Exchange Rate Policy and LDC Foreign Borrowing�

Samir Jahjah, Bin Wei, Vivian Zhanwei Yuey

July 2010

Abstract

This paper empirically analyzes how the exchange rate policy a¤ects the issuing and pricingof international bonds issued by less developed countries (LDCs). We measure an exchangerate policy by the de facto exchange rate regime and the real exchange rate overvaluation.We �nd that countries with a less �exible exchange rate regime are less likely to issue bondsand pay higher spreads. Furthermore, we �nd that the real exchange rate overvaluationsigni�cantly increases the bond spread and the bond issuance probability. Moreover, suche¤ects of the real exchange rate overvaluation tend to be magni�ed for countries with a�xed exchange rate regime.

Keywords: Sovereign Credit Spread, Exchange Rate Regime, Overvaluation, Debt CrisisJEL Classi�cations: E58, F31, F33, F34

�We are very grateful to two anonymous referees and Pok-sang Lam (the editor) for o¤ering manyinsightful comments and suggestions that have improved the paper immensely. We would also like tothank Frank Diebold, Mark Gertler, and Martin Uribe, and the participants at the IMF Institute Seminarfor their comments. We thank Carmen Reinhart for providing us with the data on crises. This paper waspreviously titled �Exchange Rate Policy and Sovereign Bond Spreads in Developing Countries.�The authorsare responsible for all errors and omissions. The views expressed in this paper are those of the authors anddo not necessarily represent those of the IMF or IMF policy.

yJahjah is at the International Monetary Fund, 700 19th Street, N.W., Washington, D.C. 20431. Email:sjahjah@imf.org. Wei is at the Department of Economics and Finance, Baruch College, CUNY, 55 LexingtonAvenue, New York, NY 10010. Email: bin.wei@baruch.cuny.edu. Yue is at the Department of Economics,New York University, 19 West 4th Street, New York, NY 10012. Email: zy3@nyu.edu.

1

1 Introduction

The recent turmoil in the Euro zone has disturbed the economies from Greece to Italy to

emerging European countries and raises the wide-spread concerns over the sovereign default

and Euro depreciation. Turning our attention to developing countries and historical data,

we can �nd that the relation between the exchange rate arrangement and debt management

has for long been an important policy issue for developing countries. However, the active

policy debate on exchange rate policy and country risk has yet to be studied formally in the

academic literature. The goal of our paper is to empirically examine how the exchange rate

policy a¤ects the issuing and pricing of foreign debt for less developed countries (LDCs).

Developing countries typically have a large amount of debt denominated in foreign

currency. Due to the risk of default,1 developing countries pay a sizable default risk premium

on their foreign debt. When the foreign debt is denominated in foreign currency, a weaker

local currency can exacerbate debt-service di¢ culties through the balance-sheet e¤ect and

a¤ect the country spread. Hence, the exchange rate management plays an important role

for developing countries�foreign debt �nancing. At the same time, the choice of exchange

rate regime remains an elusive part of macroeconomic policy. In this paper we analyze the

impact of the exchange rate policy on foreign borrowing using the primary bond market

data on 42 developing countries. Our main methodology is to estimate a Heckman�s sample

selection model (see Heckman (1979)). In the empirical analysis, we draw on the �ndings

in the literature to obtain a reasonable set of control variables and include the measures

of exchange rate policy as the explanatory variables of bond issuing probability and bond

spread. We examine the e¤ects of exchange rate policy on the issuing and the pricing of

international bonds by developing countries.

The �rst measure of a country�s exchange rate policy is its exchange rate regime. It

remains as an open question that how the choice of an exchange rate regime impacts a

country�s foreign debt borrowing. Firstly, there are virtually no comprehensive empirical

1Reinhart and Rogo¤ (2008) document 71 default episodes for developing countries since 1975 to 2006.They also provide a �panoramic�analysis of the history of �nancial crises dating from England�s fourteenth-century default to the current United States sub-prime �nancial crisis.

2

studies on this question.2 Secondly, whether a country issues a bond and how the bond is

subsequently priced are presumably a¤ected by the country�s overall economic performance.

However, the economic literature does not provide unambiguous implications as to which

exchange rate arrangement promotes a country�s economic performance. The impact of

exchange rate regime on the economic performance is probably one of the most controversial

topics in macroeconomic policy.3

Supporters of a �exible exchange rate system argue that countries with hard-pegged

currencies are more vulnerable to real shocks, which may adversely a¤ect growth and macro

stability. More �exible arrangements can better accommodate shocks and thus reduce the

uncertainty in the economy.4 Based on this argument, a �xed exchange rate regime results

in higher default risk in the context of foreign borrowing. Moreover, by eliminating the

monetary policy as a viable policy instrument, hard pegs may force the government to

increase its external liabilities, resulting in higher default risk. Gertler, Gilchrist, and

Natalucci (2007) show that �xed exchange rates exacerbate �nancial crises by tieing the

hands of the monetary authorities in a �nancial accelerator framework.5

On the other hand, supporters of a �xed exchange rate regime argue that this type of

exchange rate arrangement provides policy credibility. For example, pegging the exchange

rate may help to impose �scal discipline on the government.6 The disciplining e¤ect of a

peg may lead to a reduction in the country risk. Arellano and Heathcote (2010) speci�cally

show that countries with dollarization face a more favorable borrowing environment because

without the monetary policy instrument, these countries value their access to the foreign

2Obstfeld and Taylor (2003) study the impact of gold standard on country borrowing spreads on theLondon bond market from the 1870s to the 1930s. Arellano and Heathcote (2010) include a cross-countryregression of sovereign credit ratings on the exchange rate volatility from 1985-2000, while they focus on thee¤ect of dollarization on sovereign debt in their theoretical analysis.

3See Engel (2009) for a surreny of current research on exchange rate policy.4Edwards and Sturzenegger (2005) and Broda (2004) provide some empirical evidence that the terms

of trade shocks have a larger e¤ect on economic performance in countries with more rigid exchange rateregimes, than in countries with a �exible exchange rate regime.

5Gertler, Gilchrist, and Natalucci (2007) focus on the Korean experience during the 1997-1998 �nancialcrisis and quantitatively examine how defending an exchange rate peg may reinforce the �nancial crisis.Cespedes, Chang and Velasco (2004) also discuss the role of exchange rate regimes on excerbating �nancialcrisis in a qualitative analysis.

6Giavazzi and Pagano (1988) show that a government may choose a particular exchange rate arrangementto buy itself a reputation.

3

capital market more and are thus less likely to default. Moreover, a �xed exchange rate

system is believed by its supporters to foster a more stable environment and faster economic

growth. As argued in the literature, hard pegs can lead to lower interest rates and eliminate

exchange rate volatility, which stimulates investment and international trade, resulting in

faster growth.7 These growth-enhancing e¤ects suggest that a �xed exchange rate regime

may be advantageous to a country�s foreign borrowing.

As the preceding discussion suggests, determining how a country�s exchange rate regime

a¤ects its default probability and its foreign debt borrowing is ultimately an empirical issue

that can only be elucidated by analyzing the historical evidence.

Our �rst main �nding is that the choice of exchange rate regime has a signi�cant impact

on LDC foreign borrowing. Speci�cally, the less �exible a country�s exchange rate regime

is, it is less likely to issue foreign bonds and pays higher spreads. The decrease in the

bond issuance probability and the increase in the bond credit spread are both statistically

and economically signi�cant. The marginal e¤ect from changing a free �oating exchange

rate regime to an intermediate one on the bond spread is to reduce the bond issuance

probability by about 3% and increase the spread by 43 basis points, and further changing

from the intermediate one to a �xed one decreases the probability by 1.6% and increases the

spread by an additional amount of 89 basis points. Our results therefore unambiguously

point to the adverse e¤ect of a �xed exchange rate regime on a country�s foreign debt

�nancing, which is consistent with the conclusions from Gertler et al. (2007).

Another measure of exchange rate policy is the real exchange rate overvaluation. Real

exchange rate as a key relative price is important for the policy analysis because of its

implications for international trade and capital �ows.8 In our analysis, we use real exchange

rate overvaluation as a second measure of exchange rate policy, which is de�ned as the

di¤erence between the actual real exchange rate and its long-run equilibrium level. A

country�s debt policy may respond to its real exchange rate, especially when the currency

7See Dornbusch (2001), Rose (2000), and Rose and van Wincoop (2001). Please see Levy-Yeyati andStuzenegger (2003) for an extensive review.

8For example, the average level of real exchange rate matters for export-led growth for developing coun-tries, and real exchange rate is a key indicator of incipient currency crises. See Eichengreen (2008).

4

is misaligned, for the following reasons. First, an overvalued currency reduces a country�s

trade competitiveness and weakens the macroeconomic fundamentals.9 As a result, the

default risk may increase, so are the borrowing costs (See Eaton and Gersovitz (1981)).

Second, exchange rate overvaluation has been found to be a main cause of currency crises.

A vast literature �nds that the real exchange rate is overvalued during the period prior

to devaluations or crises.10 When a country borrows in foreign currency, its debt liability

becomes more costly to serve following the devaluation and hence the default risk rises.11

Lastly, the choice of exchange rate regime and real exchange rate overvaluation may have

a joint impact on sovereign debt market.12 An in�exible exchange rate regime compounds

the adverse e¤ect of a real overvaluation because the cost of correcting the exchange rate

misalignment is higher for a country with a �xed exchange rate. The overvaluation has a

larger and more persistent impact on the economy for a hard pegger. Therefore, a country

with an in�exible exchange rate regime is more likely to default on its debt when its currency

is overvalued.

Our second main �nding is that real exchange rate overvaluation signi�cantly increases

foreign bond issuing probability and generally raises bond spreads for developing countries.

The magnitude of this e¤ect di¤ers across exchange rate regimes. In our empirical analysis,

we use three measures of real exchange rate overvaluation to examine its impact on foreign

borrowing. We �nd that for all three measures the interaction between a �xed exchange rate

regime and real exchange rate overvaluation has the biggest e¤ect on the supply and pricing

of international bonds. Quantitatively we �nd that a one-standard-deviation increase of

real exchange rate overvaluation, measured by the percentage deviation of the real e¤ective

exchange rate from its ten year average, increases the spread by 86 basis points for a country

9Aghion et al. (2009) �nd that countries su¤ering from real overvaluation experience slower productivitygrowth. Eichengreen (2008) contains a survey of the literature that document how a competitive realexchange rate fosters growth and real overvaluation slows growth for developing countries. Engel (2010)�nds that currency misalignments are ine¢ cient and lower world welfare.10See Dornbusch et al. (1995), Edwards (1989), Eichengreen et al. (1995, 1996), Kaminsky et al. (1998),

and Goldfajn and Valdes (1999).11Schneider and Tornell (2004) �nd that balance-of-payments crises are preceded by lending booms and

real appreciation in a model with self-ful�lling crises and balance sheet e¤ects.12Jahjah and Montiel (2003) �nd that a hard peg increases default likelihood, especially in cases of large

exchange rate overvaluation.

5

with a �xed exchange rate regime, while the same increase only increases the spread by 33

and 29 basis points if the country is in an intermediate and �oating exchange rate regime.

The same pattern persists when the other two measures are used.

Our main results hold in a variety of robustness tests, including allowing for alternative

control variables and correcting for endogeneity. To address the endogeneity problem for

the exchange rate regime and real overvaluation, we conduct a multi-stage estimation of the

Heckman�s selection model and use clearly exogenous variables as instrumental variables for

the exchange rate regime and overvaluation. We �nd that controlling for the endogeneity

issue does not change our results qualitatively. These tests make us con�dent that our

empirical results indeed capture the impact of exchange rate policy on foreign debt for

emerging countries.

Linking explicitly the exchange rate policy to bonds issuing and pricing is our main

contribution to the literature on sovereign default risk in emerging economies. Edwards

(1984), Cline (1995), Easton and Rockerbie (1999), and others investigate the determinants

of sovereign debt spreads in sovereign loans. Eichengreen and Mody (1998) and Kamin and

Kleist (1999) analyze bond spreads on primary market using data on international bonds

issued by developing countries. However, none of these empirical works incorporates the

impact of exchange rate policy on sovereign bonds pricing and issuing. Edwards (1984)

includes nominal exchange rate devaluation as one determinant of spreads, but the impact

of devaluation is not signi�cant.

There are a few empirical analyses and event studies relating the exchange rate policy

to the country risk. Reinhart (2002) examines the linkage between default, currency crises,

and sovereign credit rating. She �nds that defaults usually follow sharp devaluation or are

responses to speculative attacks on exchange rate arrangements. Powell and Sturzenegger

(2000) evaluate the relation between the elimination of currency risk through dollarization

and country risk. Yet their analysis is limited to countries that adopted the Dollar or Euro.

This paper is also related to the recent studies on the impact of exchange rate regime and

real exchange rate volatility. Levy-Yeyati and Sturzenegger (2003) study the relationship

between exchange rate regime and growth, and �nd the less �exible exchange rate regimes

6

are associated with slower growth. Broda (2004) �nd that countries with �exible regimes

are able to bu¤er terms-of-trade shocks better than those with �xed regimes. Aghion et al.

(2009) show some empirical evidence that real exchange rate volatility can a¤ect the long-

term productivity growth rate, and �nd that the e¤ect depends critically on a country�s

level of �nancial development. Our work assesses the impact of exchange rate policy on

sovereign default risk which is another important dimension for developing countries.

In the remainder of the paper, we describe the dataset and our methodology. The main

empirical analysis is carried out in Section 3. In Section 4 we summarize the paper and

conclude.

2 Data and Methodology

2.1 The Data

Bond data come from Capital Data�s Bondware and contain the detailed terms of bonds

issued in the primary market by 42 developing countries between January 1990 and De-

cember 2006.13 The Bondware dataset contains information on the launch spreads, launch

dates of international bonds issued in dollars by developing countries. The launch spread is

de�ned as the di¤erence between the yields on a bond issued and the U.S. Treasury bond

with comparable maturity. We use the Bondware data at the individual bond level at the

monthly frequency. There are totally 2,653 bond issues in the sample. The list of countries

and the total number of bond issues in the sample period are reported in Table 1.

Insert Table 1 Here

We work with the primary bond market data because, to the best of our knowledge,

there is no secondary market bond dataset that covers a large sample of countries.14 In

13There are initially 66 countries covered in the Capital Data�s Bondware data during the sample period.Among them, four countries are dropped because they have no RR regime classi�cation and twenty countriesare further dropped from the sample due to the unavailability of some explanatory variables.14J.P. Morgan�s EMBI global and EMBI+ are the secondary market datasets constructed for 23 countries

starting in 1994 or later depending on the countries.

7

addition, using the primary market data allows us to analyze both the issuing and the

pricing decisions for developing countries.

We use the de facto exchange rate regime as a key explanatory variable in our empirical

analysis. We employ the monthly classi�cation of the de facto exchange rate regimes con-

structed by Reinhart and Rogo¤ (2002) (RR) who classify the exchange rate arrangements

based on the o¢ cial exchange rate and parallel market rates. We use the de facto exchange

rate regime as opposed to the de jure exchange rate regime because the latter is not a good

measure of a country�s exchange rate arrangement.15 In most of the analysis, we aggregate

the RR exchange rate classi�cation into three groups: �xed, intermediate, and free �oating

regimes.16 The aggregation of exchange rate regimes is summarized in Table 2.17 In the

empirical analysis, we use the exchange rate regime dummies of FIX (�xed regimes), INT

(intermediate regimes), and FLOAT (free �oating regimes). FIX (resp., INT or FLOAT)

takes the value 1 when the country is operating a �xed exchange rate regime (resp., an

intermediate or free �oating regime) and 0 otherwise.

Insert Table 2 Here

Next, we compute the real exchange rate overvaluation using three measures.18 The

�rst two measures of exchange rate overvaluation are computed using the monthly real

e¤ective exchange rates (REER) from the IMF Information Notice System. The REER is

a trade-weighted index of multilateral real rates measured by units of foreign goods per

domestic goods. The �rst measure of the real exchange rate overvaluation is the percentage

15A country may in practice deviate from its announced exchange rate regime. Calvo and Reinhart (2002)and Alesina and Wagner (2003) study the reasons why countries do not follow their de jure exchange rateregimes.16We also conducted the empirical analysis using the exchange rate regimes grouped into four classes:

hard peg, conventional peg, intermediate and free �oating or grouped into two classes: �xed and �oating.The di¤erent grouping methods do not change the results. The estimation is available upon request.17Two adjustments are made to the RR classi�cation. A free falling regime is de�ned as one with a

monthly in�ation rate greater than 40%. Because the in�ation is one regressor in our empirical analysis,we categorize this group using the secondary classi�cation. We discard the observations in the dual-marketregime because no secondary classi�cation is available. Our empirical analysis is robust to the exclusion ofthese two groups.18As reported in Hinkle and Montiel (1999), there is no universal method to compute the exchange rate

misalignment or real exchange rate overvalution.

8

deviation of the REER from its ten year average (ROV1). The second measure is the

percentage change in the REER over the last �ve years (ROV2).19 The third measure is the

deviation from a predicted level of the real exchange rate (ROV3). The predicted level of

the real exchange rate is based on the equilibrium concept of Purchasing Power Parity and

is adjusted from di¤erences in the relative price of non tradeables to tradeables attributed

to di¤erences in factor endowments (i.e., the �Balassa-Samuelson�e¤ect).20 The PPP real

exchange rate is from the Penn World Table (PWT). Following Dollar (1992) and Aghion

et al. (2009), we �rst perform a pooled OLS regression to obtain the predicted value as

an estimate of the equilibrium value of the real exchange rate, and then take the di¤erence

between the actual real exchange rate and its predicted value from the OLS regression as

the third measure of real exchange rate overvaluation. In the pooled OLS regression, income

per capita relative to that of the United States and geographical and year dummies are used

as proxies for factor endowments.

We draw on the �ndings in the literature to obtain a reasonable set of control variables

that have been found to be important determinants of bond spreads.21 We use real interest

rates on ten-year U.S. Treasury bonds (USRATE) and the spreads on the U.S. high yield

corporate bonds (HYD) as proxies for the global economic condition. For the domestic

economic indicators, we use the GDP growth rate (GDPGR), the GDP per capita in U.S.

dollars (GDPPC), the current account as a ratio of GDP (CA2GDP), and in�ation (INF).

We also include some liquidity and solvency variables, such as, the ratio of debt to GNP

(DT2GNP), the ratio of debt service to exports (DS2EX), and the ratio of short-term debt to

total debt (SHORTDT). In addition, we employ the regional dummies for countries in Africa

(AFRI) and the Latin America (LAT). Our objective is to use a reasonable set of controls to

test whether the exchange rate policy has a signi�cant impact on the issuing and pricing of

19These two measures are also used in Frankel and Saravelos (2010).20We also measure the exchange rate overvaluation using the di¤erence between log of the real exchange

rate and its H-P trend. The results are robust, but not reported in the paper. They are available uponrequest.21Our baseline speci�cation follows closely those reported in Edwards (1984), Eichengreen and Mody

(1995), Dell�Ariccia et al. (2002), etc. We also include control variables that are not in these earlier studiesbut have been extensively discussed as important determinants of international bond spread.

9

international bonds for emerging markets. We collect data on the macroeconomic indicators,

and country-issuer characteristics from the IMF�s International Financial Statistics (IFS),

the World Bank�s World Development Indicators (WDI), the Penn World Table (PWT),

the Global Development Finance (GDF) and the Federal Reserve Board. The detailed

description of the variables and their sources is are Table A1 in the Appendix.

2.2 The Econometric Methodology

This subsection describes the main econometric model that is based on the Heckman�s

sample selection model. The credit spread of an international bond issued by a developing

country is a measure of its default risk. As in Eaton and Gersovitz (1981), Edwards (1984)

and the subsequent studies in the literature, we assume that the logarithm of the spread is

a linear function of some explanatory variables, X, that a¤ect the default risk. Formally,

log (SPREAD) = �X + u; (1)

where u is a random error term. The explanatory variables are bond characteristics, ex-

change rate regime dummies, real exchange rate overvaluation measures, and control vari-

ables that summarize the global economic conditions and country characteristics.

Because we only observe the bond spread when a bond is issued, a sample selection

problem arises. When no spread is observed for a country in a given year, we may assume

that the missing spreads are random occurrences and ignore them, but if the gaps occur

according to some unknown but systematic selection method, estimating Equation (1) alone

leads to biased and ine¢ cient estimates. For example, a country may be excluded from the

credit market if its perceived probability of default exceeds a given level, i.e., it reaches

a �credit-ceiling�.22 Conversely, a country tends to issue international bonds when the

borrowing conditions are favorable and its �nancing need is high. To deal with the sample

selection problem, we create a binary variable for the bond issuance: BI equals 1 when we

22See Eaton and Gersovitz (1981), Sachs and Cohen (1982), and Sachs (1983).

10

observe a nonzero spread for a country at time t, and zero otherwise. We assume

BI = 1f�Z+v>0g; (2)

where Z is a set of observed variables that explain the issuing decision of a country in

a given month and v is a random error term. We can think of �Z + v as the di¤erence

between bene�t and cost from issuing bonds, and Equation (2) indicates that a bond issue

is observed if and only if the bene�t exceeds the cost.

The spread equation (1) and the issuance equation (2) consist of a standard Heckman�s

(1979) sample selection model. We can estimate Equation (2) as a probit model to get the

probability of issuing a bond. Estimating the probit model requires information on those

who did not issue bonds. To address this problem, we record a zero for each month and

country where no bond issuance is observed. The model can be identi�ed by the exclusion

requirement for the Heckman selection model. In our empirical analysis, the vector of

explanatory variable Z in the issuance equation (2) includes all the variables in X as well

as one exclusion variable that is used for identi�cation. For the exclusion variable, we use a

January dummy in the bond issuance equation. The logic behind using the January dummy

as the exclusion variable is the following. Countries are less likely to issue new bonds in

January because of the holiday seasons for the major international �nancial centers. On the

other hand, the January dummy should not enter the spread equation (1) since whether or

not the bonds are issued in January should not change the evaluation of the default risk.

We use the maximum likelihood method to estimate Equations (1) and (2) jointly un-

der the assumption that the error terms, u and v, follow a bivariate normal distribution.

The maximum likelihood method obtains the e¢ cient estimates under a correctly speci�ed

model. We also check the results by estimating the model using the Heckman�s two-stage

method.23 The two procedures give similar results.

23The two-stage estimation method of the Heckman�s model is implemented as follows. In the �rst stage,Equation (2) is estimated as a Probit model to get the probability of a bond issue. Then, the value of Mill�sratio (re�ecting the conditional probability of the observation being in the observed sample) is incorporatedin an OLS regression of (2) using the observed log (spread) only.

11

In the empirical analysis, we also quantify the impact of exchange rate regime and

real overvaluation on the issuing and pricing of the international bonds by calculating the

marginal e¤ects. The marginal e¤ects consist of two components. There is a direct e¤ect on

the mean of log (SPREAD), but also an indirect e¤ect because the exchange rate regime

or real overvaluation a¤ects the bond issuing decision and hence in�uences log (SPREAD)

indirectly.

First, the marginal e¤ect on the bond spread of changing a country�s exchange rate

regime from FLOAT to INT is given by24

E [log (SPREAD) jINT � log (SPREAD) jFLOAT jBI = 1] (4)

= �INT + ��u

"�

��Z(0;1)�v

!� �

��Z(0;0)�v

!#:

where �FIX is the coe¢ cient of FIX in Equation (1) and � (x) � � (x) =� (x) is the inverse

Mill�s ratio in which � and � are, respectively, the probability density function (PDF)

and the cumulative distribution function (CDF) of a standard normal random variable.

Let Z(0;0) be the vector of explanatory variables in the bond issuance equation (2) with

(FIX; INT ) = (0; 0) and all the other variables at their mean values. Z(0;1) or Z(1;0) is

similarly de�ned except that (FIX; INT ) is equal to (0; 1) or (1; 0), respectively.

Similarly, if the exchange rate regime changes from INT to FIX, then the marginal e¤ect

is given by

E [log (SPREAD) jFIX � log (SPREAD) jINT jBI = 1] (5)

= �FIX � �INT + ��u

"�

��Z(1;0)�v

!� �

��Z(0;1)�v

!#

where �INT is the coe¢ cient of INT in Equation (1).

24We derive the marginal e¤ects in Equations (4)-(6) by following Greene (2002). The key is to derive theconditional expectation of log (SPREAD) conditioning on the spread being observed, which is given by

E [log (SPREAD) jBI = 1] = �X + ��u� (��Z=�v) : (3)

12

Lastly, the marginal e¤ect of ROV evaluated at the sample mean in the observed sample

is given by

@E [log (SPREAD) jBI = 1]@ROV

= �ROV � ROV ��u����Z�v

�(6)

where �ROV and �ROV denote the coe¢ cients of real exchange rate overvaluation (ROV)

in Equations (1)-(2), � (x) � (� (x))2 � x� (x), and Z is the vector of explanatory variables

in the bond issuance equation (2). The marginal e¤ect of ROV in a given exchange rate

regime is similarly de�ned.

3 Empirical Analysis

In this section we empirically investigate the e¤ects of the choice of the exchange rate

regime (FIX, INT, or FLOAT) and the real exchange rate overvaluation (ROV1-ROV3) on

the issuing and the pricing of international bonds by developing countries. We �rst report

the empirical results in the baseline speci�cation, and then report in the next section the

results of various robustness tests including the endogeneity tests.

3.1 Empirical Results

We explore the e¤ects of the exchange rate regimes and real exchange rates on LDC for-

eign borrowing. Because we do not intend to reexamine results profusely analyzed in the

empirical sovereign bond spread literature, we choose a relatively noncontroversial set of

control variables.25 We then add the exchange rate regime dummies, FIX (�xed exchange

rates), and INT (intermediates), as well as the measures of real exchange rate overvaluation

(ROV1-ROV3), in the empirical analysis.

We �rst estimate the baseline model in which we include the regime dummies (FIX and

INT) together with a set of explanatory variables. The estimation results are presented in

Table 3. Ignoring the sample selection issue, we �rst run a pooled OLS regression using the

25See Eichengreen and Mody (1998), Edwards (1984).

13

bond spread as the dependent variable. The regression results are reported in the second

column of Table 3. We then take into account the sample selection issue and estimate

the Heckman�s model, as speci�ed in Equations 1 and 2, using the full sample including

the month-country pairs for which there were no bonds issued. The maximum likelihood

estimation results are reported in the last two columns of Table 3.

Insert Table 3 Here

As can be seen, the control variables behave largely as expected. In addition, most of

them have the similar coe¢ cients in both the OLS regression and the Heckman�s sample

selection model. First, the coe¢ cients on AMOUNT and ISSUES are signi�cantly positive

in the spread equation and signi�cantly negative in the issuance equation. As analyzed in

Eichengreen and Mody (1998), these variables with the coe¢ cients working in o¤setting

directions can be interpreted as proxies for the supply of bonds. Countries that issued a

large number of bonds in a big amount last year have accumulated an unsatis�ed appetite

for borrowing and tend to supply additional new issues, resulting in an outward shift in the

bond supply. Hence a higher borrowing in the past reduces the price of their bonds and

increases the spread.

Regarding the global economic condition, a higher U.S. real interest rate (USRATE)

suppresses the supply of bonds by developing countries due to the higher �nancing costs

for them, and it has an insigni�cant and negative impact on the risk premium.26 A higher

spread on the high-yield corporate bonds (HYD) signi�cantly reduces the issuance prob-

ability and tends to increase the bond spread. This result con�rms the observation that

the market requires a similar risk premium on the high-yield corporate bonds and emerging

market country bonds.

Regarding the issuing country�s macroeconomic variables, a high growth rate of per

capita GDP (GDPGR) or a high level of GDP per capita (GDPPC) enhances the market

demand for international bonds, which increases the issuance probability and decreases the

26Eichengreen and Moday (1998), Kamin and Keist (1998), and Uribe and Yue (2006) also �nd that theUS real interest rates reduces the comtemporaneous country spread.

14

spread. These variables are proxies for the demand for bonds from international investors

because their coe¢ cients work in reinforcing directions in the issuance and spread equations.

The debt to GNP ratio (DT2GNP), which is shown to be another proxy for the demand,

works in the opposite way. Speci�cally, a higher debt to GNP ratio diminishes the market

demand, reducing the probability of a bond issue, driving down the price and increasing the

spread. The other two indices of a country�s external debt (DS2EX and SHORTDT) do not

signi�cantly a¤ect the bond spread, but increase the bond issuance probability signi�cantly,

re�ecting a borrowing country�s need for liquidity. A higher in�ation on the other hand

signi�cantly increases the bond spread, but does not a¤ect the likelihood of bond issuance.27

Lastly, we �nd that countries that have a high ratio of current account to GDP (CA2GDP)

tends to supply a high volume of bonds. The prices of their issues are thus driven down

and the spreads are driven up.

Finally, the regional dummies for Africa or Latin America have positive (negative) co-

e¢ cients in the spread (issuance) equation. The dummy for the January e¤ect signi�cantly

reduces the probability of issuing bonds, serving as a valid exclusion variable. The corre-

lation between the error terms in the issuance and spread equations is equal to -0.145 and

signi�cantly negative. The negative correlation implies that some unobserved factors that

lead to a higher issuance probability also lower the bond spread. Thus these factors should

also be interpreted as unobserved determinants of demand.

Let us now focus on the impact of the exchange rate regime on the LDC borrowing.

We �rst discuss the estimation results of the Heckman�s sample selection model in the last

two columns of Table 3 regarding the role of exchange rate regime. We can see from the

table that choosing a less �exible exchange rate regime (INT or FIX) decreases the bond

issuance probability and increases the bond spread. That is, it is both more di¢ cult and

more costly to borrow for countries in intermediate or �x regimes, as if these countries were

penalized for not choosing a more �exible exchange rate regime. Further, the estimated

coe¢ cient on FIX is signi�cantly higher (lower) than the coe¢ cient on INT in the spread

27Reinhart and Rogo¤ (2010) document the high correlation between high in�ation and the occurrence ofdebt crisis using data that covers a period of over 200 years.

15

(issuance) equation, implying a monotone relation between the �exibility of the exchange

rate arrangement and the bond spread. The results indicate that a country�s exchange

rate regime impacts foreign borrowing by shifting the demand curve of its international

bonds. Speci�cally, the market is less inclined to demand the bonds of a country that has

a less �exible exchange rate regime. As a result, it is less likely to observe an issue and the

corresponding decline in demand increases the spreads on observed issues.

The impact of the exchange rate regime is not only statistically signi�cant, but also eco-

nomically signi�cant. To see the latter, we quantify the marginal e¤ect of making a country�s

exchange rate regime less �exible on the bond spread as shown in Equations (4)-(5). In the

data, the average spread among the �oaters is 319 basis points. From the OLS regression

results as in the second column in Table 3, we can see that changing from a �oating ex-

change rate regime to an intermediate one increases the spread by 319*(exp(0.137)-1)=46.7

basis points, and changing from intermediate to �xed increases the spread by an additional

amount of 92.5 (=319*(exp(0.392-0.137)-1)) basis points. The OLS regression ignores the

potential sample selection bias. After we take into account the sample selection issue by

using the Heckman�s model, the marginal e¤ect from converting a �oating exchange rate

regime to an intermediate one is 43 basis points, while the marginal e¤ect from converting

the intermediate exchange rate regime further to a �xed one increases the spread by an

additional amount of 89 basis points. So the direct use of the OLS regression without ac-

counting for the potential sample selection bias tends to slightly overestimate the impact.

Using the estimation results of the issuance equation in the last column of Table 3, we com-

pute the marginal e¤ect from a change in the exchange rate regime on the bond issuance

probability. We �nd that a country in an intermediate exchange rate regime would be 1.6%

less likely to issue a bond if its exchange rate regime had become a �xed one, but would

be about 3% more likely to issue a bond if it had become a �oater. Overall, we �nd that

countries with a less �exible exchange rate regime issue less debt and pay a signi�cantly

higher bond spread as a result of less demand for the bonds they issued in the international

market.

Next, we consider the other measure of exchange rate policy in our paper, that is, real

16

exchange rate overvaluation. To investigate its impact on the bond issuance and pricing,

we estimate the Heckman�s model in which we include measures of real exchange rate

overvaluation as well as their interaction with the exchange rate regime. As stated in Section

2.1, we use three measures of real exchange rate overvaluation, for which the estimation

results are reported in Tables 4A-4C, respectively. Each table contains three columns.

We �rst use the real exchange rate overvaluation alone as an explanatory variable in the

Heckman selection model and report the result in column (I). Column (II) shows that the

impact of the real exchange rate overvaluation and the exchange rate regime when both

of them are included. Lastly, to better identify their joint impact, we further include the

interaction terms between them (Column III), which are the products of the real exchange

rate overvaluation and the three exchange rate dummies. By construction these interaction

terms sum up to the measure of the real exchange rate overvaluation.

Insert Tables 4A-4C Here

We �nd that the real exchange rate overvaluation signi�cantly increases both the bond

spread and the bond issuance probability. This result is signi�cant and holds for all three

measures of real exchange rate overvaluation (ROV1-ROV3). Firstly, an overvalued cur-

rency makes a country�s export less competitive. Real exchange rate overvaluation is found

to be usually associated with low economic growth and loss of government revenue.28 Hence,

the borrowing country may experience greater di¢ culty in servicing its debt. When the gain

from correcting the exchange rate misalignment is high and there is little cost associated

with default, default probability increases signi�cantly. Secondly, a real exchange rate over-

valuation is highly likely to be corrected in the form of a currency devaluation or crisis,

which increases a country�s default risk due to the currency mismatch of the balance sheet.

Powell and Sturzenegger (2000) for example �nd a strong link between devaluation and

default risk. Lastly, a country experiencing real overvaluation tends to borrow more be-

cause overvaluation may signal good times (e.g., due to benign real shocks) and developing

28Prasad et al (2006), Eichengreen (2008), Aghion et al. (2009) study the impact of real exchange rateovervaluation on the economic growth.

17

countries typically borrow procyclically.29 Hence, the country supplies more bonds in the

market, which in turn drives down the price and results in a higher bond spread. In sum,

a larger real exchange rate overvaluation may increase the bond spread and bond issuance

probability through these three channels.

Based on columns (I) in Tables 4A-4C, we compute the marginal e¤ect of real exchange

rate overvaluation on the spread as speci�ed in Equation (6). We �nd that if the real ex-

change rate becomes more overvalued by one sample standard deviation of the overvaluation

measure, the average bond spread increases by 47.5, 27.6, and 20.5 basis points when the

real exchange rate overvaluation is measured by ROV1-ROV3, respectively.

When both the real exchange rate overvaluation and the exchange rate regimes are used

in the regression, from columns (II) of Tables 4A-4C the impacts of the real exchange rate

overvaluation and the exchange rate regime remain signi�cant. A �xed or intermediate

exchange rate regime has an independent positive e¤ect on the bond spread and an inde-

pendent negative e¤ect on the bond issuance probability. The coe¢ cients on the regime

dummies are slightly lower, but remain to be a monotone function of the exchange rate

�exibility.

Lastly, we investigate the combined e¤ect of real exchange rate overvaluation and ex-

change rate regime. From columns (III) of Tables 4A-4C, we �nd that among the three

interaction terms, ROV � FIX has the largest and signi�cantly positive coe¢ cients in the

issuance and spread equations (except that the coe¢ cient becomes insigni�cant in the is-

suance equation for ROV2). This result suggests that the e¤ects of the real exchange rate

overvaluation tend to be magni�ed for countries with a �xed exchange rate regime. We

can think of two possible explanations for these results. First, when a country has a hard

peg or limited exchange rate �exibility, the real overvaluation tends to be persistent.30 As

a result, servicing foreign debt can be less costly in domestic currency. Hence, countries

with a less �exible exchange rate arrangement are more likely to borrow in periods of real

29Arellano (2008), Aguiar and Gopinath (2006), and Yue (2010) document and show the procyclicality ofsovereign borrowing in a Eaton-Gersotivz framework. We thank a referee for suggsting this explanation.30Edwards (1988) �nds that the autonomous forces that move the real exchange rate back to equilibrium

operate very slowly, keeping the country out of equlibrium for a long time.

18

overvaluation. The increase in the supply of bonds from countries with a �xed exchange

rate regime and real overvaluation drives down the bond price and results in a higher bond

spread. Second, when a country is in a hard-peg regime, the overvaluation has a larger

and more persistent adverse impact on the economy.31 Debt becomes rapidly unsustainable

and the probability of default increases. By contrast, for a �oater, owing to the exchange

rate �exibility, nominal devaluation can greatly help to speed up the real exchange rate

realignment. Therefore, real exchange rate overvaluation has the least impact on the bond

spread for countries with a free-�oating regime.

We also assess the economic signi�cance of the combined e¤ect by computing the mar-

ginal e¤ect. For example, when the exchange rate overvaluation is measured using ROV1

(see column (III) of Table 4A), we �nd that a one-standard-deviation increase of ROV1

increases the spread by 86 basis points for a country with a �xed exchange rate regime,

while the same increase of ROV1 only increases the spread by 33 and 29 basis points if the

country is in an intermediate and �oating exchange rate regime. The same pattern persists

when the other two measures (ROV2 and ROV3) are used.

In summary, we �nd that a real exchange rate overvaluation increases both the bond

issuance probability and the bond spread, and such e¤ect takes place mainly when the

country has a �xed exchange rate regime.

4 Robustness

In this section we summarize the various robustness checks that we run to address some

of the concerns that our �ndings may give rise to. In particular, we discuss: (a) the ro-

bustness of our main �ndings by including more macroeconomic control variables and the

roles played by these additional variables in a¤ecting the bond issuance/pricing decisions;

(b) the endogeneity problem associated with exchange rate regime and real exchange rate

overvaluation.

31Edwards and Levy-Yeyati (2005) argue that the adjustment in equilibrium real exchange rate upon areal external shock takes longer in countries with a �xed exchange rate.

19

In the �rst robustness check, we add more macroeconomic control variables. We include

the debt crisis dummy (DCRISIS), debt rescheduling dummy (DRES), and total reserve

to GNI (RES2GNI) as additional regressors. Because of the data availability, there are 40

countries left in the sample when these controls are used. The debt crisis dataset is taken

from Reinhart and Rogo¤ (2008). The debt rescheduling dummy, constructed from GDF,

is equal to unity if there is a non-zero amount of debt rescheduled for a country and zero

otherwise.

The results are summarized in columns (I) and (II) in Table 5A. In both the OLS

regression and the Heckman�s model, the dummy for debt rescheduling (DRES) enters the

spread equation signi�cantly and positively. It also picks up the e¤ect from debt crises,

making the debt crisis dummy (DCRISIS) insigni�cant. Further, although the coe¢ cients

of the dummies for both debt rescheduling and debt crises are insigni�cant in the issuance

equation (see column (II) in Table 5A), they are positive, implying that a country that

is in crisis or is experiencing debt rescheduling �nds it more di¢ cult to issue new bonds.

Moreover, such a country is considered by investors to have higher default probability, and

thus the country has to provide a higher spread on its bond if it chooses to issue one. The

ratio of total reserve to GNI (RES2GNI) decreases both bond spreads and the likelihood of

bond issuance signi�cantly. It suggests that a country that has relatively large reserve tends

to supply a low volume of bonds and consequently the prices of their issues are driven up

and the spreads are driven down. Lastly, from the comparison between Table 5A and Table

3, we still �nd that the exchange rate regime a¤ects the issuing and pricing of international

bonds after we control for debt crisis, rescheduling, and the ratio of reserve to GNI.

In the second robustness check, we deal with the concerns that some variables, such as

the exchange rate regime and real exchange rate overvaluation, may be endogenous. In the

previous analysis, we treat all the variables as strictly exogenous for both bond issuance

and spread determination. But one concern is that the relation we �nd in the data may

be caused by the reversed causality. In particular, the choice of exchange rate regime may

be a response to a debt crisis or a mechanism to lower borrowing costs. In the subsequent

analysis, we deal with this potential endogeneity problem.

20

As a �rst attempt at the endogeneity issue, we single out observations associated with

countries with de facto pegs throughout our sample period (FIXALL) by following Levy-

Yeyati and Sturzenegger (2003) and include the dummy, FIXALL, in the OLS regression and

the Heckman�s model (see columns (I) and (II) in Table 5A). As argued by these authors,

since this group of countries corresponds to economies within long-standing currency unions,

it seems reasonable to assume that the original regime choice is independent from their

growth performance and from the bond issuance/pricing decisions. As can be seen from

columns (I) and (II) in Table 5A, the positive (negative) impact of a �xed exchange rate

regime on the spread (the likelihood of bond issuance) is signi�cant for this group of countries

relative to the rest of the countries in our sample. This presents initial evidence that the

main �ndings in our paper are not severely contaminated by the endogeneity problem.

We next correct for the endogeneity of the exchange rate regime and real exchange

rate overvaluation using a feasible generalized two-stage IV (2SIV) estimator and report

the regression results in column (III) of Table 5A and in Table 5B. To correct for the

endogeneity of the exchange rate regime, we �rst run a multivariate logit model of the

exchange rate regimes choice, R, which can take the value of FIX, INT or FLOAT. The

multinomial logit model assumes that the probability of one outcome can be expressed as

follows:

Pr (R = FIX) =exp (Y �1)

1 + exp (Y �1) + exp (Y �2)

Pr (R = INT ) =exp (Y �2)

1 + exp (Y �1) + exp (Y �2)

Pr (R = FLOAT ) =1

1 + exp (Y �1) + exp (Y �2)

where Y is the vector of variables used to explain the choice of an exchange rate regime.

��s are the associated coe¢ cients. The relative probability of choosing FIX (INT) to the

FLOAT is exp (Yt�1) (exp (Yt�2)).

Similarly, to deal with the potential endogeneity problem associated with real exchange

rate overvaluation, we run three OLS regressions on the variables Y to obtain the �tted

values for ROV1-ROV3, and then we use these �tted values as well as those for exchange rate

21

regime dummies that are obtained from the above multinomial logit regression to estimate

the Heckman�s sample selection model.

The key issue is to �nd suitable instrumental variables for the exchange rate regime and

the real overvaluation. For the exchange rate regime, following Levy-Yeyati and Sturzeneg-

ger (2003) we use the ratio of the country�s GDP over the U.S. GDP (SIZE), the geographical

area of the country (AREA), an island dummy (ISLAND), the ratio of reserve to monetary

base (RESBASE), and a regional exchange rate indicator (REGEXCH) that is equal to the

average exchange rate regime of the country�s neighbors de�ned as those under the same

IMF department. We also control for the potential endogeneity issue for the real overvalu-

ation. As in Prasad et al. (2006) and Eichengreen (2008), we use the share of working-age

persons in the population (WORKPOP) and a dummy variable for oil-exporting countries

(OILEX) as the instrumental variables for the real exchange rate overvaluation.

We use all the exogenous regressors in the baseline model and additional instrumental

variables in auxiliary regressions to obtain �tted values for the exchange rate regime and

overvaluation. The second and third columns in Table 5C report the result of the multino-

mial logit regression of the exchange rate regime over all the instruments. The coe¢ cients

are interpreted as a variation in the relative probability of choosing one regime over a free-

�oating regime. The last three columns show the estimates of the OLS regressions on the

di¤erent measures of real exchange rate overvaluation. Most variables are highly signi�cant

and have the expected signs. On the choice of the exchange rate regime, smaller countries

tend to be more open and thus are more likely to choose a �xed exchange rate regime. A

high initial level of reserves helps a country to overcome the �fear of �oating�. Finally, the

regional exchange rate indicator may indicate explicit or implicit exchange rate coordination

among neighboring countries.32 Regarding the OLS regressions for the real overvaluation,

a higher share of working-age population reduces the likelihood of real overvaluation.33

32See Levy-Yeyati and Sturzenegger (2003) for more details on the multinomial logit model for the exchangerate regime.33Prasad et al. (2006) argue that a rapidly growing labor force should lead to undervaluation due to the

pressure on policy makers to maintain a competitive real exchange rate in order to absorb additional workersinto employment. Eichengreen (2008) also documents a similar relation between the share of working agepopulation and real overvaluation.

22

Oil-exporting countries are more prone to overvaluation.

Insert Tables 5A-5C Here

From the multinomial logit model, we can estimate the predicted probabilities of choos-

ing a �xed or intermediate exchange rate regime. We use the predicted probabilities to

replace the corresponding regime dummies and estimate the Heckman�s model, as shown

in column (III) in Table 5A. A comparison of Table 3 and Table 5A shows that our main

�ndings hold after correcting for the endogeneity. The coe¢ cients on FIX and INT are still

signi�cantly positive in the spread equation and negative in the issuance equation. In gen-

eral, an in�exible exchange rate regime decreases bond issuance probability and increases

the bond spread, which is consistent with the results in the baseline empirical analysis.

The estimation results after the endogeneity correction for the real overvaluation are re-

ported in Table 5B. For the �rst two measures of the real overvaluation (ROV1 and ROV2),

the interaction terms with FIX and INT remain to be positive and signi�cant with compa-

rable magnitude of the coe¢ cients as in the baseline model. In addition, the impact of the

interaction terms on the bond issuance probability is also robust. For the real overvalua-

tion measure computed based on PPP (ROV3), the signs of the estimated coe¢ cients are

also positive after the endogeneity correction, although they are not statistically signi�cant

probably due to the fact that ROV3 itself is a regression residual and is thus harder to be

approximated by instrumental variables. Overall, the relation between exchange rate policy

and the bond issuing and pricing is robust to endogeneity corrections for both exchange

rate regime and real exchange rate overvaluation.

5 Conclusion

This study is the �rst empirical work on the impact of exchange rate policy on the issuing

and pricing of international bonds. The exchange rate policy is jointly measured by the

exchange rate regime and a measure of exchange rate overvaluation. The main conclusion

is that there is a signi�cant impact of exchange rate policy on LDC foreign borrowing, in

terms of bond issuance decision and the bond spread. Exchange rate policy a¤ects the bond

23

spread in a signi�cant and interlaced way. First, countries with a less �exible exchange rate

regime are less likely to issue bonds and pay higher spreads. Second, when the real exchange

rate is overvalued, countries tend to issue more debt. But depreciation risk associated with

an overvalued real exchange rate has a negative impact on debt sustainability, and thus

increases bond spreads, especially under hard pegs.

To conclude, the choice of an exchange rate policy is not neutral with respect to the

bond issuing and pricing decisions. Attempts to gain credibility in the international market

through the use of a pegged exchange rate have gained popularity. Overvaluation under

hard pegs incites governments to borrow more in the international market; however, foreign

investors internalize the risks associated with the overvaluation, increasing borrowing costs.

Our results emphasize that the choice of a hard peg does not necessarily lead to cheaper

borrowing costs, if there is a severe risk of currency overvaluation.

24

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Reinhart, Carmen M., Kenneth S. Rogo¤, and Miguel Savastano, 2003, �Debt Intolerance,�Brookings Papers on Economic Activity, Vol. 1, pp. 1�74.

Rose, Andrew K., 2000, �One Money, One Market? The E¤ects of Common Currencies onInternational Trade,�Economic Policy, Vol. 15, No. 30 (April), pp. 7�46.

Rose, Andrew K. and Eric van Wincoop, 2001, �National Money as a Barrier to Interna-tional Trade: The Real Case for Currency Union,�The American Economic Review, Vol.91, No. 2, pp. 386�390.

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Sachs, Je¤rey D. and Daniel Cohen, 1982, �LOC Borrowing with Default Risk,�NationalBureau of Economics Research, Working Paper No. 925.

Schneider, Martin and Aaron Tornell, 2004, �Balance Sheet E¤ects, Bailout Guaranteesand Financial Crises,�Review of Economic Studies, Vol. 71, No. 3, pp. 883�913.

Uribe, Martin and Vivian Z. Yue, 2006, �Country Spreads and Emerging Countries: WhoDrives Whom?�Journal of International Economics, Vol. 69, No. 1 (June), pp. 6�36.

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28

Appendix: De�nition of Variables

Table A1: Variables, De�nitions and SourcesVariable De�nitions and SourcesAFRI Dummy variable for African countriesAMOUNT US$ equivalent amount of bond (Source: Bondware)34

CA2GDP Current account balance as % of GDP(Source: WDI, variable BN.CAB.XOKA.GD.ZS )

DCRISIS Dummy for debt crisis (Source: Reinhart and Rogo¤ (2008))DRES Dummy for debt rescheduling (Source: GDF, series DT.TXR.DPPG.CD)DS2EX Total debt service (% of exports)

(Source: WDI, variable DT.TDS.DECT.EX.ZS)DT2GNP External debt stocks (% of GNI)

(Source: WDI, variable DT.DOD.DECT.GN.ZS)GDPGR GDP growth rate (Source: WDI, variable NY.GDP.MKTP.KD.ZG)GDPPC GDP per capita (current US$) (Source: WDI, variable NY.GDP.PCAP.CD)HYD Log of Moody�s seasoned Baa corporate bond yield less USRATE

(Source: Federal Reserve Board)INF In�ation, consumer prices (Source: WDI, variable FP.CPI.TOTL.ZG)ISSUES Total number of bond issues in a given year (Source: Bondware)LAT Dummy variable for Latin American countriesRES2GNI Total reserves (% of GNI) (Source: WDI, variables FI.RES.TOTL.DT.ZS

*DT.DOD.DECT.GN.ZS/100)ROV1 REER Deviation from 10-year average, monthly (Source: IMF)ROV2 REER 5-year percentage appreciation, monthly (Source: IMF)ROV3 Exchange rate misalignment measure (Source: PWT)35

SHORTDT Short-term debt (% of total external debt)(Source: WDI, variable DT.DOD.DSTC.ZS)

SPREAD Launch spreads in basis point, monthly (Source: Bondware)USRATE The yield on ten-year U.S. treasury bonds at time of issue (log)

(Source: Federal Reserve Board)

34Unless otherwise speci�ed, the explanatory variables are obtained at an annual frequency and are laggedfor one year to avoid the simultaneity issue.35 It is constructed by following Dollar (1992) and Aghion et al. (2009). Speci�cally, we perform the

following pooled OLS regression: log (REERi;t) = � + �dt + log (GDPPCi;t) + �LACi + �AFRIi + �i;t,where dt is the year dummy. The regression results are consistent with Aghion et al. (2009): b = 0:210c,b� = 0:077c, b = 0:068c, and the adjusted R-square is 0:24, where c denotes 1% signi�cance.

29

Table 1: List of Countries and The Number of Bond Issues

This table lists the names of 42 countries and the number of bond issues in the sample.

Country # Country # Country #Argentina 289 El Salvador 14 Peru 19Azerbaijan 2 Grenada 1 Philippines 130Bolivia 1 Guatemala 8 Poland 20Brazil 692 India 60 Romania 5Bulgaria 3 Indonesia 107 Russia 190Chile 71 Jamaica 20 South Africa 22China, P. R. 93 Jordan 5 Sri Lanka 4Colombia 58 Kazakhstan 69 Thailand 78Congo, Republic of 1 Latvia 1 Turkey 97Costa Rica 11 Malaysia 54 Ukraine 36Croatia 4 Mauritius 7 United Arab Emirates 32Dominican Republic 8 Mexico 336 Uruguay 30Ecuador 5 Moldova 2 Venezuela 56Egypt 3 Pakistan 8 Vietnam 1

Table 2: Exchange Rate Regime Classi�cation

Exchange rate regimes are aggregated to three groups: �xed, intermediate, and �oatingregimes. We use the exchange rate classi�cation from Reinhart and Rogo¤ (2002).

Aggregate Class Reinhart and Rogo¤ (2002) Classi�cationFixed (1) No separate legal tender(FIX) (2) Pre-announced peg or currency board arrangementIntermediate (3) Pre-announced horizontal band that is less than or equal to �2%(INT) (4) De facto peg

(5) Pre-announced crawling peg(6) Pre-announced crawling band that is less than or equal to �2%(7) De factor crawling peg(8) De facto crawling band that is less than or equal to �2%(9) Pre-announced crawling band that is greater than or equal to �2%(10) De facto crawling band that is less than or equal to �5%(11) Moving band that is less than or equal to �2%

(i.e., allows for both appreciation and depreciation over time)Floating (12) Managed �oating(FLOAT) (13) Freely �oating

30

Table 3 Baseline Model with Exchange Rate Regime

This table reports the regression results regarding the role of the exchange rate regimein a¤ecting launch spreads. The second column shows the result using the pooled OLSregression. The third and fourth columns show the MLE result using the Heckman�s sampleselection model. t-statistics are shown in parentheses for key variables of exchange rateregimes (FIX and INT). We calculate t-statistics using robust standard errors.36

OLS Heckit ModelSpread Spread Issuance

FIX 0.392c 0.398c -0.178a

(7.120) (7.600) (-1.921)INT 0.137c 0.152c -0.120a

(2.976) (3.365) (-1.702)AMOUNT 0.015b 0.009 0.138c

ISSUES 0.005c 0.005c 0.039c

USRATE -0.218 -0.159 -1.198a

HYD 0.859 0.976 -2.274b

GDPGR -0.009b -0.011b 0.030c

GDPPC -0.094c -0.106c 0.176c

CA2GDP 0.026c 0.024c 0.050c

DT2GNP 0.007c 0.007c -0.007c

DS2EX 0.061 -0.023 1.419c

SHORTDT -0.003 -0.003 0.009c

INF 0.010b 0.008b 0.017AFRI 0.416c 0.488c -0.901c

LAC 0.341c 0.361c -0.319c

JAN -0.149b

CONSTANT 5.841c 5.936c -0.066No of bonds 1976 1976No of obs. 1976 7410rho -0.145b

lambda -0.082

36The superscripts a; b; c denote the signi�cance level � a : signi�cant at 10%; b : signi�cant at 5%; c :signi�cant at 1%. We use them in all the other tables as well.

31

Table 4A: Model with Exchange Rate Regime and Real Overvaluation (ROV1)

This table reports the regression results based on the Heckman�s sample selection modelregarding the role of exchange rate regimes and exchange rate overvaluation in a¤ectinglaunch spreads. t-statistics are shown in parentheses for key variables of exchange rateregimes (ROV1, FIX, INT and their interaction terms). ROV1 is de�ned as the percentagedeviation of the REER from its ten year average. We calculate t-statistics using robuststandard errors.

Heckit Model (I) Heckit Model (II) Heckit Model (III)Spread Issuance Spread Issuance Spread Issuance

ROV1 0.010c 0.003b 0.009c 0.004c

(11.126) (2.276) (8.520) (2.693)ROV1 0.015c 0.021c

�FIX (8.795) (5.689)ROV1 0.007c 0.002�INT (5.406) (1.087)

ROV1 0.007c -0.005a

�FLOAT (3.544) (-1.701)FIX 0.201c -0.193b 0.030 -0.460c

(3.464) (-2.020) (0.419) (-3.860)INT 0.092b -0.102 0.134c -0.048

(2.122) (-1.412) (2.977) (-0.671)AMOUNT 0.003 0.171c 0.011 0.161c 0.013 0.174c

ISSUES 0.004c 0.032c 0.003c 0.033c 0.003b 0.030c

USRATE 0.106 -1.121a 0.101 -1.117a 0.184 -1.103a

HYD 1.300b -2.248b 1.313b -2.243b 1.416b -2.230b

GDPGR -0.009a 0.021c -0.011b 0.022c -0.017c 0.016b

GDPPC -0.243c 0.160c -0.256c 0.159c -0.235c 0.187c

CA2GDP 0.030c 0.050c 0.027c 0.051c 0.026c 0.051c

DT2GNP 0.010c -0.007c 0.009c -0.006c 0.009c -0.007c

DS2EX 0.267b 1.379c 0.243b 1.363c 0.186 1.177c

SHORTDT 0.001 0.010c 0.001 0.010c 0.001 0.009c

INF 0.001 0.018 0.003 0.017 0.004 0.014AFRI 0.639c -0.936c 0.689c -0.959c 0.650c -1.030c

LAC 0.475c -0.399c 0.481c -0.390c 0.455c -0.432c

JAN -0.130a -0.130a -0.138a

CONSTANT 6.325c -0.120 6.384c -0.042 6.088c -0.191No of bonds 1934 1934 1934No of obs. 6836 6836 6836rho -0.179b -0.183b -0.194b

lambda -0.099 -0.101 -0.107

32

Table 4B: Model with Exchange Rate Regime and Real Overvaluation (ROV2)

This table reports the regression results based on the Heckman�s sample selection modelregarding the role of exchange rate regimes and exchange rate overvaluation in a¤ectinglaunch spreads. t-statistics are shown in parentheses for key variables of exchange rateregimes (ROV2, FIX, INT and their interaction terms). ROV2 is de�ned as the percentagechange in the REER over the last �ve years. We calculate t-statistics using robust standarderrors.

Heckit Model (I) Heckit Model (II) Heckit Model (III)Spread Issuance Spread Issuance Spread Issuance

ROV2 0.004c 0.000 0.004c 0.001(8.170) (0.473) (6.787) (0.794)

ROV2 0.004c 0.003�FIX (5.347) (1.443)

ROV2 0.004c 0.000�INT (4.180) (0.154)

ROV2 0.003b -0.002�FLOAT (2.493) (-0.821)

FIX 0.298c -0.189b 0.296c -0.249b

(5.476) (-2.039) (4.988) (-2.453)INT 0.145c -0.109 0.150c -0.095

(3.280) (-1.540) (3.212) (-1.323)AMOUNT 0.000 0.152c 0.012 0.141c 0.013 0.147c

ISSUES 0.004c 0.036c 0.003c 0.037c 0.003b 0.036c

USRATE 0.001 -1.078a 0.040 -1.079a 0.045 -1.111a

HYD 1.245a -2.115b 1.333b -2.120b 1.352b -2.149b

GDPGR -0.007 0.025c -0.010b 0.026c -0.011b 0.024c

GDPPC -0.142c 0.194c -0.179c 0.196c -0.180c 0.201c

CA2GDP 0.030c 0.050c 0.025c 0.051c 0.024c 0.050c

DT2GNP 0.009c -0.007c 0.009c -0.007c 0.009c -0.007c

DS2EX 0.133 1.419c 0.132 1.397c 0.094 1.361c

SHORTDT 0.001 0.008c 0.000 0.008c 0.000 0.008c

INF 0.014c 0.017 0.015c 0.017 0.015c 0.017AFRI 0.487c -0.903c 0.589c -0.931c 0.582c -0.952c

LAC 0.415c -0.370c 0.432c -0.362c 0.432c -0.374c

JAN -0.146a -0.147a 5.886c -0.148a

CONSTANT 5.758c -0.433 5.870c -0.352 5.886c -0.316No of bonds 1970 1970 1970No of obs. 7264 7264 7264rho -0.164b -0.170b -0.177b

lambda -0.092 -0.094 -0.099

33

Table 4C: Model with Exchange Rate Regime and Real Overvaluation (ROV3)

This table reports the regression results based on the Heckman sample selection modelregarding the role of exchange rate regimes and exchange rate overvaluation in a¤ectinglaunch spreads. t-statistics are shown in parentheses for key variables of exchange rateregimes (ROV3, FIX, INT and their interaction terms). ROV3 is de�ned as the deviationfrom a predicted level of the real exchange rate, which is obtained based on the equilibriumconcept of Purchasing Power Parity and is adjusted for the �Balassa-Samuelson�e¤ect. Wecalculate t-statistics using robust standard errors.

Heckit Model (I) Heckit Model (II) Heckit Model (III)Spread Issuance Spread Issuance Spread Issuance

ROV3 0.222c 0.021 0.097 0.021(3.864) (0.352) (1.474) (0.331)

ROV3 1.576c 0.685c

�FIX (9.108) (2.736)ROV3 0.048 -0.058�INT (0.691) (-0.831)

ROV3 -0.044 0.235�FLOAT (-0.316) (1.190)

FIX 0.339c -0.033 -0.152b -0.114(6.621) (-0.360) (-2.044) (-1.134)

INT 0.091b -0.041 0.044 -0.078(2.030) (-0.628) (1.022) (-1.123)

AMOUNT -0.004 0.125c 0.007 0.123c 0.005 0.115c

ISSUES 0.002c 0.047c 0.002b 0.047c 0.003c 0.047c

USRATE 0.593c -0.218 0.479c -0.206 0.447c -0.221HYD 2.314c -1.000c 2.255c -1.004c 2.278c -1.014c

GDPGR -0.014c 0.034c -0.018c 0.035c -0.023c 0.033c

CA2GDP 0.025c 0.042c 0.020c 0.042c 0.024c 0.042c

DT2GNP 0.007c -0.006c 0.006c -0.006c 0.006c -0.006c

DS2EX 0.193b 1.334c 0.199b 1.319c 0.127 1.201c

SHORTDT -0.002 0.008c -0.004a 0.008c -0.004a 0.008c

INF 0.010c 0.001 0.014c 0.000 0.014c -0.000JAN -0.162b -0.159b -0.156b

CONSTANT 3.847c -0.739a 3.969c -0.727a 4.049c -0.647a

No of bonds 7410 7410 7410No of obs. 1976 1976 1976rho -0.224c -0.186c -0.134b

lambda -0.133 -0.109 -0.077

34

Table 5A: Exchange Rate Regime: Endogeneity Correction

This table reports the regression results regarding the role of the exchange rate regimein a¤ecting launch spreads. Column (I) shows the result using the pooled OLS regression.Column (II) shows the MLE result based on the Heckman�s sample selection model. Col-umn (III) shows the MLE result from using instrumental variables (IV) to deal with thepotential endogeneity problem associated with exchange rate regime. t-statistics are shownin parentheses for key variables of exchange rate regimes (FIX, INT, FIXALL). FIXALLis a dummy variable for countries with de facto pegs throughout our sample period. Wealso include additional control variables of DRES, DCRISIS and RES2GNI. We calculatet-statistics using robust standard errors.

OLS (I) Heckit Model (II) Heckit Model (III)Spread Spread Issuance Spread Issuance

FIX 0.350c 0.353c -0.115 0.496c -0.233(6.527) (6.866) (-1.146) (7.124) (-1.546)

INT 0.235c 0.241c -0.069 0.347c 0.313b

(5.096) (5.553) (-0.934) (4.573) (2.269)FIXALL 0.729b 0.761c -0.349

(2.567) (4.324) (-1.529)DRES 0.168c 0.168c -0.027 0.182c -0.030DCRISIS -0.012 0.001 -0.278 0.056 -0.061RES2GNI -0.030c -0.030c -0.011c -0.030c -0.014c

AMOUNT 0.009 0.006 0.127c 0.014 0.150c

ISSUES 0.001 0.001 0.038c -0.000 0.036c

USRATE -0.266 -0.232 -1.148a -0.240 -1.198a

HYD 0.576 0.646 -2.272b 0.530 -2.378b

GDPGR -0.007a -0.009a 0.029c -0.010b 0.025c

GDPPC 0.007 -0.003 0.288c 0.002 0.377c

INF 0.003 0.002 0.013 0.005 0.014CA2GDP 0.040c 0.038c 0.052c 0.035c 0.054c

DT2GNP 0.009c 0.009c -0.005c 0.008c -0.005c

DS2EX 0.021 -0.024 1.270c 0.018 1.395c

SHORTDT 0.001 0.001 0.006c 0.001 0.006c

AFRI 0.159 0.204 -0.998c 0.140 -0.987c

LAC 0.168c 0.184c -0.447c 0.161c -0.489c

JAN -0.142a -0.142a

CONSTANT 5.600c 5.672c -0.744 5.601c -1.557No of bonds 1972 1972No of obs. 1972 7262 7262rho �0.087 -0.081lambda -0.047 -0.043

35

Table 5B: Exchange Rate Regime and Overvaluation: Endogeneity Correction

This table reports the regression results from using instrumental variables (IV) to dealwith the potential endogeneity problem associated with both exchange rate regime andreal overvaluation. Heckit Models (I-III) are for ROV1-ROV3, respectively. t-statisticsare shown in parentheses for key variables of exchange rate regime, overvaluation and theirinteractions. We also include additional control variables of DRES, DCRISIS and RES2GNI.We calculate t-statistics using robust standard errors.

Heckit Model (I) Heckit Model (II) Heckit Model (III)Spread Issuance Spread Issuance Spread Issuance

ROV 0.058c 0.069c 0.027c 0.004 0.468 -1.233a

�FIX (6.470) (4.386) (5.224) (0.436) (1.530) (-1.958)ROV 0.036c 0.022c 0.014c -0.013b 0.177 -0.322b

�INT (6.229) (2.824) (3.375) (-2.046) (1.551) (-2.110)ROV 0.023c 0.012 0.008a -0.013a 0.263 -0.684a

�FLOAT (4.025) (1.286) (1.753) (-1.857) (1.066) (-1.646)FIX -0.240 -1.148c -0.049 -0.378 0.443c -0.098

(-1.602) (-4.417) (-0.400) (-1.593) (5.079) (-0.635)INT 0.308c 0.338b 0.373c 0.459c 0.272c 0.095

(3.560) (2.258) (4.284) (2.973) (3.510) (0.745)DRES -0.051 -0.247c 0.023 0.098 0.180c 0.062DCRISIS 0.347c 0.113 0.121 -0.051 -0.085 -0.457b

AMOUNT -0.017c -0.006 -0.022c -0.020c -0.026c -0.002ISSUES 0.049c 0.184c 0.041c 0.157c 0.016a 0.151c

USRATE -0.006c 0.027c -0.004b 0.033c -0.002 0.046c

HYD 0.385 -0.718 0.258 -1.530b 0.146 -0.134GDPGR 1.610c -1.520 1.231b -2.893c 1.875c -0.980c

GDPPC -0.016c 0.017c -0.007 0.017b -0.017c 0.031c

INF -0.418c 0.122 -0.210c 0.567c

CA2GDP -0.001 0.017 0.035c 0.011 0.008c 0.004DT2GNP 0.026c 0.050c 0.026c 0.054c 0.037c 0.039c

DS2EX 0.009c -0.003b 0.010c -0.006c 0.007c -0.006c

SHORTDT 0.744c 1.837c 0.441c 1.090c 0.216b 1.612c

RES2GNI 0.007c 0.008c 0.005b 0.004b 0.000 0.010c

AFRI 0.329b -0.790c 0.305a -1.153c

LAC 0.328c -0.359c 0.285c -0.624c

JAN -0.140a -0.143a -0.154b

CONSTANT 7.026c -0.944 5.783c -2.081 4.642c -1.150c

No of bonds 1972 1972 1972No of obs. 7262 7262 7262rho 0.003 -0.026 -0.056lambda 0.528 0.531 0.549

36

Table 5C: Instruments for Exchange Rate Regime and Overvaluation

The second and the third columns in this table report the multinomial logit regressionresults, which are used to generate the �tted values of exchange rate regimes FIX and INT astheir instruments. The dependent variable is the categorical exchange rate class (FIX, INT,or FLOAT). The last three columns report the OLS regression results, which are used togenerate the �tted values of the three exchange rate overvaluation measures (ROV1, ROV2,ROV3), respectively. The explanatory variables include all the exogenous variables used inTables 5A-5B, as well as seven additional variables WORKPOP, OILEX, AREA, ISLAND,REGEXCH, RESBASE, and SIZE as proposed in Levy-Yeyati and Sturzenegger (2003),Prasad, Rajan, and Subrahmanian (2006) and Eichengreen (2008). WORKPOP, obtainedfrom WDI (variable SP.POP.1564.TO.ZS), is the proportion of total population whose agesare between 15 and 64. OILEX is a dummy for oil exporting countries. AREA, obtainedfrom WDI (variable AG.LNK.TOTL.k2) is land area in sq. km. ISLAND is a dummy forcountries with no mainland territory. RESBASE, obtained from IMF (line 11/line 14), is theinitial ratio of �International Reserves�to �Monetary Base�. RESEXCH is the (monthly)average RR exchange rate regime of the region where the regions are de�ned as those underthe same IMF department. SIZE, obtained from WDI (variable NY.GDP.MKTP.CD), isa country�s GDP in dollars over U.S. GDP. For simplicity, only the regression coe¢ cientsand the corresponding t-statistics for the seven additional variables are reported below.t-statistics are shown in parentheses, which are calculated using robust standard errors.

Multinomial Logit OLSFIX INT ROV1 ROV2 ROV3

WORKPOP -0.181c 0.019 -0.428c 0.060 -0.022c

(-5.936) (0.864) (-6.777) (0.605) (-26.111)OILEX 6.814c 1.454c 6.708c 8.824c 0.032c

(20.722) (7.160) (13.056) (10.822) (4.173)AREA -0.827c 0.344c -0.214b -0.521c -0.017c

(-6.346) (10.792) (-2.110) (-3.584) (-12.087)ISLAND -0.357 -1.775c 1.186b -2.272c -0.263c

(-1.479) (-11.181) (2.215) (-2.619) (-30.524)REGEXCH 0.900c 1.246c -2.945c -1.758c -0.146c

(4.633) (8.917) (-7.530) (-2.791) (-25.899)RESBASE -1.918c -0.206c -1.554c -2.829c 0.012c

(-16.116) (-3.119) (-6.875) (-7.809) (3.471)SIZE -25.353c -4.900c -0.758 -4.570c 0.078c

(-16.024) (-17.506) (-0.727) (-2.727) (4.695)No of obs. 7572 7078 7453 7572pseudo R2 0.589 0.400 0.286 0.503

37