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Borrowing costs after debt relief

The Covid-19 pandemic is straining the public finances of many developing countries (Djankov and Panizza 2020). In response, a series of proposals and calls to action have been launched by experts and policy makers (Bolton et al. 2020a, 2020b, Bulow et al. 2020; Horn et al. 2020; Landers et al. 2020). In a short time, the international community – under the leadership of the G20 – agreed to help poor countries by proposing a suspension of debt service due in the second half of 2020. As part of the Suspension of Service Initiative debt (DSSI), participating countries can ask their bilateral lenders to defer debt service repayments for three years without affecting the net present value (NPV) of public debt. The size of the liquidity provision under the DSSI is not negligible. For all eligible countries, it stands at $ 10.2 billion and represents around one-fifth of the budget deficit due to the Covid-19 shock. However, many eligible countries have so far been reluctant or refused to participate in DSSI. It may seem like a confusing answer to what at first glance is free money in times of great need. Yet these countries fear that participation in the DSSI may signal debt sustainability issues that could trigger sovereign ratings downgrades and higher sovereign borrowing costs.1

In a recent article (Lang et al. 2020), we provide a first assessment of the short-term impact of DSSI on sovereign bond spreads. In particular, we test whether the potential benefits of providing short-term liquidity outweigh the stigma effects that may be associated with participating in the debt relief initiative. Estimating the effect of debt relief on sovereign bond spreads is generally difficult, as debt relief initiatives are generally not attributed to chance. Comparing debt relief recipients to other countries is therefore not instructive. However, the case of the DSSI makes it possible to construct plausible counterfactuals. Unlike most debt restructurings, the DSSI was announced simultaneously for the 73 eligible countries and, therefore, was not tailored to the needs of each country. In addition, the eligibility criteria were based on pre-existing income thresholds rather than financing needs or the severity of the shock, which crucially influence borrowing costs.

Sovereign borrowing costs fell by around 300 basis points

We use this event to analyze its impact on the spreads of sovereign bonds of the 16 countries eligible for DSSI with access to the international market and daily data available. We have used the Synthetic Control Method (SCM) developed by Abadie and Gardeazabal (2003) and now increasingly used in similar contexts (see Marchesi and Masi 2020). For each country eligible for DSSI, we build a synthetic control (or “doppelganger”) combining countries from a pool of middle-income countries not eligible for DSSI.2

Figure 1 shows our main result. The comparison of the spreads of the sovereign bonds of the countries eligible for the DSSI with their synthetic controls shows that the sovereign spreads decreased considerably after the debt relief. Several days after the DSSI announcement, spreads in eligible countries were down about 300 basis points (bps) more than in comparable untreated doppelganger countries. This average effect differs from country to country, but it is negative for all borrowers eligible for debt relief. This result is robust to the different specifications of the model, including the generalized synthetic control method (Xu 2017). In addition, a set of placebo tests in space and time shows that the effect on spreads is due to the DSSI and cannot be explained by the (contemporary) demand of an IMF program.

Figure 1 Spreads of sovereign bonds in DSSI-eligible countries compared to their synthetic controls

Remarks: The figure represents the difference between the real spreads of sovereign bonds and those of the synthetic control (spread gap) for the countries eligible for the DSSI. The solid red line is the average of the country specific spreads. Solid gray lines refer to countries that joined the DSSI on September 17, 2020, while dotted gray lines refer to countries that have not officially applied to join the initiative (Ghana, Honduras, Kenya, Mongolia, Nigeria and Uzbekistan). The vertical lines indicate the announcement of the DSSI on April 15, 2020 (solid line) and the first participation in the DSSI on May 1, 2020 (dotted line). The dots indicate the participation of each country in the DSSI. See description in main text. Source: Bloomberg, Our World in Data and IMF World Economic Outlook.

The fall in spreads seems to be due to the provision of liquidity

To discriminate between two mechanisms that could drive the results, we test the heterogeneous effects of debt relief. We focus on two sources of heterogeneity – the size of DSSI relief and the share of private creditors in debt service – and estimate their effects in a difference-in-differences framework using the projection method. local. This analysis shows that the decline in bond spreads for DSSI-eligible countries is greater for countries that have a higher share of debt service due during the eligibility period (between May and December 2020, graph 2, part A). On the other hand, the fall in spreads does not depend on the size of private creditors (Chart 2, Panel B). As there is no increase in spreads, not even for countries that owe a large portion of repayments to private creditors, these results do not support the presence of a stigma effect. On the contrary, the results are consistent with a positive liquidity effect due to the postponement of debt service due in 2020.

Figure 2 Cash flow versus stigma

A) Size of DSSI relief

B) Share of private creditors

Remarks: The figures plot the impulse response functions of the differential effect of the DSSI announcement (t = 0) between eligible and non-eligible countries on sovereign bond spreads. Panels A and B divide the sample between eligible countries that have benefited from DSSI relief greater or less than 0.5% of GDP and those whose debt service due to private creditors is greater or less than 60% of the total debt service due under the DSSI (the two thresholds are median values). See description in main text. Data source: Bloomberg and IMF World Economic Outlook.


The international community is currently discussing the possibility of extending the current initiative to suspend debt service in developing countries until 2021. Our results suggest that this simple moratorium on neutral NPV debt – involving no discount for creditors – can effectively help countries overcome the crisis.

Our findings also add to the larger literature on debt restructuring. They show that rapid and unconditional debt rescheduling to countries facing short-term liquidity shocks can be an effective instrument of financial support that can help avoid severe defaults (Trebesch and Zabel 2017). In addition, our results support the design and adoption of simple conditional government debt instruments with floating grace periods to help poor countries mitigate their exposure to negative shocks (Cohen et al. 2008).

Two final qualifications are important. First, our results could be generalized to other situations where countries face a short-term crisis. In the presence of severe negative shocks, only the deferral of debt service could help reduce borrowing costs. However, this does not mean that the suspension of debt service will be the optimal response to the Covid-19 crisis in the months to come. If the shock persists, the liquidity crisis could evolve into a solvency crisis, as a change in the long-term growth rate of the economy would affect debt sustainability. In such a scenario, a reduction in the debt stock might be necessary to reduce debt distress and restore debt sustainability. Second, our analysis focuses on NPV neutral debt relief provided by the public sector. How the markets would react if private creditors also joined the initiative (as requested by the G20 and major international financial institutions) remains an open question.

The references

Abadie A and J Gardeazabal (2003), “The Economic Costs of Conflict: A Case Study of the Basque Country”, American Economic Review 93 (1): 113-132.

Bolton P, L Buchheit, PO Gourinchas, M Gulati, CT Hsieh, U Panizza and B Weder di Mauro (2020a), “Born of Necessity: A Debt Stop for COVID-19”, CEPR Policy Insight n ° 103.

Bolton P, M Gulati and U Panizza (2020b), “Legal air coverage»,, October 13.

Bulow J, C Reinhart, K ​​Rogoff and C Trebesch (2020), “The debt pandemic», IMF Finance and Development, Fall.

Cohen, D, H Djoufelkit-Cottenet, P Jacquet and C Valadier (2008), “Lending to the Poorest Countries: A New Counter-Cyclical Debt Instrument”, Working Paper 269, OECD Development Center.

Djankov S and U Panizza (2020), “COVID-19 in Developing Economies: A New eBook»,, June 22.

Cor S, C Reinhart and C Trebesch (2020), “China’s foreign lending and the looming developing country debt crisis»,, May 4.

Landers C, N Lee and S Morris (2020), “Over $ 1 trillion in MDB firepower exists as COVID-19 ‘shattering glass’ moment approaches”, Center for Global Development.

Lang V, D Mihalyi and AF Presbitero (2020), “Debt relief, liquidity provision and sovereign bond spreads”.

Marchesi S and T Masi (2020), “Debt restructuring during COVID-19: private and official agreements»,, May 4.

Trebesch C and M Zabel (2017), “The output cost of hard and soft sovereign default”, European Economic Review 92: 416-432.

Xu Y (2017), “Generalized synthetic control method for causal inference with cross-sectional time series data”, Policy Analysis 25: 57-76.

End Notes

1 See reports from international institutions (IMF 2020, World Bank 2020), Think Tanks (ODI 2020) and press articles in The Economist and Reuters, among others. More details on DSSI can be found here and on the World Bank website.

2 Since the dynamics of sovereign spreads depend on fiscal and economic performance, we take the growth of real GDP, the current account, the fiscal balance and the public debt (all in shares of GDP) as macroeconomic variables to construct the synthetic control. . Additionally, to compare countries with similar bond spread dynamics before DSSI, we match the spread levels to specific dates. Finally, to take into account the differences in the intensity of the Covid-19 crisis, we use the number of cases per inhabitant. See Lang et al. (2020) for more details.

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