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Demographics and Current Account Imbalances: Accounting for the Full Age Distribution

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Abstract

This paper investigates the relationship between demographics and the current account (CA) employing a polynomial measure that considers the full age distribution. Using a panel of 49 countries over 30 years of data, the polynomial produces economically more intuitive and stable results than previously used demographics measures. We find strong and robust non-linear effects. Specifically, we find that a relatively larger share of young cohorts is correlated with a lower CA, while a larger share of working-age cohorts is correlated with a higher CA. Our results show that disregarding any age group may fail to account for the full demographic effect on the CA over time and across countries. Accounting for the full age distribution thus allows to explain a larger share of observed CA surpluses and deficits.

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Notes

  1. Studies distinguish between the young and old-age dependency ratio (Chinn and Prasad 2003; Bosworth and Chodorow-Reich 2007; Gruber and Kamin 2007; Chinn and Ito 2008a), the domestic and foreign dependency ratio (Hung and Gamber 2010), or the current and future dependency ratio (Jaumotte and Sodsriwiboon 2010).

  2. Our sample covers 49 countries, among which 24 AEs and 25 EMEs. Sect. 3 describes the sample in more detail.

  3. For a detailed explanation of this restriction, see Almon (1965) and Smith and Giles (1976).

  4. For a detailed discussion of the choice of fundamentals, we refer to Phillips and others (2013) and Cubeddu and others (2019).

  5. Indeed, the EBA regression finds a negative and significant relationship for life expectancy of prime savers and CA balances, and a positive and significant coefficient for the interaction. Dao and Jones (2018) argue these results imply that increasing life expectancy has a positive effect on CA balances only for AEs facing unfavorable old-age support ratios. Following their model, they further argue that higher future old-age dependency ratios are associated with less generous pay-as-you-go pension systems.

  6. This assumption can easily be tested using different polynomial degrees in the estimation equation. Section 5 presents the results of these tests.

  7. Runge’s phenomenon describes the oscillation at the edges of an interval that occurs when using polynomial interpolation with high-order polynomials. Runge discovered this problem by looking at the errors that are produced when using polynomial interpolation to approximate certain functions. It shows that increasing the polynomial order does not always improve accuracy, as it may introduce too much noise.

  8. As in the EBA framework, variables are converted into deviations from a weighted sample mean. The controls are defined as in the EBA model with the exception of the fiscal balance and private credit to GDP, which are not cyclically adjusted. Instead, the fiscal balance to GDP is the general government net/lending borrowing using WEO data. Private credit is the private credit by deposit money banks to GDP, or, if missing, domestic credit to the private sector, from the World Bank GFDD.

  9. In a robustness check not shown, we find that our polynomial specification is robust to time and country fixed effects when using the yearly frequency.

  10. We use the medium-variant projections.

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Acknowledgemets

We wish to thank two anonymous referees, Mitali Das, Joschka Gerigk, Gian Maria Milesi-Ferretti, Pinar Yesin, the participants of the Aussenwirtschaft workshop 2018, the NBP-SNB Joint Seminar 2019, and an SNB Brownbag Seminar for very helpful comments on this and earlier versions of this study. We also wish to thank Jeremias Kläui and Adrien Wicht for invaluable research assistance. The views expressed in this paper are strictly the authors’ and do not necessarily reflect those of the Swiss National Bank (SNB). All remaining errors are our own.

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  1. Swiss National Bank, Börsenstrasse 15, P.O. Box, 8022, Zurich, Switzerland

    Miriam Koomen & Laurence Wicht

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  1. Miriam Koomen
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Correspondence to Miriam Koomen.

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Appendix

Appendix

See Tables A1, A2, A3 and A4.

Table A1 Description of the EBA sample and variables
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Table A2 Full regression results
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Table A3 Regression results: age-group coefficients
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Table A4 F-test results for polynomials
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Koomen, M., Wicht, L. Demographics and Current Account Imbalances: Accounting for the Full Age Distribution. IMF Econ Rev 71, 719–758 (2023). https://doi.org/10.1057/s41308-022-00176-6

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