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What determines the share of non-resident public debt ownership? Evidence from Euro Area countries


This paper provides, for the first time, a detailed picture of the composition of public debt by type of holder (foreign vs. domestic) and type of holding institution for a set of 7 Euro Area countries between 1991Q1 and 2015Q4. In addition, it empirically inspects the determinants of nonresident public debt ownership, accounting for both domestic and external factors and paying special attention to the global financial crisis period. Using a previously unexplored dataset and by means of panel and country-specific time series regressions, we find that improved fiscal positions, systemic stress and financial volatility, a strong business cycle position, all increase share of public debt held by non-residents. Also, a higher share of monetary and financial institutions cross-border holdings of sovereign debt issued by the other Euro Area countries was correlated with higher share of public debt held by non-residents. Finally, results are robust to outliers inspection and other sensitivity checks.

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Fig. 1

Source: own calculations with Merler and Pisani-Ferry (2012) data

Fig. 2

Source: own calculations with Merler and Pisani-Ferry (2012) data

Fig. 3

Source: own calculations with Merler and Pisani-Ferry (2012) data. Ireland time series is read in the RHS axis. All other countries are read on the LHS axis

Fig. 4

Source: ECB


  1. 1.

    These generally include (but are not limited to) lengthening maturity of debt, improving market liquidity and reducing borrowing costs.

  2. 2.

    If banks fail, government’s spending increases directly (bailout) or indirectly (recessionary economic impact).

  3. 3.

    For details refer to ECB (2014).

  4. 4.

    Measuring the stabilizing effect of fiscal policy requires assessing how fiscal policy affects aggregate demand. In Furceri and Jalles’ (2016) conceptual framework, assessing the degree of fiscal stabilization in a given country implies estimating a country specific time-varying regression of the budget balance-to-GDP ratio on real GDP growth (or a measure of the output gap). The dataset includes 69 countries over the period 1980 throughout 2016.

  5. 5.

    The VIX, often called the ‘investor fear gauge’ since it tends to spike during market turmoil periods (Whaley 2000), is a reasonable proxy for international financial risk (Mody 2009) and has been extensively used in the literature on euro area government bond spreads (Beber et al. 2009; Gerlach et al. 2010).

  6. 6.

    Note that in some specifications where we include regressors that do not vary across countries (e.g. VIX), country fixed effects are dropped from the estimated specification.

  7. 7.

    One concern when working with time-series data is the possibility of spurious correlation between the variables of interest (Granger and Newbold 1974). This situation arises when series are not stationary, that is, they contain stochastic trends.

  8. 8.

    The advantage of panel data integration is threefold: firstly, enables to by-pass the difficulty related to short spanned time series; secondly, the tests are more powerful than the conventional ones; thirdly, cross-section information reduces the probability of a spurious regression (Barnerjee 1999).

  9. 9.

    The idea of the safe haven would work for some country sovereign debt but hardly for all. Typically, in times of financial volatility and/or stress, investors (whether private or institutional) then to reallocate investments away from stocks and equities and into bonds and treasury bills. While this rearrangement of the financial portfolio is the norm, the extent to which one country’s sovereign debt becomes more or less attractive (from a risk aversion perspective) depends on the country’s reputation and credibility. That being said, inspecting the political economy and institutional determinants of non-resident public debt ownership goes beyond the scope of this paper but it could be an interesting area for future research. We thank an anonymous referee for this point.

  10. 10.

    Commonly used examples include the Hodrick-Prescott, Baxter-King and Christiano-Fitzgerald.

  11. 11.

    Statistical methods suffer from the end-point problem, that is, they are extremely sensitive to the addition of new data and to real-time data revisions. Structural models, on the other hand, may be difficult to implement consistently in cross-sectional environments and rely on the imposition of pre-determined assumptions.

  12. 12.

    As far as other institutions are concerned, the OECD uses a method which stands somewhere in between a univariate approach and a model-built measure to calculate trend participation rates, trend hours worked and trend total factor productivity (Giorno et al. 1995; Cotis et al. 2004). According to Denis et al. (2002), the European Union’s method of computing potential output is very close to the OECD's using a Cobb–Douglas production function with an exogenous trend.


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We thank an anonymous referee for useful comments and suggestions. The usual disclaimer applies. Any remaining errors are the author’s sole responsibility. The opinions expressed herein are those of the author and do not reflect those of his employer.

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Correspondence to João Tovar Jalles.



See Tables 6, 7, 8, 9, 10, 11, 12 and 13.

Table 6 Summary statistics
Table 7 Dependent variable, quarterly difference in non-resident-to-resident ratio, France, Newey–West
Table 8 Dependent variable, quarterly difference in non-resident-to-resident ratio, Germany, Newey–West
Table 9 Dependent variable, quarterly difference in non-resident-to-resident ratio, Greece, Newey–West
Table 10 Dependent variable, quarterly difference in non-resident-to-resident ratio, Ireland, Newey–West
Table 11 Dependent variable, quarterly difference in non-resident-to-resident ratio, Italy, Newey–West
Table 12 Dependent variable, quarterly difference in non-resident-to-resident ratio, Netherlands, Newey–West
Table 13 Dependent variable, quarterly difference in non-resident-to-resident ratio, Spain, Newey–West

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Jalles, J.T. What determines the share of non-resident public debt ownership? Evidence from Euro Area countries. Ann Finance 14, 379–414 (2018).

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  • Sovereign debt
  • Central bank
  • Financial markets
  • Monetary and financial institutions
  • Europe

JEL Classification

  • C22
  • E44
  • F34
  • G15
  • H63