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Currency concentration in sovereign debt, exchange rate cyclicality, and volatility in consumption

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Abstract

For emerging economies, borrowing abroad is a double-edged sword: it can buffer against adverse economic shocks and smooth their domestic consumption; however, it can also amplify volatility in consumption, depending on the currency in which the debt is denominated and cyclicality in the borrower’s exchange rate. We empirically investigate the nexus among external debt portfolios, exchange rate cyclicality, and volatility in consumption of low- and middle-income countries. Since 1980, many countries have concentrated their external debt portfolios’ currency composition. By constructing debt-weighted effective exchange rates, we find that currency concentration magnifies exchange rate pro-cyclicality, making domestic consumption more volatile when national income fluctuates. Our results endorse diversifying the currency composition of external debt to mitigate the negative consequences of “original sin.”

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Notes

  1. Recently some emerging market economies improved their ability to borrow abroad in their home currencies (Arslanalp & Tsuda, 2014; Du & Schreger, 2016). Nonetheless, original sin remains generally prevalent among LMICs.

  2. Several studies (Claessens, 1992; Dodd & Spiegel, 2005; Fujii, 2017) endorse a portfolio perspective when considering the currency composition of indebtedness among emerging economies.

  3. For consistency and comparability of results, we limited our sample to countries with effective observations of currency denominations, exchange rates, and output for pre-EUR and EUR periods.

  4. An exception is China during 2016 and 2017 when the Chinese yuan (CNY) comprised 10.92% of the SDR basket.

  5. The “synthetic EUR” share in Figs. 1 and 2 connects the two series, i.e., the sum of debt denominated in DEM and FRF for 1980–2000 and the EUR for 2001–2017.

  6. We regressed foreign currency shares along a constant and time trend to see if the time trend’s coefficient is statistically significant at the 5% level (positive or negative).

  7. Until 1980, SDRs contained 16 currencies with weights changing annually. Their composition was revised during 1981 to feature the USD, DEM, FRF, JPY, and GBP with weights revised every 5 years. The EUR replaced the DEM and FRF during 1999, and CNY joined the basket during 2016. For 1980, we use the weights applicable in 1981.

  8. A rise in the value of DEER indicates effective depreciation in country i's currency.

  9. We used GDP-deflator inflation rates from the World Development Indicators (WDI) database.

  10. Another contributing factor may be the dollarization that countries such as Ecuador and El Salvador adopted during the 2000s.

  11. See Cordella and Gupta (2015) for an analysis of nominal effective exchange rate cyclicality of advanced and emerging market economies.

  12. The literature concerning this contentious topic is too voluminous to cite here. See Jappelli and Pistaferri (2010) for a comprehensive review.

  13. Highlighting the drawbacks of the Hodrik–Prescott filter, Hamilton (2018) proposes a better alternative that uses the linear population projection of yt+h on a constant and p most recent values of y at date t. For the annual frequency data, we set the parameter values as h = 2 and p = 2, following the suggestions of Hamilton (2018).

  14. Savings to GDP are lagged to avert simultaneity with consumption.

  15. See the Data Appendix for the list of sampled countries.

  16. If perfectly smoothed, consumption is constant and the intercepts are 0. The effect of capital account openness reduces the positive intercepts of exchange rate regimes.

  17. The exchange rate regime dummies we included in estimating Eqs. (5) and (6) capture only the regime-specific consumption growth that remain constant irrespective of income changes.

  18.  ± 2% is the standard width of exchange rate bands adopted by the regimes in the rigid category. We also used 5% as an alternative threshold to find qualitatively similar results.

  19. Unlike RDEER, a rise in REER indicates real appreciation for domestic countries.

  20. Fujii (2017) argues that correspondence between the currency compositions of external debt and international trade have important implications. Specifically, the author finds that LMICs with more accordant debt–trade currency compositions tend to have growth advantages over those without.

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Acknowledgements

This research is supported by JSPS KAKENHI Grant Number 18K01715. The author is grateful to two anonymous referees and an associate editor of the journal for helpful comments and suggestions. He also thanks **ngwang Qian, Isabelle Mejean, workshop participants at Université Catholique de Louvain, and seminar participants at the Center for Risk Research of Shiga University for helpful comments on early versions of the paper. All errors are solely the author’s.

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Authors and Affiliations

  1. School of Economics, Kwansei Gakuin University, 1–155 Uegahara Ichiban-cho, Nishinomiya, Hyogo, 662-8501, Japan

    Eiji Fujii

  2. Center for Economic Studies and Ifo Institute, Munich, Germany

    Eiji Fujii

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  1. Eiji Fujii
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Correspondence to Eiji Fujii.

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Data Appendix

Data Appendix

1.1 Sources

Currency composition of external debt: World Bank’s International Debt Statistics.

Exchange rate regime indicators: Ilzetzki et al. (2019)a

Index of capital account openness: Chinn and Ito (2006)b

Incidents of coups d’état: Powell and Thyne (2011)c

Other macroeconomic and external account variables: World Bank’s World Development Indicators and International Monetary Fund’s International Financial statistics.

Notes:

aWe downloaded the regime index from http://www.carmenreinhart.com/data/ and https://www.ilzetzki.com/irr-data. We use coarse classifications.

bThe index is based on binary dummy variables that codify restrictions on cross-border financial transactions reported in IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions. It is the first principal component of the original variables on regulatory controls over current or capital account transactions, the existence of multiple exchange rates, and requirements of surrendering export proceeds.

cWe use “Dataset 2: Coup Attempts, 1950–Present” in Powell and Thyne (2011). Coups are illegal and overt attempts by the military or other elites within the state apparatus to unseat a sitting executive. Our dummy variable equals 1 for successful and unsuccessful coup attempts because they indicate political instability.

1.2 Frequency

Annual for all series.

1.3 Sample periods

The primary sample period is 1980–2017. The pre-EUR and EUR sub-periods are 1980–2000 and 2001–2017, respectively. Depending on data availability, some countries have smaller samples.

Euro: exchange rate 1999–2017; currency composition 2001–2017.

Deutsche mark and French franc: exchange rate 1973–1998; currency composition 1973–2000; Exchange rates for 1999 and 2000 are set to €1 = 1.95583 DEM and €1 = 6.55957 FRF.

Synthetic EUR: 1980–2017, of which the 1980–2000 period is calculated by the weighted sum of DEM- and FRF-denominated debt.

1.4 Sampled countries

Our sample consists of low-, lower-middle-, and upper-middle-income countries listed in the WDI, for which data for the currency composition of external debt, exchange rates, and GDP growth rates are available for pre-EUR and EUR periods. The primary sample includes 106 countries (24 low-income and 82 middle-income countries) listed in the income stratification section.

The Sect. 5 analyses reduce the effective number of sampled countries through limited data to construct variables for estimations. More specifically, for the estimates in Tables 4, 5, and 6, the effective number of countries is 86. The following countries were dropped for data constraints: Angola, Central African Republic, China, Dominica, Ethiopia, Fiji, Gambia, Grenada, Guyana, St. Lucia, the Maldives, Malaysia, Panama, Papua New Guinea, the Solomon Islands, Tonga, Vanuatu, Samoa, Yemen, and Zambia.

For the estimates in Table 7, the sample consists of the following 37 countries whose REER data are available: Albania, Armenia, Burundi, Bulgaria, Bolivia, Brazil, Bhutan, Cote d'Ivoire, Cameroon, the Democratic Republic of the Congo, Colombia, Costa Rica, Dominican Republic, Algeria, Gabon, Georgia, Ghana, Iran, Lesotho, Morocco, Moldova, Mexico, Malawi, Nigeria, Nicaragua, Pakistan, the Philippines, Paraguay, Romania, Russia, Sierra Leone, Togo, Tunisia, Uganda, Ukraine, St. Vincent and the Grenadines, and Venezuela.

1.5 Income stratification

Low-income countries comprise the following 24 countries: Burundi, Benin, Burkina Faso, Central African Republic, the Democratic Republic of the Congo, Comoros, Eritrea, Ethiopia, Guinea, Gambia, Guinea-Bissau, Haiti, Madagascar, Mozambique, Malawi, Niger, Nepal, Rwanda, Senegal, Sierra Leone, Chad, Togo, Tanzania, and Uganda.

Lower-middle-income countries comprise the following 42 countries: Angola, Armenia, Bangladesh, Bolivia, Bhutan, Cote d'Ivoire, Cameroon, Republic of the Congo, Cabo Verde, Egypt, Georgia, Ghana, Guatemala, Honduras, Indonesia, India, Jordan, Kenya, Kyrgyz Republic, Cambodia, Lao, Sri Lanka, Lesotho, Morocco, Moldova, Mongolia, Mauritania, Nigeria, Nicaragua, Pakistan, the Philippines, Papua New Guinea, Sudan, the Solomon Islands, El Salvador, Tajikistan, Tunisia, Ukraine, Vietnam, Vanuatu, Yemen, and Zambia.

Upper-middle-income countries comprise the following 40 countries: Albania, Azerbaijan, Bulgaria, Belarus, Belize, Brazil, Botswana, China, Colombia, Costa Rica, Dominica, Dominican Republic, Algeria, Ecuador, Fiji, Gabon, Grenada, Guyana, Iran, Jamaica, Kazakhstan, Lebanon, St. Lucia, the Maldives, Mexico, Mauritius, Malaysia, Panama, Peru, Paraguay, Romania, Russia, Serbia, Thailand, Tonga, Turkey, St. Vincent and the Grenadines, Venezuela, Samoa, and South Africa (Figs. 1, 2, 3).

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Fujii, E. Currency concentration in sovereign debt, exchange rate cyclicality, and volatility in consumption. Rev World Econ 160, 169–192 (2024). https://doi.org/10.1007/s10290-023-00493-6

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