NBER WORKING PAPER SERIESWHO HOLDS SOVEREIGN DEBT AND WHY IT MATTERS
Xiang Fang
Bryan Hardy
Karen K. Lewis
Working Paper 30087
http://www.nber.org/papers/w30087

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
May 2022, Revised April 2023

We thank Maria Gelrud, Sophia Hua, and Max Yang for excellent research assistance. We also
benefited from comments by Karlye Dilts Stedman, Ester Faia, Gregor Matvos and participants at
the Bank of International Settlements, Banque de France, China International FinanceConference, Econometric Society North American Winter Meeting, Federal Reserve Board,
Federal Reserve Bank of Dallas, Hong Kong University, INSEAD, London Business School,
London School of Economics, the Macro Finance Society, the Midwest Finance Association, theNational Bureau of Economic Research, Society of Economic Dynamics, and the Wharton
School of the University of Pennsylvania. Any errors or omissions are our own. The views
expressed here are those of the authors and not necessarily those of the Bank for International
Settlements or the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2022 by Xiang Fang, Bryan Hardy, and Karen K. Lewis. All rights reserved. Short sections oftext, not to exceed two paragraphs, may be quoted without explicit permission provided that fullcredit, including © notice, is given to the source.

Who Holds Sovereign Debt and Why It Matters
Xiang Fang, Bryan Hardy, and Karen K. Lewis
NBER Working Paper No. 30087
May 2022, Revised April 2023
JEL No. F34,F41,G11,G15
ABSTRACTThis paper studies the impact of investor composition on the sovereign debt market. We construct
a data set of sovereign debt holdings by foreign and domestic bank, non-bank private, andofficial investors for 95 countries over twenty years. Private non-bank investors absorb
disproportionately more sovereign debt supply than other investors. Moreover, non-bank investor
demand is most responsive to the yield. Counterfactual analysis of emerging market sovereigns
shows a 10% increase in debt leads to a 6.7% increase in costs, but an out-sized 9% increase if
non-bank investors are absent. We conclude that these sovereigns are vulnerable to losing nonbank investors.
Xiang Fang
Faculty of Business and Economics
University of Hong Kong
818 KK Leung Building
PokfulamHong Kong, China
xiangf@hku.hk

Karen K. Lewis
Department of Finance, Wharton School
2300 SHDH
University of Pennsylvania
Philadelphia, PA 19104-6367
and NBER
lewisk@wharton.upenn.edu

Bryan Hardy
Bank for International Settlements
Centralbahnplatz 24051 Basel
Switzerland
bryan.hardy@bis.org

A data appendix is available at http://www.nber.org/data-appendix/w30087

1

Introduction

The ability to issue debt is an important instrument at the government’s disposal. Sovereign
borrowing can help buffer the economy from the impact of adverse macroeconomic shocks.
Conversely, indebtedness can also make a country vulnerable to financial distress, as crisis episodes have illustrated. Indeed, the sharp increase in fiscal expenditures and debt
issuance during the recent pandemic period, as well as concerns from the fallout of war,has brought more urgency to understanding how a government can borrow. Answering
this question requires knowledge of who invests in sovereign debt and how these investors
impact borrowing costs. Therefore, this paper provides an analysis of who holds sovereign
debt and what this investor composition implies for governments’ borrowing costs.
We begin by documenting who holds government debt around the world and establishing some new empirical regularities. For this purpose, we first assemble a dataset that distinguishes the holders of each country’s sovereign debt into investor groups that have beenhighlighted in the literature. That is, we disaggregate debt held by foreign and domestic
investors and into three subgroups within those categories: private banks, other private investors that we term ”non-banks”, and official creditors consisting largely of central banks
and international organizations like the World Bank.1 Assembling these data series provides
1744 country-year observations for which we can decompose the holders of debt, spanning
95 countries over 1991-2018.2
Figure 1 shows both the growing importance of government debt as well as how the investor base varies in our data set. Specifically, Panel (a) shows that aggregate government
debt as a proportion of GDP has increased to the highest levels in recent history, spurred onby the Covid-19 pandemic for both for advanced and emerging economies. Furthermore,
the composition of investor shares has changed over time. For example, as Panel (b) shows,the share of domestic versus foreign investors has evolved with marked differences between
advanced economies (AEs) and emerging markets (EMs). The share of aggregate debt hold1 This decomposition follows that of Arslanalp and Tsuda (2012) and Arslanalp and Tsuda (2014).
2 A partial decomposition is available for 151 countries.

1

ings by domestic investors has decreased for AEs while that same share has increased forEMs. The share of debt held by non-bank private investors, shown in Panel (c), shows more
short-term fluctuations.
Figure 1: Trends in General Government Debt

03

06
AE

09

12

15

18

EMDC

(a) Debt/GDP

21

100

0.8

0.8

75

0.6

0.6

50

0.4

0.4

25

0.2

0.2

0

00

03

06

09

12

AE (excl US)

15

18

US

(b) Share Held Domestically

0.0

00

03

06

09

EM (excl CN)

12

15

18

0.0

CN

(c) Share Held by Non-banks

Note: Panel (a) of this figure plots the time-series of general government debt-to-GDP ratio for the advanced economies,
emerging market and developing countries from IMF WEO. Panel (b) plots the share of general government debt held bydomestic investors by country group. Panel (c) plots the share of general government debt held by non-banks. Panels (b)and (c) consist of balanced samples of 15 AEs including the US and 15 EMs including China (CN).

In this paper, we use these data to document how increases in sovereign debt are absorbed by the six different investor groups (foreign vs domestic and bank vs non-bankvs official). Strikingly, we find that non-bank private investors increase their holdings ofsovereign debt at significantly higher rates than any other group, including private banks.
Furthermore, this absorption rate is greater than proportional to their average holdings.
Across all countries for instance, when there is an increase in debt, 69% of the increase isallocated to non-bank investors, even though they make up only 46% of holdings on average. Moreover, for increases in foreign-held debt, 75% of the increase is due to non-bankinvestors, even though they comprise only 42% of all foreign holdings. By contrast, banks
absorb less than their average holdings of sovereign debt: they hold 28% of the debt on average, but only take up 20% of new debt on the margin. This pattern holds for increases inforeign and domestically held debt as well as in subsamples of AEs and EMs. Furthermore,
the general results are robust to accounting for currency valuation effects on foreign-held2

debt. Thus, when the supply of government debt increases, private non-bank investors playa significantly larger role than other investors in holding this expansion.
These aggregate results highlight the importance of non-bank private investors, a large
and heterogeneous group. To disentangle the behavior of different investors within thisgroup, we turn to more granular data sets. We begin by using data on Euro Area investors
to disaggregate the foreign non-bank investor group into non-financial corporations, pensions and insurance companies, households, and a category of other financial institutions,largely representing investment funds. Our analysis indicates that within this group, financial institutions such as investment funds drive the large response of non-banks. We thenrepeat this analysis for domestic investors who hold US Treasuries and UK Gilts, finding
again that non-banks are the most responsive set of investors, and within domestic nonbanks that funds are the key players.
Given these findings for who holds sovereign debt, we next consider why this composition matters to borrowers. To answer this question, we estimate a system of funding equations representing the willingness to extend credit. Since this willingness to fund depends
endogenously on the interest paid by the government, we require an instrument for theseborrowing costs. For this purpose, we exploit the fact that our data set provides the full
breakdown of sovereign debt by investor type for each country. As shown by Koijen and
Yogo (2020), such a market clearing condition can be used to provide an instrument thatis uncorrelated with unobserved latent factors that drive investors’ willingness to extend
credit. Accordingly, we use this concept to construct an instrument for the sovereign yield.
Our estimates provide striking results that again point to the importance of non-bankinvestors. Specifically, for both Emerging Markets and Advanced Economies, non-bankinvestors are the creditor group most responsive to changes in yields. Moreover, investorsof emerging market sovereign debt generally dislike inflation and prefer income growthfrom the borrowing country.
While these results provide measures of the sovereign’s view of each creditor group’s
willingness to fund, investors will perceive the returns on these investments differently for3two main reasons. First, while governments issue debt and pay interest over time, investors
focus upon holding returns per period as measured in the secondary market. Second, since
governments primarily issue debt in local currency, foreign investors who hold this debt
will incur currency risk in addition to the holding returns.
Although the aggregate data do not contain the identity nor portfolio of holdings bythese investor groups, we show that under some additional conditions we can provide investor demand estimates similar to those in Koijen and Yogo (2020) and Jiang, Richmond,and Zhang (2022). First, we focus only on Emerging Markets because the identity of domestic versus foreign investor groups is likely to be more comingled for Advanced Economies.
As a second condition, foreign investors are assumed to be US dollar-based. Importantly,
as shown by Koijen and Yogo (2020), the demand system under these assumptions allow
for substitution across debt issued by different countries. This estimation shows that theelasticity of non-bank investors is again higher than that of banks, and that the elasticity offoreign non-bank investors is the greatest.
To assess what these estimates imply for the sovereign borrower, we ask how much thefinancing cost would increase for a hypothetical debt increase. Our estimates show that a
10% increase in debt corresponds to a 6.7% increase in yield for the average EM borrower.
However, if non-bank investors are not present and borrowers must borrow from banks,
the same 10% increase in debt corresponds to a substantially higher 9.1% increase in yield.
Thus, EM sovereigns appear highly exposed to the availability of non-bank investor funding. We also study the trade-off between borrowing costs and characteristics. For instance,
if inflation increases by 1%, the required yield for the average EM would increase by 2.5%.
The structure of the paper is as follows. We next provide a brief review of the literature. Section 2 describes the data and some basic stylized facts, including a decomposition
that highlights how much government debt each investor group absorbs on the margin. It
also reports the same decomposition using disaggregated non-bank investor holdings in
the Euro Area, the US Treasuries, and the UK Gilts. Section 3 sets up a framework to explore funding and investor demand in the sovereign debt market and reports estimates of4this demand by investor groups. Section 4 combines the country-level investor demand
identification with the marginal absorption estimates to develop a counterfactual analysis.
Concluding remarks follow.
Related Literature Since our paper studies investor behavior of sovereign debt holdings,
it relates to a number of different strands of research in macro-finance. First, it contributes
to a growing literature that uses the demand system approach to asset pricing introduced
by Koijen and Yogo (2019) and applied in domestic and international financial markets as
in Koijen and Yogo (2020), Koijen, Richmond, and Yogo (2020), Jiang et al. (2022), Bretscher,
Schmid, Sen, and Sharma (2020), and Koijen, Koulischer, Nguyen, and Yogo (2021). While
our estimation of different investor groups’ demand and the construction of instruments
follows the basic approach of this literature, we exploit the market clearing condition fromthe issuer side. This identity arises naturally in our data because the supply of debt foreach country is matched to the full breakdown of holdings by investor groups. As such,
this feature contrasts with the common data structure in the literature that focuses upon
portfolio allocation of specific investors as in, for example, Maggiori, Neiman, and Schreger(2020). We instead implement a complementary borrower-focused approach that analyzes
the investors of a given country government debt to understand the exposure of issuers.
Our paper is also related to the literature on investor demand for Advanced Economy
sovereign debt. This strand of research includes, for example, Krishnamurthy and VissingJorgensen (2012), Jiang, Krishnamurthy, and Lustig (2018), Jiang, Lustig, Van Nieuwerburgh, and Xiaolan (2019), Liu, Schmid, and Yaron (2020), and Liu (2022). Consistent withthe view in this literature that banks and the official sector hold government debt for liquidity and other purposes, we show that holdings by these groups are relatively insensitive
to yields. We also analyze the role of the global financial intermediaries and financial conditions, as highlighted by Bruno and Shin (2015), Gabaix and Maggiori (2015), Fang and
Liu (2020), and Miranda-Agrippino and Rey (2021). By contrast to these papers, we alsofind that non-bank private investors increase holdings of AE sovereign debt in response toincreases in yield, thereby demonstrating the importance of non-bank investors.
5

By studying investor types jointly, we also further the defaultable sovereign debt literature that has instead highlighted the importance of individual investor types. For instance,
an important part of the EM literature focuses only on the role of foreign investors, particularly foreign banks. See, for example, Eaton and Gersovitz (1981), Arellano (2008), Arellano and Ramanarayanan (2012), Mendoza and Yue (2012), Cruces and Trebesch (2013), andArellano, Bai, and Mihalache (2020). On the other hand, the sovereign-bank nexus (“doomloop”) literature focuses on domestic bank investors as in, for example, Gennaioli, Martin,
and Rossi (2014), Perez (2014), Bocola (2016), Brunnermeier et al. (2016), Fahri and Tirole(2018), and Chari, Dovis, and Kehoe (2020). A further area focuses on explaining the reserve
accumulation behavior of foreign official investors, as can be seen in Wooldridge (2006),
Dominguez, Hashimoto, and Ito (2012), Ghosh, Ostry, and Tsangarides (2017), Bianchi,
Hatchondo, and Martinez (2018), and Bianchi and Sosa-Padilla (2020), to name a few. We
instead evaluate the set of creditors together and thereby contribute to these literatures byshowing the relative importance of each investor group.
Lastly, we contribute to the literature by constructing a comprehensive dataset of investor
groups’ holdings to analyze the impact of investor composition on sovereign financing cost.
As noted above, this data decomposition is close to Arslanalp and Tsuda (2012, 2014), andthe extension in Arslanalp and Sunder-Plassmann (2022).3 In order to examine the impact
of marginal investors and their demand, however, we construct a dataset that begins earlier
spanning all investor types and encompasses a broader set of countries. This more expansive base allows us to better estimate the impact of investor composition on sovereigns’ financing costs. It also complements other studies based upon securities level issuances such
as Maggiori, Neiman, and Schreger (2020) and Faia, Salomao, and Veghazy (2022). Relative
to these granular data sets, our analysis provides a complete accounting of the investors ofa given sovereign and spans both domestic and foreign investors.
3 The first two of these papers also analyse a funding shock scenario, but largely emphasize the role of foreign investors.
We emphasize the role of non-bank investors, particularly noting the composition of the marginal investors (as opposed toaverage holdings) as important for understanding debt financing exposure of sovereigns.

6

2

Evolving Composition of Sovereign Debt Investor Groups

We begin by analyzing the holders of sovereign debt and examining who are the marginal
investors that purchase new debt. We first describe the structure of the data and the investor group definitions, and highlight the broad trends in sovereign debt holdings. We
then analyze the marginal investment in sovereign debt for each group. We finish the section examining non-bank investors in more depth with alternative datasets.
2.1

Investor Group Definitions

We consider three basic types of investors: (1) private banks; (2) non-banks; and (3) official
creditors. We describe briefly these three categories below before detailing their constructionin the data. We also split these investor groups by their location: foreign vs domestic. This
gives us six groups: domestic banks, foreign banks, domestic non-banks, foreign non-banks,
domestic central banks, and foreign official.
The first group is private banks. These institutions are often considered primary intermediaries for debt markets including their function as primary dealers (Arnone & Ugolini,2005). They have therefore been the focus of both the emerging market (EM) and advanced
economy (AE) branches of sovereign debt studies. In the literature on emerging market
borrowing, foreign global banks are often modeled as the primary creditor. More broadly,liquidity and capital regulation incentivize domestic banks to hold domestic government
debt, potentially creating a bank-sovereign doom loop.4
The second group of private investors is a combination of all private investors who are
not banks. These investors are not subject to bank regulatory restrictions but may face otherconstraints depending on the nature of their business. Overall, this investor group encompasses financial institutions such as pensions and insurance firms, endowments, mutual
funds, and hedge funds, as well as non-financial entities like corporations and households.
4 Bank regulation typically assigns risk-weights to different assets that banks hold for computing the required capital ratios.
So, acquiring sovereign debt, which typically carries a zero risk weight, does not reduce the bank’s regulatory capital ratios,
though it can affect other bank constraints like the leverage ratio. On the bank-sovereign doom loop, see Fahri and Tirole(2018) and the Related Literature section above.

7

Finally, we consider official creditors. “Domestic Official” creditors are simply the “Domestic Central Bank”, while the “Foreign Official” group includes foreign central banks,
foreign governments, and international organizations such as the World Bank and International Monetary Fund.
This data set is organized around the sovereign issuers of debt, rather than tracking thesecurities held by investors. Specifically, we are interested in how the holdings of each
investor group responds to changes in the sovereign’s total debt. To see this decomposition,
it will therefore be useful to define the book value of sovereign debt of a given country
indexed by n, as D (n) and the amount held by each investor group i, as Hi (n). Then, theinvestor groups for country n debt can be aggregated across the investor groups to provide
a measure of the holdings of each country’s creditors as:I∑ Hi (n)D (n) ≡

(1)i =1where I is the number of investor groups. For example, in our aggregate data set above,I = 6 since we have three types of investors with domestic and foreign counterparts foreach. We will use the decompositions in Equation (1) to uncover the marginal ownership
responses to supply changes over time below. Before doing so, we describe the data.
2.2

Data

The annual data series for the debt and holding groups come from various sources. Here
we describe briefly the overall approach in constructing these data series, relegating a more
complete discussion to Appendix A. The general approach follows the work of Arslanalp
and Tsuda (2012, 2014), and the recent expansion of their dataset (Arslanalp & SunderPlassmann, 2022). We modify their methodology in order to broaden further the time period
and sample of countries for which we have data for all investor groups. We point interested
researchers to their papers and associated database for a full description of the approach.
We first derive each sovereign’s total debt from the IMF Historical Public Debt Database(HPDD), which provides debt-to-GDP for a large number of countries over a long time8

horizon. We multiply this series by GDP from the World Bank to recover the value of debt incurrent US dollars.5 The total foreign holdings for each sovereign are constructed following
the methodology in Avdjiev, Hardy, Şebnem Kalemli-Özcan, and Servén (2022) (hereafter,
AHKS), which combines International Investment Position (IIP) data, the Quarterly External
Debt Statistics (QEDS), and the BIS international banking and international debt securities
statistics. The domestic total holdings are computed as the difference between total debt
and foreign debt holdings.
The domestic and foreign holdings are further decomposed into the three groups described above. Data for foreign bank holdings are estimated using the approach in AHKS.
Foreign official holdings for advanced economies and China are taken directly from Arslanalp and Tsuda (2012, 2014), capturing the use of such debt as foreign reserves. For allother countries for which their debt is less commonly held as reserves, we capture foreign
official lending as the sum of bilateral and multilateral lending from the World Bank Debtor
Reporting System (DRS). Foreign non-bank holdings are the difference between these measures for banks and foreign official with the total foreign holdings.
Sovereign debt holdings by domestic banks and domestic central banks are taken fromthe IMF’s International Financial Statistics (IFS) dataset, supplemented with data from theofficial websites of central banks when the data was incomplete. Domestic non-bank holdings are measured as the difference between the domestic total and the sum of domestic
banks and domestic central banks. All holdings in the baseline data series are measured incurrent US dollars.
Overall, this construction provides a sample of 95 countries with a breakdown of debt
by investor, with time series spanning 1991 to 2018.6 For some of the analysis, we split
the sovereigns into 3 groups: advanced economies (AEs), emerging markets (EMs), anddeveloping countries (DCs). Details of the country groups can be found in Appendix A.
5 For some countries, the HPDD data series stop in 2015. For these countries, we obtain post-2015 values by applying the
growth rate in total debt from the Quarterly Public Debt Statistics (QPSD), which has excellent coverage of the recent period,to the last available level computed from the HPDD.
6 For a larger sample of 151 countries starting in 1991, partial data are available (i.e. data for some investor groups are
missing).

9

Advantages and limitations of the data.

The main advantages of our dataset are that it allo-

cates all of a sovereign’s debt to individual investors, and that it spans a large number ofcountries over a long time period. However, such coverage comes with drawbacks on detail.
For instance, our dataset does not include detailed information on the currency or maturityof the debt or the specific location of the foreign investor. It does not have details on the nonsovereign portfolios of these investors. Moreover, the data is annual, and so misses higher
frequency dynamics. In the conclusion, we discuss avenues for future research to build on
our analysis.
2.3

Investor Trends and Marginal Holders

We next examine some general trends in holdings and the relative behavior of each investor
group’s holdings as government debt changes. Panels (a), (b), and (c) of Figure 2 shows
the average investor holdings-to-GDP shares of advanced economy (AE), emerging market(EM) and developing economies (DC) debt, respectively. These figures show distinctive differences within the groups. For all groups, the foreign bank and non-bank shares have beenstable over time.7 However, the proportion of foreign official holdings has become larger
for AEs, as central banks have increased their holdings of safe haven government debt forreserve purposes, while the proportion of foreign official creditors has declined for emerging markets. Strikingly, the share of domestic non-banks has increased over time for EMs
relative to AEs, perhaps reflecting growing financial development within these economies.
By contrast, the AE holdings of domestic central banks has expanded over time, tied to theuse of unconventional monetary policies and other programs.
These trends raise an important question. When the size of debt increases, which investors absorb the additional amount? In other words, who are the marginal investors forthe sovereign? To explore this question, we regress the change in debt held by each investor
7 Foreign official data for AEs are not available until 2000, so foreign holdings are not fully reported before that point.

Coverage for EMs and DCs is broader after 1995.

10

Figure 2: Trends in Sovereign Debt
50

80

40

60

DomBK

2010

2014

2018

0

DomNB

20

10
2000 2003 2006 2009 2012 2015 2018
DomCB(a) Advanced economies

40

20

20

2006

60

30

40

2002

80

ForBK

(b) Emerging markets

01998 2002 2006 2010 2014 2018
ForNB

0

ForOff(c) Developing countries

Note: Aggregate debt by country group detailed in Appendix A divided by the aggregate GDP of the country group. Eachpanel shows a balanced sample over the given time frame.

group on the change in total debt:
Hi,t (n) − Hi,t−1 (n)Dt ( n ) − Dt − 1 ( n )= ai+ a(n) + at + ei,t (n),Dt −1 ( n )Dt −1 ( n )

∀i

(2)

where the subscript t indicates time, and where a(n) and at represent country fixed effects
and time fixed effects, respectively. Using the identity in Equation (1), the sum of the investor groups add up to the total so that:
I H (n) − H
Dt ( n ) − Dt − 1 ( n )i,t−1 ( n )= ∑ i,t.
Dt − 1 ( n )Dt −1 ( n )
i =1

(3)

Since all of the debt is absorbed by some investor, the coefficients estimated from this reI

gression will sum to 1: ∑ ai = 1. Each coefficient ai reflects the marginal holding response
i =1

of investor group i to variations in the supply of debt.
Table 1 shows the results of this regression. Panel A provides a baseline estimate labeled”All” based upon a balanced sample of all countries. The first two columns provide theseresults for an aggregated group of domestic and foreign investors, respectively. Columns (1)and (2) show that for every additional unit of debt supplied, 60% is absorbed by domestic
investors while the other 40% is picked up by foreign investors. In AEs and DCs, this split
is roughly equal, whereas for EM sovereign debt, domestic investors take over two-thirdsof additional debt.
11

Breaking down foreign and domestic investors by type in columns (3) through (8) reveals
additional insights. For the ”All” country estimates, non-bank investors tend to be the mostimportant, taking on 39% and 30% of additional debt holdings for domestic and foreign entities, respectively. As reported in the following rows, decomposing estimates into countrygroups shows the relative importance of investor groups across these countries. In particular, foreign non-banks are more important for AEs and DCs, while domestic non-banks
matter more for EMs.
Table 1: Marginal Holders of Sovereign Debt
(1)
Dom

(2)
For

(3)
DomBK

(4)
DomNB

(5)
DomCB

(6)
ForBK

(7)
ForNB

(8)
ForOffPanel A: Marginal ShareAll

0.60***(0.05)

0.40***(0.05)

0.16***(0.04)

0.39***(0.05)

0.05***(0.01)

0.04***(0.01)

0.30***(0.05)

0.06***(0.02)

AE

0.50***(0.12)

0.50***(0.12)

0.02(0.03)

0.39***(0.09)

0.09(0.07)

0.06**(0.02)

0.40***(0.12)

0.04(0.03)

EM

0.68***(0.05)

0.32***(0.05)

0.21***(0.05)

0.43***(0.07)

0.04**(0.02)

0.05***(0.02)

0.24***(0.05)

0.04(0.03)

DC

0.50***(0.09)

0.50***(0.09)

0.11***(0.03)

0.34***(0.09)

0.05***(0.02)

0.01(0.02)

0.40***(0.10)

0.09***(0.02)

Panel B: Average Share
All

0.57

0.43

0.22

0.28

0.07

0.06

0.18

0.19

AE

0.55

0.45

0.19

0.32

0.04

0.10

0.21

0.14

EM

0.61

0.39

0.26

0.28

0.07

0.06

0.17

0.16

DC

0.49

0.51

0.16

0.24

0.09

0.04

0.17

0.30

Note: Panel A of the table reports the regression coefficients for Equation (2) for each investor group. The first two columns
represent domestic and foreign investors, respectively. Columns (3) through (8) correspond to the six investor groups.
Standard errors clustered at the country level are reported in the parentheses. Panel B of the table reports the average shareof holding by each investor group.

More important, however, is the role of investor groups relative to each other. Here, nonbank investors again demonstrate the most striking results. For example, foreign non-banks
play a much stronger role in expanding holdings in response to new debt than do foreign12

banks. In particular, the take-up of new EM debt by foreign non-bank investors is 24% of
the total in contrast to only 5% by foreign banks. These findings are in stark contrast to aview that foreign banks play the biggest role in the EM debt market. And lastly, foreign
official investors are more important as marginal investors for DCs, where they serve as animportant source of financing (9% of the increased supply). Appendix B illustrates thesepatterns in panel (a) of Figure B1.
These results consider the marginal decomposition of investor groups in response to an
expansion in country debt and therefore one might wonder whether these holdings simply
represent proportional expansions to average holdings. Panel B of Table 1 reports the average holdings by investor group over the period and shows this is not the case. The large
marginal contribution of non-bank investor holdings are greater than the average holdings.
For example, in the baseline ”All” results, the average holdings of domestic non-banks andforeign non-banks sum to only 46% (that is, 0.28 + 0.18). At the same time, the marginal
share reported in Panel A are jointly 69% (0.39 + 0.30) of the change. This pattern is clearlyrobust across all the remaining decompositions including AE, EM, and DC.8
Appendix B.1 reports an extensive set of results of the same analysis under different
scenarios of recessions vs. non-recessions, crisis vs. non-crisis periods, as well as in different
subperiods. The general pattern holds in all subsamples.
The responses reported in Table 1 simply capture variations in holdings by investor
groups measured in a common currency. However, such changes in holdings may arise
from currency valuation effects for debt that is originally issued in local currency. Indeed,
the importance of valuation effects in the balance sheet adjustment of countries has beenshown in a number of papers (e.g. Gourinchas and Rey (2007)).9 To consider this possibility, Appendix B.2 repeats the analysis taking into consideration the currency valuation
effect. The results are largely unchanged: non-banks have a larger response to increased
8 The most recent shares in the sample are essentially the same as the averages, which rules out the possibility that nonbanks’ average shares have an upward trend.
9 Valuation effects have also been shown to be important more generally in the portfolio allocation of investors using
disaggregated data on holdings of specific investors. See, for example, Curcuru, Thomas, Warnock, and Wongswan (2011) foran analysis of US portfolio investment in foreign equity markets.

13

debt supply than other investor groups.
2.4

The Role of Non-Bank Investors

As we showed above, private investors who are not banks play an important role in thesovereign debt market. This finding raises the obvious questions: who are these investors
and how do they respond to changes in sovereign debt? To shed light on this question,
we turn to other more disaggregated data sets: (i) the Euro area securities holding statistics(SHS) produced by the European Central Bank; (ii) US TIC data; and (iii) UK Gilt Holders
data.10 We utilise the Euro area data to shed light on the foreign non-bank segment, and the
US and UK data to examine the domestic non-bank segment.
We first examine the Euro Area SHS data. The SHS provides data on the holdings of
securities for all investors located in the Euro Area, split by sector of the holder/investor,
the type (e.g., equity vs debt) of security, the sector of the issuer, and the residence of theissuer. Thus, we can observe the holdings of government debt issued by specific countries
for all Euro area investors, disaggregated by investor type. Specifically, the non-bank sector
in this data has the more granular breakdown: (a) households and non-profits, (b) insurance
and pensions, (c) non-financial corporations; and (d) ”other financial institutions” which includes entities like hedge funds and mutual funds. As above, we also study the holdings bybanks and the official sector. However, in this data source, the official sector is the government, excluding the central bank. These data are quarterly, with sample taken over 2013 Q4
to 2020 Q3.
This setup allows us to examine how Euro Area non-bank investors behave relative toeach other and relative to other Euro area investors in regards to their holdings of foreign
sovereign debt (i.e. outside the Euro Area). Since Euro Area investors do not account for
the full set of foreign investors for a given government, we cannot replicate our previous
analysis. Nevertheless, we can adapt that approach to establish which types of non-bank
10 Faia, Salomao, and Veghazy (2022) analyze the underlying data for the Euro Area SHS, focusing on corporate bonds inthe euro-area, and find significantly different behavior for mutual funds relative to insurance and pension funds, similar to
our results below for sovereign bonds.

14

investors in the Euro Area drive the overall behavior of Euro Area non-bank investors in foreign sovereign debt markets. We therefore modify our regression in Equation (2) so that thedenominator is the total holdings of foreign sovereign debt by Euro Area investors, rather
than total debt. When we examine within the non-bank group, we replace the denominatorwith the total holdings of sovereign n’s debt by Euro Area non-bank investors.
The coefficient results are summarized in Figure 3 while the regression results are relegated to Table B8 in Appendix B. Similar to the aggregate data, the non-bank private investors are the most marginal group within the set of Euro Area investors (excluding central
banks). For the base case of all non-Euro Area countries, 77% of every additional unit ofsovereign debt picked up by these investors is held by Non-Banks, while only 23% is heldby Banks. These general patterns hold for all cases except for Advanced Economies in thebaseline estimates shown in Figure 3 panel (a), where banks seem to absorb the largest shareof the increase.11
Figure 3: Foreign Non-bank Marginal Holders: Euro Area Investors
0.5
0.4
0.3
0.2
0.1
Marginal share
Banks

0.0

NonBank(a) Advanced Economies

Share Non-Bank marginal
HH

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.2

0.0

Marginal share

OthFin0.0Gov(b) Advanced Economies(c) Emerging Markets

Share Non-Bank marginal
I&P

0.0

NFC

(d) Emerging Markets

Note: This figure plots the regression coefficients in Equation (2) for euro area investors’ holdings of non-euro area
government debt. Panels (a) and (c) report the coefficients for regressions of EA investor holdings of non-EU government
debt for AE governments and EM governments, respectively, reported in columns (1), (2), and (3) in Table B8. These groups
are private Banks, Non-Banks, and other governments ”Gov”. Panels (b) and (d) report the coefficients for regressions of
disaggregated Non-bank EA investor groups based upon columns (4), (5), (6), and (7) of Table B8) for Households, Insurance
and Pensions (IP), Nonfinancial Corporations (NFC), and Other Financial Institutions (OthFin). The results use the total EA
investor holdings of non-EA debt by AE and by EM separately as the total denominator in Equation (2).
11 Debt from large AE countries such as the US are important for this group of investors, but are weighted equally withother countries. If we weight this regression by the size of the total holdings of that sovereign’s debt by European investors,
then non-banks are again the most marginal investor in foreign AE sovereign debt.

15More importantly, panels (b) and (d) in Figure 3 provide a decomposition within the
Non-bank investor group. Insurance and pension (IP) account for 12 to 14% of the additional sovereign debt picked up by foreign non-banks in both (non-Euro Area) advanced
and emerging markets. This is dwarfed by ”other financial institutions” (OthFin) such ashedge funds and mutual funds, which picked up 85%. By contrast, the other two categories
of households (HH) and non-financial companies (NFC) essentially take on little to none of
the additional foreign government debt.
We next turn to the US and UK data to examine the domestic non-bank group. These
data breakout the total debt issuance by the central government by who is holding the debt.
Thus, we apply our standard approach in Equation (2), dropping the time and country fixed
effects, as we are considering a single country. These datasets provide a detailed breakdownof domestic holders of these securities, but provide little or no sector breakdown of theforeign holders. We utilize data over 1995 Q1 to 2020 Q4 for comparability with the otherdata. While we note that the US and UK are not typical countries, so results may not be
fully generalizable, they nevertheless provide a useful window into domestic non-banks’holdings of government debt.
The coefficients from these regressions are plotted in Figure 4 while the details are reported in Tables B9 and B10 in Appendix B. For US Treasuries and UK Gilts, respectively,
Panels (a) and (c) depict the regression coefficients for the aggregated groups of total foreign
investors, domestic central banks, and domestic non-banks. As shown there, domestic nonbanks are the most marginal investors in these markets, accounting for nearly half of thevariation. Panels (b) and (d) break out the non-bank marginal share by underlying sector
for holdings of US Treasuries and UK Gilts, respectively. In the US, we see that investment
funds, including hedge funds and Money Market Funds (MMFs), are the most importantgroup, accounting for over half of the non-bank marginal variation (despite making up only
26% of non-bank holdings on average).12 The Insurance and Pension (I&P) sector accounts
for about 15%. In the UK, the I&P sector plays a much larger role for non-bank holdings,
12 The “household” sector includes hedge funds, private funds, and private trusts.

16

accounting for nearly half of the marginal absorption. Funds and other non-bank financial
institutions account for the other half. The large presence of I&P is notable especially in lightof the Sept 2022 turmoil in the Gilt market regarding pension funds.13
Figure 4: Domestic Non-bank Marginal Holders: US and UK
0.5
0.4
0.3
0.2
0.1
Marginal share
Foreign

0.0

DomCB
DomBank
DomNB(a) US Treasuries

0.3

0.4

0.4

0.2

0.3

0.3

0.1

0.2

0.2

0.0

0.1

0.1

0.0

0.0

–0.1

Share Non-bank marginal
MMF
I&P
Non FinMarginal share

HF/HH
Oth FinForeign(b) US Treasuries

DomCB
DomBank
DomNB(c) UK Gilts

Share Non-bank marginal
Funds
& Oth
Non Fin

I&P(d) UK Gilts

Note: This figure plots the regression coefficients in Equation (2) for the holders of US Treasuries and the holders of UK Gilts
over 1995q1-2020q4. The regression results are reported in Tables B9 and B10, for US Treasuries and UK Gilt, respectively.
Panels (a) and (c) give the aggregate breakdown for all foreign (Foreign), domestic central bank (DomCB), domestic bank
(DomBank), and domestic non-bank (DomNB). In panel (b), “HF/HH” includes Hedge funds, private equity, private trusts,
and direct household holdings. “Non Fin” is all non-financial holders excluding households. “Oth Fin” is all other financial
institutions apart from funds and Insurance and Pension (I&P). In panel (d), “Funds & Oth” comprises all funds andfinancial institutions except for I&P. “Non Fin” includes all non-financial holders.

Overall, these disaggregated data suggest that the foreign non-bank investor group islargely driven by the behavior of investment funds, with modest participation from the
I&P sector. For the domestic non-bank investor group, the I&P sector likely plays a largerrole alongside funds. Other domestic players may be context specific (e.g., the large role of
MMFs in the US may be unique), but the results suggest that investment and other funds
are the primary non-bank players in the market.
13 The higher marginal participation of this sector is driven by their large average holdings, rather than I&P being quite
active traders. In the US, this sector makes up 43% of domestic non-bank holdings, while in the UK it accounts for 81%. This
fits with the perception that this sector is more stable in their holdings.

17

3

Sovereign Debt, Investor Holdings, and Yields

We showed above who holds sovereign debt and how the composition of investor holdings
varies when that debt expands on the margin. In this section, we describe a framework to
study the behavior of creditors, allowing us to consider why that composition matters. Section 3.1 begins by considering how a sovereign may view the propensity for its investors toprovide credit as it varies debt and the yield on that debt. Although this framework couldin principle be used for any country, we study debt issued by AE and EM countries separately because investors view these two types of assets differently. Investors hold AE debt
(for example, US Treasuries) mainly for its safety and liquidity, but investors primarily holdEM debt for its yield. Therefore, in Sections 3.2 and 3.4 we estimate the funding propensities from the point of view of EM and AE sovereigns, respectively. Section 3.3 then showsthat under some additional assumptions, this framework may be used to identify and estimate the investor demand similarly to the approach in Koijen and Yogo (2020). In Section
4, we combine these estimates with the marginal investor results above to determine thesensitivity of financing cost in borrowing countries.
3.1

Sovereign Funding

We now describe a framework to relate the debt funding of a sovereign to its creditor holdings and to study how those creditors respond to the yield. Clearly, the sovereign debt
market is characterized by both supply and demand, as we describe in turn.
The supply of debt depends upon factors that impact the desire to borrow along with thecost of doing so. Specifically, a long literature has related government financing needs witheconomic downturns and macroeconomic variables such as inflation (e.g. Aguiar, Chatterjee, Cole, and Stangebye (2020); Reinhart and Rogoff (2011)). Moreover, a government’s
ability to borrow depends upon its perceived riskiness. For example, in the defaultable
sovereign debt literature, debt levels depend upon the country’s income level and its default probability (e.g., Arellano (2008)). We summarize this relationship by treating the debt

18

supply-to-GDP as dependent on a vector of country-specific variables that impact the borrowing decision, defined as xt (n) for a given country n, as well as its own lag. Thus, defining
the book value of country n’s debt as a share of GDP to be d(n) ≡ D (n)/Y (n), where Y (n)is country n’s GDP level, we can write the debt supply function as: dt (n) = d (xt (n), dt−1 ).
Our analysis below uses the functional form for this process given by:
Dt (n) − γ0 Dt−1 (n)≡ dt (n) − γ0 dt−1 (n) gt (n)−1 = g(xt (n)),
Yt (n)(4)

where γ0 is the legacy debt that has not matured and gt (n) = Yt (n)/Yt−1 (n) is country
n’s GDP growth rate. Governments mostly borrow long-term so that (1 − γ0 )−1 is approximately the maturity of country n’s debt. The newly issued debt depends on the set
characteristics xt (n). Below, we will assume that investors and governments view the same
country-specific variables xt (n) as exogenous to their own decisions. An interesting question for future research is to ask whether they differ.
Given a desired supply of book value for this debt, a government then raises the debt
according to the willingness to fund by a set of creditors. To examine the funding provided
by these investors, we define Pt (n) as the price of debt of government n at time t and theshare of holdings of that country’s debt-to-GDP held by investor i at time t as:hi,t (n) ≡Hi,t (n)Dt ( n )d t ( n ),(5)m ( n ) ≡ P ( n ) h ( n ). Investor i’s
Then the market value of this share can be written as: hi,tti,tholdings of country n’s debt are assumed to depend on the price of debt Pt (n) and otherm (n) =
country characteristics xt (n). We capture this relation as a funding function: hi,thi ( Pt (n), xt (n)). The market clearing condition in Equation (1) can then be rewritten as:I

Pt (n)dt (n) = ∑ hi ( Pt (n), xt (n)).

(6)

i =1

This condition implies an equilibrium price level of country n’s debt, Pt (n), as a function of
the book value of debt-to-GDP, dt (n), and the propensity for investors to fund debt depending on the characteristics of country n, xt (n).
19The ability for the government to sustain debt will depend upon its borrowing costs,
captured by the price of debt. In general, the price of debt depends upon the interest rate,
debt maturity and coupon repayment schedule. While this information may be available forsome individual bond issuances, the debt maturity and payment schedules for total debt are
not available. Therefore, we assume that the annualized interest cost per period is proxiedby the yield-to-maturity on pure discount government bonds. In this case, the price of debt
is: Pt (n) = (1 + yt (n))−T , where y(n) is the yield of T-year debt issued by government n.
We now use this framework to estimate the investor willingness to fund debt viewedm ( n ) asfrom the point of view of the borrowing government. We specify this function hi,ta log-linear function of yield yt (n) and exogenous characteristics xt (n).14 The empiricalregression model of investor i’s holding shares of country n’s debt-to-income is then:m0
ln hi,t(n) = θ0,i yt (n) + θ1,i
xt (n) + θi (n) + θi,t + ε i,t (n)(7)

As Equation (7) shows, the willingness to fund by investor group i depends not only on
the yield and country-specific characteristics x(n), but also on country and year fixed effects. Country fixed effects θi (n) are included to capture the time-invariant characteristics
of countries that affect the overall ability to fund from a given investor group.15 Year fixed
effects θi,t are included to capture worldwide economic shocks that are common to all countries. Finally, ε i,t (n) captures investor i’s latent demand for country n’s debt.
Estimating holdings shares as in Equation (7) presents two identification issues. First,
since the yield of debt is determined in equilibrium by equating demand to supply, it islikely to be correlated with latent demand ε i,t (n). To address this problem, we construct
an instrument for yield yt (n) based on the insight of Koijen and Yogo (2020) that utilizes
14 Since P ( n ) is a monotonic transformation of y ( n ), we replace P ( n ) in the H function with y ( n ).
t
t
t
t
i
15 The inclusion of fixed effects is a departure from Koijen and Yogo (2019, 2020). It is motivated by the different structureof our data: allocating all of a borrower’s debt by the investors, rather than allocating all of the investors’ assets to theborrowers or equity issuers. Foreign investors in our data are composed of different entities for each country. The identifying
assumption of our approach is that all countries face a common willingness to fund debt by a given foreign investor groupbased on their exogenous characteristics, x(n), and yield. Including a sovereign country fixed effect within the vector ofcharacteristics better captures constant funding differences across sovereigns for a given investor type that are not capturedby the other characteristics. Moreover, domestic investors operate in very different contexts within their own countries. Thus,
including fixed effects also help to make domestic investor behavior more comparable across borrowers by controlling fortime-invariant differences of each country within each domestic investor group.

20

the market clearing condition to construct an implied yield that is orthogonal to the latentdemand and correlated with the observed yield. Our dataset includes a full set of creditors
for a particular sovereign, which enables us to construct the instrument using this insight.
Specifically, we begin by estimating the market value of holdings per investor group asa reduced-form function of country characteristics xt (n) using the regression:m(n) = αi0 xt (n) + αi (n) + αi,t + ui,t (n).
ln hi,t(8)m ( n ) = exp( α̂0 x ( n ) +The fitted values of this regression for each investor defined as ĥi,ti tα̂i (n) + α̂i,t ) provides an exogenous measure of the right-hand side of the market clearing
condition in Equation (6), which only depends on exogenous characteristics xt (n).
The second identification issue concerns the endogeneity of the supply of debt. Therefore, according to Equation (4), we project current debt-to-GDP on the characteristics andlagged debt as in the following equation:dt (n) = γ0 dt−1 (n) gt (n)−1 + γ1 xt (n) + γ(n) + γt + et (n)(9)

Substituting the fitted values of Equation (9), defined as dˆt (n), for the realized debt alongm ( n ), into the market clearwith the fitted values from the market value of holding shares, ĥi,ting in Equation (6) implies:Imdˆt (n)(1 + ỹt (n))−T = ∑ ĥi,t
(n)

(10)

i =1

where the fitted value is: dˆt (n) = γ̂0 dt−1 (n) gt (n)−1 + γ̂1 xt (n) + γ̂(n) + γ̂t . Based uponthis condition, we then solve for the hypothetical yield ỹ j,t that clears the market given theexogenous variables. The hypothetical yield ỹ j,t is then used as an instrument for the actual
yield y j,t in the estimation of Equation (7).
3.2

Funding Emerging Market Sovereign Debt

We now use this framework above to estimate the investor funding shares for EM sovereigns.
Note that estimating Equation (7) requires specifying a set of country-specific characteristics,
xt (n). For this purpose, we draw on a large literature on emerging market sovereign debt
21

that has provided a rich set of variables that may impact investor demand. In general, creditors prefer a higher yield, but are concerned about potential default risk. Thus, creditors are
more likely to invest in countries with characteristics that they view as correlated with lower
default. For example, they are likely to be attracted to countries with higher growth, but dislike inflation.16 In light of the literature on sovereign debt and following Koijen and Yogo
(2020) that estimates asset demand across countries, we include real GDP growth (“GDPgrowth”), inflation, the logarithm of the export-to-GDP ratio (“Exp-to-GDP”), the logarithmof the nominal GDP (”GDP”) and sovereign credit ratings (”Ratings”).17 We use the fiveyear local currency government bond yield (”Bond Yield”) as the relevant price variable.18
Table 2 reports the IV estimation results, relegating the reduced form holding estimates ofEquation (8) and the debt-to-GDP estimates of Equation (9) to the Appendix Section C.1 andC.2, respectively. As would be expected on economic grounds, all investor groups increase
holdings in response to a higher yield. Moreover, most investor groups increase demand fora country’s sovereign debt when GDP grows and inflation declines, although insignificantly
so for some groups. Exports-to-GDP and Ratings are relatively insignificant across groups.
The responsiveness of the holdings to yield provide an important first look at the relevance of different investor groups for the sovereign, as reported in the first row of Table 2.
Within domestic investors, both banks and non-banks have a significant response to yield,
but the non-bank group is almost twice as responsive. That is, in response to a 1 percent
increase in yield, domestic non-banks increase their debt holding by 23% while domestic
banks do so by 12%. Similarly, among the foreign investors, foreign non-banks are the most
responsive as they increase holdings by 38% for a 1 percent increase of yield. Thus, nonbanks appear to vary their sovereign debt holdings by more than other investor groups in
16 See for example Arellano (2008) and Aguiar and Gopinath (2007) on output growth; Arellano et al. (2020) and Reinhart

and Rogoff (2011) on inflation. Aguiar et al. (2020) provides a survey.
17 Using a data base of bilateral holdings across countries with a larger number of observations, Koijen and Yogo (2020) also
include some other variables such as imports-to-GDP, real GDP per capita, and equity volatility. Using our much smaller database of holdings by country, we found that these variables were generally insignificant
18 We use five-year yields because the coverage of countries is greater than that of other maturities. Similarly, we use theyields in local currency since EM governments primarily borrow in their local currency, and have been increasingly borrowing
from abroad in local currency, thereby making the liquidity in these markets greater. For discussion, see for example Du andSchreger (2016) and Onen, Shin, and von Peter (2023), as well as Table C4 in the BIS Debt Securities Statistics.

22

response to yield changes.
Table 2: Emerging Market Funding
(1)
DomBK

(2)
DomNB

(3)
DomCB

(4)
ForBK

(5)
ForNB

(6)
ForCB

Bond yield

12.073∗(6.833)22.914∗∗∗(8.754)27.368(17.226)

28.167∗∗(13.689)

38.410∗(19.704)

24.890∗(14.538)GDP-0.257(0.164)0.039(0.276)-0.716(0.476)-0.403(0.329)-1.156∗(0.658)-1.692∗∗(0.307)

GDP growth3.554∗(2.019)3.949(2.687)8.568(5.413)

3.325(4.045)16.068∗∗(6.307)6.728(4.221)

Inflation-3.486∗∗∗(0.946)-3.471∗∗∗(1.304)-4.424∗(2.322)-6.013∗∗∗(1.896)-3.967(2.619)-4.068∗∗(1.976)

Exp-to-GDP-0.249(0.193)-0.266(0.298)-0.689(0.565)-0.536(0.387)-0.094(0.650)-0.362(0.369)Rating0.018(0.013)-0.001(0.020)0.049(0.035)-0.023(0.026)0.012(0.040)0.025(0.025)

350

323

288

350

305

342

Observations

Note: This table reports the IV estimates of the funding shares in Equation (7), with country and year
fixed effects for the EM sovereign debt. The sample spans 1996-2018 at annual frequency. The
dependent variable is holding of the group indicated in the column title to GDP. The standard errors
clustered at country and year levels are reported in the parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

These results provide insights from the sovereign’s perspective about how funding varies
across investor groups in response to changes in borrowing costs, captured by the local
currency yield. However, given that the data do not include information about the identityof the investor, this analysis presumes governments do not have separate information about
the investor demand in the funding market. Nevertheless, in the next subsection, we showthat under some additional assumptions, our framework can be viewed as consistent withthe literature of demand system asset pricing as described in Koijen and Yogo (2019, 2020)and our estimated demand elasticities are similar to those reported in the literature.

23

3.3

Investor Demand Estimation for Emerging Market Debt

As noted above, our data set does not provide the identity of the investor other than whether
it is domestic or foreign and its sectoral classification. Therefore, it does not provide information about what the other holdings of these investors are. This omission is important because, in reality, investors substitute between debt issued by different countries. Moreover,foreign investors care about returns denominated in their own currency, while domestic investors care about returns denominated in local currency. In principle, these issues could be
addressed with a more granular data set that included the identity of these investors andtheir portfolios. We leave this important question for future research.
Despite these issues, under two additional assumptions, our data can be used to estimate a demand system model of EM debt by different investor groups following Koijen and
Yogo (2019, 2020). We summarize these assumption below, although Appendix D provides
more details. First, we assume that all foreign investors within a specific investment groupcan be treated as a single investor. This assumption requires, for instance, that a foreignbank does not become a domestic bank within the sample. This assumption appears morereasonable for EM sovereign debt since many of their foreign investors are from advanced
economies. Second, since we do not know the identity of the foreign investors, we assume
that these investors are all US dollar-based investors. As such, they face foreign exchange
risk in holdings of non-US dollar denominated debt and base their risk-free rate on US Treasuries. Although this assumption is inaccurate for some foreign investors, the continued
importance of the US dollar in the global market suggests that it provides a useful benchmark. We assume domestic investors are local-currency-based.
Under these assumptions, we then treat the six creditor groups as distinct investor types
with desired portfolio shares of an unobserved wealth. As in Koijen and Yogo (2020), we
model investor i’s portfolio share of country n’s debt as a logistic function of expected returns and the set of country characteristics xt (n).19 That is, we denote the portfolio holdings
19 Unlike Koijen and Yogo (2020), we do not have a breakdown of total debt holdings to wealth by investor group andtherefore we collapse the two-step decision process within and across asset classes in that paper into one single step.

24

m ( n ) = ω ( n ) A where A isof investor i of sovereign debt holdings of country n as: Hi,t
i,t
i,t
i,t

the wealth of investor i in US dollars and ωi,t (n) is the share of country n’s sovereign debt ininvestor i’s portfolio. Similarly, Hi,t (0) = Ai,t ωi,t (0) is the amount of investor i outside asset
holdings in US dollars and ωi,t (0) is the portfolio share of that asset. The regression modelis then given as:δi,t (n) ≡ ln

ωi,t (n)ωi,t (0)



= ln

m (n)
Hi,t

Hi,t (0)!
0= λ0,i µi,t (n) + λ1,ixt (n) + λi,t + λi (n) + ε i,t (n) (11)where λi,t and λi (n) are year and country fixed effects and µi,t (n) is the expected excess
return of country n’s sovereign debt faced by investor i. Similar to our analysis in Section
3.2, country fixed effects are included to capture time-invariant country characteristics whiletime fixed effects are included to capture global-wide shocks that are common to all countries.
Estimating Equation (11) poses two challenges. First, we do not observe the net worth or
the outside asset held by investor i. Second, the expected excess return faced by domestic
and foreign investors differ according to their base currency. Therefore, we next discuss how
we approach these two challenges.

Outside Assets

We first describe our approach to the outside asset for the foreign investors

and the domestic investors, in turn. As noted above, we treat foreign investors as representative investors in the global economy who make debt allocation decisions across all the
EM countries. Given that we do not observe the wealth or outside asset holding of such investors, we assume that their outside asset holding can be absorbed by the time fixed effectand thus not affect our parameter estimation. Clearly, this treatment requires the foreign
investor group composition to be relatively constant for all EM borrowing countries.
Domestic investors are different from foreign investors, however, as they differ by country. For example, for a particular investor group such as ”banks”, we have N distinct domestic banks in this investor group, one for each country. As such, we do not have a sufficientnumber of observations for a given domestic investor to identify a separate response per25

country. Therefore, we instead treat the responses of the domestic investors jointly acrosscountries but condition on a proxy for the outside asset, defined for a particular domestic
investor group as Hi,t (o, n). Thus, Equation (11) can be rewritten for domestic investors as:
!
m (n)
Hi,t
0
= λ0,i µi,t (n) + λ1,ixt (n) + λi,t + λi (n) + ε i,t (n)(12)
lnHi,t (o, n)
In our baseline results below, we consider the level of GDP as a proxy of the outside asset
held by investor i of country n, allowing comparability to the foreign investor results. Our
results are qualitatively similar based on measures such as bank credit and the equity market
cap.

Expected Excess Returns

We next discuss our treatment of excess returns for foreign anddomestic investor groups. All foreign investors are assumed to be US dollar-based, as noted
earlier. Thus, for foreign investors, we follow Koijen and Yogo (2020) in treating the expectedexcess return for a US dollar-based investor as the fitted value of a predictive regression ofholding period return of debt plus the exchange rate change given by:
rt+1 (n) + st+1 (n) − st (n) − yt (US) = φ f pt (n) + ψ f (st (n) − zt (n)) + χ f (n) + ν f ,t+1 (n) (13)where rt+1 (n) is the holding return of sovereign debt of country n in local currency, st (n)is the log of exchange rate St (n), and yt (US) is the short-term yield of US Treasuries. The
predictive variables are the log of the bond price, pt (n), and the log of the real exchange rate
given by the difference between the log nominal exchange rate, st (n), and the difference of
price levels between country n and the US, zt (n). A country fixed effect χ f (n) is included tocapture time-invariant characteristics of country n. Constructed expected excess returns arethen:µ f ,t (n) = φ̂ f pt (n) + ψ̂ f (st (n) − zt (n)) + χ̂ f (n)(14)

By contrast, domestic investors are local-currency based and view the realized returns inexcess of their own short term rate given by predictive regressions for each currency n:
rt+1 (n) − yt (n) = φd pt (n) + χd (n) + νd,t+1 (n)
26

(15)

where yt (n) is the short term rate in local currency of country n. The constructed expected
returns for domestic investors are then:µd,t (n) = φ̂d pt (n) + χ̂d (n)(16)

Appendix D.2 provides the estimation results of the return system as well as a descriptionof their connection to investor demand elasticities with respect to yield.

Estimation Results

Based upon these modifications for outside assets and expected returns,

we can now turn to estimating the investor demand equations. To address the endogeneityof the expected excess return µi,t (n) for each investor group i, we first construct an instrument following the same steps as in Section 3.2. These instruments are then used to estimate
the foreign and domestic investor demand in Equation (11) and Equation (12).
Table 3 provides the results of this baseline estimation in Panel A. The first two rows
again show the importance of non-bank investors, with the first row showing the dependence on domestic excess returns and the second on foreign excess returns. Among the foreign investors, foreign non-banks are the most sensitive as a 1% change in expected excess
returns corresponds to a 10% change in debt holdings of a particular country. Among thedomestic private investors, domestic non-banks are more responsive than domestic banks
at 7.7% and 4.2%, respectively. Domestic central banks are more responsive, although this
response is likely motivated by policy decisions and not by the desire for a return. Acrossinvestor groups, higher inflation leads to a decline in holdings while higher GDP growth
generally increases these holdings.
The estimates of expected return responses can further be used to obtain implied investor demand elasticities. Investors who increase their holdings of a particular country’s
sovereign debt in response to an increased yield would necessarily have to reduce portfolio shares in another asset. This feature of the asset demand system allows for substitution
across debt issued by different countries and different assets more generally. As shown in
Koijen and Yogo (2019) and detailed in Appendix D.2, investor group i’s elasticity of de27

Table 3: Investor Demand: Emerging Markets
(1)
DomBK

(2)
DomNB

(3)
DomCB

(4)
ForBK

(5)
ForNB

(6)
ForCB

8.034∗∗(3.924)10.056∗(5.199)6.428∗(3.844)

Panel A: IV Estimates
Return- Domestic

4.150∗(2.190)7.753∗∗∗(2.770)10.347∗(6.021)Return- ForeignGDP

0.767∗∗∗(0.152)

1.099∗∗∗(0.252)0.330(0.467)1.718∗∗∗(0.610)1.299(1.251)0.203(0.587)

GDP Growth3.739∗(1.968)4.264(2.590)10.080∗(5.850)5.124(4.892)17.428∗∗(6.982)

7.543(4.761)

Inflation-3.542∗∗∗(0.910)-3.526∗∗∗(1.235)-4.894∗∗(2.424)-7.217∗∗∗(2.468)-5.071(3.171)-4.734∗∗(2.398)Exp-to-GDP-0.222(0.175)-0.216(0.271)-0.662(0.542)-0.502(0.400)-0.167(0.642)-0.304(0.363)Ratings

0.016(0.012)-0.005(0.018)0.046(0.033)-0.016(0.029)0.008(0.039)0.028(0.026)

Panel B: Shares and Elasticities
Mean ωi (n)

0.140

0.340

NA

0.01

0.015

NA

Elasticity

10.64

14.89

NA

29.05

35.45

NA

Note: This table reports the EM sovereign debt IV estimates of the demand function in Equation (12) for the first three columns and in
Equation (11) for the last three columns, including country and year fixed effects. The sample spans 1996-2018 at annual frequency. The
dependent variable is holding of the group indicated in the column title to GDP. The standard errors clustered at country and year level arereported in the parentheses. Panel A reports the instrumental variable estimates. * p < 0.10, ** p < 0.05, *** p < 0.01. Panel B gives the sharesand implied demand elasticities.

mand with respect to expected excess return equals λ0,i (1 − ωi,t (n)). Therefore, calculating
the elasticities requires adjusting the investors’ yield coefficients by their portfolio share ofcountry n debt holdings, ωi,t (n). Although we do not have measures of portfolio sharesof each investor groups, we can get estimates of domestic and foreign shares. For domestic banks and domestic non-banks, we proxy for this share using the average ratio acrossemerging markets of investor group holdings of own government bonds to assets. Taken

28

from the IMF Financial Soundness Indicators, these ratios are 0.14 for banks and 0.34 fornon-banks. For foreign investors, we take the data from the Euro area SHS data and BIS
banking statistics to compute proxies. In particular, holdings of EM sovereign debt by euroarea non-bank investors is about 1.5% of their portfolio. From the BIS banking statistics,
foreign banks have roughly 1% of claims on foreign sovereigns (Hardy and Zhu (2023)).
Panel B of Table 3 reports these shares and their implied elasticities with respect to yield.
These elasticities vary from 10 for domestic banks to around 35 for foreign non-banks. Although much of the comparable elasticities in the literature relate to private assets instead ofsovereign debt, the estimates are consistent with other debt instruments. For example, forten year bonds, Jiang et al. (2022) find a range of 13 to 44, while the estimates in Koijen et al.
(2021) vary between 12 to 30 but reach 72 for foreigners. The similarity between these elasticity estimates suggests that there may be a high degree of substitutability between privateand sovereign debt, an interesting question that we leave on the agenda for future research.
3.4

Funding Advanced Economy Sovereign Debt

We now return to consider the funding of Advanced Economy debt based upon the framework described in Section 3.2. The approach to estimating AE sovereign debt funding issimilar to that of EM sovereigns with two modifications. First, since variations in yields aremuch lower for these countries, we regress the holdings on the logarithm of the yield.20 Second, we omit some countries. In particular, given the special status of the US Treasury Market, we exclude the U.S. sovereign debt from the AE sample. Indeed, as is well recognizedin the literature, US Treasuries are often treated as special safe assets in the international
financial market, a status has been described as an ”exorbitant privilege.”21 Moreover, we
exclude Greece for the period of 2009 to 2015 when Greece was experiencing a debt crisis
and its yield spiked to an excessively high level.
Table 4 reports the IV estimation results for AEs. The results for the first-step regressions
20 For some countries, the yields are very low during the Zero Lower Bound period implying implausibly out-sized responses.
21 On the safe asset status, see for example, Jiang, Krishnamurthy, and Lustig (2018)and the discussion in Gourinchas, Rey,and Govillot (2017).

29

Table 4: Advanced Economy Funding: Instrumental Variable Estimates
(1)
DomBK

(2)
DomNB

(3)
DomCB

(4)
ForBK

(5)
ForNB

(6)
ForCB

Bond yield

1.377∗∗∗(0.497)2.179∗∗(0.865)1.790∗∗(0.782)0.979∗∗(0.391)3.111∗∗(1.373)0.686∗∗(0.301)

GDP growth6.990∗(3.726)4.897(6.659)15.057∗∗(6.723)1.915(3.039)11.634(9.600)5.246∗∗(2.338)GDP-0.742(0.595)-2.161∗(1.130)-1.482(1.086)0.705(0.494)0.021(1.510)0.915∗∗(0.380)Exp-to-GDP

4.363∗∗∗(1.536)

5.103∗∗(2.408)6.124∗∗(2.540)2.957∗∗(1.203)8.735∗∗(3.938)1.384(0.926)

Rating-0.469∗(0.279)-1.128∗∗(0.498)-0.110(0.462)-0.560∗∗(0.221)-1.358∗(0.724)-0.013(0.170)Inflation

3.705(5.994)

4.007(9.532)20.007∗(11.488)9.769∗∗(4.940)8.820(14.611)11.810∗∗∗(3.801)

274

264

249

275

267

275

Observations

Note: This table reports the IV estimates of the funding function in Equation (7), with country andyear fixed effects. The sample spans 2000-2018 at annual frequency. The dependent variable is holdingof the group indicated in the column title to GDP. The standard errors clustered at country and year
level are reported in the parentheses.* p < 0.10, ** p < 0.05, *** p < 0.01.

for the instrument and results for the projected debt are reported in Appendices C.3 and C.4,
respectively. As the first row shows, the responses of each investor group’s holdings to yieldchanges are all positive. Moreover, as with the holdings of EM debt, the non-banks are themost responsive to changes in yields within the domestic and foreign investor groups. In
particular, a one percent change in the local currency yield leads to a 2.2% change in holding
shares by domestic non-banks compared to a 1.4% change in domestic banks. Similarly,
foreign non-banks respond to the same change with a 3.1% change in holdings compared toa 0.98% change by foreign banks. Overall, the pattern that non-banks are more responsive
as found among emerging markets appears robust to advanced economies as well.
It would be useful to estimate these holdings as a demand system based upon the investors in the AE country government debt as we did for EM sovereigns. However, as

30noted earlier, in our data set foreigners are only identified as living outside the given country. Thus, we cannot cleanly identify which investors are foreign for a given sovereign.
For example, German banks will be in the ”Domestic bank” group for German debt but inthe ”Foreign bank” group for French debt. Thus, we cannot estimate realistic investor demand systems for AE governments with this data set. A more granular data set of portfolio
holdings of sovereign debt and other securities would be required for the estimation of thedemand system for AE sovereign debt, an important study that we leave for future research.

4

Cost of Financing Sensitivity and Counterfactual Analysis

Section 3 reported estimates of the demand response of different investors to, alternatively,changes in the yield or the expected excess return of debt and to other country characteristics. This section illustrates why the investor composition of sovereign debt holdings matters. For this purpose, we develop a measure of financing costs and show how it depends
upon the investor elasticity to yield. We then consider how this financing cost differs according to the characteristics preferred by the various investor groups. All of the analysis in
this section is based upon the Emerging Market sample using estimates from the demandsystem approach in Section 3.3. Using the estimates in Section 3.2 leads to similar results.
In this section, we conduct counterfactual analyses for private investors only. Despitethat official lenders’ holdings respond to the price of debt and other characteristics changes,
we treat official lenders’ holding as policy choices instead of market behaviors. The marginal
or average shares of official lenders are redistributed proportionally to private bank andnon-bank sectors.
4.1

Debt Increase

We begin by asking the question: how much will the cost of financing increase if the government wants to increase the debt by one percent? This question is clearly an important
one given the projected rise of debt-to-GDP in the next few years. To calculate this measure,
we first note that the present value of the cost of borrowing to the country is equal to the31present value of debt to the creditors according to the market-clearing condition in Equation (6). For expositional parsimony, we also define the market-value of debt as: Dtm ≡ Dt Pt
and subsume the dependence upon country n. Then, Appendix E.1 shows that the overall
response in the yield for a percentage increase in debt is:ξ≡dy∑iI=1 ai /( T + ηi )=d ln D m∑iI=1 ai ηi /( T + ηi )(17)

where ηi is the yield elasticity of demand by investor group i and ai is the change in bookvalue holdings by investor group i in response to changes in debt as estimated in Section
2. We call this measure ξ the ”financing cost sensitivity.” This calculation is a partial equilibrium calculation as it does not account for substitution effects that arise from investors
adjusting their portfolios towards or away from non-EM sovereign debt assets in response
to prices.
The interpretation of this measure is straightforward. The numerator is the sensitivity of
cost of financing in response to a one percent change in the book value of debt. This sensitivity equals the marginal absorption-weighted average of the inverse demand elasticities.
Intuitively, if the sovereign issues one percent more debt, ai percent is absorbed by investor
group i. To induce investor group i to absorb this amount of debt, the yield must rise byai /(ηi + T ). By contrast, the denominator shows how much debt should be issued at bookvalue in order to collect one more percent in market value. Since demand is downwardsloping, the price of debt will in general decrease when the debt level increases. Thus, theincrease in market value of debt will be lower than the increase in book value of debt. If
the aggregate elasticity is relatively high, the price change is mild so that the increase in
debt revenue will be close to the increase in book value. We use our estimates to study the
relative importance of these effects.
Panel A of Table 5 reports these financing cost sensitivities calculated from Equation (17)using the marginal responses a from Table 1 and elasticities η from Table 3. For convenience,
Panel D repeats the estimates used in this calculation. The first row of Panel A shows the financing sensitivity using the total composition of investors in our sample estimates. Specif32

ically, if a government increases debt by 10 percent, the cost of financing given by the debt
yield, will increase by 59 basis points. The average 5-year yield of EM debt in our sample is8.8%, so that an increase of 59 basis points represents a 6.7% increase in cost of financing.
Next, we consider how a change in the investor base would impact this financing sensitivity. These counterfactuals evaluate how the financing costs are affected if the absorption
shares of a given investor group is set to zero while the remaining shares are distributed
proportionately across the remaining investors according to their marginal absorption levels. As Table 5 Panel A shows in the row labeled ”No Bank”, excluding the bank investor
groups as a whole would move the cost of financing sensitivity from 59 basis points to 52
basis points in response to a 10% increase in debt revenue. By contrast, the next row labeled ”No Non-bank” illustrates how excluding the non-bank investor groups (domestic
and foreign) would impact financing. As reported, eliminating non-banks would significantly increase the overall financing cost sensitivity to 80 basis points, representing a 9%
increase for the average emerging market. This greater sensitivity is due to the higher elasticities of non-bank investors as reported in Panel D. Thus, when the investor base has more
non-banks, the required increase in yield for the sovereign is less.
4.2

Changes of Characteristics

We next consider how a change in the characteristics of the borrower would impact the government’s financing costs. Specifically, in this second set of illustrations, we ask the question:
how much would the yield have to change in response to changes in characteristics in order to keep the market value of debt unchanged? This counterfactual provides information
about the investor substitutability between the borrowing costs and characteristics.
For this purpose, we define ζ as the change of yield in response to a unit change in oneof the characteristics, Xt (n), given the market value debt. Then, Appendix E.2 shows that ζ
can be expressed as:ζ≡ζ̃
dyt (n)=dXt (n)1 − T ξ̃

33

(18)

where1ζ̃ = −
T

I

∑

i =1

m ( n ))∂ ln Hi,t∂Xt (n)!
ψiI÷ 1− ∑i =1

m (n)∂ ln Hi,t∂Pt (n)!
ψiand where ψi is the average share of emerging market debt held by investor group i, as given
in Table 1. The numerator ζ̃ is the response of yield to a unit change of characteristic Xt (n)for a given book value of debt Dt (n). However, if the yield of debt changes in response tochanges in X, the market value of debt will move accordingly. Therefore, the book value ofdebt should adjust to offset the yield change as captured by dividing by (1 − Tξ ).
To analyze this relationships, we focus on two characteristics: real GDP growth and theinflation rate. Panel B of Table 5 reports the response of yield to a one percent decrease
in real GDP growth. As shown in Table 3 (repeated in Table 5 Panel D), a decrease of real
GDP growth induces investors to reduce their demand of debt, so that the yield increases.
In response to a one percentage point decrease of real GDP growth, the yield increases by
36 basis points, which is equal to a 4.1% rise in the average yield to EM debt. Similarly, if
inflation increases by one percent, the yield would have to increase by 22 basis points, whichis equal to 2.5% of the average yield to EM debt. The reason is that, other things equal, allinvestors dislike investing in the debt of countries with low GDP growth and high inflation.
To evaluate how investors differ according to their view of these attributes and their impact on funding, we next consider how the the cost of financing sensitivity changes withdifferent investor bases. Table 5 Panel B shows the results of this analysis under the rows
labeled ”No Bank” and ”No Non-bank.”22 Excluding the bank investor groups as a whole,yield will increase by 41 basis points in response to a one-percent real GDP growth decrease
and 19 basis points in response to a one-percent inflation increase. By contrast, excluding thenon-bank investor groups implies that the yield would have to increase by 29 basis points
to a one percent decrease in real GDP growth but a significantly higher 28 basis points to anincrease in inflation. As Panel D shows, the reason is that foreign non-banks’ holdings ofdebt have the strongest response to GDP growth and foreign banks’ holdings of debt have
22 As before, the absorption shares of different investor groups are set to zero with the remaining shares distributed propor-

tionately across the remaining investors according to their marginal absorption.

34

the strongest response to inflation among all investors. Thus, excluding investor groups
with strongest responses reduces the yield pressure from these changes. Overall, these results highlight the importance of non-banks not only for their sensitivity to yield, but tocharacteristics as well.
4.3

Changes of Investors’ Latent Demand

Finally, we analyze the impact of changes in investor group latent demand on the borrowing costs. In particular, we ask: how much would yield change in response to a one percent
increase in a given investor group’s latent demand given the market value of debt? Intuitively, if investors in a particular group, k, increase their latent demand, the price of debt
will increase. Thus, the government can issue less book value of debt to maintain the same
market value. The reduced debt issuance will further lower debt yield. Appendix E.3 showsthat this sensitivity of financing costs to latent demand can be expressed as:ϕ≡where ϕ̃ = ψk /T ÷

1 + T1 ∑iI=1 ψi ηiϕ̃
dyt (n)=dek,t (n)1 − Tξ

(19). Table 5 Panel C reports the results of this analysis.

Since domestic investors are in general less elastic than foreign investors and also hold moredebt, a demand increase leads to the larger yield reduction for domestic investors than foreign investors. An increase in demand by foreign non-banks generates a sizable shift incosts despite holding a lower share.

5

Concluding Remarks

The rising levels of government debt worldwide in the wake of the Covid-crisis have madeurgent the answers to questions about their repayment. At the front of those questions is:
who holds this debt and does it matter to borrowers in this market? In this paper, we addressthese questions by analyzing a unique data set that decomposes sovereign debt into investor
holding groups for a large number of countries over almost three decades.
Based upon our analysis, the answers to these questions are striking. First, private fi35

Table 5: Borrowing Cost Sensitivity and Counterfactuals
Panel A: Borrowing Cost Sensitivity to Debt Increase
Investor Composition

Sensitivity a% Yield change b

Actual

0.59

6.70%

No Bank

0.52

5.91%

No Non-bank

0.80

9.09%

Panel B: Borrowing Cost Sensitivity to Characteristics
Investor Composition

Real GDP growth

Inflation

Sensitivity% Yield change b

Sensitivity% Yield change b

Actual

0.36

4.09%

0.22

2.50%

No Bank

0.41

4.66%

0.19

2.16%

No Non-bank

0.29

3.30%

0.28

3.18%

Panel C: Borrowing Cost Sensitivity to Latent Demand
Investor Group

Domestic Bank

Domestic Non-bankForeign Bank

Foreign Non-bank

Sensitivity

-1.32

-1.42

-0.30

-0.86

Panel D: Components from Estimates
Component

Domestic Bank

Domestic Non-bankForeign Bank

Foreign Non-bank

Elasticity η

10.64

14.89

29.05

35.45

Marginal Shares a

0.21

0.43

0.05

0.24

Average Shares ψ

0.26

0.28

0.06

0.17

GDP growth: ∂H/∂X

3.74

4.26

5.12

17.43

Inflation: ∂H/∂X

-3.54

-3.53

-7.22

-5.07

Panel A reports calculations of the borrowing cost sensitivity in Equation (17). Panel B gives the effects on borrowing costs
due to the characteristics as in Equation (18). Panel C provides the effects of latent demand by investor group given in
Equation (18). Panel D provides the measures used in the counterfactual calculations. The marginal shares a and average
shares ψ are from Table 1 while the semi-elasticities η, and coefficients for GDP growth and inflation are from Table 3.
Footnotes: a scaled by 10% ; b based upon average EM five-year yield in our sample of 8.8%

36

nancial institutions that are not banks absorb substantially more of the variation in outstanding government debt than other investor groups. Further decomposing this non-bankinvestment group using country/region specific data, we find that investment funds are
the primary drivers of this larger group. Next, we identify the propensity to provide funding across investor groups by exploiting the market clearing condition provided by the fullset of creditors in our data. Using these estimates, we find that the elasticity of demandby non-bank investors is higher than other groups. Finally, we use both the marginal increase of holdings together with EM investor demand estimates to calculate the financing
cost sensitivities. An average EM country faces significant financing cost sensitivity since a
10% increase would lead to a nearly 7% increase in borrowing costs. These countries face
the greatest sensitivity of financing cost against losing non-bank investors compared to any
other investment groups, as removing them from the investor pool increases the borrowing
cost response from 7% to 9%. We conclude that EM sovereign investors are highly vulnerable to the presence or absence of non-bank investors. Thus, the behavior of non-bankinvestors is crucial for understanding sovereign debt sustainability.
Our analysis opens up several avenues for future research, particularly given the limitations of our data. For example, our data is annual and lacks detailed data on the currency
and maturity structure of sovereign debt. It also does not report the location of foreign
investors, identities of investors, or the non-sovereign portion of investor portfolios. Data
built from the investor side, such as fund level data or granular data underlying the euroarea SHS data, would allow researchers to dig further into the aspects of the data missing
from our study and provide greater analysis from the investors’ perspectives. They would
allow a deeper understanding of how investors trade off their holdings of sovereign debt
with other assets, how they respond differently to local currency versus foreign currency
debt of emerging market sovereigns, how the yield elasticity varies by maturity, or how interventions by AE central banks affect their presence in the market. They would also allow
for greater analysis of the heterogeneity of this very diverse group of investors. We leavethese important questions on the agenda for future research.
37

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