International Debt Sustainability Reconciling Debt Intolerance with Original Sin

Julia Nicole Thornton Department of Economics

Stanford University Stanford, CA 94305 USA

May 2004 Email: julieta@stanford.edu

Abstract In the recent literature on debt sustainability, two competing viewpoints have emerged addressing the underlying causes of many emerging market countries’ difficulty in sustaining their debt. Carmen Reinhart, Kenneth Rogoff, and Miguel Savastano, authors of the 2003 article “Debt Intolerance,” argue that institutional weaknesses, evidenced by histories of default and inflation, are the root causes of many countries’ inability to sustain advanced country levels of debt, or debt intolerance. On the other hand, Barry Eichengreen, Ricardo Hausmann, and Ugo Panizza, authors of the 2003 article “The Pain of Original Sin,” argue that while institutional history certainly matters, the nature of the international financial system plays a more important role. More specifically, they argue that original sin, or many countries’ inability to borrow externally in their own currency, takes a primary role in explaining debt intolerance. This paper attempts to reconcile the two views by showing that both institutional history and the incidence of original sin matter empirically when attempting to explain intolerance of external debt. The analysis contains two main analytical chapters: one in which long-term debt intolerance is explained through cross-sectional analysis, and another where year-to-year changes in debt intolerance are explained through panel analysis. In all of the paper, debt intolerance is proxied with measures of country risk such as credit ratings or sovereign bond interest rate spreads. The empirical work shows that once factors such as levels of external debt and whether the country is in an advanced country “club” are controlled for, both variables relating to a country’s default and inflation history and variables indicating its incidence of original sin are economically and statistically significant.

Acknowledgements: First and foremost, I would like to thank my thesis advisor, Professor Ronald McKinnon, for his infinite support and patience. Without his guidance, this project would certainly not have been possible. I am also truly grateful to University of Maryland professor Carmen Reinhart for sharing much of her data with me. In addition, I would like to thank my major advisor, Professor David McKenzie for encouraging my interest in graduate school and thesis writing early on, and Professor Geoffrey Rothwell, director of the economics honors program, for his insight and humor. Finally, many thanks to my friends, my family, and especially my roommate for their patience and understanding during the long and at times arduous process of preparing this paper. Any remaining mistakes are solely attributable to the author.

TABLE OF CONTENTS

Introduction …………………………………………………………………………….. 3

Literature Review ……………………………………………………………………… 9

Cross-Sectional Analysis ……………………………………………………………... 22

An Analysis of Debt Intolerance ………………………………………………….. 23

An Alternative Measure of Debt Intolerance ………………………………………. 32

The Role of Original Sin …………………………………………………………. 36

Adding Original Sin to the Empirical Analysis ……………………………………. 41

Lessons from the Cross-Sectional Analysis ……………………………………….. 47

Panel Analysis ………………………………………………………………………… 49

A Basic Panel Regression ………………………………………………………… 50

Adding Historical Variables ……………………………………………………… 54

Estimating the Fixed Effects Coefficients …………………………………………. 57

Explaining the Fixed Effects ……………………………………………………… 59

Lessons from the Panel Analysis …………………………………………………. 60

Conclusions ……………………………………………………………………………. 62

References ……………………………………………………………………………... 64

Appendix ………………………………………………………………………………. 66

2

INTRODUCTION

This is the age of globalization. One can find the word everywhere: on the front

page of the newspaper, in countless college syllabi, in discussions at globally-billed

conferences, hand-painted on the signs of protesters on the street. More than a half

century ago, the General Agreement on Tariffs and Trade, or GATT, signed in Bretton

Woods, New Hampshire, ushered in an era of diminished tariff protection in the

internationals goods markets. Yet the 1990s bore witness not only to a proliferation of

traditional trade agreements dealing with goods and services, but also to a dramatic

liberalization of the international financial system.

If Adam Smith and David Ricardo gave economists a rationale for encouraging

the flow of goods (and even services) between nations, then Robert Solow, with his

theory of growth and convergence, gave economists a further rationale for also

liberalizing the flow of capital from rich nations to poor ones. If poor countries lack the

abundant capital stocks of rich countries, then investing in their capital stock should

exhibit high rates of return, to the benefit of both the poor countries and their creditors.

Yet real life has not always mirrored this simple, optimistic vision, and consequently, the

literature on macroeconomic development has become much more nuanced since Solow

first presented his theory.

Nevertheless, a strong rationale for at least some borrowing by developing

countries still exists. For example, in their 1996 article “The Overborrowing Syndrome,”

Ronald McKinnon and Huw Pill defend the welfare-improving potential of international

borrowing by suggesting that it may aid a country in developing a new method of

production, that, due to high startup costs, would have been difficult or even impossible

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to adopt in an initial state of an economy at or barely surpassing subsistence. The lure of

such welfare improvements achieved as a result of borrowing and investing has thus

allowed the past decade’s move toward greater financial liberalization to proceed despite

its potential pitfalls.

Although this liberalization was ushered in with substantial promise, the past

decade also provides testimony to the considerable dangers that come hand in hand with

the potential benefits of financial liberalization. Crises erupted in Mexico in 1995, in

Indonesia, Thailand, Malaysia, the Philippines, and South Korea in 1997, in Russia in

1998, and in Argentina and Uruguay in late 2001, and this is not even a complete list. As

the author knows from personal experience, as late as June 2003, more than a year and a

half after the outbreak of the Argentine crisis, people were still combing the streets at

night to search the trashcans for food or any object to sell. The effects of liberalization

that gives way to crisis can be truly tragic, and the possibility of such tragedy has left

some clamoring to halt the process.

Despite the risks it brings, however, the promise of liberalization is too great to be

passed up, and in any case, it is unlikely that the process of globalization could be

stopped in its tracks so far down the road. Thus if there is any lesson to be learned from

the series of debt crises of the late 1990s and early 2000s, it is that not only the desired

results but also the process matters. Economists must attain a better understanding of the

factors that lead to financial vulnerability in order to help developing countries, and the

international community, install the proper safeguards against future debt crises. This is

the aim of this paper.

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In the recent literature on debt sustainability, a debate has been taking place

between the adherents of the school of debt intolerance and those of the school of original

sin. “Debt Intolerance” is a term coined by Carmen Reinhart, Kenneth Rogoff, and

Miguel Savastano in a 2003 article bearing the same name. It refers to some countries’

inability to sustain levels of debt that would be unproblematic for the more industrialized

countries. In their article, Reinhart et al. propose that debt intolerance is mostly

attributable to the institutional weaknesses of some debtor countries. They argue that a

history of default and high inflation renders a country more debt intolerant, and such

countries are accordingly considered a greater credit risk and must bear higher interest

premiums.

On the other hand, Barry Eichengreen, Ricardo Hausmann, and Ugo Panizza,

authors of the 2003 article “The Pain of Original Sin”, contend that it is the structure of

the international financial system that makes some countries less able to sustain their

debts. More specifically, they suggest that original sin, or many countries’ inability to

borrow externally in their own currency, makes the afflicted countries particularly

vulnerable to exchange rate fluctuations, which can bring adverse balance sheet effects

and render a once-manageable debt unsustainable.

This paper attempts to provide a reconciliation of the two views. Through

empirical analysis, it shows that both historical variables relating to a country’s fiscal and

financial responsibility and variables indicating its incidence of original sin matter when

seeking to explain debt intolerance. First, the literature review provides an overview of

the traditional debt sustainability literature and recent amendments to it, including the

theories of debt intolerance and original sin. Then, in its empirical section, two main

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chapters are presently to simultaneously test the relevance of institutional history and

original sin in explaining debt intolerance, as proxied alternately by credit ratings and

interest rate spreads.

The first empirical chapter uses cross-sectional analysis to study the relationship

between country risk and the historical and original sin variables. The chapter begins

with a discussion of the theory of “Debt Intolerance,” followed by a set of cross-sectional

regressions that replicate those in “Debt Intolerance” as closely as possible to test their

sensitivity to factors such as sample selection. Average Institutional Investor credit

ratings, or IIR, from 1970 to 2002 are regressed on inflation history, countries’ average

level of external debt to GDP multiplied by a dummy to tell whether they are in the

advanced countries’ “club” or not, and three alternative measures of default history:

history of default and restructuring between 1824 and 2001, history of default and

restructuring between 1946 and 2001, and a dummy indicated whether or not a country

has defaulted or restructured multiplied by the number of years since the last default or

restructuring. The major conclusions of the “Debt Intolerance” regressions are

confirmed: namely, that default and inflation history matter when seeking to explain a

country’s debt intolerance. In the next regressions of the chapter, GDP growth is added

as an explanatory variable, and IIR are replaced with long-term sovereign bond interest

rate spreads as the dependent variable, but the importance of institutional history is

upheld.

In the second half of the cross-sectional chapter, original sin is added to the

equation. First, the theory of original sin is discussed in the context of debt sustainability,

and the criticisms that Eichengreen et al. make of the “Debt Intolerance” model are

6

addressed. Next original sin is added to the cross-sectional models of the first half of the

chapter to test whether they are robust to the inclusion of original sin. In fact, it is found

that while original sin is highly economically and statistically significant, this does not

chip away from the significance of a country’s default and restructuring history.

The next analytical chapter employs a methodological change to test the relevance

of default history and original sin in the context of year-to-year fluctuations in interest

rate spreads rather than in long-term averages of interest rate spreads or credit ratings, as

in the previous chapter. For this purpose, panel regressions are used instead of cross-

sectional analysis. The first regression presents a basic model seeking to explain yearly

changes in interest rate spreads with GDP growth and countries’ external debt, marked

with a dummy to indicate whether or not they are in the advanced countries’ “Club A.”

GDP growth shows more power to explain these yearly changes than the long-term

averages of credit ratings or interest spreads, suggesting a pro-cyclicality of interest rate

premiums that could aggravate countries’ economic troubles during a recession. Next,

variables representing a country’s default history and incidence of original sin are added

to the basic panel model, and the significance of those variables is generally confirmed in

this context.

Finally, a dummy variable regression is used to estimate the fixed effects

coefficients for the panel regression, and the fixed effects are saved to test to what extent

they can be explained by default history and original sin. Both original sin and default

history lend a great deal of explanatory power to the regression. This finding reflects the

overall conclusion of this paper: that both original sin and a country’s institutional history

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are fundamentally important, and that debt sustainability cannot be thoroughly

understood without a careful analysis of both concepts.

8

LITERATURE REVIEW

The bulk of the current literature on debt sustainability has its roots in the debt

crisis of the 1980s. During the 1950s and 1960s, growth rates were typically high and

interest rates low in developing countries. In contrast, during the 1970s and 1980s, in

part as a result of oil supply shocks, debt crises, and turmoil in the foreign exchanges,

growth rates fell and interest rates rose, making it impossible for countries to service

existing debt merely by issuing new debt. Consequently, fiscal adjustment became

necessary for debt service obligations to be met, and a wealth of literature prescribing

sustainable budgets to developing countries was born of the crisis.

The primary focus of this paper is to explore the relationship between historical

variables and debt sustainability. As debt sustainability is a bit of a nebulous concept, at

least empirically, in this paper it will primarily be interpreted as the risk associated with a

sovereign’s government debt, as measured by credit ratings or interest rate spreads. In

exploring this relationship, much inspiration is taken from Carmen M. Reinhart, Kenneth

S. Rogoff, and Miguel Savastano’s 2003 paper “Debt Intolerance.” Yet to fully

understand the role of historical variables in debt sustainability calculations, it is

important to acquire a sense of the traditional debt sustainability literature and the ways

that it has been expanded upon in recent years.

Thus, the literature review will first examine the traditional view of debt

sustainability calculations. The main source for this purpose will be Pierre-Richard

Agénor and Peter J. Montiel’s 1996 textbook, Development Macroeconomics, which

draws heavily on evidence from the 1980s debt crisis to make its conclusions. The next

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part of this review will look at the limitations of such traditional methods of debt

sustainability calculations. It will turn first to the role of the exchange rate in debt

sustainability calculations as outlined in Guillermo Calvo, Alejandro Izquierdo, and

Ernesto Talvi’s “Sudden Stops, the Real Exchange Rate and Fiscal Sustainability:

Argentina’s Lessons” (2003). They explain that the real exchange rate is especially

important in calculating the impact of sudden stops in capital inflows on debt

sustainability, particularly in small and relatively closed economies where internal prices

are relatively sticky.

Next, the review will shift its focus to Carmen M. Reinhart, Kenneth S. Rogoff,

and Miguel A. Savastano’s 2003 article “Debt Intolerance” in order to examine the

historical and endogeneity issues it brings to light. The role of historical variables in debt

sustainability analysis will be particularly important for this paper. In addition, the

results of Sumit Khedekar’s empirical work in “Pricing Sovereign Debt as an Option:

Theory and Evidence” (2000) will then be compared with the concepts and results put

forward by Reinhart et al. It will be shown that Khedekar’s results are consistent both

with traditional debt sustainability theory as well as with the concept of “debt

intolerance.”

Finally, this review will explore the concept of original sin, as introduced in Barry

Eichengreen and Ricardo Hausmann’s 1999 article “Exchange Rates and Financial

Fragility.” This concept is further elaborated upon in Eichengreen, Hausmann, and Ugo

Panizza’s 2003 articles “The Pain of Original Sin,” “The Mystery of Original Sin,” and in

Eichengreen and Hausmann’s introduction to Debt Denomination and Financial

Instability in Emerging Market Economies (2003), a book which the two edit. The

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authors’ theory of original sin seeks to explain the same phenomenon as does the theory

of debt intolerance proposed by Reinhart et al., namely, developing countries’ frequent

difficulty in servicing and paying off their debts. Thus it follows that one must also

consider the role of original sin when analyzing issues of debt sustainability.

Eichengreen, Hausmann, and Panizza themselves sought to clarify the relationship

between debt intolerance and original sin and how each affects debt sustainability in their

2003 article “Currency Mismatches, Debt Intolerance and Original Sin: Why They Are

Not the Same and Why it Matters.”

To present the traditional view of debt sustainability, Agénor and Montiel

carefully construct an economic model with which to calculate a country’s sustainable

level of debt. They first present an equation representing the consolidated budget identity

of a nation’s general government and another equation representing the central bank’s

balance sheet. To find the overall public sector deficit, the general government and the

central bank need to be jointly considered.

The general government budget identity presented is as follows:

• • • L + B + EFg = P(g – τ) + iB + i*EFg +icL (1)

Here L is the nominal stock of credit allocated by the central bank, including its holdings

of government bonds, B is the stock of domestic currency-denominated interest-bearing

public debt held outside the central bank, g real public spending on goods and services, τ

real tax revenue (net of transfer payments), i the domestic interest rate, i* the foreign

interest rate, ic ≤ i the interest rate paid by the government on central bank loans, E the

nominal exchange rate, and P the domestic price level (Agénor & Montiel, 1996, pp. 137

– 138).

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The central bank balance sheet is represented as follows:

M = L + ER - Ω (2)

Here M represents the nominal stock of base money, R the stock of foreign exchange

reserves, and Ω the central bank’s net worth. Throughout the model, an asterisk (*)

denotes foreign variables. Changes in Ω are defined as

• • Ω = I*ER + icL + ER. (3)

For simplicity, Agénor and Montiel assume that the interest rate earned on reserves is the

same as that paid on the government’s foreign debt (1996, p. 139).

Now that the central bank and the general government’s balance sheets have been

modeled, to obtain the overall public balances, the central bank’s profits must be

subtracted from the general government deficit and the increase in its net worth must be

subtracted from the increase in the government’s liabilities. This gives:

• • • • • L + B + EFg - Ω = P(g - τ) + iB + i*E(Bg – R) – ER. (4)

Using (3) to substitute for changes in the central bank’s net worth and defining net public

foreign debt as F* = Fg – R yields the final version of the equation:

• • • M + B + EF* = P(g - τ) + iB + i*EF*. (5)

The right-hand side of the equation thus represents the overall public sector deficit, and

the left-hand side represents means available to finance it (Agénor & Montiel, 1996, p.

139).

Agénor and Montiel then derive an intertemporal solvency constraint. They

calculate that the government will be considered solvent as long as the expected present

value of the future resources available to service the debt is greater than or equal to the

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face value of the initial stock of debt (1996, p. 156). Finally, they show that the resources

used to service the debt are equal to any seigniorage revenue plus the primary budget

surplus:

• (r – n)∆ - ∆ = s – d (6)

Here ∆ is the total public debt over GDP, d is the primary deficit over GDP, s is

seigniorage revenue over GDP, r is the real interest rate at time t, and n is the rate of real

GDP growth (Agénor & Montiel, 1996, p. 550). This equation generally agrees with the

“traditional” debt sustainability equations put forward by Reinhart et al. (2003, p. 38) as

well as Calvo et al. (2003, pp. 20 - 21). The only differences are that none of the latter

authors include seigniorage revenue, and Reinhart et al., who focus on external debt, refer

to the trade balance surplus instead of the primary balance surplus as the country’s main

means of servicing its debt.

Yet while these authors present a model of debt sustainability almost identical to

that found in Agénor and Montiel’s work, their purpose in doing so is not to adopt it

unthinkingly but rather to point out essential factors that the traditional debt sustainability

model neglects. Calvo et al. devote a great deal of their analysis to the role of exchange

rate fluctuations in fiscal sustainability considerations. For their part, Reinhart et al.

show that the traditional methods are limited by their neglect of interest rate endogeneity

issues as well as their failure to incorporate historical factors in sustainability

calculations.

One of the chief functions of the piece by Calvo et al. is to illuminate the role of

the exchange rate in debt sustainability calculations. As the authors show, this

consideration can be especially relevant in some cases. They focus on the case of

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Argentina’s recent crisis, in which exchange rates certainly had a crucial role in

determining the country’s sustainable level of debt.

Let us now turn to their model of debt sustainability. The authors define a given

time period’s debt recursively as follows:

bt +1 = bt[(1 + r)/(1 + θ)] – st. (7)

Here b is the debt-to-GDP ratio, r the real interest rate on debt, θ the GDP growth rate, s

the primary surplus, and t is time. The steady state of debt is thus:

s = b([(1 + r)/(1 + θ)] – 1). (8)

or equivalently:

b = s/[(1 + r)/(1 + θ) – 1] (9)

This is a familiar equation in traditional debt sustainability calculations, but Calvo et al.

point out that it hides the true composition of the debt-to-GDP ratio b. They show that

b = (B + eB*)/(Y + eY*) (10)

where B is the debt balance payable in terms of non-tradable goods, B* is the debt

balance payable in terms of tradable goods, Y is output of non-tradables, Y* is output of

tradables, and e is the real exchange rate, here defined as the price of tradables over the

price of non-tradables.

The two limit cases for the composition of the debt-to-GDP ratio would thus show

when real exchange rate movements have the greatest and least possible effect on debt

sustainability (Calvo et al., 2003, p. 21). The first limit case is then

b = eB*/Y (11)

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where all debt is foreign debt, and all domestic output is non-tradable. Here all effects of

real exchange rate valuation impact only debt and not output. Thus real exchange rate

depreciation has the strongest possible effect on sustainability. The other limit case is

[(B/eB*)]/[(Y/eY*)] = 1 (12)

so that the composition of debt is perfectly matched to the composition of output. Thus

changes in the real exchange rate will have no effect on fiscal sustainability. The authors

calculate [(B/eB*)]/[(Y/eY*)] for five South American countries in 1998 to determine

whose debt was better or worse matched to their income (2003, p. 22). Out of Argentina,

Brazil, Chile, Colombia, and Ecuador, Argentina showed the worst match, with a value of

0.01. (Values can range from zero to one.) On the other end of the spectrum, Chile, a

considerably more open economy than Argentina, had the best match, with a value of

0.45.

In their paper “Debt Intolerance,” Reinhart et al. analyze a dataset of general

macroeconomic figures (including debt, inflation, and GDP) for a wide range of countries

over various time periods in the past 180 years in order to define the concept of “debt

intolerance.” Debt intolerance is the authors’ way of explaining how certain countries

can be deemed quite risky to lend to at debt levels that would be considered quite

manageable for other countries.

They begin by classifying countries into three “clubs.” Club A has nearly

continual access to world credit markets, and consists primarily of industrialized

countries. Club C consists of countries that are virtually shut of from world credit

markets. The authors focus their analysis on Club B, which represents the middle range

of countries, and subdivide it into four “types” depending on the countries’ external debt-

15

to-GDP ratio and Institutional Investor ratings (IIR) (2003, p. 24). The authors also use

IIR as a proxy for perceived default risk and thus debt intolerance (2003, pp. 26 - 27).

After defining these clubs and types, Reinhart et al. run several regressions in an

attempt to quantify debt intolerance (the dependent variable), again using IIR as a proxy

(2003, p. 29). The ratio of external debt-to-GDP is statistically significant in each of the

six regressions performed, and in each case the coefficient is at least –0.27. The authors

also find, however, that the effect of the external debt-to-GDP ratio on debt intolerance

depends on whether the country in question belongs to Club A or Club B. In five out of

six regressions, the value of the coefficient of the external debt-to-GDP variable was

larger in magnitude and more strongly statistically significant for Club B countries than

for Club A countries. Thus an increase in external debt-to-GDP is perceived as a larger

risk for a Club B country, and accordingly has a larger impact on its IIR.

The tool of separating countries into “clubs” of risky and less risky borrowers also

lent much explanatory power in Sumit Khedekar’s 2000 empirical work seeking to

explain sovereign debt pricing. He performed iterated regressions on a panel sample of

18 countries until he was left with four statistically significant explanatory variables:

average GDP growth, private external debt, public external debt, and stock market return.

When he subdivided his sample of debtor countries into “safer” and “riskier” countries

(i.e. more or less along the lines of Club A and Club B), he found that the magnitudes of

all the coefficients in the regression for “riskier” countries were almost four times as

large in magnitude as those in the regression for “safer” countries (2000, p. 77).

Interestingly, average GDP growth and external debt-to-output ratios, the crux of

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traditional debt sustainability analysis, were by far the most statistically significant

variables.

Reinhart et al. point out that the interest rate that a country must pay when

servicing its debt is actually an endogenous variable, and thus may increase as debt itself

increases (2003, p. 38). The endogeneity of the interest rate has previously been

overlooked by much of the debt sustainability analysis. Yet more relevant to this paper,

Reinhart et al. argue that such historical factors as inflation history, past default and debt

restructuring history, and whether or not a country belongs to Club A can have a

tremendous effect on how much external debt it is able to sustain. They caution that a

country’s initial level of debt-to-output may already be near its maximum sustainable

value given the country’s credit and inflation history, a danger that could easily be

overlooked using alternate methods of debt sustainability analysis (2003, p. 39). Reinhart

et al. show that a large percentage of the variation in IIRs (and thus in debt intolerance)

can be explained by three variables alone: one for the country’s inflation history, another

for the country’s default history (measured various ways in the paper), and another for its

level of debt to output. Debt-to-output is allowed to have a different slope for Club A

and Club B countries. They R2 they obtain are between 0.74 and 0.77, despite the fact

that they have omitted key variables in the traditional debt sustainability analysis, such as

GDP growth.

Yet Eichengreen, Hausmann, and Panizza would argue that these regressions are

misleading. In “Currency Mismatches, Debt Intolerance and Original Sin: Why They

Are Not the Same And Why it Matters,” they state pointedly that “[a] correlation between

past defaults and current defaults is insufficient to establish this case [that institutional

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weakness has led to current difficulties in servicing debt]. That correlation could reflect

any omitted variable that matters persistently for debt-servicing difficulties and is slow to

change over time” (2003a, pp. 42-43). A key variable that they are alluding to is original

sin, which refers to the fact that all but a few countries in the world are unable to borrow

internationally in their own currencies. Indeed, this variable has the potential to explain

much of the observed debt intolerance, as the countries with the weakest institutions and

the strongest debt intolerance tend also not to be able to issue debt in their own

currencies.

Eichengreen and Hausmann introduced the concept of original sin in 1999, in

their article entitled “Exchange Rates and Financial Fragility.” In it they take the recent

East Asian financial crisis as a gateway to reexamine financial vulnerability and crisis.

They emphasize the role of the exchange rate in this context, providing three alternate

hypotheses as to its importance: moral hazard, commitment problems, and original sin.

Of these, original sin is the one with the most relevance to this paper. Original sin creates

currency mismatches in balance sheets, which make it difficult to service debt when

shocks hit the exchange rate. Moreover, in the context of original sin, both fixed and

flexible exchange rate can be troublesome. One the one hand, defending a peg by selling

reserves and raising interest rates can lead to defaults on short-term, domestic debt. Yet

on the other hand, a flexible exchange rate can become quite volatile, tending to

depreciate most severely when the efficacy of the economy is in doubt (Eichengreen &

Hausmann, 1999, p. 3). This makes the external debt difficult to service in general, but

this difficulty would hit especially hard in bad economic times.

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Eichengreen, Hausmann, and Panizza further expound on the concept of original

sin in Eichengreen and Hausmann’s book Debt Denomination and Financial Instability

in Emerging Markets. In the introduction to the book, Eichengreen and Hausmann reflect

on the liberalization of international financial markets in recent years, on the hopes that

advocates of liberalization put forward, and the disappointment that came with the many

subsequent crises. They acknowledge that institutional weaknesses have certainly played

a role in the development of such crises, but explain that their focus with be on another

contributing factor, namely, their concept of original sin (Eichengreen & Hausmann,

2003, pp. 1 - 2).

In the chapter entitled “The Pain of Original Sin” (2003c), Eichengreen,

Hausmann, and Panizza describe the negative effects of original sin on the countries that

suffer from it. Yet the bulk of the chapter is dedicated to the task of constructing an

index of original sin so that other authors might gain a more solid empirical grip on the

concept. They provide four alternative indices. The first simply measures the ratio of

securities issued by a country in its own currency to the total value of securities issued by

that country. The other three indices provide variations on this theme.

Later, in the chapter entitled “The Mystery of Original Sin” (2003b), Eichengreen,

Hausmann, and Panizza turn to the question of what root cause is behind original sin. In

contrast to Reinhart et al., Eichengreen et al. emphasize not just a country’s institutions

but also “the structure and operation of the international financial system that matters”

when seeking to explain a country’s level of financial stability (p. 3). They suggest that

the latter causes original sin as much as the former, if not more. The authors then run a

series of regressions to test their hypothesis about the causes of original sin. The two

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variables in their regressions that are always statistically significant are the size of a

country and whether it is a financial center1 (p. 48). This confirms their theory that the

international financial system is a primary determinant of original sin. It leaves open,

however, to what extent original sin leads to debt intolerance, and to what extent

institutional weaknesses such as inflation and default history are at root.

This brings us to the starting point of the analysis. Traditional measures of debt

sustainability can provide much insight into which factors most strongly influence the

amount of debt that a country can “safely” acquire. For example, in Khedekar’s work it

is clear that GDP growth and the ratio of debt to output bear great weight in determining

how risky a country’s debt is perceived to be (2000). Yet these traditional measures

nevertheless leave some important aspects unexplored. As Reinhart et al. have shown,

countries’ individual histories of default and inflation are closely linked to the “debt

intolerance” they face. The link of the historical variables to their debt intolerance will

be thoroughly tested in this analysis. Reinhart et al. also stress the endogeneity of the

interest rate on debt service, which can increase as debt-to-GDP itself rises. Finally, the

exchange rate can also play a vital role in determining debt sustainability, as formulated

by Calvo et al. and Eichengreen et al. Whereas Calvo et al. lay out the foundations of a

model in which currency mismatches are present and seek to explain the often disastrous

implications of those mismatches, Eichengreen et al. question where they come from.

Original sin is their answer, and they purport that original sin is as fundamentally

important in determining a country’s financial stability as its history of inflation and

1 The interplay between being a financial center and facing original sin, however, is actually quite subtle. For their purposes, Eichengreen et al. examined countries’ international borrowing. Yet if one were to look at international lending, the results would be a bit different. For example, Japan can borrow extensively in

20

default. This analysis will thus explore the role of such historical variables in

determining debt intolerance while remaining aware of the possible effect of original sin,

and controlling for that effect wherever possible.

its own currency, but it can make very few loans in yen, and generally must denominate them in dollars instead.

21

CROSS-SECTIONAL ANALYSIS

This chapter will present a series of cross-sectional regressions to explain

countries’ varying degrees of debt sustainability in terms of their current ratio of debt to

GDP, their credit history, the degree of their affliction with original sin, and their

respective growth rates. The first section will discuss the cross-sectional regressions

performed by Reinhart et al. in “Debt Intolerance” and will provide a replication of those

regressions. Average GDP growth will also be added as an explanatory variable to those

regressions, and the effect of doing so will be discussed. The second section will turn to

an alternative method of measuring debt intolerance, namely, with interest rate spreads

rather than credit ratings. As will be shown, both methods produce similar results, as

both provide a fairly telling measure of country risk. The regressions of the first section

will be repeated in this context, and the results discussed.

Next there will be a discussion of the theory of original sin and its implications

for the analysis of debt intolerance. The original sin and debt intolerance schools of

theory differ in what they consider to be the primary source of many countries’ limited

capacity to sustain debt. While the debt intolerance school emphasizes a country’s

responsibility to create and maintain good institutions in order to sustain its debt, the

original sin school instead puts the focus on the workings of the international financial

system. Thus, in the final section of this chapter, the compatibility of these views will be

explored. A measure of original sin will be added to the previous regressions to test

whether the historical variables introduced in “Debt Intolerance” still have a significant

effect on country risk once original sin is controlled for. Theoretical implications of the

findings will also be discussed. But first, let us turn to the theory of debt intolerance.

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An Analysis of Debt Intolerance.

Why can some countries safely sustain so much more debt than others? On the

one hand, there are countries like Argentina, which imploded in crisis in late 2001,

despite the fact that as late as 2000, its external debt-to-GDP ratio was only 51 percent.

Yet on the other hand, there is Japan, which in 2001 had a government debt-to-GDP ratio

of an astounding 122 percent, with no one doubting its solvency. Carmen Reinhart,

Kenneth Rogoff, and Miguel Savastano (2003) purport that a root cause of these

inconsistencies can be found in a country’s default and inflation history, coining the term

“debt intolerance” to refer to the fact some countries are much less able to sustain debt

than others.

The crux of the analysis presented by Reinhart et al. is that debt intolerance is

above all a result of the weak institutions of many emerging market economies. They

argue that debt intolerant countries tend to have fiscal and financial systems that are less

well functioning than those in the advanced economies. These institutional weaknesses

make debt intolerant countries more prone to default. Yet once default has occurred, it

generally aggravates the existing fiscal and financial problems, making subsequent

defaults more likely. The result is a pattern of poor debt sustaining capacity, which, once

established, tends to be quite long-lived. In stark contrast to Barry Eichengreen, Ricardo

Hausmann, and Ugo Panizza, who place much more emphasis on the role of the

international financial system, Reinhart et al. are vehement in attributing this inability to

sustain debt to institutional weaknesses. Indeed, they are quite skeptical of the idea that

original sin, or a country’s inability to borrow externally in its own currency, offers much

23

explanatory power for debt intolerance. They sharply state that “the notion that the

‘original sin’ of the serial defaulters can be extinguished through any stroke of financial

engineering, thereby allowing these countries to borrow advanced economies quantities

at advanced-country rates, is sheer folly” (2003, p. 2).

Empirically, Reinhart et al. test their hypothesis by performing a series of cross-

sectional regressions (and later panel regressions as well). They formulate that debt

intolerance can be proxied by country risk, and can be explained by a handful of variables

relating to a country’s default and inflation history. They also include debt relative to

GDP as a regressor to demonstrate that the effect of increasing debt is dramatically

different for debt intolerant countries than for advanced economies, and to show at which

levels of debt certain countries’ measured creditworthiness begins to unravel, given their

institutional history.

Default (or urgent restructuring) represents a failure to meet the commitment a

country has made to its creditors. Virtually no country chooses to default on its debt

while it is politically stable and experiencing economically prosperous times. Instead,

defaults generally arise when countries are faced with political difficulties or economic

troubles. It is true that defaulting on one’s sovereign debt may provide temporary fiscal

relief, and indeed, in certain situations default may become extremely difficult to avoid.

Yet the act of default signals to creditors to be wary of the country’s commitment to

meeting its payment obligations, not just for the current debt, but also for any debt that

the country accrues in the future. Moreover, default tends to reap havoc on a country’s

financial system and can also undermine a country’s tax system by encouraging capital

flight and tax avoidance. (Eichengreen et al. (2003a), however, criticize Reinhart et al.

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for failing to detail and provide evidence for a specific mechanism through which these

financial and tax troubles are brought on by past defaults and lead to future defaults.) For

all these reasons, Reinhart et al. believe that default (and restructuring) history would be a

key determinant of debt intolerance. In fact, one of the chief points that Reinhart et al.

make is that debt intolerance brought on by past defaults generally takes an extremely

long time to overcome.

Default and restructuring, however, are not the only ways for a sovereign to

renege on its payment obligations. When dealing with debt in domestic currency,

inflation can provide a powerful tool for altering the real values of payments. When

inflation is not indexed into contracts or when a unexpected bout of high inflation occurs,

sovereign debtors benefit at the expense of the suppliers of domestic currency-

denominated debt, usually their own citizens. High inflation also signals instability and

substantial ineffectiveness or lack of prudence in a country’s economic policy. To define

high inflation, Reinhart et al. set a threshold at forty percent, chosen for reasons

explained in William Easterly’s 2001 book, The Elusive Quest for Growth. They then

suggest that a country’s history of high inflation would directly affect its perceived

riskiness in taking on external debt, and should thus be added as a potential explanatory

variable of debt intolerance.

As will be seen, however, inflation history, at least in the form in which it was

measured in this analysis, provided much less explanatory power than default history. In

fact, it was rarely statistically significant. This is most likely because, beyond its

capacity as a signal of instability, inflation history is less relevant to the intolerance of

external debt (the focus of this analysis), which for most debt intolerant countries is not

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denominated in their own currency. It might also have to do, however, with the fact that

counting only inflation at or above forty percent is a rather clumsy measure of inflation

that perhaps makes overly simple a rather complicated relationship between inflation and

debt sustainability.

When observing that some countries appear to be more debt intolerant than

others, Reinhart et al. decided to sort the countries in their sample into “clubs” based on a

country’s perceived creditworthiness in international financial markets. As explained in

the literature review, Club A consists of the countries with the most access to the world’s

credit markets, Club C countries are virtually shut off from international credit markets,

and Club B countries comprise the middle group. In their empirical work, Reinhart et al.

confined themselves primarily to countries belonging to clubs A and B.

Finally, traditional debt sustainability analysis tells us that the amount of debt that

a country takes on should be of importance when calculating the likelihood of crisis.

Although Reinhart et al. emphasize the role of historical factors, they do not deny the

importance of this key variable. Indeed, including the ratio of debt to GDP is necessary

to be able to discern different thresholds of sustainable debt for different countries.

Reinhart et al. include the ratio of debt to GDP as an explanatory variable, but they

multiply it by dummies to distinguish whether a country is in Club A or Club B.

With these various factors to guide the theory, Reinhart et al. perform three types

of cross-sectional regression. In all the regressions, a country’s average IIR from 1979

until 2002 serves as the dependent variable. Institutional Investor ratings range from 1 to

100, with the highest marks being given to the countries deemed most creditworthy. In

all three, a country’s average ratio of external debt to GDP (or central government debt in

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the case of the OECD countries) from 1970 until 2000, multiplied by a Club A or Non-

Club A dummy, serves as an independent variable. Inflation history, represented as the

percentage of 12-month periods with inflation over 40 percent from 1958 to 2001, serves

as another independent variable in all three.

The difference between the three regressions is in the way that default history is

represented. In the first, it is represented as the percentage of years that a country was in

a state of default or restructuring between 1824 and 2001. In the second, the time period

is 1946 until 2001. Finally, in the third, default history is represented as the number of

years since a country has last defaulted or restructured its debt. In all the regressions,

endogeneity is generally avoided by sampling the independent variables over a much

longer time period than the dependent variable, so that they are largely predetermined in

comparison to the IIR. The “Debt Intolerance” results are presented below for easy

contrast with the results of the replicated regressions and expanded regressions performed

in this paper. The figures given are the OLS coefficients for the variable in the table

heading. Robust t-statistics are in parentheses.

Table 1. “Debt Intolerance” Regressions Explaining 1979-2002 Average IIR No. Inflation

HistoryaDefault History

Since 1824b

Default History

Since 1946

b

Years Since Last Default

Average Debt-to-

GDP*Non-Club A

Dummyc

Average Debt-to-GDP*Club A Dummyc

Number of Obs.

Adjusted R2

I. -0.16 -0.21 -0.33 0.28 53 0.77 (-2.97) (-2.10) (-5.40) (3.63)

II. -0.16 -0.17 -0.34 0.29 53 0.76 (-1.87) (-1.53) (-4.49) (3.68)

III. -0.11 0.05 -0.29 0.27 53 0.79 (-1.37) (1.93) (-4.03) (3.62)

a Percentage of 12-months periods with inflation over 40 % from 1958 to 2001 b Percentage of years in a state of default or restructuring c Average from 1970 to 2000

In preparing the analysis for this paper, the first step taken was the most

straightforward: simply to replicate the regressions performed by Reinhart et al. and

summarized above. The variables were defined in exactly the same way, and the sample

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of countries used was as close as possible to the original sample2. The results of this

replication are below. As in the previous table, OLS coefficients are given for the

variable in the column heading, and due to the detected presence of heteroskedasticity in

the residuals, robust t-statistics are reported in parentheses.

Table 2. Duplicating the “Debt Intolerance” Regressions Explaining 1979-2002 Average IIR No. Inflation

HistoryaDefault History Since 1824b

Default History Since 1946 b

Has Defaulted

Since 1824c

Years Since Last

Default

Average Debt-to-

GDP*Non-Club A

Dummyd

Average Debt-to-

GDP*Club A Dummyd

Number of Obs.

Adjusted R2

CS1 -0.123 -0.198 -0.138 0.519 59 0.62 (-1.63) (-1.68) (-1.40) (3.93)

CS2 -0.083 -0.306 -0.120 0.526 59 0.64 (-1.14) (-2.71) (-1.39) (4.15)

CS3 -0.053 -11.22 0.169 -0.155 0.494 59 0.63 (-0.62) (-2.04) (2.00) (-1.67) (3.77)

a Percentage of 12-months periods with inflation over 40 % from 1958 to 2001 b Percentage of years in a state of default or restructuring c Dummy variabled Average from 1970 to 2000

Comparing the two sets of results, several key features arise. First of all, the two

sets of regressions agree in sign on each coefficient, and on all but the coefficients for

debt-to-GDP ratios for the Club A and Non-Club A countries, the coefficients are

approximately the same in magnitude. Although the magnitudes of the coefficients for

the debt ratios differ between the two sets of regressions, in each case the gap between

Club A and Non-Club A is about the same, namely about 0.6. The adjusted R2 are in

each case smaller for the second set of regressions, suggesting some sensitivity of the

model to sample selection.

2 Reinhart et al. use a sample of the 53 countries, yet it is not entirely clear which 53 countries they use, as the list in the appendix that they refer the reader to contains 62 countries. This paper kept as many of the 62 countries in the sample as the data could be replicated for. The result was a sample of the following 59 countries: Algeria, Argentina, Australia, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Czech Republic, Denmark, Dominican Republic, Ecuador, Egypt, El Salvador, Ethiopia, Finland, Ghana, Greece, Hungary, India, Indonesia, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kenya, Malaysia, Mexico, Nepal, New Zealand, Nigeria, Norway, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Romania, Singapore, South Africa, South Korea, Spain, Sri Lanka, Swaziland, Sweden Tanzania, Thailand, Turkey, United Kingdom, United States, Uruguay, Venezuela, Zimbabwe.

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The signs of the coefficients conform to expectation with no surprises, with the

possible exception of debt-to-GDP. Raising the number of 12-month periods a country

has had inflation above 40 percent by ten percent lowers average IIR by about one point.

Increasing the percent of years in a state of default or restructuring since 1824 by ten

percent lowers average IIR by about 2 points both in the regression performed by

Reinhart et al. and in the one performed for this paper. Increasing the percent of years in

a state of default or restructuring since 1946 decreases IIR by different amounts in the

regression by Reinhart et al. and in the one for this paper, but only the coefficient for this

paper was found to be statistically significant. In the regressions performed for this

paper, default history since 1946 consistently produced regressions with a higher R2

when it was used as an explanatory variable rather than one of the other two measures of

default history. Here it has a characteristically large effect, as raising the percent of years

in a state of default or restructuring since 1946 by ten percent decreases average IIR by

about 3 points.

Average debt-to-GDP is perhaps the most interesting variable, as increasing it

lowers average IIR for the Non-Club A countries, as one would expect, yet it raises it for

the Club A countries. This discrepancy in sign between the Club A countries and Non-

Club A countries is statistically significant across the board in the regressions by Reinhart

et al. In the regressions for this paper, the positive coefficients are always statistically

significant for Club A countries, while the negative coefficient for the Non-Club A

countries is only statistically significant in the last of the three regressions. One possible

interpretation of this discrepancy in sign is endogeneity. A country may have a higher

IIR because it is deemed more creditworthy and thus can take on more debt, rather than

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its debt dictating its credit rating. Yet the causality may also flow in the other direction,

as an exogenous increase in external debt might scare investors and damage credit

ratings. These two possibilities would tend to confuse the sign on debt-to-GDP,

especially for the Non-Club A countries.

The fact that the negative coefficients on debt-to-GDP were not statistically

significant in most of the regressions performed for the paper, however, is somewhat

troubling. Debt intolerance, as defined by Reinhart et al., is precisely the phenomenon

that some countries are much less able to sustain given amounts of debt than others.

Thus, to provide support for the phenomenon of debt intolerance, Reinhart et al. point to

the negative coefficient on debt-to-GDP for the Non-Club A countries. The negative

coefficient shows that, for a given default and inflation history, raising a country’s level

of debt brings it closer and closer to having a serious implied risk of default, as evidenced

by its credit ratings. That this negative coefficient was generally not found to be

statistically significant in this paper is problematic, in that it makes it more difficult to say

concretely at exactly which levels of debt a country comes into danger. It should also be

noted, however, that default history still maintains much significance, both statistically

and economically, as a factor related to country risk.

One noteworthy aspect of the regressions performed by Reinhart et al. is the fact

that they exclude most of the traditional debt sustainability variables. In fact, this was

intentionally done to show how much explanatory power the historical factors had once

debt levels and country club were controlled for. As the authors state, “Our key finding

… is that a country’s external debt intolerance can be explained by a very small number

of variables about its repayment history, debt levels, and its history of macroeconomic

30

stability” (2003, p. 3)3. Yet what effect adding traditional variables would have is an

interesting question, and so the next set of regressions presented will add average GDP

growth from 1960 to 2000 to show its effect and significance4. The theoretical

justification for adding GDP growth to the debt intolerance model is that countries with

high GDP growth should be more able to “outgrow” their debt, and thus should be

perceived as less risky. The following set of OLS regressions reported is identical to CS1

through 3 except that average GDP growth is added as another explanatory variable. As

before, the t-statistics in parentheses are robust.

Table 3. Explaining 1979-2002 Average IIR with GDP Growth Added as an Explanatory Variable No. Inflation

Historya Default History Since 1824b

Default History Since 1946 b

Has Defaulted

Since 1824c

Years Since Last

Default

Average Debt-to-

GDP*Non-Club A

Dummyd

Average Debt-to-

GDP*Club A Dummyd

GDP Growthe

No. of Obs.

Adjusted R2

CS4 -0.087 -0.188 -0.150 0.518 1.15 59 0.62 (-0.98) (-1.64) (-1.51) (3.90) (0.95)

CS5 -0.065 -0.290 -0.129 0.526 0.664 59 0.63 (-0.76) (-2.52) (-1.43) (4.11) (0.54)

CS6 -0.030 -10.48 0.168 -0.163 0.496 0.863 59 0.63 (-0.32) (-1.88) (1.97) (-1.74) (3.75) (0.73)

a Percentage of 12-months periods with inflation over 40 % from 1958 to 2001 b Percentage of years in a state of default or restructuring c Dummy variable

d Average from 1970 to 2000 d Average from 1960 to 2000

As evidenced by the above table, GDP growth yields the expected sign as an

explanatory variable, yet in none of the regressions is it statistically significant.

Moreover, it detracts from the statistical significance of the default and inflation history

variables, suggesting a quasi-collinearity problem. Indeed, one would expect that the

countries with the most troubled default and inflation histories also tended to have low

3 Eichengreen et al., however, would argue that limiting the regressions to so few variables is misleading. As they state in their 2003 article “Currency Mismatches, Debt Intolerance, and Original Sin: Why They Are Not the Same and Why it Matters,” “That the same countries have defaulted in both the distant and more recent past may in fact reflect other characteristics of those countries that are slow to change but which are omitted from this analysis of debt intolerance” (p. 9). They suggest that original sin may take a prominent role among such omitted variables. This is an issue which this paper aims to address. 4 That GDP growth plays a central role in the traditional debt sustainability calculus can be seen in Agénor and Montiel’s model in the literature review.

31

GDP growth. Later in this paper it will be shown that short-run GDP growth has a more

statistically significant effect on perceived sovereign risk than does long run GDP

growth, suggesting that the market is very reactive to short run output volatility.

An Alternative Measure of Debt Intolerance.

At this point in the chapter, the dependent variable is changed to see if the results

hold when debt sustainability is measured in an alternative way. Instead of average IIR

from 1970 to 2000, the dependent variable used in the next set of regressions is an

interest rate spread. Interest rate spreads are meant to represent premiums borne by

certain assets; thus they are calculated by subtracting a benchmark interest rate from the

interest rate in question. By subtracting the yield of a “riskless” asset from the yield of an

asset of interest, one can find the risk premium that the latter asset bears. For the

purposes of this analysis, interest rate spreads will measured as the difference between

long-term maturity sovereign bond5 yields for the US versus other countries. Thus, in

this case, the interest rate spreads can be interpreted as a premium for perceived default

risk, making them a suitable measure of a country’s debt intolerance or ability to sustain

its debt. All of the Non-Club A sovereign bonds in the sample were denominated in US

dollars, which suggests that they may be traded internationally and eliminates currency

risk from the interest rate premium. The Club A sovereign bonds were not issued in US

dollars; however, their currencies, such as the pound and yen, are generally not deemed

5 Treasury bill spreads were also used as the dependent variable instead of sovereign bond spreads, but the results were not particularly enlightening. The adjusted R2 was only 0.25, as compared with figures in the 0.5 to 0.6 range for the sovereign bond spread regressions. (Please see the appendix for the particulars of the Treasury bill regressions.) This is most likely due to the fact that governments can often coerce their citizens to buy Treasury bills, which are typically denominated in domestic currency, by selling them to the government pension fund or forcing other government institutions to hold them. Thus their prices, and hence their yields, are not necessarily very meaningful.

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to be risky, so currency risk should be minimal6. The spreads were controlled for

inflation where applicable (namely for the Club A countries, whose bonds were issued in

domestic currency rather than dollars).

Table 4. Explaining Average Sovereign Bond Interest Rate Spreads, 1996-2001 No. Inflation

Historya Default History Since 1824b

Default History Since 1946 b

Has Defaulted

Since 1824c

Years Since Last

Default

Average Debt-to-

GDP*Non-Club A

Dummyd

Average Debt-to-

GDP*Club A Dummyd

Average Debt-to-

GDPd

GDP Growthe

No. of Obs.

Ad-justed

R2

CS7 0.040 0.113 0.003 -0.072 20 0.57 (1.43) (1.78) (0.08) (-1.37)

CS8 0.038 0.085 0.004 -0.075 20 0.64 (1.50) (2.88) (0.14) (-1.63)

CS9 0.005 2.09 -0.036 -0.003 -0.104 20 0.43 (0.11) (1.13) (-1.63) (-0.06) (-1.40)

CS10 0.077 0.158 0.018 20 0.50 (3.11) (2.77) (0.43)

CS11 0.004 0.117 -0.058 15f 0.24 (0.08) (1.88) (-1.06)

CS12 0.035 0.109 0.001 -0.076 -0.208 20 0.54 (1.01) (1.78) (0.02) (-1.30) (-0.43)

a Percentage of 12-months periods with inflation over 40 % from 1958 to 2001 b Percentage of years in a state of default or restructuring c Dummy variable

d Average from 1970 to 2000 d Average from 1960 to 2000 f Club A countries removed

The same independent variables were used as in the previous six regressions to

test their sensitivity to the measurement of debt intolerance as the dependent variable.

The above table presents the results, which were estimated by OLS. Due to a lack of

available data, the sample had to be cut back to 20 countries7, and the sampling period of

the dependent variable to 1996 through 2001. Robust t-statistics are in parentheses.

Several observations can be made about these regressions. To begin with, the

signs of the coefficients are exactly opposite what they were in the previous regressions.

6 Ideally, the sample would consist of only countries whose debt was denominated in dollars to make the interest rate spreads most comparable. Yet as data on dollar-denominated debt could be found for only relatively few countries, cutting out the Club A countries would have a dramatic effect on the sample size. One regression without the Club A countries, CS11, is reported to give an idea of the effect of reducing the sample to the more comparable countries. 7 The countries included in the small sample are Argentina, Australia, Brazil, Bulgaria, Canada, Indonesia, Jamaica, Japan, Lebanon, Morocco, Nigeria, Panama, Peru, the Philippines, Poland, Russia, Spain, Turkey, the United Kingdom, and Venezuela. Of these, the Club A countries are Australia, Canada, Japan, Spain, and the United Kingdom.

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This is logical, as a higher interest rate spread implies a riskier country, whereas a higher

IIR implies that a country is perceived as more creditworthy.

Next to note is the fact that although default history since 1824 or 1946 retains

significance at the 90 percent level or better in all of the regressions, inflation history is

only statistically significant in regression CS10, where Club A debt and Non-Club A debt

are not distinguished. As it turns out, inflation history is one of the ways that Club A and

Non-Club A tend to differ, so in regression CS10 inflation history is likely serving to

some extent in the same manner as the Club A and Non-Club A dummies.

In the other regressions in Table 4, the insignificance of inflation history is

probably due in part to the small sample size, yet, as mentioned previously in this paper,

it most likely also has to do with the fact that the Non-Club A countries’ sovereign bonds

were not issued in their domestic currency, making their inflation history less relevant

(although it still serves to some extent as a signal of macroeconomic instability, lending it

the appropriate plus sign in all the regressions). In addition, the form of measurement of

inflation history, counting only inflation over forty percent as high, is quite blunt, and

ignores much subtlety. Finally, the fact that inflation history loses its statistical

significance in these regressions provides a contrast to the strong and consistent negative

impact of increased default history on a country’s ability to sustain debt that is

maintained. In this set of regressions, raising the number of years between 1824 and

2001 spent in a state of default or restructuring by 10 percent increases a country’s risk

premium by about one percent.

Also important is the fact that although default history has an effect which is both

large in magnitude and statistically significant, a country’s specific level of debt never

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bore statistical significance in these regressions. T-statistics for the Non-Club A

countries’ debt coefficients are quite dismally low. (On the other hand, the more

significant negative coefficient on debt-to-GDP for the Club A countries is probably

owing in large part to Japan.) This suggests that it does not matter whether a country is

Club A or not, so in CS10 the distinction between Club A and Non-Club A countries was

not made. (Yet in this regression inflation history becomes statistically significant, so the

country groupings are nevertheless roughly made.) Debt in this regression, however, also

yielded a coefficient that was not statistically significant, most likely owing to the

problem of the endogeneity of debt accumulation and credit ratings. As mentioned

previously, the statistical insignificance of debt-to-GDP in these regressions has troubling

implications when attempting to carry out precise debt sustainability calculations.

In regression CS11, the sample was reduced to the 15 Non-Club A countries,

which all released U.S. dollar-denominated sovereign bonds. The purpose was to see if

the explanatory power of the regression was improved by removing the non-US dollar

denominated assets from the sample, restricting the sample to a group of countries which

all found it necessary to borrow in a currency other than their own. In fact, the adjusted

R2 decreased dramatically, from 0.50 to 0.24, although this was surely owing in part to

the diminished sample size. Default history retained its statistical significance in CS11,

but the other variables were not statistically significant at the 90 percent level or better.

In the final cross-sectional regression with interest rate spreads as the dependent

variable, the original sample of 20 was used, and GDP growth was added as an

explanatory variable. As in the regressions with IIR as the dependent variable, GDP

growth was not statistically significant. Default history, however, retained its statistical

35

significance at the 90 percent level. The magnitude of the coefficient remained at about a

one percent increase in interest rate premium for a ten percent increase in the number of

years in a state of default or restructuring since 1824.

The findings of the cross-sectional regressions in this section are fairly consistent.

Inflation history and levels of debt matter when predicting a country’s sustainable level

of debt, but the variable that consistently has a negative, statistically significant impact on

debt sustainability is default history. The fact that the effect of this variable is large and

statistically significant whether measured from 1946 or 1824 suggests that debt

intolerance rising from past defaults is extremely long-lived. Yet some causality issues

come to light here. Are countries perceived as not credit worthy because they default, or

is there some other factor that causes them both to default and to be perceived as not

creditworthy? Any such factor would have to be considerably long-lived and slow to

change over time, as debt intolerance stemming from default has been shown to endure

for decades, if not centuries. Eichengreen, Hausmann, and Panizza assert that such a

factor can be found in the phenomenon of original sin, which brings us to the next section

of this chapter.

The Role of Original Sin.

Original sin is defined by Eichengreen et al. as a country’s inability to borrow

externally in its own currency. The source of original sin, however, is not so easy to

pinpoint, as the authors point out in “The Mystery of Original Sin” (2003b). They find

that empirically, original sin cannot be well explained by the level of economic

development (as proxied by the natural logarithm of GDP), monetary credibility (as

36

proxied by various measures of inflation), fiscal sustainability (as proxied by various

measures of a country’s debt and deficit), the rule of law (as defined by various indices),

or openness to trade. They do find, however, that the size of a country’s financial system

and the size of its economy in general, both provide statistically and economically

significant explanations for the incidence of original sin. This leads them to the

conclusion that original sin is not as much a product of emerging market countries’ own

institutional weakness so much as a result of the nature of the international financial

system.

Having all the world’s internationally traded securities in exactly one currency

would not be an optimal situation, as added additional currencies provides diversification

as a protection from currency risk. Yet in a world where there are international

transaction costs, the ideal number of currencies to be traded in internationally becomes

finite. The data also supports this conclusion: The vast majority of internationally-issued

debt is denominated in a handful of currencies, most prominent among these being the

United States dollar. For example, Eichengreen et al. state in chapter one of Debt

Denomination and Financial Instability in Emerging Markets, that of the nearly $5.8

trillion in outstanding international securities in the period from 1999 through 2001, fully

96 percent of it was denominated in just five currencies: the US dollar, the euro, the

British pound, the Japanese yen, and the Canadian dollar (2003c, p. 4). Large countries

provide more benefit from diversification when their currencies are traded

internationally, and there is also a large degree of historical path dependence, so countries

that have historically been financial centers in the past will tend also to fill that role

today.

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Eichengreen et al. argue that while the prevalence of original sin is largely not the

fault of the countries afflicted with it, it does, however, have a significant impact on the

level of debt that afflicted countries can safely sustain. To begin with, one should note

that while acquiring a net foreign debt may be a reasonable idea for a developing country

interested in making investments in its capital stock, if the country suffers from original

sin, it will have an aggregate currency mismatch on its balance sheets. Currency

mismatches are problematic because any shock to the exchange rate will cause an

imbalance in assets and liabilities on the balance sheet. For example, a country that has

debt denominated primarily in dollars but assets denominated primarily in pesos will find

itself in a precarious position to repay its debt if the peso is suddenly devalued (as was

recently the case in Argentina).

Currency mismatches on the balance sheet tend to make developing countries

afraid to let their exchange rate move freely, hence the “Fear of Floating” described by

Guillermo Calvo and Carmen Reinhart in their 2002 article with the same name. In order

to maintain a fixed (or officially floating but in reality tightly controlled) exchange rate,

developing country central banks must hold more reserves (thus reducing net foreign

debt) and be prepared to adjust short-term interest rates. This will force the interest rates

to be more volatile. Moreover, as the country’s exchange rate will tend to come under

downward pressure when its economic times are not so good, hiking short-term interest

rates to defend the exchange rate constitutes a pro-cyclical monetary policy.

Yet if the central bank wishes to avoid these issues and instead chooses to float

the exchange rate, then exchange rate fluctuations will aggravate the currency

mismatches in the banking system and cause bankruptcies. Moreover, if the central

38

government has a net foreign debt denominated in foreign currency, its obligations in

terms of the local currency will be greatest in bleak economic times when the exchange

rate is weak, encouraging (and quite possibly forcing) the government to conduct a pro-

cyclical fiscal policy. This is in stark contrast to countries which do not suffer from

original sin. During a recession, they can let their exchange rate depreciate to some

extent to ease their debt burden. They are also free to use their interest rates in a counter-

cyclical fashion so as to stabilize the economy.

This all has some rather bleak implications for the debt sustaining capacity in

countries suffering from original sin. The exchange rate suddenly becomes a crucial

variable in determining these countries’ ability to sustain debt. This means that, all else

equal, countries facing original sin will be riskier to invest in than other countries, and

they will have to pay interest rate premiums. All this serves to exacerbate any difficulties

they may have already had in sustaining their debt.

The theory of original sin thus suggests that the degree to which a country is

affected by original sin will have a profound impact on its ability to sustain debt. This

implies that some measure of original sin would be a key variable in any debt

sustainability calculus. Indeed, this is one of the chief criticisms that Eichengreen et al.

make of the work done by Reinhart et al. They claim that predicting current risk of

default based on a country’s history of default in the past is susceptible to omitted

variable bias, as there could easily be some third factor that is slow to change over time,

causing countries to default both in the past and in the present. Original sin, they suggest,

could serve as such a variable.

39

The other two main issues that Eichengreen et al. take with the regressions

performed in “Debt Intolerance” are the fact that IIR metrics are used to group the

countries in the sample into Clubs A, B, and C8, and the fact that the only difference

allowed for between Club A countries and Non-Club A countries is in separate slopes for

the debt-to-GDP ratio. In “Currency Mismatches, Debt Intolerance and Original Sin,”

Eichengreen et al. perform a series of similar regressions, however, and find that once

separate intercepts are allowed for Club A countries and Non-Club A countries, the

difference in slope loses its statistical significance. Indeed, in the regressions performed

for this paper as well, the difference between the debt-to-GDP slope for Club A and Non-

Club A tended not to be statistically significant.

Yet nowhere in their analysis do Eichengreen et al. refute the claim that Reinhart

et al. make about the importance of historical variables. Although they perform a series

of regressions in “Currency Mismatches, Debt Intolerance and Original Sin” (2003a) to

test for the significance of original sin and levels of external debt in determining debt

intolerance (as proxied by credit ratings), they exclude the historical variables. The next

section of this paper will therefore attempt to fill in the major gap in the “Debt

Intolerance” style regressions that Eichengreen et al. pointed out: namely, the potential

omitted variable problem. While there are several variables that could potentially explain

both past and present defaults, the one that will be included here is original sin. A key

variable left out is, of course, institutional weakness. This variable is presumably an

underlying cause for both previous defaults and current ones (and to some extent, also for

original sin). Thus the effect of previous defaults on current defaults can be interpreted

8 That is, the mean and standard deviation of IIR were taken for the sample. Countries with IIR at least one standard deviation above the mean were placed in Club A, and those with IIR at least one standard

40

not only as being the damage they cause to a country’s fiscal and financial systems, but

also the institutional weakness they signal. The following section will provide a series of

cross-sectional regressions including original sin in order to make these relationships

more precise.

Adding Original Sin to the Empirical Analysis.

The regressions in this section generally duplicate the regressions presented in the

first part of this paper, with the exception that they include alternately two measures of

original sin, OSIN1 and OSIN3, defined by Eichengreen et al. in the “Pain of Original

Sin” (2003c). OSIN1 is defined as “one minus the ratio of the stock of international

securities issued by a country in its own currency to the total stock of international

securities issued by the country” (Eichengreen et al., 2003c, p. 9). Less formally, it

represents the portion of a country’s international securities that it issues in foreign

currency. A drawback of this measure, however, is that it does not take into account a

country’s ability to perform swaps in order to hedge its currency exposure. Eichengreen

et al. (2003c) therefore define OSIN3 with this problem in mind. OSIN3 is equal to one

minus the ratio of all international securities issued in a country’s currency to the total

international securities issued by the country or zero, whichever is greater.

The OSIN measures used in this analysis were figures given for 1993 through

1998. As early a period as possible was chosen in order to avoid endogeneity by having

the original sin index mostly predetermined relative to the dependent variable. This was

impossible for the regressions using IIR as the dependent variable; however, it should

serve some purpose in the regressions using interest rate spreads as the dependent

deviation below the mean were placed in Club C. The rest comprise Club B.

41

variable, as the spreads are averages from 1996 to 2001. The OSIN figures are taken in

exactly the same form as given in the appendix to “The Pain of Original Sin” (2003c).

The first set of regressions presented in this section provides a duplication of the

original “Debt Intolerance” OLS regressions, with the addition of original sin as an

explanatory variable. As in the original “Debt Intolerance” regressions, the dependent

variable here is average IIR from 1979 until 2002, and robust t-statistics are in

parentheses. Since Eichengreen et al. suggest that the “Debt Intolerance” regressions

may be spurious, as any omitted variable that affects both past and present defaults (or

incidences of restructuring) could bias the results, here one very important such variable

is controlled for: namely, original sin. As can be seen in Table 5, original sin proves to

be both extremely statistically and economically significant in the resulting regressions;

however, it does not strip the default history variables of their statistical significance.

Table 5. Explaining 1979-2002 Average IIR with Original Sin Added as an Explanatory Variable No. Inflation

Historya Default History Since 1824b

Default History Since 1946 b

Has Defaulted

Since 1824c

Years Since Last

Default

Average Debt-to-

GDP*Non-Club A

Dummyd

Average Debt-to-

GDP*Club A Dummyd

OSIN1, 1993-1998

OSIN3, 1993-1998

No. of

Obs.

Ad-justed

R2

1.1 -0.163 -0.230 -0.094 0.375 -49.76 59 0.72 (-2.40) (-2.16) (-1.17) (3.74) (-5.01)

1.2 -0.148 -0.234 -0.097 0.388 -17.22 59 0.69 (-2.02) (-2.18) (-1.11) (3.23) (-2.37)

2.1 -0.122 -0.322 -0.081 0.382 -51.68 59 0.73 (-1.82) (-3.14) (-1.18) (3.96) (-5.02)

2.2 -0.107 -0.319 -0.085 0.397 -18.00 59 0.70 (-1.45) (3.10) (-1.10) (3.43) (-2.54)

3.1 -0.090 -11.92 0.151 -0.118 0.357 -47.12 59 0.72 (-1.11) (-2.27) (1.89) (-1.54) (3.65) (-4.60)

3.2 -0.064 -14.45 0.077 -0.118 0.333 -20.94 59 0.71 (-0.77) (-2.66) (1.05) (-1.54) (3.02) (-3.30)

a Percentage of 12-months periods with inflation over 40 % from 1958 to 2001 b Percentage of years in a state of default or restructuring c Dummy variable

d Average from 1970 to 2000

Compared with the previous “Debt Intolerance” style regressions, these provide

no surprises in sign, and have a similar pattern of statistical significance. Inflation and

default histories have a negative impact on IIR (although the number of years since last

default, quite logically, has a positive impact). The effect of inflation is statistically

42

significant in the first three regressions at the 90 percent level or better, but not in the

latter three. The coefficients of the default and inflation history variables are about the

same as in the regressions in Table 2, or if anything, slightly larger in magnitude,

suggesting that once original sin is controlled for, default and inflation histories become

even more important in determining country risk. Compared with Table 2, the gap in the

coefficients of average debt-to-GDP for Club A versus Non-Club A countries has

narrowed, and only the figures for Club A are statistically significant.

Original sin is revealed to be highly significant, and also has a large impact in

terms of its effect on IIR. It should be noted, however, that the fact that the coefficients

are also large because the OSIN indices range from 0 to 1, whereas the other variables are

measured as percent from 0 to 100 (or higher, as sometimes in the case of debt-to-GDP).

Yet even controlling for this difference, the effect is still astoundingly large in magnitude.

The OSIN1 regressions (which, as will be noted below, consistently produced a higher

adjusted R2) suggest that taking a country from a situation of absolutely no original sin to

complete original sin (i.e. bring OSIN1 from 0 to 1) yields an astonishing drop of about

50 points in IIR, or half the scale!

Also interesting to note is the fact that OSIN1 proved both to be more statistically

significant, to have a larger coefficient, and to produce a higher adjusted R2 in all the

above regressions. This suggests that in creating its ratings, the Institutional Investor is

more concerned with the volume and composition of a country’s own securities rather

than the number securities issued internationally in a country’s currency, as is picked up

in OSIN3. As will be noted later, however, OSIN3 turns out to provide more explanatory

power when the dependent variable used is instead sovereign bond spreads.

43

As in the previous section, the next step taken with the OSIN regressions is to add

GDP growth as an explanatory variable in order to gauge its impact on the preceding

calculus. Table 6 below outlines the results of those regressions, estimated by OLS. As

in Table 5, the dependent variable is average IIR and robust t-statistics are in parentheses.

As in the regressions without original sin as a variable, GDP growth turns out to

contribute little to the explanatory power of the regressions. Only in regression 4.2 is its

coefficient statistically significant at conventional levels. The coefficients on growth also

vary quite wildly between the OSIN1 and OSIN3 regressions, but this should not be

surprising, given the low t-statistics. The positive sign, however, does confirm to

expectations, and the magnitude of the coefficients is also in the general ballpark of those

in regressions CS4 through 6.

Table 6. Explaining 1979-2002 Average IIR with GDP Growth and Original Sin Added as Explanatory Variables No. Inflation

Historya Default History Since 1824b

Default History Since 1946 b

Has Defaul-

ted Since 1824c

Years Since Last

Default

Average Debt-to-

GDP*Non-Club A

Dummyd

Average Debt-to-

GDP*Club A Dummyd

GDP Growthe

OSIN1, 1993-1998

OSIN3, 1993-1998

No. of

Obs.

Ad-justed

R2

4.1 -0.127 -0.219 -0.107 0.371 1.07 -51.49 59 0.71 (-1.63) (-2.12) (-1.28) (3.69) (1.07) (-4.88)

4.2 -0.081 -0.213 -0.115 0.373 1.81 -20.82 59 0.69 (-0.94) (-2.06) (-1.31) (3.09) (1.82) (-2.75)

5.1 -0.103 -0.307 -0.090 0.380 0.636 -52.67 59 0.73 (-1.30) (-2.99) (-1.23) (3.93) (0.61) (-4.86)

5.2 -0.061 -0.285 -0.101 0.385 1.40 -20.76 59 0.70 (-0.67) (-2.75) (-1.27) (3.33) (1.30) (-2.75)

6.1 -0.069 -11.12 0.150 -0.126 0.357 0.761 -48.79 59 0.72 (-0.78) (-2.06) (1.87) (-1.57) (3.64) (0.75) (-4.50)

6.2 -0.025 -13.24 0.067 -0.130 0.327 1.30 -23.37 59 0.71 (-0.26) (-2.38) (0.90) (-1.64) (2.96) (1.32) (-3.38)

a Percentage of 12-months periods with inflation over 40 % from 1958 to 2001 b Percentage of years in a state of default or restructuring c Dummy variable

d Average from 1970 to 2000 d Average from 1960 to 2000

As before, it should be noted that OSIN1 consistently yielded more explanatory

power than OSIN3. The coefficients on the variables representing debt and inflation

history are approximately the same as in regressions CS4 through 6, although the OSIN3

regression coefficients provide a closer approximation of those in CS4 through 6,

whereas the OSIN1 regressions tend to yield coefficients on inflation and default history

44

that are slightly larger in magnitude. As the OSIN1 regressions are the ones with the

greater explanatory power, this reinforces the idea that the effect of default and inflation

history on credit ratings is even larger once original sin is controlled for (as suggested in

discussing Table 5). As in Table 5, average debt-to-GDP only proved to be statistically

significant for Club A countries.

Finally, as in the previous section, the dependent variable is changed from IIR to

long-term sovereign bond spreads to test the sensitivity of the above results to the choice

of measurement of debt intolerance. Table 7 presents the results, which were estimated

with OLS, with robust t-statistics in parentheses.

Table 7. Explaining Average Sovereign Bond Interest Rate Spreads, 1996-2001, with Original Sin Added as an Explanatory Variable

No. Inflation Historya

Default History Since 1824b

Default History Since 1946b

Has Defaul-

ted Since 1824c

Years Since

Last De-fault

Average Debt-to-

GDP* Non-Club

A Dummyd

Average Debt-to-

GDP* Club A

Dummyd

Average Debt-to-

GDPd

GDP Growthe

OSIN1, 1993-1998

OSIN3, 1993-1998

No. of

Obs.

Ad-jus-ted R2

7.1 0.042 0.114 -0.001 -0.079 -1.25 20 0.53 (1.25) (1.59) (-0.02) (-1.11) (-0.12)

7.2 0.031 0.099 -0.011 0.002 6.84 20 0.57 (1.11) (1.55) (-0.25) (0.04) (1.73)

8.1 0.036 0.083 -0.008 -0.054 5.51 20 0.64 (1.39) (2.68) (-0.30) (-1.09) (0.84)

8.2 0.030 0.086 -0.015 0.047 10.30 20 0.72 (1.31) (3.35) (-0.67) (0.65) (2.08)

9.1 -0.017 3.43 -0.084 -0.052 0.000 20.64 20 0.57 (-0.43) (2.38) (-2.44) (-0.98) (0.00) (1.89)

9.2 -0.012 4.16 -0.054 -0.039 0.062 12.93 20 0.60 (-0.33) (2.45) (-2.50) (-0.81) (0.68) (2.00)

10.1 0.051 0.116 -0.003 7.47 20 0.51 (1.23) (1.65) (-0.06) (1.19)

10.2 0.031 0.099 -0.010 6.03 20 0.60 (1.14) (1.62) (-0.25) (2.83)

11.1 -0.016 0.086 -0.087 87.73 15f 0.30 (-0.37) (1.43) (-1.50) (1.28)

11.2 -0.022 0.081 -0.087 22.72 15f 0.37 (-0.49) (1.33) (-1.54) (2.97)

12.1 0.036 0.107 -0.004 -0.080 -0.207 -0.206 20 0.49 (0.83) (1.59) (-0.09) (-1.09) (-0.38) (-0.02)

12.2 0.028 0.096 -0.012 -0.003 -0.161 6.66 20 0.54 (0.80) (1.54) (-0.26) (-0.04) (-0.30) (1.56)

a Percentage of 12-months periods with inflation over 40 % from 1958 to 2001 b Percentage of years in a state of default or restructuring c Dummy variable

d Average from 1970 to 2000 d Average from 1960 to 2000 f Club A countries removed

An immediate result that springs forward from the sovereign bond spread

regressions is that here, unlike in the IIR regressions, OSIN3 yields much more

45

explanatory power than OSIN1, producing higher R2 across the board. In addition,

OSIN3 is statistically significant at least at the 90 percent level (any generally much

better) in all but one regression, whereas OSIN1 is only statistically significant in one

regression. This suggests that unlike the IIR, which seem to focus only on the currency

denomination of a country’s own debt, the market determines sovereign interest spreads

also taking a country’s ability to hedge into account. (Again, only OSIN3 is sensitive to

such hedging ability.) Therefore, let us focus on the OSIN3 regressions in the analysis.

First to note is that the variable of inflation history does quite badly here in terms

of its explanatory power. Across the board, it is much less significantly significant than

in the corresponding regressions without original sin. One reason for this could be that

once it is controlled for to what extent a country is borrowing in foreign currency, its own

inflation history matters less, as countries without original sin tend to have quite

impeccable inflation histories, whereas those that do suffer from original sin are

borrowing in a foreign currency anyway (and hence can not inflate away their debt). As

mentioned before, another reason is likely the imprecision with which the inflation

history variable measures a history of high inflation.

Also noteworthy is the fact that although default history since 1824 is not once

statistically significant at conventional levels, default history since 1946 and the number

of years since a country’s last default both have sizeable coefficients as well as statistical

significance at the 95 percent level or better. This suggests that bond markets pay more

attention to recent default history (i.e. from 1946) than to more distant default history (i.e.

from 1824), and that the time elapsed since a country has last defaulted matters.

Increasing the percentage of years in a state of default or restructuring since 1946

46

increases interest rate spreads by almost one point. Conversely, each ten years since

default last occurred lower interest rate premiums by about half of a point. These

coefficients are similar to those in regressions CS8 and 9, the analogous regressions

without original sin

As in CS12, GDP growth has the expected coefficient, but is not statistically

significant. Likewise, in none of these regressions is debt-to-GDP significant for the

Non-Club A countries, although in contrast to the regressions excluding original sin, the

coefficient on debt-to-GDP also loses its significance for the Club A countries as well.

One explanation for this could be that there is most likely a great degree of collinearity

between membership to Club A and the absence of original sin. This would make it

difficult to precisely estimate the effect of both of these variables within one regression.

Lessons from the Cross-Sectional Analysis.

To wrap up this section, let us reflect on several key findings that emerge.

Prominent among these is the importance of history when predicting debt sustainability.

Countries’ inflation history often mattered when trying to explain sovereign credit risk,

and default history was revealed to matter even more. It was generally always

statistically significant, and provided the most explanatory power when looking at

defaults since 1946. Original sin was shown to also bear considerable importance, both

statistically and economically. It should be noted, however, that default history retained

it significance even when original sin was controlled for. This implies that if the omitted

variable problem that Eichengreen et al. suspect the “Debt Intolerance” regressions of

having does bear weight, it arises from something more than just original sin. Another

47

key theme of this analysis was the lack of significance of debt-to-GDP. This is likely

owing to the endogeneity of country risk and levels of external debt. On the one hand, a

country deemed creditworthy will generally be permitted by the international markets to

take on a greater amount of debt without damaging its credit rating, but on the other hand,

an exogenous increase in debt may still cause some countries to see their credit ratings

deteriorate. While the insignificance of the debt-to-GDP variables does not invalidate the

importance of the historical variables, it does make it quite difficult to say with precision

exactly how much debt a given country can sustain before its credit risk becomes

dangerous.

All these regressions are based on cross-sections of long-term averages, however,

and so they fail to provide a picture of how debt intolerance responds to macroeconomic

variables on a short-term basis. Thus the next chapter will present a series of panel

regressions to examine the more short-term aspects of the phenomenon of debt

intolerance.

48

PANEL ANALYSIS

In the previous chapter, debt intolerance and original sin were analyzed from a

long-term perspective. Cross-sectional regression analysis was used to study the

relationships between variables that were averaged over long time periods. The emphasis

was thus placed on long-term patterns, and not on monthly or yearly fluctuations in those

patterns. In this section, the opposite approach will be taken. Panel techniques will be

used to study what determines how interest rate spreads (a measure of country risk)

change from one year to the next. Although new variables will gain significance in

explaining the yearly fluctuations, a key conclusion will be that many of the same

variables matter as in the cross-sectional analysis.

This chapter will have the following layout. In the first section, a basic panel

regression will be presented, seeking to explain sovereign bond interest rate spreads in

the years 1997 through 2001 (inclusive) as a function of GDP growth, external debt-to-

GDP (as done previously, with different slopes for Club A and Non-Club A Countries),

and year dummies. Unlike in the cross-sectional analysis, these variables are not

averages, but rather are figures that change from one year to the next. The sample will be

the same as in the cross-sectional analysis of sovereign bond interest rate spreads.

In the next section, a set of panel regressions expanded to include historical

variables will be presented in order to study the role that default and original sin history

play in determining sovereign bond interest rate spreads. These historical variables

change only slowly over time, so the same value will be used for all five years of the

panel. Thus, the function of these variables will be to provide insight into the premiums

that they generate on sovereign bonds over the entire five-year period.

49

In the following section, the focus will be on the fixed effects for the various

countries in the panel regressions. Due to the possibility of serial correlation in the

errors, there is reason to suspect that not controlling for fixed effects might tend to bias

the estimation of the coefficients. Therefore a dummy variable regression (with dummies

for all but one of the countries) will be used to estimate the fixed effects coefficients.

The method of fixed effects estimation, however, does not allow the inclusion of

any variables that do not change over time during the sample, so the history variables

(which would show only minimal change during the years of the sample period) cannot

be included in a fixed effects regression. Thus the dummy variable regression used to

estimate the fixed effects coefficients is used for a second purpose. The fixed effects

taking the form of country dummies in the regression are set aside, and together with the

constant they are used to construct interest rate premiums for the various countries.

Then, another set of regressions is performed to test to what extent the fixed effects can

be explained by default and original sin history.

Lastly, to conclude the chapter, there will be a discussion of the general lessons

taken from the panel section.

A Basic Panel Regression.

In this section, a basic panel regression model will be presented to examine which

factors affect sovereign bond interest rate spreads on a yearly basis (as opposed to long-

term averages, as in the cross-sectional chapter). One might expect that historical

variables (such as those relating to default, inflation, or original sin history) would have a

strong impact on the fixed effects pertaining to each country that do not change from one

50

year to the next. Yet a major finding of the previous chapter was that the effect of such

historical variables is extremely long-lived. Thus it is quite likely that other variables

would gain significance in explaining variations in sovereign bond spreads from one year

to the next.

It is also important to note that the years 1997 through 2001 comprise an

especially turbulent period, with much year-to-year variation to explain. The East Asian

crises, the Russian Crisis, and the beginning of the Argentine crisis all occurred within

this period. A graph of the interest rate spreads for the sample countries during this

period is given below.

Interest Rate Spreads, 1997-2001

-40.00

-30.00

-20.00

-10.00

0.00

10.00

20.00

30.00

40.00

50.00

01/3

1/19

97

03/3

1/19

97

05/3

0/19

97

07/3

1/19

97

09/3

0/19

97

11/2

8/19

97

01/3

0/19

98

03/3

1/19

98

05/3

1/19

98

07/3

1/19

98

09/3

0/19

98

11/3

0/19

98

01/2

9/19

99

03/3

1/19

99

05/3

1/19

99

07/3

1/19

99

09/3

0/19

99

11/3

0/19

99

01/3

1/20

00

03/3

1/20

00

05/3

1/20

00

07/3

1/20

00

09/2

9/20

00

11/3

0/20

00

01/3

1/20

01

03/3

0/20

01

05/3

1/20

01

07/3

1/20

01

09/2

8/20

01

11/3

0/20

01

Argentina

Australia

Brazil

Bulgaria

Canada

Indonesia

Jamaica

Japan

Lebanon

Morocco

Nigeria

Panama

Peru

Philippines

Poland

Russia

Spain

Turkey

United Kingdom

Venezuela

51

To test whether more short-term variables do explain year-to-year fluctuations in

the interest rate spreads, this section provides a basic panel regression model9. (More

elaborate models will be presented in later sections of this chapter.) The dependent

variable is the same set of long-term sovereign bond interest rate spreads as in the cross-

sectional chapter, except that here they are taken in March of each year10 from 1997 to

2001 (inclusive), as opposed to averaging them over the period. The sample is also the

same.

The independent variables consist of GDP growth and external11 debt-to-GDP,

with different slopes for Club A and Non-Club A countries, both measured on a yearly

basis, and dummies for the years 1998 through 200112. With the exception of the year

dummies, all the independent variables were taken for the year preceding the sovereign

bond interest rate spreads, so as to avoid endogeneity. The resulting regression follows

below, with t-statistics given in parentheses below their respective coefficients. The

number of observations was 93, and the R2 was 0.33.

(P1) interest spread = constant - 0.199 GDP growth + 0.018 Non-Club A debt (-1.70) (0.77)

– 0.060 Club A debt - 0.354 year 1998 + 3.05 year 1999 +2.02 year 2000 (-2.11) (-0.26) (2.25) (1.48)

+ 2.72 year 2001 (1.99)

9 Panel regressions P1 through P7 were estimated with random effects, and P8 uses dummy variables to estimate the fixed effects coefficients. Due to possible serial correlation of the errors, fixed effects seems a more appropriate method. The random effects models were used so that historical variables could be inserted directly into the panel regression, since a fixed effects model would difference them out. 10 There were two exceptions to this rule. February 2000 was used instead of March 2000 for Jamaica, and April 1997 was used instead of March 1997 for the Philippines. 11 As in the cross-sectional chapter, the setup of “Debt Intolerance” was mimicked, with central government debt as opposed to external debt being used for the Club A countries. 12 Current inflation was also used as an independent variable in various regressions, but it never showed statistical significance, so it is omitted from the models presented in this chapter.

52

One of the more striking features of this regression is the newly acquired

significance of GDP growth, a variable that was generally not statistically significant in

its long-term average form in the cross-sectional regressions. As economic theory would

suggest, GDP growth has a fairly strong, negative impact on interest rate premiums, with

a 5 percent growth rate leading to a premium decreased by about 100 basis points. This

also suggests a pro-cyclicality of interest-rate premiums: They tend to fall when the

economy is doing well and debts are the easiest to pay, whereas they tend to rise when

the economy hits recession and paying sovereign debt becomes more burdensome. This

implies that a sovereign debt that appears sustainable under current growth rates and

interest rates may become unsustainable if growth weakens and interest rates rise as a

consequence.

Next to note is that the signs of the debt-to-GDP ratio for Club A and Non-Club A

countries yield no surprises given the findings of the previous chapter. As before, Non-

Club A countries face higher interest rate premiums when they acquire more debt,

although the effect is not statistically significant. (It also might be rational to expect the

opposite sign, as countries that pose little credit risk might be rewarded with more loans.)

On the other hand, Club A countries see their interest rate premiums fall as a debt rises,

which is likely due to endogeneity. The sample of Club A countries, being extremely

small, is also greatly affected by the presence of Japan, a country with very good credit

and a very large debt. In fact, the effect will be reversed in a later section, when fixed

effects for the different countries are controlled for, and in other places it will have little

statistical significance.

53

A final observation to make here is the effect of the year dummies on interest rate

spreads. The years 1999 and 2001 both saw very large and highly statistically significant

increases in interest rate premiums for the various countries. The effect in 1999 can

almost certainly be attributed to the Russian crisis, and the lingering effects of the East

Asian crisis. Likewise, in 2001 the Argentine crisis was imminent, and skittish investors

demanded higher interest rate premiums in anticipation of a possible disaster. This

overall pattern reveals the unpredictable nature of interest rate spreads, which can

suddenly burst upward as a response to exogenous shocks that often have little to do with

a country’s own fiscal and financial health. In this way, the mere expectation of a

financial crisis, by increasing emerging market risk premiums, can make the

materialization of such a crisis more likely.

Adding Historical Variables.

In the previous section, variables that explain the short-term volatility of interest

rate spreads were discussed. Now the questions addressed will relate to more long-term

factors as well. For instance, what are the fixed effects pertaining to each country (that

is, characteristics that do not change from one year to the next in the sample), and how

can they be explained? The most obvious approach for answering this question is to

estimate the fixed effects coefficients and find the fixed effects, and indeed, that will be

the approach of the next section. But first, for the sake of comparison, variables

explaining the countries’ default and original sin histories will be added to the regression

model of the previous section. Here the purpose is to see how much the historical

variables can explain within the panel model. In contrast, in the next two sections, a two-

54

step process will be used, whereby the fixed effects are first estimated within a dummy

variable regression, and then a subsequent set of regressions is performed to determine to

what extent the historical variables can explain the fixed effects.

Table 8. Explaining Yearly Sovereign Bond Interest Rate Spreads, 1997-2001 Regression No. P2 P3 P4 P5 P6 P7 GDP Growtha -0.202 -0.217 -0.239 -0.259 -0.199 -0.202

(-1.73) (-1.87) (-2.09) (-2.30) (-1.71) (-1.75) Non-Club A 0.022 0.013 0.007 0.000 -0.001 -0.004

Debt-to-GDPa (0.88) (0.54) (0.30) (-0.02) (-0.03) (-0.14) Club A -0.038 -0.007 -0.034 0.007 -0.001 0.031

Debt-to-GDPa (-1.24) (-0.17) (-1.23) (0.19) (-0.03) (0.60) Year 1998b -0.615 -0.626 -0.457 -0.526 -0.198 -0.244

(-0.45) (-0.45) (-0.33) (-0.38) (-0.15) (-0.18) Year 1999b 2.86 2.88 3.07 3.02 3.46 3.42

(2.04) (2.07) (2.21) (2.18) (2.50) (2.49) Year 2000b 1.67 1.70 1.87 1.81 2.28 2.24

(1.19) (1.21) (1.34) (1.30) (1.64) (1.63) Year 2001b 1.98 2.03 2.24 2.25 2.50 2.49

(1.41) (1.45) (1.60) (1.61) (1.79) (1.80) Default History 0.103 0.087

Since 1824c (2.35) (2.16) Default History 0.075 0.075

Since 1946c (3.50) (3.86) Has Defaulted 2.36 2.70

Since 1824b (1.33) (1.60) Years Since -0.049 -0.038 Last Default (-1.46) (-1.29)

OSIN1, -0.945 3.99 10.06 1993-1998 (-0.14) (0.75) (1.17)

OSIN3, 4.01 6.45 8.18 1993-1998 (0.91) (1.88) (1.73)

Observations 88 88 88 88 88 88 R2 0.41 0.42 0.45 0.47 0.38 0.40

a During the previous year b Dummy variable c Percentage of years in a state of default or restructuring

As in the previous section, the dependent variable in all the above regressions is

the sovereign bond interest rate spread, measured in March of the given year. GDP

growth and debt-to-GDP are defined in exactly the same way as before. In addition,

default history since 1824, default history since 1946, a dummy for whether or not a

country has defaulted, the number of years since the last default or restructuring episode

(only for those countries that have defaulted), and the indices OSIN1 and OSIN3 are used

in various combinations as further explanatory variables. All of the above are defined in

the same manner as in the cross-sectional chapter. Since the historical variables are all

largely predetermined relative to the sample period, endogeneity should be avoided. The

55

regressions are presented in the table above. The sample is the same, except that Nigeria

was removed, as no original sin data for it was available. T-statistics are in parentheses.

Looking at the results, a number of key features arise. First of all, GDP growth

maintains its sign and significance. Its magnitude also remains in a similar range. Debt-

to-GDP for the Club A and Non-Club A countries provides some strange results,

sometimes yielding positive signs and sometimes negative signs for the two groups of

countries, but the effect is never statistically significant. In a sense this is troubling, as it

makes it quite difficult to estimate at what precise levels of debt countries will see their

credit ratings fall. It does suggest, however, that other factors can have a profound

influence in debt sustainability calculations, perhaps even surpassing the actual amount of

debt itself in importance. The year 1999 continues to have a large, statistically significant

upward impact on interest rate spreads. Likewise, while impact of the year 2001

diminishes a bit in magnitude, it retains statistical significance in two of the six

regressions (and also in the fixed effects regression of the next section).

The historical variables added to the model generally prove useful in terms of the

explanatory power that they add to the model. Default and restructuring history since

1824 and since 1946 both show statistically significant upward effects on interest rate

spreads. For example, increasing the number of years in a state of default or restructuring

since 1824 by ten percent raises the interest rate premium paid on sovereign debt by

about one point. As for the effect of original sin, OSIN1 is never statistically significant

at traditional levels, but OSIN3 is in two out of three regressions. This suggests, as in the

cross-sectional chapter, that markets are particularly sensitive to a country’s ability to

hedge when determining interest rates, whereas the Institutional Investor seems to pay

56

more attention to a country’s own debt when deciding its credit ratings. The two

regressions in which OSIN3 was significant indicate that moving from a state of no

original sin to a state of complete original sin would be associated with an increase in

interest rate premiums of anywhere from about six and a half to about eight points. That

moving from no original sin to total original sin is an extremely dramatic example should

not detract from the astounding magnitude of the effect.

Finally, inflation history is not included among the historical variables in Table 8

as it provided little explanatory when included in the regression model13. A possible

reason for this is the way in which inflation was measured. In only tracking what

percentage of twelve-month periods inflation is at or above 40 percent, the subtleties of

an inflation problem are ignored. Certainly inflation below 40 percent can also be

problematic, and none of this information is captured in the inflation history variable used

for this analysis. A more precise tool would likely show a more significant negative

impact of inflation history on sovereign credit. As mentioned before, this should be true

for the cross-sectional analysis as well.

Estimating the Fixed Effects Coefficients.

The previous section showed that when included in a panel regression model, the

historical variables examined in this paper yield economically and statistically significant

effects. Such variables cannot be included in a fixed effects model, as they do not change

from one year to the next, and are thus differenced away. Yet the nature of the data

suggests that there may well be serial correlation among the errors, so this section uses a

13 For the results of the regressions in which inflation history was included as an explanatory variable, please see the appendix.

57

dummy variable regression to estimate the fixed effects coefficients, thus controlling for

any possible fixed term in the errors. The variables were all defined as in the other

sections of this chapter, and in the interest of saving space, the fixed effects themselves

are listed in the appendix. All twenty countries from the sovereign bond interest rate

spreads sample were used. The resulting regression is given below, with t-statistics in

parentheses. The number of observations was 93, and the R2 was 0.59.

(P8) interest spread = constant - 0.137 GDP growth + 0.024 Non-Club A debt (-1.10) (0.59)

+ 1.42 Club A Debt - 0.334 year 1998 + 3.08 year 1999 + 2.09 year 2000 (1.29) (-0.25) (2.22) (1.50)

+ 2.72 year 2001 + fixed effects dummies (1.96)

There are a few points about this regression that merit attention. First of all, the

years 1999 and 2001 maintain their effects in the same magnitude as in (P1), and also

retain their statistical significance. Debt-to-GDP is estimated to increase interest rate

spreads for both Club A and Non-Club A countries, an effect that contrasts with the

findings of the cross-sectional chapter, yet it is not statistically significant. Troubling to

note is the fact that GDP growth loses its statistical significance, and, although it

maintains the expected sign, the effect diminishes considerably in magnitude.

Finally, fixed effects were estimated for each country except for Argentina, which

was captured in the constant. Those fixed effects are given in the appendix in their

original form, but for the purpose of further analysis, they were used to construct interest

rate premiums. For example, since the constant is estimated at 3.76 and Bulgaria’s fixed

effect is estimated at –1.59, Argentina is calculated to have an interest rate that is on

average 3.76 points above the US sovereign bond yield, while Bulgaria’s is only 3.76 –

58

1.59 = 2.17 points above the US, all else equal being held equal between the two

countries. The interest rate spreads implied by those fixed effects are analyzed in the

next section.

Explaining the Fixed Effects.

This section marks the second part of a two-step process to test to what extent a

country’s fiscal and financial history can explain the part of its sovereign bond interest

rate spreads that is slow to change over time. The fixed effects and the constant of the

last section were used to construct the implied interest rate spreads, which will serve as

the explanatory variable in this section. The aim is to see to what extent the fixed effects

can be explained by the sample countries’ default and original sin history14. The equation

estimated was interest rate spread (from fixed effects) = ß0 + ß1 default history

(measured in various ways) + ß2 OSIN (either OSIN1 or OSIN3). The explanatory

variables were defined as previously. Also, as in regressions (P2) through (P7), Nigeria

had to be removed from the sample due to a lack of original sin data. The OLS results

are presented in the following table. T-statistics are in parentheses.

Table 9. Explaining the Fixed Effects from Regression P8 Regression No. FE1.1 FE1.2 FE2.1 FE2.2 FE3.1 FE3.2 Default History 0.081 0.065

Since 1824a (0.99) (1.17) Default History 0.094 0.081

Since 1946a (2.15) (2.72) Has Defaulted 2.69 2.93

Since 1824b (1.20) (1.60) Years Since -0.106 -0.057 Last Default (-3.43) (-1.90)

OSIN1, 26.63 26.13 26.53 1993-1998 (3.03) (3.74) (3.90)

OSIN3, 16.31 15.79 14.61 1993-1998 (5.74) (6.99) (5.11)

Observations 19 19 19 19 19 19 Adjusted R2 0.51 0.75 0.60 0.81 0.69 0.77

a Percentage of years in a state of default or restructuring

14 Inflation history was also included in another set of regressions, but it did not prove statistically significant, so it is omitted here.

59

b Dummy variable

Overall, this table presents few surprises. Both default history since 1946 and

years since last default prove to be quite statistically significant, suggesting that it is the

more recent default history that matters the most. Increasing the number of years spent in

a state of default or restructuring since 1946 by ten percent raises a country’s interest rate

premium by about one point. The effect of increasing the number of years since the last

default or restructuring is less precisely defined: Each ten years lower interest rates by

about one point in the regression including OSIN1, but only by about 0.6 points in the

regression including OSIN3. As before, OSIN3 proved to be more statistically

significant in explaining sovereign bond interest rates than OSIN1, although both are

highly significant and yield enormous effects.

Lessons from the Panel Analysis.

This chapter presents two major lessons for debt sustainability analysis. First and

foremost, the basic message of the cross-sectional chapter was confirmed: Both default

history and the incidence of original sin matter when it comes to explaining country risk.

This finding was upheld in the panel analysis both when the historical variables were

added directly to the panel model and when they were used in a two-step process to

explain the fixed effects from a dummy variable regression. The opportunity to hedge, as

taken into account in OSIN3, and the more recent default history seem to be the more

relevant forms of these variables.

The other major lesson is that different factors matter in explaining year-to-year

fluctuations in country risk than in explaining long-term patterns. While the actual level

of debt proved to be statistically significant in several of the cross-sectional regressions

60

whereas GDP growth provided little explanatory power, in the panel analysis, GDP

growth suddenly gained in importance. This suggests a pro-cyclicality of debt

intolerance. Sovereign debt become more difficult to sustain precisely when the

economy hits troubled times. The fact that interest rates often rise in response to a poor

economic outlook or recession only aggravates the problem of debt sustainability. Thus a

concluding message for this chapter would be that while historical variables have a

profound importance in explaining debt sustainability, the structure of the international

financial system also matters, both in the form of original sin and in the volatile reactions

that investors often have to exogenous shocks.

61

CONCLUSIONS

The central message of this paper is that there is no single culprit for problems

concerning debt sustainability. One of the sources of debt intolerance is a poor

institutional history. Previous defaults can reap havoc on a country’s financial system,

rendering future defaults more likely. Moreover, when a government defaults on its debt,

it also encourages capital flight and tax avoidance, thereby making its future fiscal

situation more precarious. Hence a cycle of debt intolerance is created, in which a

history of frequent defaults in the past leaves a country more vulnerable to defaults in the

future, and is accordingly faced with poorer credit ratings and higher interest rate

premiums.

Another (not entirely independent) source of debt intolerance is original sin.

Countries unable to take on external debt in their own currency are left dangerously

susceptible to adverse balance sheet effects brought on by exchange rate fluctuations.

Neither a peg nor a flexible exchange rate can keep an original sin country safe from

these hazards: A peg requires a pro-cyclical monetary policy because the authorities

must defend the peg by raising interest rates when the exchange rate faces downward

pressure, which is usually during times of recession. Such practices also make a

country’s interest rates more volatile and often higher on average than in a country with a

flexible exchange rate regime. Yet a float also fails to provide an answer, because swings

in the exchange rate, when the country has a net external debt, lead to adverse balance

sheet effects and sometimes to bankruptcy.

Eichengreen et al. criticize the work done by Reinhart et al., suggesting that any

variable that increases the likelihood of default and is slow to change over time could

62

cause debt intolerance both in the past and in the future. They suggest that original sin is

a prime candidate for such a variable. The work in this paper, however, confirms that

both original sin and institutional history are crucial in determining a country’s perceived

risk of default, which directly influences its ability to sustain its debt. This idea was

confirmed in several different contexts: with both credit ratings and interest rate spreads

representing debt intolerance as the dependent variable, both in cross-sectional and in

panel regressions, and with several different further variables controlled for (such as

current inflation, levels of debt to GDP, and GDP growth).

Since default and inflation history do have such a strong impact on a country’s

(perceived) creditworthiness, one obvious implication for policy is that emerging market

countries should strive to maintain responsible fiscal and monetary policy as much as

possible, keeping inflation low and stable, and buying insurance against the temptation to

default by running surpluses in good years to make fiscal policy less strained during a

recession. Yet it is also clear from this paper that original sin matters as well, and that,

unfortunately, is a factor largely out of developing countries’ control. One idea proposed

by Eichengreen et al. is to create a basket of emerging market country currencies so that

the countries could issue debt in their own currency while investors received the benefits

of diversification by owning assets denominated in the makeup of the basket. This plan

seems to hold some promise, and should likely be investigated further. What is clear,

however, is that conducting research to better understand the causes of debt intolerance

and its possible solutions is a desirable goal, as alleviating problems of debt sustainability

allows developing countries to invest in themselves, thereby making both their own

citizens and citizens of the world better off.

63

REFERENCES

Agénor, P. R., & Montiel, P. J. (1996). Development Macroeconomics (2nd Ed.).

Princeton, NJ: Princeton University Press. Beim, D. O. & Calomiris, C. W. (2001). Emerging Financial Markets. New York:

McGraw-Hill/Irwin. Beers, D. T. (1999). Sovereign Defaults: Hiatus in 2000? Standard and Poor’s Credit

Week, 19 (51), 9 – 21. Calvo, G., Izquierdo, A., & Talvi , E. (2003). Sudden Stops, the Real Exchange Rate

and Fiscal Sustainability: Argentina’s Lessons (NBER Working Paper No. w9828). Cambridge, MA: NBER.

Calvo, G., & Reinhart, C. (2002). Fear of Floating. Quarterly Journal of Economics,

117, 379 – 408. Easterly, W. R. (2001). The Elusive Quest for Growth. Cambridge, MA: MIT Press. Eichengreen, B., & Hausmann, R. (1999). Exchange Rates and Financial Fragility.

(Working Paper). San Francisco: Center for Pacific Basin Monetary and Economic Studies, Federal Reserve Bank of San Francisco.

Eichengreen, B., & Hausmann, R. (2003). Editors’ Introduction. In Barry Eichengreen

and Ricardo Hausmann (Eds.), Debt Denomination and Financial Instability in Emerging Markets. Chicago: University of Chicago Press.

Eichengreen, B., Hausmann, R., & Panizza, U. (2003a). Currency Mismatches, Debt

Intolerance and Original Sin: Why They Are Not the Same And Why it Matters (NBER Working Paper No. 10036). Cambridge, MA: NBER.

Eichengreen, B., Hausmann, R., & Panizza, U. (2003b). The Mystery of Original Sin. In

Barry Eichengreen and Ricardo Hausmann (Eds.), Debt Denomination and Financial Instability in Emerging Market Economies. Chicago: University of Chicago Press.

Eichengreen, B., Hausmann, R., & Panizza, U. (2003c). The Pain of Original Sin. In

Barry Eichengreen and Ricardo Hausmann (Eds.), Debt Denomination and Financial Instability in Emerging Market Economies. Chicago: University of Chicago Press.

Khedekar, S. (2000). Pricing Sovereign Debt as an Option: Theory and Evidence

(Center for Research in Economic Growth Memorandum No. 332). Stanford, CA: Center for Research in Economic Growth.

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McKinnon, R. I., & Pill, H. (1996). Credible Liberalizations and International Capital

Flows: The ‘Overborrowing Syndrome.’ In Takatoshi Ito and Anne O. Krueger (Eds.), Financial Deregulation and Integration in East Asia (pp. 7-40). Chicago: University of Chicago Press.

Reinhart, C. M. (2002). Credit Ratings, Default, and Financial Crises: Evidence from

Emerging Markets. World Bank Economic Review, 16 (2), 151 – 170. Reinhart, C. M., Rogoff, K. S., & Savastano, M. A. (2003). Debt Intolerance.

Washington, D.C.: International Monetary Fund Research Department. Ricardo, D. (1821). On the Principles of Political Economy and Taxation (3rd Ed.).

John Murray: London. Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations.

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Quarterly Journal of Economics, 70, 65-94.

65

APPENDIX

Table A1. Data Sources Series Data Sources Sample Period Institutional Investor country credit ratings Institutional Investor, various issues; Reinhart,

Rogoff and Savastano (2003) 1979-2002

External debt/GDP World Bank, Global Development Finance; International Monetary Fund International Financial Statistics

1970-2001

Central government debt/GDP (OECD countries)

OECD 1970-2001

Club A/Non-Club A dummy Reinhart, Rogoff and Savastano (2003) - Probability of inflation above 40 percent Global Financial Data 1958-2001 Probability of being in a state of default or restructuring

Beim and Calomiris (2001); Standard and Poor's Credit Week, various issues; Beers (1999); Reinhart (2002)

1824-2001

GDP growth World Bank, Global Development Finance 1960-2001 Long-term maturity sovereign bond interest rate spreads (cross-sectional)

Global Financial Data and author's own calculations 1996-2001

Long-term maturity sovereign bond interest rate spreads (panel)

Global Financial Data and author's own calculations 1997-2001

OSIN1 and OSIN3 Eichengreen, Hausmann and Panizza (2003) 1993-1998 Current inflation Global Financial Data 1996-2000

Table A2. Cross-Sectional Regressions with Treasury Bill Spreads as the Dependent Variablea Inflation

Historyb Default History

Since 1824c

Average Debt-to-GDP*Non-Club A

Dummyd

Average Debt-to-GDP*Club A

Dummyd

OSIN1, 1993-1998

OSIN3, 1993-1998

No. of Obs.

Adjusted R2

TB1 0.085 0.060 -0.005 -0.039 24 0.25 (1.04) (1.02) (-0.28) (-1.88)

TB2 0.083 0.056 -0.007 -0.036 3.60 24 0.23 (1.01) (0.93) (-0.40) (-1.80) (1.16)

TB3 0.077 0.057 -0.006 -0.033 1.743 24 0.24 (0.97) (0.98) (-0.38) (-1.50) (1.06)

a Average 3-month treasury bill spread over the US 3-month Treasury Bill, 1994 – 2001, robust t-statistics in parentheses b Percentage of 12-months periods with inflation over 40 % from 1958 to 2001 c Percentage of years in a state of default or restructuring d Average from 1970 to 2000

66

Table A3. Panel Regressions Explaining Sovereign Bond Interest Rate Spreads, 1997-2001, with Inflation Included as an Explanatory Variablea

CI1b CI2c IH1.1 IH1.2 IH2.1 IH2.2 IH3.1 IH3.2 Current 0.001 0.002

Inflationd (0.16) (0.22) GDP Growthe -0.201 -0.125 -0.168 -0.193 -0.209 -0.236 -0.198 -0.201

(-1.54) (-0.91) (-1.43) (-1.65) (-1.81) (-2.07) (-1.68) (-1.73) Non-Club A 0.015 0.022 0.028 0.016 0.009 0.001 -0.001 -0.004

Debt-to-GDPe (0.63) (0.52) (1.07) (0.66) (0.41) (0.04) (-0.03) (-0.14) Club A -0.064 0.141 -0.026 -0.003 -0.024 0.011 -0.001 0.031

Debt-to-GDPe (-2.28) (1.25) (-0.79) (-0.07) (-0.81) (0.31 ) (-0.03) (0.60) Year 1998f -0.358 -0.308 -0.571 -0.580 -0.378 -0.463 -0.195 -0.242

(-0.26) (-0.22) (-0.42) (-0.42) (-0.28) (-0.34) (-0.14) (-0.18) Year 1999f 3.279 3.332 2.909 2.946 3.184 3.110 3.462 3.425

(2.27) (2.21) (2.11) (2.13) (2.31) (2.25) (2.48) (2.47) Year 2000f 2.184 2.291 1.741 1.768 1.998 1.913 2.284 2.247

(1.49) (1.51) (1.26) (1.27) (1.45) (1.38) (1.63) (1.62) Year 2001f 2.889 2.876 1.960 2.036 2.274 2.282 2.498 2.487

(2.01) (1.94) (1.41) (1.46) (1.64) (1.64) (1.78) (1.79) Inflation 0.043 0.030 0.033 0.027 0.005 0.002 History g (1.36) (1.00) (1.28) (1.15) (0.14) (0.05)

Default History 0.128 0.101 Since 1824h (2.61) (2.30)

Default History 0.082 0.081 Since 1946h (3.61) (4.02)

Has Defaulted 2.349 2.692 Since 1824f (1.32) (1.58) Years Since -0.048 -0.038 Last Default (-1.42) (-1.28)

OSIN1, -4.529 2.386 9.766 1993-1998 (-0.60) (0.43) (1.11)

OSIN3, 2.632 5.626 8.126 1993-1998 (0.55) (1.60) (1.67)

Observations 90 90 88 88 88 88 88 88 R2 0.34 0.59 0.43 0.43 0.46 0.48 0.38 0.40

a Long-term sovereign interest rate spreads are the dependent variable; t-statistics in parentheses b Mirrors the basic regression model presented in regression P1 c Fixed effects coefficients estimated through a dummy variable regression as in P8 d Annual inflation in the year preceding the dependent variable e During the previous year f Dummy variable g Percentage of 12-months periods with inflation over 40 % from 1958 to 2001

h Percentage of years in a state of default or restructuring

67

Table A4. Dummy Panel Regression Fixed Effectsa Country Fixed Effect Country Fixed Effect Constant 3.76 Nigeria -0.23

(Argentina) (1.42) (-0.08) Australia -4.90 Panama -2.79

(-1.37) (-1.02) Brazil 3.05 Peru -2.04

(1.25) (-0.80) Bulgaria -1.59 Philippines -2.69

(-0.57) (-1.09) Canada -14.39 Poland -3.63

(-1.76) (-1.51) Indonesia -2.32 Russia 7.21

(-0.76) (3.02) Jamaica -1.40 Spain -11.87

(-0.50) (-1.81) Japan -17.69 Turkey -0.27

(-1.81) (-0.11) Lebanon -2.68 United Kingdom -11.47

(-1.13) (-1.92) Morocco -1.48 Venezuela 1.09

(-0.61) (0.45) a T-statistics in parentheses

68