2019
DOI: 10.1016/j.euroecorev.2019.04.009
|View full text |Cite
|
Sign up to set email alerts
|

Sovereigns going bust: Estimating the cost of default

Abstract: This paper estimates the cost of sovereign default by using novel econometric methods-dynamic local projections applied to a sample that is re-randomised using inverse propensity score weights. We find that the impact of default on output is negative, significant and persistent-around 2.8% of GDP on impact and 4.8% at peak. The downturn is driven by sharp falls in investment, accompanied by a collapse in gross trade. The cost rises dramatically if the default is followed by a systemic banking crisis, peaking a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
51
1

Year Published

2020
2020
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 52 publications
(52 citation statements)
references
References 54 publications
0
51
1
Order By: Relevance
“…Forni et al (2016) use the same approach and consider private restructurings after a Paris Club event as exogenous. Kuvshinov and Zimmermann (2016) present an alternative approach to measure the effects of PSI by estimating impulse-response functions (IRFs). They argue that this modeling technique is suitable because it explicitly controls for the endogenous feedbacks inherent to the dynamic relation between defaults and the macroeconomic context in which they occur.…”
Section: Methodsology: Local Projectionsmentioning
confidence: 99%
See 1 more Smart Citation
“…Forni et al (2016) use the same approach and consider private restructurings after a Paris Club event as exogenous. Kuvshinov and Zimmermann (2016) present an alternative approach to measure the effects of PSI by estimating impulse-response functions (IRFs). They argue that this modeling technique is suitable because it explicitly controls for the endogenous feedbacks inherent to the dynamic relation between defaults and the macroeconomic context in which they occur.…”
Section: Methodsology: Local Projectionsmentioning
confidence: 99%
“…In turn, Asonuma and Trebesch (2016) study the different dynamics created by pre-preemptive and post-default restructurings. There are also two very recent papers dealing with the costs of debt restructurings: and Kuvshinov and Zimmermann (2016).…”
Section: Introductionmentioning
confidence: 99%
“…6 Using higher frequency data, Levy Yeyati and Panizza (2011) actually show that output contraction precedes default and that default episodes seem actually already to mark the beginning of the economic recovery. Furceri and Zdzienicka (2012) and Kuvshinov, and Zimmermann (2016) Trebesch and Zabel (2017) have investigated the economic consequences of debt restructurings, focusing in particular on their outcomes in terms of economic growth. Asonuma and Trebesch (2016) consider the asymmetric output costs between preemptive -that can be implemented prior to a payment default-and post-default restructurings.…”
Section: Related Literaturementioning
confidence: 99%
“…We need this assumption that default costs are larger in "good times" than in "bad times" in order to match empirical observations that a country is more likely to default when it is facing with a lower level endowment or a lower GDP growth rate (e.g., Kuvshinov and Zimmermann, 2017). This assumption is also consistent with models with endogenous production.…”
Section: Calibration and Simulationmentioning
confidence: 83%
“…Following Furceri and Zdzienicka (2012), we take the data on default episodes from five different sources (De Paoli et al, 2006;Detragiache and Spilimbergo, 2001;Laeven and Valencia, 2008;Levy-Yeyati and Panizza, 2011;Reinhart et al, 2003). In order to follow up more recent studies on sovereign defaults, we also collect default episodes from Asonuma and Trebesch (2016), Trebesch and Zabel (2017), and Kuvshinov and Zimmermann (2017). These data sources cover defaults on their debt to private creditors.…”
Section: Cross-sectional Regressionsmentioning
confidence: 99%