Life After Default: Private vs. Official Sovereign Debt Restructurings

Abstract

This paper studies the relationship between sovereign debt default and annual GDP growth distinguishing between private and o¢ cial deals. Using the Synthetic Control Method to analyze 23 o¢ cial and private defaulters from 1970 to 2017, we find that private and o¢ cial restructurings are associated to di¤erent growth outcomes. Private defaults generate output losses both during the crisis and persisting over time. Conversely, official defaulters do not show a permanent drop in GDP per capita, neither during the crisis nor in its aftermath. We present further evidence for the heterogeneity of the economic impact of debt restructurings by controlling for the severity of the default and distinguishing between debt flow and stock reduction. Using panel data analysis to analyze 548 restructuring episodes, we confirm that o¢ cial and private defaults may have di¤erent e¤ects on GDP growth and should then be treated di¤erently.

The Output Costs of Hard and Soft Sovereign Default

“…Asonuma and Trebesch (2016) study output losses in preemptive vs. post-default debt restructurings. Relatedly, a recent paper byMarchesi (2016) also uses the haircut data byCruces and Trebesch (2013) and confirms our finding that more severe defaults (with high haircuts) are associated with lower growth. See alsoTrebesch (2009) for a similar setup using the coerciveness index.…”

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

“…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). From a policy perspective, these findings have important implications.…”

“…We 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).3 SeeBroner et al (2013),Reinhart et al (2016),Kaminsky and Vega-García (2016), andArteta and Hale (2008) for capital flows and sovereign debt crises.4 SeeAsonuma and Trebesch (2016) for empirical and theoretical findings on the choice of restructuring strategies.5 Our 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.6 Our finding on smaller output costs in post-default restructurings with small haircuts is in line withTrebesch and Zabel (2017)-"soft defaults" classified by both government's behavior (coerciveness) and haircuts-andMarchesi (2016).©International Monetary Fund. Not for Redistribution…”

“…See alsoBocola (2016),Arellano et al (2017),Engler and Große Steffen (2016),Bolton and Jeanne (2011), Perez (2016),Pei (2016), andMallucci (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.13 SeeDas et al (2012),Cheng et al (2018), andMarchesi (2016) for work on official external debt restructurings,Reinhart and Rogoff (2011a) andErce and Mallucci (2018) for work on restructurings involving domestic debt.14 Their classification of the nature of the restructuring strategy is only made after its completion.©International Monetary Fund. Not for Redistribution…”

Is to Forgive to Forget? Sovereign Risk in the Aftermath of Private or Official Debt Restructurings

“… 39 Hence, the positive growth prospect observed for official defaulters after the end of the default might be due to the absence of a negative stigma in the credit markets (see, for example, Marchesi and Masi 2021 ). …”