
30
0.361
(2.504)
371
14
5
0.263
(2.940)
371
14
16
-8.786***
(2.145)
847
23
10
-13.811***
(2.638)
847
23
31

-37.57***
(2.257)
720
27
10
-8.876**
(4.292)
720
27
30
-7.893***
(2.938)
357
14
5
-1.038
(3.855)
357
14
16
-17.66***
(2.522)
825
23
10
-22.09***
(3.385)
825
23
31

-127.8***
(6.919)
716
27
10
-42.83***
(10.847)
716
27
30
-23.48***
(8.938)
357
14
5
-13.62
(10.516)
357
14
16
-63.46***
(7.332)
818
23
10
-60.38***
(9.577)
818
23
30

Notes: All regressions include a constant term, the lagged dependent variable, cyclical component of log of GDP obtained
from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects.

A15

Panel B: Investment
Dep. Var. = 100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t)

Post-default, with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Post-default, without sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive, with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive, without sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly pre., with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly pre., without sudden stops

# of obs.
# of countries
# of debt restructuring obs.

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-4.072***
(0.321)
737
26
16
0.060
(0.235)
737
26
24
-3.059***
(0.197)
343
13
8
1.596***
(0.277)
343
13
12
-3.121***
(0.205)
791
20
18
0.422
(0.259)
791
20
21

-3.607***
(0.380)
737
26
16
-1.354***
(0.315)
737
26
24
-4.517***
(0.267)
343
13
8
2.342***
(0.353)
343
13
12
-4.967***
(0.385)
791
20
18
1.238***
(0.343)
791
20
21

-2.156***
(0.369)
737
26
16
0.338
(0.309)
737
26
24
-2.861***
(0.311)
343
13
8
2.134***
(0.311)
343
13
12
-3.006***
(0.292)
771
20
18
1.863***
(0.372)
771
20
21

-0.434
(0.531)
737
26
16
-0.037
(0.363)
737
26
24
-3.504***
(0.312)
343
13
8
1.216***
(0.368)
343
13
12
-3.281***
(0.298)
751
20
18
1.501***
(0.415)
751
20
21

-0.401
(0.622)
711
26
16
-0.932**
(0.429)
711
26
24
-4.084***
(0.341)
330
13
8
-0.427
(0.446)
330
13
12
-4.015***
(0.317)
731
20
18
0.292
(0.412)
731
20
21

-10.70***
(2.076)
711
26
16
-1.565
(1.453)
711
26
24
-18.04***
(1.170)
330
13
8
7.278***
(1.475)
330
13
12
-18.44***
(1.281)
731
20
18
5.391***
(1.472)
731
20
21

Notes: All regressions include a constant term, the lagged dependent variable, cyclical component of log of GDP obtained
from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects.

A16

Panel C: GDP
Dep. Var. = 100 × (GDPi,t+h − GDPi,t)/GDPi,t

Post-default, with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Post-default, without sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive, with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive, without sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly pre., with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly pre., without sudden stops

# of obs.
# of countries
# of debt restructuring obs.

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-4.049***
(0.243)
825
29
17
-1.371***
(0.231)
825
29
27
-3.149***
(0.304)
371
14
9
-1.667***
(0.278)
371
14
12
-2.347***
(0.222)
910
23
21
-2.816***
(0.251)
910
23
21

-4.839***
(0.303)
825
29
17
-2.412***
(0.388)
825
29
27
-3.583***
(0.406)
371
14
9
-1.802***
(0.361)
371
14
12
-3.810***
(0.420)
910
23
21
-1.672***
(0.296)
910
23
21

-5.462***
(0.332)
825
29
17
-2.718***
(0.422)
825
29
27
-2.259***
(0.419)
371
14
9
-1.152***
(0.433)
371
14
12
-3.299***
(0.337)
887
23
21
-0.177
(0.450)
887
23
21

-4.845***
(0.446)
825
29
17
-0.710
(0.482)
825
29
27
-0.188
(0.580)
371
14
9
-4.718***
(0.518)
371
14
12
-1.730***
(0.393)
864
23
21
-2.269***
(0.653)
864
23
21

-6.507***
(0.472)
796
29
17
-3.727***
(0.810)
796
29
27
1.174*
(0.665)
357
14
9
-7.958***
(0.647)
357
14
12
-2.974***
(0.475)
841
23
21
-5.212***
(0.893)
841
23
21

-24.89***
(1.421)
796
29
17
-10.17***
(1.884)
796
29
27
-7.849***
(1.893)
357
14
9
-16.67***
(1.720)
357
14
12
-14.25***
(1.409)
841
23
21
-11.78***
(2.116)
841
23
21

Notes: All regressions include a constant term, the lagged dependent variable, cyclical component of log of GDP obtained
from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects.

A17

Figure D2: Post-Default Debt Restructurings with/without Severe Sudden Stops,
AIPW, One-type Model

Panel I: Post-default

Notes: Severe sudden stops are deﬁned as observations where the percentage point diﬀerence in the net capital inﬂows-to-
GDP ratio from year 0 to year 1, dN CI
i,t+1 = 100 × (N CIi,t+1/GDPi,t+1 − N CIi,t/GDPi,t), is less than the 25th percentile
among the observations experiencing that type of restructurings. The ﬁgure shows AIPW estimates of the variable shown

in each panel for h = 1, 2, .., 5, where h indicates horizon. Solid lines in brown and purple are point estimates. Gray

bands and dotted lines in brown are 95% conﬁdence intervals. The sample period is 1970–2013.

A18

with Sudden Stopswithout Sudden Stops−40−2002040Cumulative rate of change from year 0012345YearsPanel A: Private Creditwith Sudden Stopswithout Sudden Stops−5−2.502.55Cumulative percentage point diff. from year 0012345YearsPanel B: Investmentwith Sudden Stopswithout Sudden Stops−8−4048Cumulative rate of change from year 0012345YearsPanel C: GDPTable D3: Estimation Results, Restructurings with Severe Sudden Stops, AIPW, One-
type Model

Panel A: Private Credit
Dep. Var. = 100 × (Crediti,t+h − Crediti,t)/Crediti,t
h = 2

h = 1

h = 3

h = 4

h = 5

Sum

Post-default, with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Post-default, without sudden stops

# of obs.
# of countries
# of debt restructuring obs.

-5.608***
(1.048)
385
14
16
-2.593***
(1.133)
505
18
25

-20.98***
(1.976)
385
14
16
-11.25***
(2.193)
503
18
24

-25.64***
(2.489)
385
14
16
-12.41***
(3.169)
501
18
24

-29.22***
(2.930)
385
14
16
-2.012
(5.003)
500
18
24

-35.63***
(3.443)
372
14
16
0.107
(6.687)
482
18
24

-118.02***
(10.805)
372
14
16
-28.24*
(17.116)
478
18
24

Panel B: Investment
Dep. Var. = 100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t)

Post-default, with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Post-default, without sudden stops

# of obs.
# of countries
# of debt restructuring obs.

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-3.159***
(0.499)
386
14
16
-0.049
(0.280)
488
17
24

-3.153***
(0.613)
386
14
16
-1.751***
(0.400)
488
17
24

-1.685***
(0.585)
386
14
16
0.094
(0.389)
488
17
24

0.516
(0.918)
386
14
16
-0.273
(0.468)
488
17
24

0.492
(1.085)
373
14
16
-1.022*
(0.523)
471
17
24

-6.927**
(3.477)
373
14
16
-2.633
(1.751)
471
17
24

Panel C: GDP
Dep. Var. = 100 × (GDPi,t+h − GDPi,t/GDPi,t)
h = 4
h = 2

h = 3

h = 1

Post-default, with sudden stops

# of obs.
# of countries
# of debt restructuring obs.
Post-default, without sudden stops

# of obs.
# of countries
# of debt restructuring obs.

-4.219***
(0.416)
416
15
17
-0.980***
(0.321)
547
19
27

-5.348***
(0.580)
416
15
17
-2.325***
(0.548)
547
19
27

-5.398***
(0.563)
416
15
17
-2.209***
(0.571)
547
19
27

-4.962***
(0.719)
416
15
17
-0.340
(0.691)
547
19
27

h = 5

Sum

-6.389***
(0.793)
402
15
17
-2.443***
(0.954)
528
19
27

-25.43***
(2.264)
402
15
17
-7.514***
(2.545)
528
19
27

Notes: The table shows local projections (AIPW) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively.

A19

Table D4: Debt Restructurings and Severe Sudden Stops, Panel OLS & IV

Panel A: Dep. Var. = Annualized GDP Growth Rate from a Year Before and a Year After the Start of Restructurings,
100 × (ln(GDPi,t+2) − ln(GDPi,t))/2

(1)

-3.302***
(1.070)
-1.183**
(0.530)
-2.500*
(1.379)
1.370*
(0.786)
-0.430
(1.841)
0.623
(1.044)

0.616***
(0.016)

Post-default, d.N CI < 25th

Post-default, d.N CI ≥ 25th

Weakly preemptive, d.N CI < 25th

Weakly preemptive, d.N CI ≥ 25th

Strictly preemptive, d.N CI < 25th

Strictly preemptive, d.N CI ≥ 25th

Control variables

GDP growth rate, lag

Investment-to-GDP ratio

Population growth rate

ln(Population)

Government expenditure-to-GDP ratio

Human capital index

Freedom house index, civil liberty

Openness

Banking crisis dummy

Year ﬁxed eﬀects
R-squared
# of countries
# of obs.

No
0.401
64
2,309

Diﬀerence between point estimates for each strategy

Post-default, diﬀerence

# of obs. from d.N CI < 25th
# of obs. from d.N CI ≥ 25th
Weakly preemptive, diﬀerence

# of obs. from d.N CI < 25th
# of obs. from d.N CI ≥ 25th
Strictly preemptive, diﬀerence

# of obs. from d.N CI < 25th
# of obs. from d.N CI ≥ 25th

-2.120*
(1.185)
15
62
-3.871**
(1.574)
9
28
-1.054
(2.098)
5
16

OLS
(2)

-3.258***
(1.001)
-1.098**
(0.545)
-2.674*
(1.364)
1.322*
(0.761)
0.391
(2.068)
0.669
(0.939)

0.569***
(0.022)
-0.038**
(0.015)
0.162
(0.168)
0.409
(0.654)
0.018
(0.026)
-0.129
(0.801)
-0.118
(0.0978)
1.217*
(0.690)
-0.734**
(0.310)
No
0.360
53
1,671

-2.160*
(1.126)
13
45
-3.996***
(1.537)
7
23
-0.279
(2.245)
3
15

(3)

(4)

-2.553***
(0.989)
-0.558
(0.546)
-1.736
(1.367)
1.626**
(0.760)
1.222
(2.040)
0.627
(0.922)

0.547***
(0.022)
-0.034**
(0.016)
0.320*
(0.167)
-1.502
(1.032)
0.014
(0.026)
-2.224**
(0.995)
0.0451
(0.103)
0.814
(0.754)
-0.458
(0.310)
Yes
0.410
53
1,671

-1.996*
(1.110)
13
45
-3.362**
(1.523)
7
23
0.595
(2.212)
3
15

-2.917***
(1.131)
-1.217**
(0.553)
-3.591**
(1.530)
1.199
(0.821)
-0.352
(1.870)
1.009
(1.093)

0.625***
(0.016)

No

64
2,245

-1.701
(1.252)
15
62
-4.791***
(1.679)
9
28
-1.360
(2.102)
5
16

IV
(5)

-3.366***
(0.993)
-0.664
(0.541)
-3.098**
(1.390)
1.480*
(0.762)
1.826
(2.014)
0.881
(0.939)

0.575***
(0.021)
-0.085***
(0.017)
0.250
(0.196)
1.340
(0.871)
0.074**
(0.033)
-0.843
(1.032)
-0.0905
(0.136)
2.253***
(0.820)
-1.191***
(0.344)
No

(6)

-2.513**
(0.985)
-0.256
(0.535)
-1.940
(1.404)
1.884**
(0.767)
2.861
(1.990)
0.739
(0.920)

0.559***
(0.021)
-0.075***
(0.018)
0.360*
(0.196)
-2.327
(1.425)
0.056*
(0.034)
-3.065**
(1.278)
0.198
(0.144)
1.839*
(0.960)
-0.920***
(0.346)
Yes

52
1,614

52
1,614

-2.701**
(1.125)
13
45
-4.578***
(1.539)
7
23
0.945
(2.173)
3
15

-2.257**
(1.112)
13
45
-3.825**
(1.539)
7
23
2.121
(2.142)
3
15

Notes: The sample period is 1970–2013. All regressions include country ﬁxed eﬀects. Robust standard errors are in
parentheses. ***, **, and * indicate the statistical signiﬁcance at 1%, 5%, and 10% level, respectively. Debt restructuring
samples are divided into two groups depending upon percentage point changes in the net capital inﬂows-to-GDP ratio
from the pre-crisis level to 1 year after the crisis and the cutoﬀ is the 25th percentile of distribution. IV columns report
the results from the Arellano-Bond estimator, controlling for country ﬁxed eﬀects by taking ﬁrst diﬀerences and ﬁrst
diﬀerences of independent variables are employed as IVs.

A20

Panel B: Dep. Var. = Annualized Percentage Point Diﬀerence in Investment-to-GDP ratio from a Year Before and a
Year After the Start of Restructurings, 100 × (Investmenti,t+2/GDPi,t+2 − Investmenti,t/GDPi,t)/2

(1)

-3.894***
(0.785)
-1.244***
(0.398)
-2.794***
(0.978)
0.614
(0.589)
-1.093
(1.305)
1.116
(0.739)

0.361***
(0.0211)

Post-default, d.N CI < 25th

Post-default, d.N CI ≥ 25th

Weakly preemptive, d.N CI < 25th

Weakly preemptive, d.N CI ≥ 25th

Strictly preemptive, d.N CI < 25th

Strictly preemptive, d.N CI ≥ 25th

Control variables

GDP growth rate, lag

Investment-to-GDP ratio

Population growth rate

ln(Population)

Government expenditure-to-GDP ratio

Human capital index

Freedom house index, civil liberty

Openness

Banking crisis dummy

Year ﬁxed eﬀects
R-squared
# of countries
# of obs.

No
0.156
61
2,017

Post-default, diﬀerence

# of obs. from d.N CI < 25th
# of obs. from d.N CI ≥ 25th
Weakly preemptive, diﬀerence

Diﬀerence between point estimates for each strategy
-2.650***
(0.874)
14
55
-3.409***
(1.131)
9
25
-2.210
(1.486)
5
16

# of obs. from d.N CI < 25th
# of obs. from d.N CI ≥ 25th
Strictly preemptive, diﬀerence

# of obs. from d.N CI < 25th
# of obs. from d.N CI ≥ 25th

OLS
(2)

-3.917***
(0.788)
-0.948**
(0.441)
-2.757**
(1.075)
0.386
(0.599)
-0.840
(1.630)
0.975
(0.739)

0.480***
(0.0264)
-0.110***
(0.0134)
0.0293
(0.132)
0.642
(0.518)
-0.0139
(0.0201)
-0.755
(0.637)
-0.0750
(0.0774)
0.144
(0.550)
-0.156
(0.242)
No
0.202
52
1,657

-2.969***
(0.895)
13
42
-3.143***
(1.211)
7
23
-1.814
(1.770)
3
15

(3)

(4)

-3.554***
(0.778)
-0.603
(0.442)
-1.708
(1.074)
0.639
(0.598)
-0.791
(1.604)
1.005
(0.725)

0.489***
(0.0268)
-0.121***
(0.0138)
0.0904
(0.131)
-0.259
(0.813)
-0.0215
(0.0201)
-2.169***
(0.792)
0.0153
(0.0814)
-0.988*
(0.600)
0.124
(0.243)
Yes
0.268
52
1,657

-2.951***
(0.879)
13
42
-2.347***
(1.197)
7
23
-1.796
(1.740)
3
15

-3.751***
(0.793)
-1.185***
(0.406)
-3.349***
(1.031)
0.790
(0.595)
-0.454
(1.277)
1.284*
(0.748)

0.359***
(0.0194)

No

60
1,955

-2.565***
(0.890)
14
55
-4.138***
(1.162)
9
25
-1.738
(1.444)
5
16

IV
(5)

-4.410***
(0.807)
-0.800*
(0.452)
-3.285***
(1.123)
0.469
(0.618)
-0.848
(1.639)
1.266
(0.771)

0.520***
(0.0259)
-0.195***
(0.0159)
-0.121
(0.162)
1.479*
(0.771)
0.00807
(0.0241)
-2.322***
(0.883)
-0.123
(0.118)
1.309*
(0.722)
-0.315
(0.278)
No

(6)

-4.139***
(0.796)
-0.542
(0.455)
-2.117*
(1.121)
0.800
(0.624)
-0.628
(1.606)
1.232
(0.757)

0.543***
(0.0266)
-0.213***
(0.0166)
0.0474
(0.160)
0.473
(1.181)
-0.0109
(0.0240)
-4.755***
(1.181)
-0.0349
(0.120)
-0.458
(0.790)
0.166
(0.280)
Yes

52
1,601

52
1,601

-3.610***
(0.922)
13
42
-3.755***
(1.251)
7
23
-2.114
(1.766)
3
15

-3.598***
(0.906)
13
42
-2.917***
(1.239)
7
23
-1.860
(1.730)
3
15

Notes: The sample period is 1970–2013. All regressions include country ﬁxed eﬀects. Robust standard errors are in
parentheses. ***, **, and * indicate the statistical signiﬁcance at 1%, 5%, and 10% level, respectively. Debt restructuring
samples are divided into two groups depending upon percentage point changes in the net capital inﬂows-to-GDP ratio
from the pre-crisis level to 1 year after the crisis and the cutoﬀ is the 25th percentile of distribution. IV columns report
the results from the Arellano-Bond estimator, controlling for country ﬁxed eﬀects by taking ﬁrst diﬀerences and ﬁrst
diﬀerences of independent variables are employed as IVs.

A21

Panel C: Dep. Var. = Annualized Rate of Change in Private Credit from a Year Before and a Year After the Start of
Restructurings, 100 × (ln(Crediti,t+2) − ln(Crediti,t))/2

(1)

-6.114**
(2.656)
-5.546***
(1.226)
-4.757
(2.924)
0.444
(1.667)
-1.475
(3.914)
-3.045
(2.286)

0.614***
(0.0166)

Post-default, d.N CI < 25th

Post-default, d.N CI ≥ 25th

Weakly preemptive, d.N CI < 25th

Weakly preemptive, d.N CI ≥ 25th

Strictly preemptive, d.N CI < 25th

Strictly preemptive, d.N CI ≥ 25th

Control variables

GDP growth rate, lag

Investment-to-GDP ratio

Population growth rate

ln(Population)

Government expenditure-to-GDP ratio

Human capital index

Freedom house index, civil liberty

Openness

Banking crisis dummy

Year ﬁxed eﬀects
R-squared
# of countries
# of obs.

No
0.416
63
2,047

Diﬀerence between point estimates for each strategy

Post-default, diﬀerence

# of obs. from d.N CI < 25th
# of obs. from d.N CI ≥ 25th
Weakly preemptive, diﬀerence

# of obs. from d.N CI < 25th
# of obs. from d.N CI ≥ 25th
Strictly preemptive, diﬀerence

# of obs. from d.N CI < 25th
# of obs. from d.N CI ≥ 25th

-0.567
(2.910)
11
52
-5.202
(3.340)
9
28
1.570
(4.491)
5
15

OLS
(2)

-6.574**
(2.622)
-5.651***
(1.304)
-3.330
(3.128)
1.355
(1.747)
2.593
(4.754)
-3.290
(2.227)

0.602***
(0.0196)
-0.0385
(0.0377)
0.222
(0.422)
1.706
(1.629)
-0.207***
(0.0655)
-1.775
(1.952)
0.502**
(0.248)
3.065*
(1.712)
-3.981***
(0.730)
No
0.444
52
1,561

-0.923
(2.907)
10
41
-4.685
(3.525)
7
23
5.884
(5.185)
3
14

(3)

(4)

-6.783***
(2.621)
-4.967***
(1.327)
-1.986
(3.180)
1.690
(1.773)
2.307
(4.743)
-2.304
(2.215)

0.600***
(0.0198)
-0.0556
(0.0393)
0.401
(0.424)
-1.210
(2.657)
-0.208***
(0.0667)
-3.900
(2.459)
0.739***
(0.256)
-0.0644
(1.923)
-3.770***
(0.738)
Yes
0.478
52
1,561

-1.816
(2.901)
10
41
-3.675
(3.538)
7
23
4.611
(5.166)
3
14

-7.200***
(2.629)
-5.781***
(1.176)
-4.789
(2.989)
0.296
(1.593)
-0.590
(3.673)
-4.758**
(2.245)

0.612***
(0.0152)

No

63
1,974

-1.419
(2.876)
11
52
-5.085
(3.302)
9
28
4.167
(4.173)
5
15

IV
(5)

-7.422***
(2.515)
-5.658***
(1.231)
-4.874
(3.081)
1.096
(1.637)
3.769
(4.353)
-4.723**
(2.137)

0.597***
(0.0176)
-0.126***
(0.0440)
-0.274
(0.484)
1.775
(2.068)
-0.247***
(0.0775)
-3.171
(2.465)
1.033***
(0.324)
5.482***
(2.015)
-5.095***
(0.751)
No

52
1,500

-1.763
(2.791)
10
41
-5.970*
(3.393)
7
23
8.493*
(4.742)
3
14

(6)

-7.603***
(2.515)
-5.124***
(1.250)
-3.480
(3.161)
1.173
(1.661)
2.801
(4.359)
-3.176
(2.127)

0.599***
(0.0177)
-0.111**
(0.0454)
-0.0141
(0.487)
-2.566
(3.506)
-0.263***
(0.0804)
-7.288**
(3.421)
1.346***
(0.337)
-0.180
(2.428)
-4.913***
(0.764)
Yes

52
1,500

-2.479
(2.776)
10
41
-4.654
(3.443)
7
23
5.977
(4.743)
3
14

Notes: The sample period is 1970–2013. All regressions include country ﬁxed eﬀects. Robust standard errors are in
parentheses. ***, **, and * indicate the statistical signiﬁcance at 1%, 5%, and 10% level, respectively. Debt restructuring
samples are divided into two groups depending upon percentage point changes in the net capital inﬂows-to-GDP ratio
from the pre-crisis level to 1 year after the crisis and the cutoﬀ is the 25th percentile of distribution. IV columns report
the results from the Arellano-Bond estimator, controlling for country ﬁxed eﬀects by taking ﬁrst diﬀerences and ﬁrst
diﬀerences of independent variables are employed as IVs.

A22

D.2 Duration, Haircuts and Missed Payments

Table D5: First-Stage Regression Results, Predicting Post-default Debt Restructurings
with High/Small GDP Forecasts, Long/Short Expected Durations, and High/Small Ex-
pected Haircuts

GDP growth rate, lag

Cyclical component of ln(GDP per capita), lag

# of past post-default cases

# of past preemptive cases

# of past banking crises

Interest payments-to-GDP ratio, lag

External debt-to-GDP ratio, lag

Constant

Pseudo R-sq.
Log-likelihood ratio
p-value for log-likelihood ratio
# observations
Area under ROC curve

GDP Forecasts
High

Low

Expected Durations
Short

Long

Expected Haircuts
Small
High

0.0246
(0.0332)
-7.765*
(4.065)
1.769***
(0.560)
4.847
(465.9)
-1.271***
(0.488)
58.14***
(19.11)
-1.299
(0.820)
-12.73
(1,129)
0.417
46.25
0.000
456
0.95

-0.0490***
(0.0152)
2.706*
(1.461)
0.597***
(0.132)
0.351*
(0.182)
-0.348***
(0.0682)
11.06***
(2.548)
-0.819***
(0.299)
-1.837***
(0.478)
0.180
75.79
0.000
1,260
0.84

-0.0260*
(0.0149)
-0.322
(1.634)
0.592***
(0.127)
0.578***
(0.218)
-0.593***
(0.108)
12.47***
(2.724)
-0.767**
(0.311)
-1.810***
(0.510)
0.241
100.24
0.000
1,276
0.87

-0.145*
(0.0742)
36.12***
(12.22)
6.604***
(1.948)
51.53
(5,524)
-1.204**
(0.472)
31.17
(24.45)
0.825
(1.332)
-62.63
(5,579)
0.606
72.37
0.000
481
0.98

-0.0295**
(0.0149)
-0.166
(1.640)
0.615***
(0.129)
0.498**
(0.202)
-0.504***
(0.0892)
12.96***
(2.795)
-0.948***
(0.337)
-1.817***
(0.500)
0.235
96.82
0.000
1,235
0.87

-0.129**
(0.0652)
27.17***
(9.351)
5.964***
(1.880)
45.73
(1,829)
-1.339**
(0.543)
9.967
(23.96)
1.060
(1.004)
-56.31
(1,923)
0.543
64.87
0.000
481
0.97

Notes: The table shows ﬁrst-stage regression results for AIPW estimation. All regressions include a constant term
and country ﬁxed eﬀects. Robust standard errors, clustered at the country-level, are in parentheses. ***, **, and *
indicate statistical signiﬁcance at 1%, 5%, and 10% level, respectively. The estimates are based on the same sample of
restructuring observations.

A23

Figure D3: First-Stage Estimation Results, ROC

Panel I: GDP Forecasts

Panel II: Length of Duration

Panel III: Size of Haircuts

Notes: Panels I, II, and III show the area under the ROC curve. The ROC area takes a value between 0.50 and 1. Taking

a value of 0.50 indicates that regressors have no classiﬁcation power when they diﬀerentiate observations of the start of

debt restructurings from other observations. Taking a value of 1 indicating that regressors have the perfect classiﬁcation

power. See the text for the deﬁnition of x1 and x2.

A24

0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.94x2 ROC area: 0.94Panel A: Low GDP Forecasts0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.89x2 ROC area: 0.88Panel B: High GDP Forecasts0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.90x2 ROC area: 0.88Panel A: Long Expected Duration0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.91x2 ROC area: 0.89Panel B: Short Expected Duration0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.87x2 ROC area: 0.87Panel A: High Expected Haircuts0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.91x2 ROC area: 0.88Panel B: Small Expected HaircutsFigure D4: GDP after Post-default Restructurings

Panel I: AIPW, Allowing the First-Stage Parameters to Diﬀer across the Two Groups of
Observations (Two-type Model)

Panel II: AIPW, Assuming the Same First-Stage Parameters between the Two Groups of
Observations (One-type Model)

Panel III: OLS Local Projections

Notes: The ﬁgure shows dynamics of GDP after post-default debt restructurings, classifying the sample into those high

and low GDP growth forecasts (Panel A), those with short and long expected duration (Panel B), and those with small

and high expected haircuts (Panel C). Panel A classiﬁes post-default restructurings into two groups based on the long-

run GDP growth forecasts. Panel B and C classify post-default restructurings into two groups based on the estimated

duration and haircuts obtained in columns (2) and (5) in Table 4, respectively. Solid lines in red and purple are point

estimates. Gray bands and dotted lines in purple are 95% conﬁdence intervals.

A25

Low GDP forecastsHigh GDP forecasts−10−505Cumulative rate of change x 100012345YearsPanel A: GDP ForecastsLong expected durationShort expected duration−10−505012345YearsPanel B: DurationHigh expected haircutsSmall expected haircuts−10−505012345YearsPanel C: HaircutsLow GDP forecastsHigh GDP forecasts−10−505Cumulative rate of change x 100012345YearsPanel A: GDP ForecastsLong expected durationShort expected duration−10−505012345YearsPanel B: DurationHigh expected haircutsSmall expected haircuts−10−505012345YearsPanel C: HaircutsLow GDP forecastsHigh GDP forecasts−15−10−505Cumulative rate of change x 100012345YearsPanel A: GDP ForecastsLong expected durationShort expected duration−15−10−505012345YearsPanel B: DurationHigh expected haircutsSmall expected haircuts−15−10−505012345YearsPanel C: HaircutsTable D6: GDP after Post-default Debt Restructurings, AIPW, Allowing the First-Stage
Parameters to Diﬀer across the Two Groups of Observations (Two-type Model)

Post-default, with lower GDP forecasts

# of obs.
# of countries
# of S&P defaults
Post-default, with higher GDP forecasts

# of obs.
# of countries
# of debt rest. obs.

Panel A: GDP Forecasts
h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-5.866***
(0.425)
446
12
12
-2.023***
(0.211)
394
13
13

-3.713***
(0.603)
446
12
12
-0.520
(0.402)
394
13
13

-6.078***
(0.602)
434
12
12
-0.833**
(0.393)
381
13
13

-5.301***
(0.823)
422
12
12
4.202***
(0.548)
368
13
13

-2.809***
(0.979)
410
12
12
1.693**
(0.702)
355
13
13

-22.85***
(2.942)
410
12
12
2.826
(1.873)
355
13
13

Post-default, with longer expected duration

# of obs.
# of countries
# of S&P defaults
Post-default, with shorter expected duration

# of obs.
# of countries
# of debt rest. obs.

Post-default, with higher expected haircuts

# of obs.
# of countries
# of S&P defaults
Post-default, with smaller expected haircuts

# of obs.
# of countries
# of debt rest. obs.

Panel B: Duration
h = 2
h = 1

-6.656***
(0.449)
437
12
12
-2.023***
(0.211)
740
13
13

-5.093***
(0.586)
437
12
12
-0.520
(0.402)
740
13
13

Panel C: Haircuts
h = 2
h = 1

-7.207***
(0.447)
437
12
12
-2.129***
(0.212)
413
13
13

-7.652***
(0.500)
437
12
12
0.326
(0.366)
413
13
13

h = 3

h = 4

h = 5

Sum

-6.432***
(0.586)
425
12
12
-0.833**
(0.393)
740
13
13

-5.908***
(0.742)
413
12
12
4.203***
(0.548)
740
13
13

-3.058***
(0.768)
401
12
12
1.693**
(0.702)
713
13
13

-26.09***
(2.677)
401
12
12
2.826
(1.873)
713
13
13

h = 3

h = 4

h = 5

Sum

-7.357***
(0.586)
425
12
12
-1.799***
(0.391)
400
13
13

-7.519***
(0.711)
413
12
12
3.155***
(0.514)
387
13
13

-5.292***
(0.700)
401
12
12
2.554***
(0.657)
374
13
13

-34.08***
(2.426)
401
12
12
2.620
(1.703)
374
13
13

Notes: The table shows local projections (AIPW) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁler with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively. The estimates are based on the same sample of restructuring observations.

A26

Table D7: GDP after Post-default Debt Restructurings, AIPW, Assuming the Identical
First-Stage Parameters between the Two of Observations (One-type Model)

Post-default, with lower GDP forecasts

# of obs.
# of countries
# of S&P defaults
Post-default, with higher GDP forecasts

# of obs.
# of countries
# of debt rest. obs.

Panel A: GDP Forecasts
h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-4.144***
(0.165)
1467
38
12
-1.366***
(0.178)
1438
38
13

-4.254***
(0.262)
1467
38
12
-2.249***
(0.251)
1438
38
13

-7.297***
(0.300)
1429
38
12
-1.458***
(0.301)
1400
38
13

-8.446***
(0.424)
1391
38
12
2.532***
(0.371)
1362
38
13

-7.700***
(0.556)
1353
38
12
2.925***
(0.452)
1324
38
13

-30.70***
(1.458)
1353
38
12
1.148
(1.303)
1324
38
13

Post-default, with longer expected duration

# of obs.
# of countries
# of S&P defaults
Post-default, with shorter expected duration

# of obs.
# of countries
# of debt rest. obs.

Post-default, with higher expected haircuts

# of obs.
# of countries
# of S&P defaults
Post-default, with smaller expected haircuts

# of obs.
# of countries
# of debt rest. obs.

Panel B: Duration
h = 2
h = 1

-4.972***
(0.180)
1438
38
12
-1.366***
(0.178)
1438
38
13

-5.313***
(0.261)
1438
38
12
-2.249***
(0.251)
1438
38
13

Panel C: Haircuts
h = 2
h = 1

h = 3

h = 4

h = 5

Sum

-8.048***
(0.309)
1400
38
12
-1.458***
(0.301)
1400
38
13

-9.144***
(0.428)
1362
38
12
2.532***
(0.371)
1362
38
13

-8.039***
(0.544)
1324
38
12
2.925***
(0.452)
1324
38
13

-34.50***
(1.486)
1324
38
12
1.148
(1.303)
1324
38
13

h = 3

h = 4

h = 5

Sum

-5.019***
(0.179)
1438
38
12
-1.366***
(0.178)
1438
38
13

-7.922***
(0.238)
1438
38
12
-0.777***
(0.237)
1438
38
13

-8.416***
(0.306)
1400
38
12
-1.327***
(0.299)
1400
38
13

-9.145***
(0.426)
1362
38
12
2.404***
(0.371)
1362
38
13

-8.322***
(0.535)
1324
38
12
2.970***
(0.452)
1324
38
13

-37.89***
(1.416)
1324
38
12
2.729**
(1.300)
1324
38
13

Notes: The table shows local projections (AIPW) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively. The estimates are based on the same sample of restructuring observations.

A27

Table D8: GDP after Post-default Debt Restructurings, OLS Local Projections

Post-default, with lower GDP forecasts

Post-default, with higher GDP forecasts

R-squared
# of observations
# of countries

Panel A: GDP Forecasts

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-4.661***
(1.079)
-1.148**
(0.561)
0.321
2,102
59

-4.161***
(1.356)
-2.661**
(1.209)
0.493
2,102
59

-6.214***
(2.184)
-2.735
(1.889)
0.595
2,043
59

-6.845**
(3.348)
1.216
(1.760)
0.619
1,984
59

-6.417
(4.616)
1.993
(1.985)
0.599
1,925
59

-27.77**
(10.69)
-2.496
(5.684)
0.659
1,925
59

Post-default, with longer expected duration

Post-default, with shorter expected duration

R-squared
# of observations
# of countries

Post-default, with higher expected haircuts

Post-default, with smaller expected haircuts

R-squared
# of observations
# of countries

Panel B: Duration

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-4.661***
(1.079)
-1.148**
(0.561)
0.321
2,102
59

-4.161***
(1.356)
-2.661**
(1.209)
0.493
2,102
59

-6.214***
(2.184)
-2.735
(1.889)
0.595
2,043
59

-6.845**
(3.348)
1.216
(1.760)
0.619
1,984
59

-6.417
(4.616)
1.993
(1.985)
0.599
1,925
59

-27.77**
(10.69)
-2.496
(5.684)
0.659
1,925
59

Panel C: Haircuts
h = 2
h = 1

h = 3

h = 4

h = 5

Sum

-4.671***
(1.078)
-1.139**
(0.556)
0.321
2,102
59

-5.589***
(1.294)
-1.348
(1.014)
0.494
2,102
59

-6.378***
(2.172)
-2.585
(1.866)
0.595
2,043
59

-6.763**
(3.364)
1.139
(1.747)
0.619
1,984
59

-6.404
(4.615)
1.980
(1.973)
0.599
1,925
59

-29.31***
(10.32)
-1.080
(5.653)
0.659
1,925
59

Notes: The table shows local projections (AIPW) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively. The estimates are based on the same sample of restructuring observations.

A28

E Robustness Checks

E.1 Large Sample Including Countries without Restructurings

Previous studies on sovereign defaults use a wider coverage of countries including those that have
never defaulted: on defaults on private external debt, Furceri and Zdzienicka (2012) and Kuvshinov
and Zimmermann (2016) use a sample of 154 countries and 114 countries, respectively. We set our
sample to follow as close as possible the conventional approach in these studies. We exclude high
income countries where the Purchasing Power Parity (PPP) adjusted GDP per capita is higher than
the 80th percentile of the entire sample in 2000 since we do not have any restructuring episodes for
advanced economies (Greece’s 2011–12 restructuring takes place outside of the time sample used in this
paper). That leaves 122 countries in the sample, a similar number to that in Furceri and Zdzienicka
(2012) and Kuvshinov and Zimmermann (2016).

Figure E1 and Table E1 report the OLS estimates for GDP, investment, private credit, lending
interest rates and net capital inﬂows—since we include country sample which restructuring dummies
indicate 0, we cannot apply AIPW but OLS. The results are quantitatively similar to Figure 7 and
Table C1 conﬁrming the robustness of our baseline (AIPW) results.

Table E1: Estimation Results with Expanded Sample, OLS

Panel A: Dep. Var. = 100 × (GDPi,t+h − GDPi,t)/GDPi,t
h = 2

h = 1

h = 4

h = 3

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of obs.
# of countries
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

-1.978***
(0.706)
-1.736***
(0.660)
-1.757***
(0.558)
0.418
3,834
115
65
31
11

-3.181***
(0.771)
-1.948**
(0.911)
-1.313
(1.086)
0.566
3,834
115
65
31
11

-3.337***
(0.735)
-1.208
(1.356)
-0.777
(1.402)
0.611
3,719
115
65
31
11

-2.859**
(1.150)
-0.797
(2.013)
-0.619
(1.283)
0.595
3,604
115
65
31
11

h = 5

-2.285
(1.997)
-2.003
(2.199)
-0.698
(1.916)
0.527
3,489
115
65
31
11

Sum

-13.22***
(3.704)
-7.700
(6.011)
-5.095
(3.351)
0.662
3,489
115
65
31
11

Panel B: Dep. Var. = 100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t)
h = 2

h = 5

h = 4

h = 3

h = 1

Sum

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of obs.
# of countries
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

-1.806**
(0.810)
-1.876***
(0.713)
-0.788
(0.533)
0.069
3,431
109
56
28
11

-3.179***
(0.945)
-2.463**
(1.064)
0.700
(0.951)
0.103
3,425
109
56
28
11

-1.920**
(0.960)
-2.117**
(0.973)
2.040*
(1.105)
0.143
3,313
109
56
28
11

-1.455
(0.899)
-2.080**
(0.971)
2.026*
(1.195)
0.161
3,201
109
56
28
11

-2.160**
(0.971)
-2.643**
(1.310)
1.726*
(0.957)
0.149
3,091
109
56
28
11

-10.30***
(3.713)
-11.34**
(4.355)
5.594
(3.745)
0.178
3,085
109
56
28
11

Notes: The table show local projections (OLS) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1 and lag 2), cyclical component of log
of GDP obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust
standard errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%,
5%, and 10% level, respectively.

A29

Panel C: Dep. Var. = 100 × (Crediti,t+h/GDPi,t+h − Crediti,t/GDPi,t)
h = 5

h = 1

h = 4

h = 3

h = 2

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of obs.
# of countries
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

-0.0313
(0.368)
-0.157
(0.451)
0.227
(0.330)
0.100
3,421
113
57
31
10

-1.943***
(0.523)
-1.475**
(0.672)
-0.867**
(0.411)
0.062
3,410
113
56
31
10

-2.778***
(0.648)
-2.026**
(1.010)
-0.865
(1.564)
0.051
3,294
113
56
31
9

-3.218***
(0.848)
-2.400*
(1.417)
-0.800
(2.325)
0.043
3,178
113
56
31
10

-3.788***
(0.806)
-2.868*
(1.588)
-1.155
(3.423)
0.036
3,064
112
56
31
10

Panel D: Dep. Var. = 100 × (LendingRatei,t+h − LendingRatei,t)
h = 5
h = 2

h = 1

h = 4

h = 3

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of obs.
# of countries
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

4.542**
(2.078)
8.712
(5.734)
3.983
(2.627)
0.117
1,880
98
24
15
9

10.55**
(4.218)
11.26
(9.291)
7.251
(5.956)
0.218
1,856
97
24
15
9

10.34***
(3.501)
10.87
(8.322)
3.434
(3.441)
0.481
1,754
97
23
15
9

9.884**
(4.183)
13.26
(9.394)
-0.597
(3.876)
0.503
1,656
97
23
14
9

12.09**
(4.938)
18.36
(11.13)
-1.793
(5.218)
0.511
1,560
97
23
14
9

Panel E: Dep. Var. = 100 × (N CIi,t+h/GDPi,t+h − N CIi,t/GDPi,t)
h = 5
h = 2

h = 4

h = 1

h = 3

Post-default

Weakly preemptive

Strictly preemptive

R-squared
# of obs.
# of countries
# of post-default obs.
# of weakly preemptive obs.
# of strictly preemptive obs.

-2.458**
(1.190)
-6.123***
(1.569)
1.580
(1.304)
0.116
2,936
110
62
31
10

-4.784***
(1.820)
-4.919**
(2.194)
3.534
(2.787)
0.174
2,912
110
62
31
10

-0.258
(2.508)
-3.543
(2.362)
5.130**
(2.153)
0.206
2,790
110
62
31
10

-2.029
(1.634)
-5.065*
(2.586)
4.185*
(2.324)
0.198
2,674
109
62
31
10

-3.754***
(1.327)
-4.512
(3.250)
9.227***
(1.852)
0.184
2,559
109
60
31
10

Sum

-11.97***
(2.910)
-9.128**
(4.584)
-3.388
(8.256)
0.070
3,018
112
56
31
9

Sum

48.77***
(18.39)
76.76
(50.89)
15.75
(16.04)
0.412
1,524
97
23
14
9

Sum

-14.16***
(5.242)
-22.62**
(9.833)
23.61***
(3.680)
0.268
2,542
109
60
31
10

Notes: The table show local projections (OLS) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1 and lag 2), cyclical component of log
of GDP obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust
standard errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%,
5%, and 10% level, respectively.

A30

Figure E1: Local Projections with Expanded Sample, OLS

Notes: The ﬁgure shows local projections of the variables shown in each panel for h = 1, 2,.., 5, where h in-
dicates horizon. Dependent variables are as follows. Panel A: 100 × (GDPi,t+h − GDPi,t)/GDPi,t, Panel B:
100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t), Panel C: 100 × (Crediti,t+h/GDPi,t+h − Crediti,t/GDPi,t),
Panel D: 100 × (LendingRatei,t+h − LendingRatei,t), and Panel E: 100 × (N CIi,t+h/GDPi,t+h − N CIi,t/GDPi,t). Solid
lines in red, blue, and green are point estimates. Gray bands and dotted lines in blue and green are 95% conﬁdence

intervals. The sample period is 1970–2013.

A31

Post−defaultWeakly preemptiveStrictly preemptive−6−4−2024Cumulative rate of change from year 0012345YearsPanel A: GDPPost−defaultWeakly preemptiveStrictly preemptive−6−4−2024Cumulative percentage point diff. from year 0012345YearsPanel B: InvestmentPost−defaultWeakly preemptiveStrictly preemptive−6−3036Cumulative percentage point diff. from year 0012345YearsPanel C: Private CreditPost−defaultWeakly preemptiveStrictly preemptive−10010203040Cumulative percentage point diff. from year 0012345YearsPanel D: Lending Interest RatesPost−defaultWeakly preemptiveStrictly preemptive−10−5051015Cumulative percentage point diff. from year 0012345YearsPanel E: Net Capital InflowsE.2 Standard Poor’s Default Episodes

Some studies on sovereign debt use S&P default data, for instance Borensztein and Panizza (2009),
Levy-Yeyati and Panizza (2011), or Kuvshinov and Zimmermann (2016). S&P data do not diﬀerentiate
restructuring strategies and cover only 88 “default” episodes in countries where ratings are assigned:
(i) post-default restructurings and (ii) only exchange events—not duration from the start to the
exchange—for preemptive restructurings. In this regard, the S&P default sample is quite similar to
those of post-default restructurings in our sample. Figure E3 reports the AIPW estimates—Figure E2
and Table E2 report the 1st-stage probit regression results—for GDP, investment, private credit, and
net capital inﬂows in S&P defaults. The results for GDP, investment, private credit, and net capital
inﬂows in S&P defaults (Panels A, B, C, and D in Figure E3) largely mirror those in post-default
restructurings (Panels A, B, C, and E in Figure 7).

Table E2: Predicting the Start of S&P Defaults, Probit

Dependent Variable = Dummy Taking 1 at the Start of the S&P Defaults
x1

Set of instruments

x2

x3

# of past post-default

# of past weekly preemptive

# of past strictly preemptive

# of past banking crises

0.153*
(0.0845)
0.0590
(0.121)
-0.0978
(0.120)
-0.148***
(0.0450)

External debt-to-GDP ratio, lag

Interest payments-to-GDP ratio, lag

Change in credit ratings, lag

Cyclical comp. of log of GDP per capita, lag

GDP growth rate, lag

Freedom index, civil liberty, lag

# of years from the previous debt rest.

Legislative index of electoral comp., lag

Executive index of electoral comp., lag

Coerciveness index, lag

0.282
(0.178)
-0.659***
(0.245)
0.419
(0.516)
-0.130*
(0.0707)
-1.052**
(0.429)
20.02***
(6.867)
0.0154
(0.0306)
3.320
(2.114)
-4.439**
(2.051)
0.190*
(0.105)

Country ﬁxed eﬀects
# of observations
# of countries
# of S&P default episodes
Likelihood ratio
(p-value)
Pseudo R-squared
ROC

Yes
2,068
74
46
34.53
(0.953)
0.054
0.70

Yes
815
39
29
59.59
(0.014)
0.190
0.87

1.765***
(0.549)
-0.502
(0.433)
-8.958
(456.2)
-0.0914
(0.183)
-3.355**
(1.589)
2.861
(18.56)
0.0542
(0.0574)
-2.817
(4.728)
-10.99**
(4.917)
0.054
(0.289)
-0.256***
(0.0787)
0.239
(0.199)
-0.0990
(0.217)
-0.317**
(0.133)
Yes
415
39
25
91.03
(0.000)
0.529
0.96

Notes: All regressions include country ﬁxed eﬀects. The sample period is 1970–2013. ***, **, and * indicate statistical
signiﬁcance at 1%, 5%, and 10% level, respectively. Standard errors are in parentheses.

A32

Figure E2: First-Stage Estimation for AIPW, S&P Defaults

Notes: The ﬁgure shows eﬀectiveness of instruments used to estimate the ﬁrst-stage probit model.

Figure E3: S&P Defaults, AIPW

Notes: The ﬁgure shows local projections of the variables shown in each panel for h = 1, 2, .., 5, where h indicates horizon.

Red and blue solid lines are point estimates for S&P default episodes. Gray bands and blue dotted lines indicate 95%

conﬁdence intervals. The sample period is 1970–2013.

A33

0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.95x2 ROC area: 0.87Panel A: Classification Power of InstrumentsTreatment groupControl group02468Kernel density0.2.4.6.81Estimated probabilitiesPanel B: Kernel with x1Treatment groupControl group05101520Kernel density0.2.4.6Estimated probabilitiesPanel C: Kernel with x2S&P default, with x1S&P default, with x2−8−6−4−202Cumulative rate of change from year 0012345YearsPanel A: GDPS&P default, with x1S&P default, with x2−6−4−202Cumulative percentage point diff. from year 0012345YearsPanel B: InvestmentS&P default, with x1S&P default, with x2−6−4−202Cumulative percentage point diff. from year 0012345YearsPanel C: Private CreditS&P default, with x1S&P default, with x2−20−15−10−505Cumulative percentage point diff. from year 0012345YearsPanel D: Net Capital InflowsTable E3: Estimation Results, S&P Defaults, AIPW

Panel A: Dep. Var. = 100 × (GDPi,t+h − GDPi,t)/GDPi,t

S&P defaults, with x1

# of obs.
# of countries
# of S&P defaults
S&P Defaults, with x2

# of obs.
# of countries
# of S&P defaults

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-2.859***
(0.530)
372
16
21
-3.383***
(0.370)
786
28
38

-5.453***
(0.467)
372
16
21
-4.366***
(0.566)
786
28
38

-1.244*
(0.639)
372
16
21
-3.634***
(0.707)
786
28
38

0.192
(0.677)
372
16
21
-1.950
(1.267)
786
28
38

-0.670
(0.689)
359
16
21
-4.057**
(1.629)
758
28
38

-9.216***
(2.359)
359
16
21
-16.68***
(4.029)
758
28
38

Panel B: Dep. Var. = 100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t)

S&P defaults, with x1

# of obs.
# of countries
# of S&P defaults
S&P defaults, with x2

# of obs.
# of countries
# of S&P defaults

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-3.937***
(0.230)
372
16
21
-3.057***
(0.395)
740
27
35

-4.459***
(0.384)
372
16
21
-3.774***
(0.521)
740
27
35

-3.610***
(0.468)
372
16
21
-3.591***
(0.615)
740
27
35

-1.711***
(0.332)
372
16
21
-3.458***
(0.589)
740
27
35

-2.362***
(0.342)
359
16
21
-3.757***
(0.624)
713
27
35

-15.65***
(1.423)
359
16
21
-17.31***
(2.555)
713
27
35

Panel C: Dep. Var. = 100 × (Crediti,t+h/GDPi,t+h − Crediti,t/GDPi,t)

S&P defaults, with x1

# of obs.
# of countries
# of S&P defaults
S&P defaults, with x2

# of obs.
# of countries
# of S&P defaults

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-0.206
(0.236)
369
16
20
0.686***
(0.214)
759
28
36

-2.236***
(0.421)
369
16
20
-0.778***
(0.283)
758
28
35

-3.404***
(0.422)
369
16
20
-1.642***
(0.348)
757
28
35

-4.478***
(0.457)
369
16
20
-2.251***
(0.388)
757
28
35

-5.133***
(0.528)
356
16
20
-2.407***
(0.443)
730
28
35

-15.37***
(1.875)
356
16
20
-6.474***
(1.489)
722
28
35

Panel D: Dep. Var. = 100 × (N CIi,t+h/GDPi,t+h − N CIi,t/GDPi,t)
h = 5

h = 1

h = 2

h = 3

h = 4

PS&P defaults, with x1

# of obs.
# of countries
# of S&P defaults
S&P defaults, with x2

# of obs.
# of countries
# of S&P defaults

-7.432***
(1.190)
367
16
21
-0.610
(1.084)
756
28
38

-16.44***
(1.598)
366
16
21
-7.680***
(1.313)
751
28
38

-9.251***
(1.314)
366
16
21
-2.656**
(1.243)
747
28
38

-4.606***
(1.071)
366
16
21
-1.861*
(1.082)
743
28
38

-5.307***
(1.047)
353
16
21
-4.117***
(0.931)
713
28
38

Sum

-15.37***
(1.875)
356
16
20
-6.474***
(1.489)
722
28
35

Notes: All regressions include a constant term, the lagged dependent variable, cyclical component of log of GDP obtained
from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects.

A34

E.3 Restructurings with IMF-supported Programs and Paris Club Restructurings

Next, we check how the GDP and investment respond under the three diﬀerent restructuring
strategies once we take into account whether the country has an IMF-supported program or an oﬃcial
debt (Paris Club) restructuring. Under an IMF-supported program or an oﬃcial debt restructuring,
the availability of oﬃcial (multilateral or bilateral) ﬁnancing with oﬃcial debt being restructured
through Paris Club deals can mitigate some of the adverse eﬀects on GDP and investment (through
a capital inﬂow-credit-investment channel).

We apply panel OLS regression to capture the inﬂuence of an IMF-supported program or an oﬃcial
debt restructuring occurred over the forecast horizon. Figure E4 reports the panel OLS estimates for
GDP and investment for the three restructuring strategies with and without IMF-supported programs
or Paris Club restructurings. Declines in GDP and investment following a post-default restructuring
or a weakly preemptive restructuring are signiﬁcantly moderated by oﬃcial ﬁnancing or oﬃcial debt
treatment (Left and Center in Panels A and B).

Figure E4: Private External Debt Restructurings with/without IMF-Supported Pro-
grams or Paris Club Restructurings, Panel OLS

Panel A: GDP

Panel B: Investment

Notes: The ﬁgure shows panel OLS regressions of the variables shown in each panel for h = 1, 2, ..., 5, where h indicates

horizon. Square dots in red and circle dots in blue are point estimates for private debt restructurings with and without

IMF-supported programs or oﬃcial (Paris Club) debt restructurings. Upper and lower limits of lines in red and blue

indicate 95% conﬁdence intervals. The sample period is 1970–2013.

A35

−30−20−1001020Cumulative rate of change from year 0012345YearsEither IMF−supported program or Paris ClubNeither IMF−supported program nor Paris ClubPost−default−30−20−1001020012345YearsWeakly preemptive−30−20−1001020012345YearsStrictly preemptive−15−10−50510Cumulative perfentage point diff. from year 0012345YearsEither IMF−supported program or Paris ClubNeither IMF−supported program nor Paris ClubPost−default−15−10−50510012345YearsWeakly preemptive−15−10−50510012345YearsStrictly preemptiveE.4 “Single” External Debt Restructurings

In this sub-section, we consider whether large costs in restructurings (in particular post-default
cases) are driven solely by external debt restructurings or also by associated domestic debt restructur-
ings. Asonuma and Papaioannou (2016) provide domestic debt restructuring dataset based on both
jurisdiction and creditor residence criterion—an alternative is based on currency denomination crite-
ria for instance Reinhart and Rogoﬀ (2011a), S&P (2006)—which are consistent with jurisdiction and
creditor residence criterion used to deﬁne external debt restructurings in Cruces and Trebesch (2013)
and Asonuma and Trebesch (2016). We combine the data on domestic debt restructurings on our ex-
ternal debt restructuring dataset and deﬁne a “single” external debt restructuring if no domestic debt
restructuring occurs during duration of an external debt restructuring and a “dual” domestic-external
debt restructuring if otherwise.

Table E4: External Debt Restructurings with/without Domestic Debt Restructurings

Asonuma and Trebesch (2016) and Asonuma and Papaioannou (2016)

Weakly

Debt Restructuring Episodes
“Single” External Debt Restructurings
(No domestic debt restructuring occurs within
duration of external debt restructurings)
Share of Single External Debt Restructurings

All Post-default preemptive
179

111

45

Strictly
preemptive
23

156

95

87%

86%

41

91%

20

87%

Figure E5: "Single" External Debt Restructurings, AIPW

Part I: Post-default Restructurings

Part II: Weakly Preemptive Restructurings

Notes: Solid lines in red and blue are point estimates. Gray bands and dotted lines in blue are 95% conﬁdence intervals.

The sample period is 1970–2013.

A36

All External CasesExternal Only−8−6−4−20Cumulative rate of change from year 0012345YearsPanel A: GDPAll External CasesExternal Only−3−2−1012Cumulative percentage point diff. from year 0012345YearsPanel B: InvestmentAll External CasesExternal Only−4−3−2−10Cumulative percentage point diff. from year 0012345YearsPanel C: Private CreditAll External CasesExternal Only−10−505Cumulative percentage point diff. from year 0012345YearsPanel D: Net Capital InflowsAll External CasesExternal Only−6−4−20Cumulative rate of change from year 0012345YearsPanel A: GDPAll External CasesExternal Only−3−2−101Cumulative percentage point diff. from year 0012345YearsPanel B: InvestmentAll External CasesExternal Only−4−3−2−101Cumulative percentage point diff. from year 0012345YearsPanel C: Private CreditAll External CasesExternal Only−6−4−2024Cumulative percentage point diff. from year 0012345YearsPanel D: Net Capital InflowsTable E4 shows that 87 percent of restructuring episodes in total (156 out of 179 episodes) are
single external debt restructurings. Moreover, the likelihood of “single” external debt restructurings
does not vary across restructuring strategies on external debt: 86 percent, 91 percent and 87 percent
for post-default, weakly or strictly preemptive restructurings, respectively. Sovereigns are more willing
to restructure only external debt than both domestic and external debt, and their choice of debt is
independent of restructuring strategies.

Figure E5 and Table E5 reports AIPW estimates for the eﬀect on GDP, investment, private credit,
and net capital inﬂows during post-default and weakly preemptive restructurings. We follow the same
presentation as in Figure 7. The solid lines in red and the gray bands indicate the point estimates and
95% conﬁdence intervals for all external debt restructurings, respectively. The solid and dotted lines in
blue indicate the point estimates and 95% conﬁdence intervals for single external debt restructurings,
respectively.

All Panels A, B, C, and D in Parts I and II show that estimated impacts on GDP, investment, pri-
vate credit, and net capital inﬂows in the sample of all external debt restructurings (both post-default
and weakly preemptive cases) are quite similar to (to large extent overlapping with) those of restructur-
ing only external debt. This is consistent with the large sample of single external debt restructurings
in total external debt restructuring episodes (87 percent). Clearly, large costs in restructurings are
dominantly associated with restructuring only on external debt.

Table E5: Estimation Results, Single External Debt Restructurings, AIPW

Panel A: Dep. Var. = 100 × (GDPi,t+h − GDPi,t)/GDPi,t

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

Post-default

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive

# of obs.
# of countries
# of debt restructuring obs.

-2.381***
(0.242)
825
29
33
-2.332***
(0.340)
371
14
20

-2.889***
(0.408)
825
29
33
-2.543***
(0.453)
371
14
20

-3.581***
(0.446)
825
29
33
-1.673***
(0.454)
371
14
20

-1.813***
(0.557)
825
29
33
-2.824***
(0.629)
371
14
20

-4.763***
(0.947)
796
29
33
-4.199***
(0.841)
357
14
20

Panel B: Dep. Var. = 100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t)
h = 2

h = 1

h = 3

h = 4

h = 5

-14.70***
(2.079)
796
29
33
-13.21***
(2.114)
357
14
20

Sum

Post-default

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive

# of obs.
# of countries
# of debt restructuring obs.

-1.441***
(0.358)
737
26
29
-0.046
(0.333)
343
13
19

-1.828***
(0.377)
737
26
29
-0.235
(0.442)
343
13
19

-0.322
(0.387)
737
26
29
0.328
(0.385)
343
13
19

0.477
(0.524)
737
26
29
-0.605
(0.420)
343
13
19

0.085
(0.655)
711
26
29
-1.813***
(0.481)
330
13
19

-2.884
(2.089)
711
26
29
-2.062
(1.797)
330
13
19

Notes: All regressions include a constant term, the lagged dependent variable, cyclical component of log of GDP obtained
from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects.

A37

Panel C: Dep. Var. = 100 × (Crediti,t+h/GDPi,t+h − Crediti,t/GDPi,t)
h = 5

h = 3

h = 1

h = 2

h = 4

Post-default

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive

# of obs.
# of countries
# of debt restructuring obs.

-0.151
(0.217)
752
27
30
0.244
(0.190)
371
14
20

-1.542***
(0.330)
750
27
29
-0.990***
(0.273)
371
14
20

-1.829***
(0.421)
748
27
29
-1.468***
(0.357)
371
14
20

-2.060***
(0.470)
747
27
29
-1.627***
(0.469)
371
14
20

-1.979***
(0.548)
720
27
29
-2.225***
(0.548)
357
14
20

Panel D: Dep. Var. = 100 × (N CIi,t+h/GDPi,t+h − N CIi,t/GDPi,t)
h = 5
h = 2

h = 4

h = 1

h = 3

Post-default

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive

# of obs.
# of countries
# of debt restructuring obs.

-0.745
(0.912)
764
29
33
-3.303***
(0.830)
360
14
20

-3.225***
(1.177)
757
29
33
0.372
(0.930)
358
14
20

0.937
(1.183)
751
29
33
1.899**
(0.904)
357
14
20

-0.379
(1.699)
746
29
33
-0.393
(1.082)
357
14
20

-2.123
(1.728)
716
29
32
-0.121
(1.124)
343
14
20

Sum

-7.804***
(1.812)
716
27
29
-6.590***
(1.580)
357
14
20

Sum

-7.088***
(2.015)
730
29
29
-5.635***
(1.561)
348
14
20

Notes: All regressions include a constant term, the lagged dependent variable, cyclical component of log of GDP obtained
from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects.

E.5 Non-overlapping and Sequential Restructurings

To address concerns on inﬂuence by restructurings where duration overlaps, we consider the follow-
ing three alternative classiﬁcations of restructurings: (1) individual restructurings without overlapping
years—if there are a multiple of restructurings where duration overlaps, we treat duration of the pre-
vious restructurings as one ending at year before the next restructuring starts and duration of the
next restructurings as it is deﬁned; (2) sequential restructurings with the initial strategy—if there are
a multiple of restructurings with diﬀerent strategies where duration overlaps, we consider as one “se-
quential restructuring” with the strategy of the initial individual restructurings—; and (3) sequential
restructurings with the worst strategy—similarly, we consider as one “sequential restructuring” with
the worst strategy among individual strategies of overlapping restructurings. For example, if there is a
weakly preemptive restructuring followed by a post-default restructuring, the sequential event would
be coded as a weakly preemptive restructuring under the second classiﬁcation (2) and as a post-default
restructuring under the third classiﬁcation (3).

Figure E6 reports the AIPW estimates for GDP in individual restructurings without overlapping
years and in sequential restructuring with the initial and worst strategy, respectively. Under the
alternative classiﬁcations of restructurings, GDP experiences a similar dynamic after each type of
restructurings. The results show that our baseline AIPW estimates are robust (as reported in Figure
7).

A38

Figure E6: Alternative Classiﬁcations of Restructurings, AIPW

Panel I: GDP under Individual Restructurings without Overlapping Years

Panel II: GDP under Sequential Restructurings with the Initial Strategy

Panel III: GDP under Sequential Restructurings with the Worst Strategy

Notes: The ﬁgure shows local projections of the variables shown in each panel for h = 1, 2,.., 5, where h indicates horizon.

Solid lines in red and purple are point estimates. Bands and dotted lines in purple are 95% conﬁdence intervals. The

sample period is 1970–2013.

A39

BaselineNo Operlapping Restructuring−8−4048Cumulative rate of change x 100012345YearsPost−defaultBaselineNo Operlapping Restructuring−8−4048012345YearsWeakly preemptiveBaselineNo Operlapping Restructuring−8−4048012345YearsStrictly preemptiveBaselineOnly Initial Strategies−7−3.503.57Cumulative rate of change x 100012345YearsPost−defaultBaselineOnly Initial Strategies−7−3.503.57012345YearsWeakly preemptiveBaselineOnly Initial Strategies−7−3.503.57012345YearsStrictly preemptiveBaselineOnly Worst Strategies−7−3.503.57Cumulative rate of change x 100012345YearsPost−defaultBaselineOnly Worst Strategies−7−3.503.57012345YearsWeakly preemptiveBaselineOnly Worst Strategies−7−3.503.57012345YearsStrictly preemptiveE.6 Enlarged Set of Instruments for AIPW Estimation

One might have further concerns on inﬂuence by domestic political system, banking sector, past
banking crises, government behavior, and even more how GDP growth is predicted before the debt
crisis. To address these concerns, we conduct two robustness checks.

First, we apply the AIPW estimation with the augmented set of instruments including our baseline
(selected) instruments: (i) years since the last restructurings (for any types of debt restructurings), (ii)
legislative and executive indices of electoral competitiveness (LIEC and EIEC come from the Database
of Political Institutions), and (iii) government coerciveness indicator during past restructurings (En-
derlein et al 2012).

LIEC is constructed as follows: 1 if there is no legislature; 2 if unelected legislature; 3 if elected
from one candidate; 4 if 1 party, multiple candidates; 5 if multiple parties are legal but only one
party won seats; 6 if multiple parties win seats but the largest party received more than 75% of
the seats; 7 if the largest party got less than 75%. EIEC is constructed as follows: 1 if rival chief
executives in one country, particularly in the setting of armed conﬂicts, are counted as no executives;
2 if executives elected by small appointed juntas or by appointed electoral colleges; 3 if the chief
executives of communist nations (the chairman of the Communist Party); 6 or 7 if competitively
elected prime ministers.

Figure E7: Classiﬁcation Power of the First Stage Regressors with Enlarged Set of
Instruments

Notes: The ﬁgure shows the area under the ROC curve. The ROC area takes a value between 0.50 and 1. Taking a

value of 0.50 indicates that regressors have no classiﬁcation power when they classify observations into those from the

start of debt restructurings and non-restructuring observations. Taking a value of 1 indicating that regressors have the

perfect classiﬁcation power. x1 denotes the set of instruments used in the baseline estimation and x2 includes additional

instruments in addition to the baseline instruments.

We are unable to run logit estimation for strictly preemptive case because the log likelihood function
becomes non-concave largely due to limited observations. Therefore, we take the dummy variable
taking one if there is either a weakly or strictly preemptive debt restructuring as the dependent variable
in columns (5) and (6). ROC suggests that a greater number of instruments improves instruments’
classiﬁcation power when instruments classify observations into the treatment and control groups.
ROC increases from 0.870 to 0.963 for the post-default case, from 0.953 to 0.993 for the weakly
preemptive case, from 0.911 to 0.949 for the “weakly and strictly” preemptive case, respectively.
These improvements are shown in Figure E7. In each panel, two sensitivity curves are reported. The
one is for a set of instruments x1, which is the set of instruments used in our baseline estimation. The
other curve is for another set of instruments x2, which is the set of baseline instruments plus additional
instruments. All of the three panels show that the additional instruments improve the clariﬁcation
power at the ﬁrst stage. In contrast, however, a disadvantage of the enlarged set of instruments is
a reduced number of restructuring observations as reported in Table E6. Samples of restructurings

A40

0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.89x2 ROC area: 0.80Post−default0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.91x2 ROC area: 0.81Weakly preemptive0.000.250.500.751.00Sensitivity0.000.250.500.751.001−Specificityx1 ROC area: 0.80x2 ROC area: 0.70Weakly & Strictly preemptivecovered in this robustness analysis are substantially smaller than those in our baseline: 51 and 29
episodes for post-default and weakly preemptive cases in our baseline vs 25 and 21 episodes in the
robustness analysis.

Table E6: Predicting the Start of Debt Restructurings with Additional Instruments,
Probit

Dependent Variable = Dummy Taking 1 at the Start of Debt Restructurings

Post-default

Weakly preemptive

Weakly and strictly
preemptive

(1)

(2)

(3)

(4)

(5)

(6)

0.782***
(0.183)
0.029
(0.277)
-0.037
(0.641)
-0.341***
(0.077)
-1.095***
(0.390)
21.67***
(5.927)
-0.022
(0.030)
1.563
(2.080)
-4.706**
(1.933)
0.311***
(0.110)

# of past post-default

# of past weakly preemptives

# of past strictly preemptives

# of past banking crises

External debt-to-GDP ratio, lag

Total interest payments-to-GDP ratio, lag

Change in credit ratings, lag

Cyclical comp. of log of GDP per capita, lag

GDP growth rate, lag

Freedom index, civil liberty, lag

Additional instruments

# of years from the previous debt restruct.

Legislative index of electoral comp., lag

Executive index of electoral comp., lag

Coerciveness index, lag

Country ﬁxed eﬀects
# of observations
# of countries
# of restructuring observations
Likelihood ratio
(p-value)
Pseudo R-squared
ROC

Yes
854
51
46
96.98
(0.000)
0.271
0.870

1.826***
(0.407)
0.531
(0.419)
-10.30
(857.0)
-0.229
(0.168)
-5.024***
(1.573)
39.65***
(12.51)
0.014
(0.049)
-4.246
(4.097)
-7.656**
(3.465)
0.332
(0.241)

-0.217***
(0.064)
0.317
(0.236)
-0.284
(0.264)
-0.153*
(0.086)
Yes
429
21
29
113.19
(0.000)
0.533
0.963

-1.578**
(0.616)
0.484*
(0.263)
-1.399*
(0.824)
-0.301
(0.190)
0.435
(1.431)
49.30***
(18.27)
-0.001
(0.039)
-6.755*
(3.477)
3.865
(3.662)
-0.390
(0.283)

Yes
371
51
25
82.36
(0.000)
0.450
0.953

-6.624
(709.8)
6.201***
(2.062)
-4.767
(2.932)
-0.585
(0.718)
9.974*
(5.255)
-118.3
(81.73)
0.258**
(0.128)
16.73
(12.85)
-29.60**
(13.22)
-1.066
(0.940)

-2.688***
(0.820)
1.252
(1.469)
-0.056
(0.667)
-0.108
(0.363)
Yes
298
21
21
120.39
(0.000)
0.793
0.993

0.066
(0.359)
-0.223
(0.172)
-0.080
(0.397)
0.031
(0.109)
-2.586**
(1.198)
82.18***
(16.04)
-0.031
(0.032)
-8.193***
(3.089)
4.364
(3.226)
-0.471**
(0.206)

Yes
496
51
32
82.00
(0.000)
0.346
0.911

-1.111*
(0.569)
-0.554**
(0.229)
-0.207
(0.596)
0.094
(0.142)
-0.237
(1.721)
32.74
(21.27)
0.035
(0.041)
-10.43**
(4.211)
1.499
(4.455)
-0.721**
(0.297)

-0.182***
(0.042)
-0.155
(0.254)
0.293
(0.223)
0.048
(0.125)
Yes
361
21
26
94.15
(0.000)
0.504
0.949

Notes: All regressions include country ﬁxed eﬀects. The sample period is 1970–2013. ***, **, and * indicate statis-
tical signiﬁcance at 1%, 5%, and 10% 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 past six years is used predict “weakly and strictly” preemptive cases.

Figure E8 and Table E7 report the AIPW estimates for GDP, investment, private credit, and
net capital inﬂows with the enlarged set of instruments. The results show that our baseline AIPW
estimates are robust (Table C2 in Appendix C.2); post-default restructurings experience a more se-
vere and prolonged decline in GDP, investment, private credit, and net capital inﬂows than weakly
preemptive restructurings.

A41

Table E7: Estimation Results with Enlarged Set of Instruments, AIPW

Panel A: Dep. Var. = 100 × (GDPi,t+h − GDPi,t)/GDPi,t
h = 2

h = 4

h = 1

h = 3

Post-default

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly preemptive

# of obs.
# of countries
# of debt restructuring obs.

-4.371***
(0.561)
400
16
28
-1.503**
(0.762)
96
11
21
-2.646***
(0.304)
340
13
25

-8.054***
(0.810)
400
16
28
-1.600*
(0.925)
96
11
21
-1.291***
(0.456)
340
13
25

-5.653***
(0.560)
400
16
28
-0.523
(1.026)
96
11
21
1.127
(0.744)
340
13
25

-4.242***
(0.627)
400
16
28
-3.118***
(1.023)
96
11
21
-0.253
(1.045)
340
13
25

h = 5

Sum

-4.502***
(0.725)
386
16
28
-2.801**
(1.206)
96
11
21
-2.997***
(1.053)
327
13
25

-26.28***
(2.858)
386
16
28
-9.544***
(3.428)
96
11
21
-5.861*
(3.111)
327
13
25

Panel B: Dep. Var. = 100 × (Investmenti,t+h/GDPi,t+h − Investmenti,t/GDPi,t)

Post-default

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly preemptive

# of obs.
# of countries
# of debt restructuring obs.

h = 1

h = 2

h = 3

h = 4

h = 5

-0.891**
(0.417)
397
16
27
1.103
(0.983)
92
11
21
0.613
(0.392)
340
13
25

-1.144***
(0.443)
397
16
27
1.081
(0.983)
92
11
21
1.157
(0.464)
340
13
25

-0.334
(0.414)
397
16
27
1.330
(1.078)
92
11
21
1.226***
(0.423)
340
13
25

1.088**
(0.465)
397
16
27
-1.258**
(0.596)
92
11
21 21 21
1.271**
(0.516)
340
13
25

0.775
(0.622)
383
16
27
-2.167***
(0.699)
92
11

1.008*
(0.609)
327
13
25

Sum

-0.240
(2.256)
383
16
27
0.089
(3.247)
92
11

5.456**
(2.159)
327
13
25

Panel C: Dep. Var. = 100 × (Crediti,t+h/GDPi,t+h − Crediti,t/GDPi,t)
h = 2

h = 4

h = 1

h = 3

h = 5

Post-default

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly preemptive

# of obs.
# of countries
# of debt restructuring obs.

-0.556***
(0.222)
378
15
27
-0.182
(0.188)
340
13
25

-4.184***
(0.389)
378
15
27
-1.614***
(0.287)
340
13
25

-3.757***
(0.404)
378
15
27
-1.505***
(0.379)
340
13
25

-3.552***
(0.547)
378
15
27
-1.545***
(0.534)
340
13
25

-4.679***
(0.558)
365
15
27
-1.510**
(0.627)
327
13
25

Sum

-16.85***
(1.839)
365
15
27
-6.512***
(1.816)
327
13
25

Notes: The table shows local projections (AIPW) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively.

A42

Panel D: Dep. Var. = 100 × (N CIi,t+h/GDPi,t+h − N CIi,t/GDPi,t)
h = 2

h = 1

h = 3

h = 4

h = 5

Post-default

# of obs.
# of countries
# of debt restructuring obs.
Weakly preemptive

# of obs.
# of countries
# of debt restructuring obs.
Weakly & strictly preemptive

# of obs.
# of countries
# of debt restructuring obs.

-5.067***
(1.047)
393
16
28
0.174
(1.164)
96
11
21
0.238
(0.782)
336
13
25

-7.391***
(1.493)
392
16
28
-0.138 -
(1.323)
95
11
21
3.822***
(0.927)
335
13
25

0.1705
(1.111)
392
16
28
2.467*
(1.369)
94
11
21
3.180***
(0.867)
335
13
25

2.310**
(1.091)
392
16
28
-5.727***
(1.486)
94
11
21
2.527**
(1.010)
335
13
25

-1.533
(1.020)
378
16
27
0.425
(1.587)
94
11
21
5.481***
(1.167)
322
13
25

Sum

-10.50**
(4.790)
375
16
27
-6.188
(4.907)
92
11
21
16.39***
(4.054)
319
13
25

Notes: The table shows local projections (AIPW) of the variables shown in each panel for h = 1, 2,.., 5, where h indicates
horizon. All regressions include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP
obtained from a Hodrick-Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard
errors, clustered at the country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and
10% level, respectively.

Figure E8: Local Projections with the Enlarged Set of Instruments, AIPW

Notes: The ﬁgure shows local projections (with enlarged set of instruments) of the variables shown in each panel for h

= 1, 2,.., 5, where h indicates horizon. Solid lines in red, blue and brown are point estimates. Gray Bands and dotted

lines in blue and brown are 95% conﬁdence intervals. The sample period is 1970–2013.

A43

Post−defaultWeakly preemptiveWeakly & strictly preemptive−10−505Cumulative rate of change from year 0012345YearsPanel A: GDPPost−defaultWeakly preemptiveWeakly & strictly preemptive−4−2024Cumulative percentage point diff. from year 0012345YearsPanel B: InvestmentPost−defaultWeakly & strictly preemptive−6−4−20Cumulative percentage point diff. from year 0012345YearsPanel C: Private CreditPost−defaultWeakly preemptiveWeakly & strictly preemptive−10−50510Cumulative percentage point diff. from year 0012345YearsPanel D: Net Capital InflowsSecond, we apply the AIPW estimation with GDP projections included as an additional control.
The data on GDP forecasts are available from the IMF WEO over the period 1990–2013 at an annual
frequency. We include the variable as additional instrument in both the ﬁrst-stage and second-stage
regressions. The estimation is speciﬁed as follows:

gi,t+h = αh

i + ΛP D,hDP D

i,t+1 + βF orecastgF orecast

i,t

+ Xi,tβββh + (cid:15)i,t+h,

(E.1)

h = 1, 2, .., 5

i,t

+ gF orecast,t+5

+ gF orecast,t+4

denotes the GDP growth rate forecasts deﬁned as gF orecast

+gF orecast,t+2
where gF orecast
i,t
gF orecast,t+3
)/5, which is the average of GDP growth rate forecasts over
i,t
the next ﬁve years in pre-restructuring year t. With limited sample of the GDP forecasts, our sample
of restructurings is signiﬁcantly reduced in this estimation. To be comparable with this estimation,
we also estimate our baseline speciﬁcation (equation 2 without GDP growth forecasts) with the same
sample as in equation (E.1). Results are reported in Figure E9 and Table E8. These show that our
baseline results for post-default restructurings are robust when we control for the GDP forecasts.

= (gF orecast,t+1

i,t

i,t

i,t

i,t

+

Figure E9: GDP after Post-default Restructurings Controlling for GDP Forecasts, AIPW

Notes: The ﬁgure shows local projections (with enlarged set of instruments including GDP forecasts) of GDP shown in

each panel for h = 1, 2,.., 5, where h indicates horizon. Solid lines in red and purple are point estimates. Gray Bands

and dotted lines in purple are 95% conﬁdence intervals. The sample period is 1990–2013.

Table E8: Estimation Results Controlling for GDP Forecasts, AIPW

Dep. Var. = 100 × (GDPi,t+h − GDPi,t)/GDPi,t

Post-default, with GDP forecasts

# of obs.
# of countries
# of debt restructuring obs.
Post-default, without GDP forecasts

# of obs.
# of countries
# of debt restructuring obs.

h = 1

h = 2

h = 3

h = 4

h = 5

Sum

-5.858***
(0.606)
145
8
9
-4.036***
(0.966)
145
8
9

-7.205***
(0.654)
145
8
9
-5.350***
(1.071)
145
8
9

-6.374***
(0.683)
145
8
9
-4.606***
(1.015)
145
8
9

-5.685***
(0.877)
145
8
9
-3.349***
(1.193)
145
8
9

-4.382***
(0.863)
138
8
9
-2.694***
(1.180)
138
8
9

-28.26***
(3.304)
138
8
9
-18.51***
(4.877)
138
8
9

Notes: The table shows local projections (AIPW) of GDP for h = 1, 2,.., 5, where h indicates horizon. All regressions
include a constant term, lagged dependent variables (lag 1), cyclical component of log of GDP obtained from a Hodrick-
Prescott ﬁlter with a smoothing parameter of 100, and country ﬁxed eﬀects. Robust standard errors, clustered at the
country-level, are in parentheses. ***, **, and * indicate statistical signiﬁcance at 1%, 5%, and 10% level, respectively.
The estimates are based on the same sample of restructuring observations.

A44

Post−default withGDP forecasts as a controlPost−defaultwithout GDP forecasts as a control−8−6−4−20Cumulative rate of change x 100012345Years6a Circuit de la Foire Internationale 

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