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Sovereign Risk and Exchange Rate Regime.
The Cost of Tying one's Hand
Sergio Sola, Thomas Stratmann and Sebastian Weber
SOVEREIGN RISK AND EXCHANGE RATE REGIMES:
THE COST OF TYING ONE’S HAND
Sergio Sola†, Thomas Stratmann‡, and Sebastian Weber$
December 2013 – Preliminary Draft
(Please do not cite)
Abstract
This paper investigates the relationship between fiscal policy, sovereign risk and exchange rate
regimes for a panel of emerging market and developed countries. In particular, we analyze the
role of exchange rate regimes for sovereign interest rates, distinguishing floating regimes and
regimes with pegs or monetary unions. We also study how the effect of exchange rate regimes on
sovereign interest rates varies with the state of the economy and with the degree of financial
stress. Our cross-country analysis informs the debate regarding the benefits of flexible exchange
rates relative to those of being a member of a currency union or having some other arrangements
that make the exchange rate less flexible. We document that countries with floating exchange
rates face up to 3.5 percentage points lower interest rates for government debt in times of
economic stress.
Keywords: Sovereign spreads, Exchange rate regimes, Emerging markets, Currency unions
JEL classification : E63, E44, F33, F41
†
International Monetary Fund, 700, 19th Street NW, Washington DC, 210432, USA, Email: [email protected]
George Mason University, MSN 1D3, Fairfax VA 22030, USA, Email: [email protected]
$
International Monetary Fund, , 700, 19th Street NW, Washington DC, 210432, USA, Email: [email protected]
‡
I. INTRODUCTION
Governments across countries issue bonds to finance spending and other obligations at differing
sovereign interest rates or spreads. Substantial research, dating back over twenty years ago
(Alesina et al1992), has investigated the determinants and economic conditions which give rise
to diverging sovereign interest rates. Recent developments in the Euro Area and the great
recession have reignited interest in the determinants of sovereign spreads.
Despite the significant size of the literature on the determinants of sovereign spreads, estimates
on the effect of exchange rate regimes and their interactions with fiscal policy on the spreads are
scarce (for an exception see Jahjah et al 2013). However, the relevance of exchange rate
arrangements in the determination of sovereign interest rates is particularly evident if one
considers what happened during the recent European debt crisis. For example, despite their
similar fiscal positions, in 2013 Spain and the UK faced significantly different interest rates on
their public debt. The interest rate on a ten-year Spanish bond was roughly two and a-half times
higher, around five percent, than for UK bonds, for which the interest rate was around two
percent. While this suggests that fixed exchange rates may play a role for the cost of government
debt, there are theoretical arguments, which point to potentially opposing effects of floating
exchange rates and associated monetary policy flexibility on interest rates.
Fixed exchange rate regimes might improve the reputation of the sovereign (Giavazzi and
Pagano 1989) by imposing a harder budget constraint. The stronger is the commitment to a level
or path of the exchange rate, the more the fiscal authority is constrained to ensure that fiscal
policy is consistent with the exchange rate target. Thus, a credible commitment to a fixed
exchange rate level will be associated with lower spreads, as there is lower risk of a fiscal policy
that is unsustainable.
Alternatively, the inflexibility of the exchange rate regime might make a country more
vulnerable to shocks. Gertler, Gilchrist, and Natalucci (2007) show that fixed exchange rates
exacerbate financial crises in a financial accelerator framework. The stronger the commitment to
a level or a path of the exchange rate, the less flexibility monetary policy has to act in response to
a given shock.
The extreme case is that of a currency union, a union implying that each member country gives
up the ability to engage in monetary policy. Because countries in a currency union lack control
over monetary policy they are less able to accommodate large economic shocks. This latter
consideration implies that it is important to account for the level of distress of an economy when
studying the effect of the exchange rate regime for interest rate spreads.
Our study explores the different role of exchange rate regimes for sovereign interest rates,
distinguishing floating regimes and pegs. We also contribute to the literature on sovereign
1
borrowing costs by examining the choice of the exchange rate regime on sovereign interest rates
depending on whether state of the economy is tranquil or in crisis.
This paper informs the debate regarding the benefits of flexible exchange rates relative to those
of being a member of a currency union or having some other institution that makes the exchange
rate less than flexible. Our work relates to the work of De Grauwe and Yi (2012, 2013) and
Jahjah et al (2013). De Grauwe and Yi (2012, 2013) use a sample of European countries to
assess whether the adoption of the single currency has made the sovereign interest rates more
sensitive to fiscal fundamentals. They find that public debt has a positive effect on sovereign
spreads only for countries that adopted the Euro at the time of the recent Euro crises and not for
countries with their own currency. A concern regarding the external validity of their study is that
that they look at one particular crisis and one particular regions’ exchange rate regime. Jahjah et
al (2013), using a sample of developing countries, find that countries with a less flexible
exchange rate regime face higher sovereign spreads. They show that the type of exchange regime
adopted can influence both the pattern of issuance and the pricing of government bonds. Using
primary market data for a set of developing countries, they show that the effects of real exchange
rate misalignments on sovereign spreads tend to be magnified in countries with fixed exchange
rate regimes. Jahjaj et al (2013), however, do not distinguish between periods with and without
economic stress and focus on only a subset of countries.
To fill in this gap in the literature, our study assesses the more general role of varying episodes
of stress and different exchange rate regimes for sovereign interest rates. We consider the
universe of countries for which data on sovereign bond yields are available. Consequently, we
analyze an unbalanced sample of 77 emerging and advanced economies observed between 1993
and 2013, focusing on secondary market data. We find that in times of stress, countries with a
flexible exchange rate have on average a yield on government debt, which is up to 3.5 percentage
points below the respective yields in other countries. This result applies regardless of whether we
focus only on emerging market economies or include also developed markets in the regressions.
In the next section, Section II, we discusses the related empirical literature, followed by Section
III where we describe the empirical framework and the data. We present our results in Section IV
and conclude with Section V.
II. RELATED LITERATURE
Previous work shows that many factors influence sovereign interest rates and spreads. For
example, Edwards (1984) demonstrates that the amount of foreign currency reserves a country
has matters. Cline and Barns (1997) and Baldacci and Kumar (2010) extended this research to
also include debt-to-GDP ratios and inflation, capturing each country’s fiscal history and current
monetary situation. Catão and Kapur (2004) and Catão and Sutton (2001) explain differences in
spreads by studying macroeconomic volatility. Kaminsky, Lizondo, & Reinhart (1997) and
2
Manasse, Roubini, & Schimmelpfennig (2003) examine the impact of debt crises on sovereign
interest rates. Providing evidence for the influence of third party market signals, Cantor &
Packer (1996), and Hauner et al (2010) find that countries with a better credit rating have lower
interest rates than countries with worse credit ratings. Government corruption,institutions and
sovereign spreads are examined by Ciocchini, Durbin, & Ng (2002), Block & Vaaler (2004) and
Baldacci, Gupta and Mati (2011). The tendency for banks to be “bailed out” and how this affects
sovereign interest rates was studied by Lane & Phillips (2000), and Dell’Ariccia, Schnabel, &
Zettelmeyer (2002).
More recently Schuknecht et al. (2011) analyze sovereign spreads at issuance in a panel of
fifteen EU countries. They find that a one percentage point increase in public debt with respect to
their benchmark country, increases spreads by 0.23 basis points, and that a one percentage point
increase in public deficit with respect to their benchmark country, increases sovereign spreads by
four basis points. They observe that these magnitudes increased substantially during the global
financial crisis. Sgherri and Zoli (2009) show a similar result. Analyzing a sample of European
countries, they find that sovereign risk premia tend to co-move with a “global risk” factor, and
that financial markets become more concerned about fiscal fundamentals in periods of crisis.
Jaramillo and Weber (2012) use a panel of emerging economies and show that the effects of
fiscal fundamentals on sovereign spreads are time varying. When analyzing the effects of fiscal
shocks, Akitoby and Stratmann (2008) find that reductions in public expenditure are a more
powerful tool for reducing spreads than increases in revenue.
De Grauwe and Yi (2012, 2013) show that the borrowing cost for Euro area countries responded
differently during the great recession compared to other countries with sovereignty over their
own currency. In particular they show that responsiveness of sovereign risk to fiscal
fundamentals is only present in the crisis period for EMU countries.
Jahjah et al (2013) focus on a sample of developing countries. They find that countries with less
flexible exchange rate regimes face higher sovereign bond spreads. They find that this effect is
more pronounced the more a currency is overvalued.
With respect to the De Grauwe and Yi (2012, 2013) and Jahjah et al (2013) studies, we analyze
a broader sample of countries and allow for a number of different exchange rate regimes.
Moreover, as it has been widely pointed out in the literature on exchange rate regimes, we will
investigate whether the benefits and costs of given exchange rate arrangements are state
dependent and whether, while strong commitments to exchange rate stability are good in tranquil
times, they can becomes very costly in times of financial distress or when fiscal or
macroeconomic fundamentals are not in order.
3
III. DESCRIPTIVE EVIDENCE
We start by providing descriptive evidence on the behavior of the yields on government bonds
over time, distinguishing across exchange rate regimes. First we analyze the differences in yields
of countries with de facto and de jure floating exchange rate regimes versus all other countries
(Figure 1). We then repeat a similar exercise using only the de facto exchange rate classification
and distinguishing countries also by country groups: Emerging and Advanced economies. We
show these differences in yields from 1998 to 2010 for the first exercise, and from 1998 to 2012
for the second one.1
We obtain the results in Figure 1 by estimating a cross-section regression for each half-year
period on a constant and an indicator variable that equals one if the exchange rate regime is a de
facto float (top panels of Figure 1) and another regression where the indicator variable takes
value one if the exchange rate regime is a de jure float (bottom panels of Figure 1).
The 4 panels of Figure 1 report the value of the estimated coefficient on the constant (top and
bottom left panels) and on the indicator variable (top and bottom right panels) for each semester,
together with the confidence bands. Hence, they display the evolution of the average yield over
time for all non-floater countries (left panels) and the evolution of the difference in the yield of
all the floaters relative to the non-floaters over time (right panels) distinguished by exchange rate
classification (de jure vs. de facto). The vertical axis shows the difference in yield between
countries with floating and non-floating exchange rate regimes in percent. The solid line shows
the point estimates and the two dashed lines provide the 95 percent confidence intervals.
The results show that countries with floats experienced a lower yield over the entire period,
though that for much of the period analyzed the difference is not statistically significant, as
shown by the fact that the confidence interval includes zero. However, differences in yields
relative to the comparison group became greater during the time of the euro sovereign debt crises
and the 2008 worldwide recession. While the coefficient on the float indicator variable is always
negative for each period, the point estimates is statistically significant since the start of the 2008
worldwide recession (top right panel of Figure 1), or since the euro sovereign debt crisis (bottom
right panel of Figure 1). In these times of economic downturn, countries with floating exchange
rate regimes pay up to about four percentage points less for their sovereign debt than countries in
the comparison group. The figures on the left panel of Figure 1 instead show that the behavior of
the interest rates for non-floating regimes followed a downward trend since the early 2000,
which was then interrupted by the onset of the great recession in 2008.
We then perform a similar exercise, analyzing the differences between emerging and advanced
countries. Using only the de facto classification of exchange rate regimes we estimate two
different set of cross-section regressions for each time period first for the group of advanced
1
We use slightly different time spans to be able to have enough observations to identify the effects of exchange rate
regimes and due to limitation on the availability of data on the type of exchange rate regime.
4
economies2 and then for the group of emerging economies.3 In the first regression we regress
yields on a constant and an indicator variable which takes value one when the exchange rate
regime is a de facto float, while in the second regression we define the indicator variable to take
value one when the exchange rate regime is a de facto peg.
The top and bottom panels of Figure 2 report the time path of the estimated coefficient on the
indicator variable for de facto peg and de facto float for the group of advanced economies. The
top and bottom panels of Figure 2 report the same statistics for the group of emerging
economies. As before, dashed lines represent the 95 percent confidence interval around the point
estimate. These figures provide close to a mirror image to those of Figure 1. Note that this does
not have to be necessarily the case because in each panel of Figure 2 we compare countries with
one extreme end of the exchange rate regime with all other countries. The country-episodes
which are characterized by an exchange rate regime somewhere between the extremes of nonFloat and Float are therefore included in the comparison group in all panels. The top and bottom
panels of Figure 2 show that for advanced economies floating regimes have in general
experienced lower yields in the period of the European debt crisis, even though the difference is
not statistically different from zero. Regimes with Pegs, on the other hand, have experienced
higher and increasing yields during the same periods. This last evidence reflects the experience
of the Euro area peripheral countries. The right two panels of Figure 2, instead, show that for
Emerging economies floating exchange rate regimes have experienced on average lower yields
with a large difference during the period of the crisis. In contrast, pegs seem to be associated
with higher yields throughout the entire sample period, without any distinguishable increase in
the period of the most recent crisis.
Taken together, these initial results provide preliminary evidence in support of the hypothesis
that either the independence of monetary policy or the presence of the exchange rate buffer
decreases the risk premia associated with sovereign interest rates. This effect seems to be
particularly pronounced in periods of economic downturns or financial stress.
IV. ECONOMETRIC APPROACH
A. Empirical Models
We propose three approaches to estimate the effect of the exchange rate regime on yields.
In our first approach, we estimate a standard linear model where we regress sovereign yields, ‫ݕ‬௜௧ ,
in country i in period t on a set of macroeconomic and financial variables. We define a period as
six months.
2
3
See right column of Table 1.
See first and second column of Table 1.
5
‫ݕ‬௜௧ ൌ ߙ௜ ൅ ߙ௧ ൅ ߠܺ௜௧ ൅ ݁௜௧
(1)
The vector ܺ௜௧ includes inflation, the government balance to GDP ratio, the debt to GDP ratio,
the international reserve to GDP ratio, and real GDP growth. The variables ߙ௜ and ߙ௧ indicate
country and period fixed effects. We cluster the standard errors by country.
We then compute the residual ݁ෞ
ప௧ from this regression and estimate
݁ෞ
ప௧ ൌ ߙ௜ ൅ ߚଵ ‫ݏ݅ݏ݅ݎܥ‬௜௧ ൅ ߚଶ ‫ݐܽ݋݈ܨ‬௜௧ ൅ ߚଷ ሺ‫ݏ݁ݏ݅ݎܥ‬௜௧ ‫ݐܽ݋݈ܨ כ‬௜௧ ሻ ൅ ߝ௜௧
(2)
In equation 2, Float is an indicator variable indicating whether a country in time period t has a
floating exchange rate regime, Crisis an indicator variable reflecting whether a country
experienced an economic crisis in period t, and Crisis * Float an interaction between the two
variables. The hypothesis that countries with floats have lower sovereign spreads in times of
economic crisis predicts a negative coefficient on ߚଷ . We estimate equation (2) both with and
without country fixed effects. Again, we cluster the standard errors by country.
In our second approach we estimate
‫ݕ‬௜௧ ൌ ߙ௜ ൅ ߙ௧ ൅ ߠܺ௜௧ ൅ ߚଵ ‫ݏ݅ݏ݅ݎܥ‬௜௧ ൅ ߚଶ ‫ݐܽ݋݈ܨ‬௜௧ ൅ ߚଷ ሺ‫ݏ݅ݏ݅ݎܥ‬௜௧ ‫ݐܽ݋݈ܨ כ‬௜௧ ሻ ൅ ߝ௜௧
(3)
In this specification, we allow for a correlation between economic fundamentals, captured in the
ܺ௜௧ vector and whether a country adopts a floating exchange rate regime and whether it
experiences a crises. Moreover, given this is a two-way fixed effects model, the coefficient on
ߚଷ is identified by countries that change their exchange rate regime. In the previous framework
countries that remain in all periods under the same exchange rate regime could contribute to the
estimation of the coefficient estimate, when excluding country fixed effects in the second stage
regression.
In our third approach, we add an additional indicator mesauring whether the country has a fixed
exchange rate regime and interact this variable with the Crisis indicator.
‫ݕ‬௜௧ ൌ ߙ௜ ൅ ߙ௧ ൅ ߠܺ௜௧ ൅ ߚଵ ‫ݏ݅ݏ݅ݎܥ‬௜௧
൅ߚଵ ‫ݐܽ݋݈ܨ‬௜௧ ൅ ߚଷ ሺ‫ݏ݅ݏ݅ݎܥ‬௜௧ ‫ݐܽ݋݈ܨ כ‬௜௧ ሻ
൅ߚସ ܲ݁݃௜௧ ൅ ߚହ ሺ‫ݏ݅ݏ݅ݎܥ‬௜௧ ‫݃݁ܲ כ‬௜௧ ሻ ൅ ߝ௜௧
(4)
Now the coefficients on the two exchange rate regimes and interactions measure the effect of
these regimes and interactions relative to countries that do not fall into either fixed or floating
exchange rate regime, but intermediate exchange rate regimes that lie between these two poles.
6
B. Data
We have an unbalanced sample of semi-annual data for developed and developing countries,
observed from 1993 to 2012. We report the countries included in our study and number of semiannual observations included for each country in Table 1. We report descriptive statistics in
Table 2. Our macro variables are from the semi-annual WEO forecast, which are published in
April and October of each year. Using real-time data as explanatory variables allows us to
capture the forward looking nature of financial markets.
Our dependent variable is a measure of sovereign default risk. For emerging economies, we use
the EMBI Global yields. For advanced economies, we measure the default risk by the GBI global
yields. One advantage of using the EMBIG for emerging and developing countries is that we do
not have to correct the yields to account for the exchange rate risk. For all countries, we are use
only interest rates of comparable maturities for USD issuances.4
To measure the exchange rate regime we draw on the Reinhart and Rogoff database. Their
classification is based on de facto measures of exchange rate regimes and allows fine distinctions
over 15 categories including the for our case most relevant categories (i) no separate legal tender;
(ii) currency board, (iii) peg; (vi) managed floating; and (vii) freely floating.5 We also use the de
jure measure taken from the IMF Annual Report on Exchange Arrangements and Exchange
Restrictions (AEAER) to analyze the effect of the choice of the exchange rate regime on
sovereign yields. The exact definition of the computed exchange rate dummies is provided in
Table 1 in the Appendix.
V. RESULTS
As a first step in our empirical analysis we estimate equation (1) with Ordinary Least Squares.,
estimating the effect of a number of determinants of sovereign yields. Our set of right hand side
variables includes both macroeconomic and fiscal variables, which can influence the yield on
government paper by having an effect on the risk premium. Our explanatory variables include
inflation, the ratio of government balance to GDP and the debt to GDP ratio, real GDP growth
and the net international reserves to GDP. All the right hand side variables are real-time data, and
to address potential endogeneity concerns,, they are also lagged by one period. In all the
specifications, we include both country and time fixed effects. We estimate four different
specifications: the first using the entire sample (column 1, Table 3), the second excluding
European Monetary Union (EMU) countries (column 2, Table 3); the third using only the sample
of emerging market countries (column 3, Table 3) and the fourth using only advanced countries
(column 4, Table 3).
4
The EMBIG includes maturities for longer than two years. The GBI includes maturities for one to ten years.
For the purpose of our analysis we reclassify, the euro area members as having no separate legal tender rather than
freely floating.
5
7
For most of our specifications, the regressors have the expected sign and the results are
comparable to those of the existing literature. The results however show that there is some
heterogeneity across country groups. The ratio of international reserves to GDP is statistically
significant throughout all of the specifications, although the magnitude of the coefficient is much
larger in emerging rather than advanced countries (see column 3 and 4 of Table 3). Growth is
both statistically and economically important. A one percentage point increase in real GDP
growth reduces sovereign yields by about 0.3 percentage points. This effect is however not
statistically significant in advanced economies. A one percentage point increases in debt as a
fraction of GDP increases yields by about two basis points, although this estimate is not
statistically significant for emerging market countries. Higher inflation has a positive and
statistically significant effect on yields in the subsample of emerging market and advanced
market economies. Further, in our first three specifications, that is the entire sample, the
emerging markets sample and the sample without the EMU countries, the marginal effects of
budget surpluses and international reserves on yields are negative and statistically significant.
Overall, the results of Table 3 show that – with the exception of the debt to GDP ratio – yields
are more sensitive to fundamentals in emerging market economies than they are in advanced
market economies. In fact, the results show that for advanced countries the point estimates tend
to be smaller in absolute value and have lower levels of statistical significance. One possible
explanation for this finding might be that this sample includes the euro area where yields
converged and did not always follow fundamentals.
As discussed in Section IV. A, the estimation of model (1) allows us to obtain regression
residuals. From a theoretical point of view, if model (1) was correctly specified, the regression
residuals will not exhibit any systematic difference across exchange rate regimes. To test
whether different exchange rate regimes affect sovereign yields differently, , we perform a
second step regression. In this regression we regresss the estimated residual are regressed on
country fixed effects, an indicator variable which takes value one during crisis periods, an
indicator variable which takes value one for de facto floating exchange rate regimes and an
interaction between the two indicators (equation 2 in Section IV. A).
We use five definitions of Crisis: (1) an indicator variable which takes value 1 whenever the VIX
index is in the top 5 percent of its distribution; (2) an indicator variable which takes value 1
whenever there is an episode of debt crisis or debt restructuring – as reported by the database by
Laeven and Valencia (2013); (3) an indicator variable which takes value 1 whenever the VIX
index is contained in the top 5 percent of its distribution or whenever the data by Laeven and
Valencia indicate that a debt crisis or restructuring has taken place; (4) an indicator variable
which takes value 1 whenever the standard deviation of the interest rate on government bond is
larger than its mean over the sample period;6 (5) the posterior probability of financial distress,
estimated using a Markov switching regression. We report details regarding this latter method in
the Appendix in Table A1.
6
The standard deviation is the 6 months standard deviation on government bond yields computed using daily data.
8
Using the estimated residuals as dependent variable we estimate two different sets of four
regressions for each of our four definitions of crisis. The two sets of regressions differ by the
introduction of country fixed effects. Within each set of results we show regression regressions
results for: (i) the full sample, (ii) full sample without EMU countries; (iii) only emerging
economies, and (iv) only advanced economies. We report these results in five panels in Table 4.
For the vast majority of specifications, the point estimates on our Crisis indicators are positive
and statistically significant. This is evidence that our crisis measures are a sensible definition of
crisis and capture periods of economic distress. Furthermore, the results imply that irrespective
of our definition of crisis, the presence of a floating exchange rate regime lowers sovereign
interest rates in times of distress as reflected by the negative point estimate on the interaction
term between the exchange rate indicator for floaters and the crisis indicator. Our estimated
effect is not only statistically but also economically significant. The magnitude of the reduction
in sovereign yields brought about by the presence of a floating exchange rate regime in times of
stress ranges between 1 and 3.5 percentage points depending on the definition of crisis and on
the sample used for the estimation. The results are remarkably stable whether or not we include
country specific fixed effects in our specifications.
Across the different definitions of crisis, the strongest negative effects of floating regimes occur
for emerging markets or for the full sample that excludes EMU countries. This could point to a
possible non-linearity of the exchange rate regime with the level of interest rates: the benefit of a
floating exchange rate regime in times of distress could potentially depend on the level of the
interest rate, with higher benefits for countries with higher interest rates. For the sample of
advanced countries, in fact, the benefits of floating exchange rates are relatively low and oscillate
between 0.6 and 1.2 percentage points.
As a next step in our empirical analysis we estimate equation (3). As in the previous analysis, we
investigate whether floating exchange rate regimes are associated with lower spreads during
periods of crisis. We regress yields on sovereign debt on country and time fixed effects, the usual
set of control variables, an indicator for crisis periods, an indicator for floating exchange rate
regimes and an interaction between the two. As before, we lag the macroeconomic and fiscal
variables by one period, to avoid potential endogeneity. Differently from the two-step estimation
procedure above, in this approach, we always use country and time fixed effects. Furthermore,
this specification addresses the concern that the regressors in our previous second stage might be
correlated with the regressors from the first stage. We report the estimation results in Table 5. As
before, we use five different definitions of crisis and for each definition we estimate four
specifications, which differ based on the country samples we consider.
The results in Table 5 show that the macroeconomic and fiscal control variables have the
predicted signs and tend to be statistically significant across specifications. Compared to Table 1,
now for advanced economies, the point estimates on debt are always statistically significant, and
9
those on foreign reserves tend to be statistically significant as well. However, it remains the case
that the point estimate on the government balance is statistically insignificant for advanced
economies. For emerging market economies, all estimates for the fundamentals are statistically
significant with the exception of government debt. The crisis definition has the predicted positive
sign and is statistically significant in most specifications. The indicator variable for Defacto
floating exchange rate regimes is negative though not statistically significant across the different
specifications. Thus, in tranquil times, yields appear not to differ between countries that have a
floating exchange rate regime or not. However, when interacted with the crisis indicator
variable, the exchange rate indicator for floats is statistically significant, with magnitudes
varying between 0.34 and 3.5. These magnitudes are comparable with the results obtained with
the two-step estimation, but it appears to be no longer that case that this latter effect is smaller
for advanced economies.
Table 6 reports the results for our third specification, model (3). Compared to the previous
estimates reported in Table 5, this regression includes also an indicator variable for de facto pegs
and the interaction of this indicator variable with indicator variables for crisis periods. For
parsimony, we report results only for three measures of crisis: (1) the measure based on the VIX
index; (2) the presence of a debt crisis or restructuring as in Laeven and Valencia and (3) the
measure based on the standard deviation of the interest rate. We focus only on a subgroup of our
crises definition. Our reported findings are are comparable when using our other definitions.
The results show that, in tranquil times, a peg substantially reduces the yields on government
bonds. This result, however, seems to be driven mostly by Emerging market countries. When
estimating the equation for advanced economies only, the indicator for a peg is positive, although
only marginally statistically significant. The indicator for a floating exchange rate regime,
instead, is consistently negative and statistically significant across most specifications, indicating
the benefits of floating exchange rate regimes as buffers also in normal times compared to the
base group of intermediate regimes. In terms of magnitude, however, the point estimates on the
for floating exchange rate regimes indicator variables are generally fifty percent smaller than
those estimates on the peg regimes indicator variables. Furthermore, the results show that, in
times of distress, having a floating exchange rate regime as opposed to a peg reduces sovereign
yields. The magnitude of this effect varies across country groups: it is between 0.5 and one
percentage points in advanced countries and between one and five percentage points in emerging
markets.
VI. CONCLUSION
There are only few studies regarding the effects of exchange rate regimes on sovereign yields.
Against the backdrop of the recent experience of the euro are crisis, our analysis contributes to
recent empirical advances regarding the role of the exchange rate regime, by testing whether
10
countries with floating exchange rate regimes or pegs fare better in periods of economic crises as
opposed to tranquil times in terms of higher borrowing costs.
Using various definitions of economic crisis, we consistently find that countries with floating
exchange rate regimes, as opposed to countries with less flexible regimes face lower yields in
times of crises. Our estimates imply that sovereign spreads are up to 3.5 percentage points lower
in times of crises when a country has adopted a float. This is broadly in line with the difference
between the yield on Spain’s and UK’s debt during the recent period.
We also find that floats lower spreads when we consider subsamples such as emerging market
countries and countries not including the European Union. Interestingly, the beneficial effects
appear to be smallest for advanced countries.
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13
Appendix
Table A1: Variable definition
Variable Description
US dollar interest rate on sovereign debt. For EMEs we take the
EMBIG and for the advanced economies we take the Global Bond
Index for maturities between 1 and 10 years so that they are
comparable with the bonds included in the EMBIG index. All interest
rates are on dollar denominated bonds.
Gov Bal./GDP
Real time forecast of the government balance to GDP ratio based on
the IMF’s WEO.
Net Interntl. Res. to GDP ratio Real time forecast of net international reserves to GDP ratio based
on the IMF’s WEO.
Debt/GDP (external for EMEs) Real time forecast of government debt to GDP ratio based on the
IMF’s WEO. Only external debt for EMEs.
GDP Growth
Real time forecast of real GDP growth based on the IMF WEO.
Inflation
Real time forecast of inflation based on the IMF’s WEO. To limit the
impact of outliers the variable was transformed by dividing the value
by itself plus 1.
Defacto Float
Indicator taking on the value 1 if the Reinhard and Rogoff fine
defacto classification takes on values 12 or 13, i.e. managed or
freely floating.
Dejure float
Indicator taking on the value 1 if the IMF dejure classification takes
on value 8, i.e. independently floating.
Defacto peg
Indicator taking on the value 1 if the Reinhard and Rogoff fine
defacto classification takes on values 1 to 4, i.e. no separate legal
tender, Pre announced peg or currency board arrangement, Pre
announced horizontal band that is narrower than or equal to +/-2%,
and De facto peg.
Dejure peg
Indicator taking on the value 1 if the IMF dejure classification takes
on value 1 to 3, i.e. no separate legal tender, currency board, and
conventional peg.
Defacto no sep. legal tender
Indicator taking on the value 1 if the Reinhard and Rogoff fine
defacto classification takes on the values 1, i.e. no separate legal
tender.
Dejure no sep. legal tender
Indicator taking on the value 1 if the IMF dejure classification takes
on value 1, i.e. no separate legal tender.
Crisis indicator,
Indicator taking on the value 1 if the classification by Laeven and
Gov. default/restruct.
Valencia (2013) indicates a government debt restructuring or default.
Crisis indicator, VIX
Indicator taking on the value 1 if the VIX takes on a value in the top
th
(top 5%)
5 percentile (this is equivalent to a time indicator for 2008 S2).
Crisis indicator, VIX and Gov. Indicator taking on the value 1 if either Crisis indicator, Gov.
default/restruct or Crisis indicator, VIX (top 5%) equal 1.
Crisis indicator, St Dev
Indicator taking on the value 1 if the standard deviation of the daily
(above median)
interest rate for the entire semester (normalized by the country’s
average standard deviation of the interest rate) exceeds the median
value
Crisis indicator, GDP growth
Indicator taking on the value 1 if the demeaned GDP growth is below
Variable name
Interest Rate (EMBIG, GBI)
14
(bottom 10%)
Crisis indicator, FMM comp.
(above 50%)
Crisis cont., FMM comp.
the bottom 10th percentile.
Indicator taking on the value 1 if the probability of being in a period of
distress exceeds 50 percent. The probability of being in a period of
distress is computed as the posterior probability of a 2 components
mixture model (FMM). The data are assumed to be drawn from two
distinct distributions: one for normal times and one for distressed
periods, and we estimate the posterior probability of being in either of
the two states.
This is the posterior probability of being in a period of distress,
computed using a two components mixture model as explained
above. The variable takes values between zero and one with higher
values corresponding to higher probabilities of being in a period of
distress.
Notes: Once a country becomes part of the euro zone, we no longer define that country as having separate legal
tender. Real time data is semiannual refers to the respective year’s annual forecast of the variables released in the
April WEO (first semester) and the October WEO (second semester), respectively. Our data sources are IMF WEO
(internal database) for all real time data. Laeven and Valencia (2013) for the government debt crisis indicator.
Reinhart and Rogoff (2004) and associated updates for the defacto exchange rate classifications. We obtained
interest rates from Datastream.
15
Figure 1: Time-varying Differences in Yield (Float vs. Non Float)
16
Figure 2: Time-varying Differences in Yield (Float vs. Non Float) – By country Groups
ADVANCED ECONOMIES
EMERGING MARKETS
17
Table 1: Data and sample coverage
EmergingEconomies
EMBIGL.Div.
Angola
Argentina
Azerbaijan
Belarus
Belize
Bolivia
Brazil
Bulgaria
Chile
China
Colombia
CostaRica
CoteD'ivoire
Croatia
Dom.Rep.
Ecuador
Egypt
ElSalvador
Gabon
Georgia
Ghana
Guatemala
Hungary
India
Indonesia
Iraq
Jamaica
Jordan
Kazakhstan
Latvia
Lebanon
Lithuania
Malaysia
Mexico
Mongolia
Morocco
Namibia
Nigeria
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Romania
Russia
Senegal
Serbia
SouthAfrica
SriLanka
Turkey
Ukraine
Uruguay
Venezuela
Vietnam
Zambia
<1Y
19Y
1Y
2Y
6Y
<1Y
19Y
19Y
14Y
19Y
16Y
<1Y
15Y
16Y
11Y
19Y
11Y
11Y
5Y
4Y
5Y
<1Y
14Y
<1Y
9Y
7Y
5Y
2Y
5Y
<1Y
15Y
3Y
16Y
19Y
1Y
19Y
1Y
19Y
11Y
19Y
<1Y
19Y
19Y
19Y
1Y
19Y
2Y
7Y
18Y
5Y
16Y
13Y
12Y
19Y
7Y
<1Y
AdvancedEconomies
EMBIG
Angola
Argentina
Azerbaijan
Belarus
Belize
Bolivia
Brazil
Bulgaria
Chile
China
Colombia
CostaRica
CoteD'Ivoire
Croatia
Dom.Rep.
Ecuador
Egypt
ElSalvador
Gabon
Georgia
Ghana
Guatemala
Hungary
India
Indonesia
Iraq
Jamaica
Jordan
Kazakhstan
Latvia
Lebanon
Lithuania
Malaysia
Mexico
Mongolia
Morocco
Namibia
Nigeria
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Romania
Russia
Senegal
Serbia
SouthAfrica
SriLanka
Turkey
Ukraine
Uruguay
Venezuela
Vietnam
Zambia
Source :Da ta s tre a m
18
GBI
<1Y
19Y
1Y
2Y
6Y
<1Y
19Y
19Y
14Y
19Y
16Y
<1Y
15Y
16Y
11Y
19Y
11Y
11Y
5Y
4Y
5Y
<1Y
14Y
<1Y
9Y
7Y
5Y
2Y
5Y
<1Y
15Y
3Y
16Y
19Y
1Y
19Y
1Y
19Y
11Y
19Y
<1Y
19Y
19Y
19Y
1Y
19Y
2Y
7Y
18Y
5Y
16Y
13Y
12Y
19Y
7Y
<1Y
Australia
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Japan
Korea
Netherlands
NewZealand
Portugal
Singapore
Spain
Sweden
UnitedKingdom
UnitedStates
15Y
15Y
15Y
15Y
15Y
10Y
15Y
15Y
15Y
10Y
15Y
15Y
12Y
15Y
15Y
15Y
12Y
15Y
15Y
15Y
15Y
Table 2: Summary Statistics
VARIABLES
Interest Rate (EMBIG, GBI)
Gov Bal./GDP
Net Interntl. Res. to GDP ratio
Debt/GDP (external for EMEs)
Inflation
Defacto Float
Dejure float
Defacto peg
Dejure peg
Defacto no sep. legal tender
Dejure no sep. legal tender
Crisis indicator, Gov. default/restruct.
Crisis indicator, VIX (top 5%)
Crisis indicator, VIX and Gov.
Crisis indicator, St Dev (above median)
Crisis indicator, GDP growth (bottom
10%)
Crisis indicator, FMM comp.(above 50%)
Crisis cont., FMM comp.
Number of countries
(1)
N
(2)
mean
(3)
sd
(4)
min
(5)
max
1,705
2,777
2,555
2,898
2,976
2,544
2,901
2,544
2,901
2,544
2,392
3,003
3,003
3,003
3,003
7.164
-2.634
14.87
64.23
0.781
0.176
0.257
0.371
0.300
0.146
0.185
0.0213
0.0256
0.0466
0.712
5.893
5.208
15.19
365.5
2.281
0.381
0.437
0.483
0.458
0.353
0.388
0.144
0.158
0.211
0.453
0.372
-121.6
-29.25
-46.38
-95.54
0
0
0
0
0
0
0
0
0
0
73.68
29.08
112.8
19,475
56.24
1
1
1
1
1
1
1
1
1
1
3,003
3,003
1,386
0.0989
0.704
0.423
0.299
0.456
0.414
0
0
0
1
1
1
77
Note: the interest rate variable is measured annually. All other variables are measured biannually.
19
Table 3: Determinants of yields
VARIABLES
Inflation
Gov Bal./GDP
Debt/GDP (external for EMEs)
Net Interntl. Res. to GDP ratio
Real GDP Growth
Observations
R-squared
Number of countries
Country FE
Time FE
Regressors lagged
(1)
ALL
(2)
NO EMU
(3)
EMEs
(4)
ADV
-0.001
[0.010]
-0.171**
[0.072]
0.021*
[0.012]
-0.083**
[0.034]
-0.378***
[0.113]
-0.004
[0.011]
-0.177**
[0.081]
0.021*
[0.012]
-0.080**
[0.036]
-0.383***
[0.121]
1.137***
[0.322]
-0.192**
[0.094]
0.019
[0.012]
-0.081**
[0.039]
-0.431***
[0.139]
0.007**
[0.003]
0.035
[0.048]
0.026***
[0.007]
-0.048**
[0.022]
-0.203
[0.141]
1,309
0.296
64
Yes
Yes
Yes
1,068
0.304
54
Yes
Yes
Yes
847
0.334
43
Yes
Yes
Yes
462
0.482
21
Yes
Yes
Yes
Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1
20
Observations
Number of
countries
Country FE
Defacto float *
Crisis
Defacto float
Crisis indicator
Observations
Number of
countries
Country FE
Defacto float *
Crisis
Defacto float
Crisis indicator
1,068
54
64
3.943***
(1.265)
-0.809**
(0.365)
3.743***
(1.208)
-0.744**
(0.292)
1,309
54
64
-3.274***
(1.167)
1,068
1,309
-3.034***
(1.103)
-2.779***
(0.859)
0.504
(0.650)
-0.768**
(0.343)
0.277
(0.533)
-0.739***
(0.276)
-2.090***
(0.788)
Excl. EMU.
(2)
Full
Sample
(1)
Table 4: Residual Regressions
No
No
-3.421***
(1.150)
43
847
Full
Sample
(5)
21
462
-1.237*
(0.636)
0.221
(0.244)
-0.380
(1.591)
64
1,309
-2.037**
(0.782)
0.263
(0.531)
-0.638**
(0.266)
A. Crisis Variable: VIX (top 5%)
Adv.
(4)
21
64
1,309
-3.049***
(1.108)
3.550***
(1.222)
-0.630**
(0.275)
54
1,068
-3.445***
(1.168)
3.709***
(1.286)
-0.402
(0.314)
54
1,068
-2.681***
(0.853)
0.482
(0.649)
-0.404
(0.303)
Yes
Yes
Excl. EMU.
(6)
B. Crisis Variable: Gov. debt crisis
3.858***
(1.248)
-0.561
(0.343)
43
847
-2.429**
(1.114)
0.284
(0.702)
-0.553*
(0.315)
EMEs
(3)
43
847
-3.386***
(1.154)
3.586***
(1.273)
-0.342
(0.307)
43
847
-2.350**
(1.128)
0.263
(0.704)
-0.360
(0.284)
EMEs
(7)
21
462
-1.235*
(0.632)
0.220
(0.244)
-0.418
(1.610)
Adv.
(8)
Observations
Number of
countries
Country FE
Defacto float *
Crisis
Defacto float
Crisis indicator
Observations
Number of
countries
Country FE
Defacto float *
Crisis
Defacto float
Crisis indicator
54
64
-1.147**
(0.481)
1,068
54
-1.137***
(0.440)
1,309
64
0.979***
(0.319)
-0.274
(0.432)
1,068
1,309
1.063***
(0.273)
-0.174
(0.403)
-3.616***
(0.886)
1.843***
(0.712)
-0.684*
(0.357)
-2.785***
(0.812)
1.391**
(0.639)
-0.651**
(0.286)
No
No
43
847
-1.083**
(0.497)
0.859**
(0.338)
0.102
(0.372)
43
847
-3.215***
(1.028)
1.809**
(0.744)
-0.451
(0.334)
64
1,309
-2.711***
(0.800)
1.313**
(0.636)
-0.539*
(0.275)
22
21
462
-0.781*
(0.457)
0.303
(0.323)
-0.263
(1.627)
64
1,309
-1.037**
(0.415)
0.794***
(0.272)
-0.040
(0.373)
D. Crisis Variable: St Dev.
21
462
-1.237*
(0.636)
0.221
(0.244)
-0.380
(1.591)
54
1,068
-1.174**
(0.459)
0.770**
(0.323)
0.257
(0.424)
54
1,068
-3.513***
(0.861)
1.730**
(0.713)
-0.279
(0.313)
C. Crisis Variable: Gov. debt or VIX (top 5%)
Yes
Yes
43
847
-0.989*
(0.519)
0.713**
(0.344)
0.280
(0.453)
43
847
-3.110***
(1.000)
1.671**
(0.748)
-0.235
(0.298)
21
462
-0.802*
(0.459)
0.301
(0.324)
-0.315
(1.654)
21
462
-1.235*
(0.632)
0.220
(0.244)
-0.418
(1.610)
-2.066**
(0.872)
1,068
54
1,309
64
2.116***
(0.682)
0.033
(0.419)
-2.125***
(0.815)
2.254***
(0.637)
0.105
(0.419)
No
43
847
-1.550**
(0.783)
1.900***
(0.694)
0.213
(0.421)
21
462
-0.639
(1.200)
0.811
(0.611)
-0.404
(1.590)
64
1,309
-2.024**
(0.818)
2.077***
(0.638)
0.220
(0.445)
54
1,068
-2.059**
(0.874)
1.989***
(0.685)
0.456
(0.465)
Yes
23
43
847
-1.608**
(0.786)
1.824**
(0.696)
0.443
(0.481)
Note: Robust standard errors in parentheses, constant terms not reported. *** p<0.01, ** p<0.05, * p<0.1
Observations
Number of
countries
Country FE
Defacto float *
Crisis
Defacto float
Crisis indicator
E. Crisis Variable: FMM Comp. indicator
21
462
-0.633
(1.203)
0.811
(0.614)
-0.442
(1.602)
1,309
0.133
64
-0.170*
(0.091)
-0.163***
(0.044)
0.027*
(0.015)
0.007
(0.011)
2.841***
(0.617)
-1.041
(0.639)
-2.174***
(0.792)
7.906***
(0.831)
(1)
All
1,068
0.260
54
-0.269**
(0.112)
-0.093**
(0.037)
0.026*
(0.015)
0.001
(0.009)
0.727
(1.416)
-0.575
(0.479)
-2.764***
(0.894)
10.497***
(1.484)
847
0.282
43
-0.301**
(0.133)
-0.096**
(0.041)
0.025
(0.015)
0.829***
(0.248)
1.382
(1.529)
-0.532
(0.565)
-2.476**
(1.203)
10.698***
(1.665)
(2)
(3)
Ex. EMU.
EMEs
VIX (top 5%)
462
0.445
21
-0.001
(0.050)
-0.047*
(0.025)
0.027***
(0.009)
0.011**
(0.005)
-1.354***
(0.184)
-0.756*
(0.408)
-0.435*
(0.239)
4.176***
(0.479)
(4)
Adv.
1,309
0.134
64
-0.132
(0.082)
-0.155***
(0.040)
0.024*
(0.014)
0.008
(0.011)
4.382***
(1.239)
-1.043
(0.649)
-3.475***
(1.128)
8.071***
(0.786)
(5)
All
1,068
0.272
54
-0.255**
(0.103)
-0.090**
(0.034)
0.024
(0.014)
0.002
(0.009)
3.739***
(1.041)
-0.534
(0.483)
-3.263***
(0.858)
10.267***
(1.470)
847
0.296
43
-0.288**
(0.122)
-0.093**
(0.037)
0.023
(0.014)
0.904***
(0.268)
3.682***
(1.008)
-0.472
(0.569)
-3.229***
(0.852)
10.378***
(1.670)
(6)
(7)
Ex. EMU.
EMEs
Gov. debt crisis
(8)
Adv.
24
Notes: Robust standard errors in parentheses, time and country fixed effects, all regressors lagged by one period,
*** p<0.01, ** p<0.05, * p<0.1
Observations
R-squared
Number of countries
Constant
Defacto float * Crisis
Defacto float
Crisis indicator
Inflation
Debt/GDP (external for EMEs)
Net Interntl. Res. / GDP
Gov Bal./GDP
Panel A
Table 5: The effect of floats on yields: Country and Time Fixed effects
1,068
0.275
54
64
-0.257**
(0.103)
-0.090**
(0.034)
0.024
(0.014)
0.002
(0.009)
3.746***
(0.996)
-0.491
(0.469)
-2.964***
(0.728)
10.232***
(1.465)
1,309
0.152
-0.164*
(0.087)
-0.158***
(0.041)
0.026*
(0.014)
0.008
(0.011)
3.377***
(0.621)
-0.952
(0.651)
-2.680***
(0.685)
7.818***
(0.796)
43
847
0.297
-0.289**
(0.122)
-0.093**
(0.037)
0.023
(0.014)
0.896***
(0.267)
3.672***
(0.972)
-0.438
(0.550)
-2.826***
(0.844)
10.355***
(1.666)
(10)
(11)
Ex.
EMU.
EMEs
Gov. debt cum VIX
21
462
0.445
-0.001
(0.050)
-0.047*
(0.025)
0.027***
(0.009)
0.011**
(0.005)
-1.354***
(0.184)
-0.756*
(0.408)
-0.435*
(0.239)
4.176***
(0.479)
Adv.
(12)
64
1,309
0.132
-0.140
(0.086)
-0.146***
(0.040)
0.026*
(0.014)
0.007
(0.010)
1.295***
(0.201)
-0.896
(0.590)
-0.228
(0.428)
7.298***
(0.759)
All
(13)
54
1,068
0.263
-0.265**
(0.110)
-0.088**
(0.036)
0.025*
(0.015)
0.003
(0.009)
0.905***
(0.200)
0.068
(0.461)
-1.177**
(0.476)
9.818***
(1.437)
43
847
0.286
-0.298**
(0.131)
-0.092**
(0.039)
0.025
(0.015)
0.854***
(0.239)
0.919***
(0.252)
0.064
(0.480)
-0.985
(0.607)
9.948***
(1.582)
(14)
(15)
Ex.
EMU.
EMEs
St Dev.
21
462
0.499
-0.005
(0.045)
-0.027
(0.018)
0.024***
(0.008)
0.010**
(0.005)
1.011**
(0.371)
-0.452
(0.345)
-1.309***
(0.359)
2.011***
(0.525)
Adv.
(16)
64
1,309
0.193
-0.114
(0.080)
-0.129***
(0.036)
0.025**
(0.012)
0.007
(0.008)
2.781***
(0.569)
-0.608
(0.477)
-1.004
(0.820)
6.736***
(0.842)
25
54
1,068
0.297
-0.248**
(0.107)
-0.076**
(0.031)
0.025*
(0.014)
0.003
(0.008)
2.228***
(0.644)
0.235
(0.409)
-2.002**
(0.876)
9.035***
(1.799)
43
847
0.316
-0.288**
(0.131)
-0.079**
(0.034)
0.024*
(0.014)
0.755***
(0.181)
2.162***
(0.654)
0.179
(0.472)
-1.470
(0.955)
9.158***
(1.953)
21
462
0.539
0.008
(0.048)
-0.034**
(0.015)
0.021***
(0.007)
0.007*
(0.004)
1.898**
(0.734)
-0.495
(0.288)
-2.157**
(0.895)
4.466***
(0.385)
(18)
(19)
(20)
Ex.
All
EMU.
EMEs
Adv.
FMM Comp. indicator
(17)
Notes: Robust standard errors in parentheses, time and country fixed effects, all regressors lagged by one period,
*** p<0.01, ** p<0.05, * p<0.1
Observations
R-squared
Number of countries
Constant
Defacto float * Crisis
Defacto float
Crisis indicator
Inflation
Debt/GDP (external for EMEs)
Net Interntl. Res. / GDP
Gov Bal./GDP
All
(9)
Table 5: The effect of floats on yields: Country and Time Fixed Effects
Panel B
Observations
R-squared
Defacto Float * Crisis
Defacto Peg * Crisis
Defacto Float
Defacto Peg
Crisis indicator
Real GDP Growth
Net Interntl. Res. to GDP ratio
Debt/GDP (external for EMEs)
1,237
0.703
-0.524
[0.977]
0.020
[0.021]
-0.164**
[0.066]
0.023***
[0.006]
-0.089***
[0.017]
-0.371***
[0.097]
0.123
[1.109]
-1.997**
[0.819]
Inflation
Gov Bal./GDP
ALL-OLS
VARIABLES
(1)
1,237
0.700
-2.193***
[0.831]
-0.852***
[0.308]
0.022
[0.023]
-0.182***
[0.069]
0.024***
[0.007]
-0.087***
[0.017]
-0.372***
[0.101]
0.209
[1.145]
ALL-OLS
(2)
WHOLE SAMPLE
1,237
0.705
0.018
[0.021]
-0.164**
[0.066]
0.024***
[0.007]
-0.093***
[0.017]
-0.375***
[0.097]
0.953
[1.138]
-2.016**
[0.826]
-0.936***
[0.305]
-1.119
[1.074]
-2.634***
[0.992]
ALL-OLS
(3)
26
798
0.643
1.133
[1.540]
1.058
[0.810]
-0.192**
[0.088]
0.020***
[0.006]
-0.088***
[0.020]
-0.432***
[0.109]
1.253
[1.238]
-1.798**
[0.751]
(7)
EMEsOLS
Panel A. Crises defined as VIX being in top 5 percent of volatility
Table 6: Effects of pegs and floats on yields.
798
0.640
-2.645**
[1.137]
-0.758*
[0.391]
1.249
[0.842]
-0.214**
[0.090]
0.022***
[0.007]
-0.089***
[0.021]
-0.435***
[0.113]
1.897
[1.308]
(8)
EMEsOLS
ONLY EMEs
798
0.645
1.040
[0.812]
-0.193**
[0.088]
0.021***
[0.007]
-0.094***
[0.021]
-0.434***
[0.109]
1.835
[1.258]
-1.831**
[0.761]
-0.887**
[0.389]
0.793
[1.590]
-2.337*
[1.210]
(9)
EMEsOLS
439
0.834
0.604***
[0.149]
0.001
[0.004]
0.022
[0.019]
0.017***
[0.004]
-0.022*
[0.012]
-0.052
[0.063]
-1.323***
[0.208]
0.718
[1.025]
ADV-OLS
(13)
439
0.836
-0.484**
[0.244]
-0.625***
[0.221]
0.000
[0.004]
0.024
[0.019]
0.017***
[0.004]
-0.023**
[0.012]
-0.059
[0.063]
-0.915***
[0.197]
(14)
ADVOLS
(15)
439
0.838
0.000
[0.004]
0.023
[0.019]
0.017***
[0.004]
-0.022*
[0.012]
-0.060
[0.063]
-1.405***
[0.200]
0.716
[1.029]
-0.661***
[0.221]
0.649***
[0.122]
0.003
[0.245]
ADV-OLS
ADV COUNTRIES
1,237
0.709
0.019
[0.020]
-0.158**
[0.065]
0.023***
[0.007]
-0.087***
[0.017]
-0.355***
[0.103]
3.967
[2.840]
-1.833**
[0.811]
-0.848***
[0.307]
-4.178
[3.261]
-4.609*
[2.656]
ALL-OLS
(3)
798
0.648
-3.645
[3.084]
0.989
[0.828]
-0.189**
[0.087]
0.020***
[0.007]
-0.084***
[0.019]
-0.411***
[0.115]
3.441
[2.628]
-1.578**
[0.744]
(7)
EMEsOLS
27
Robust standard errors in brackets. The regressions include time and country FE
*** p<0.01, ** p<0.05, * p<0.1
1,237
0.703
Observations
R-squared
1,237
0.707
-3.755*
[2.137]
-0.766**
[0.307]
0.025
[0.023]
-0.176***
[0.068]
0.022***
[0.007]
-0.084***
[0.017]
-0.355***
[0.106]
2.914
[2.222]
ALL-OLS
(2)
Defacto float * Crisis
Defacto peg * Crisis
Defacto Float
Defacto Peg
Crisis indicator
Real GDP Growth
Net Interntl. Res. to GDP ratio
Debt/GDP (external for EMEs)
-3.746
[3.078]
0.020
[0.020]
-0.161**
[0.065]
0.022***
[0.006]
-0.083***
[0.016]
-0.350***
[0.103]
3.540
[2.618]
-1.811**
[0.804]
Inflation
Gov Bal./GDP
ALL-OLS
VARIABLES
(1)
WHOLE SAMPLE
798
0.645
-4.273*
[2.259]
-0.649*
[0.385]
1.302
[0.853]
-0.206**
[0.089]
0.020***
[0.007]
-0.085***
[0.020]
-0.421***
[0.118]
2.888
[2.208]
(8)
EMEsOLS
ONLY EMEs
Panel B: Crises defined as Government Debt Crises (Laeven and Valencia 2013)
798
0.651
0.983
[0.828]
-0.184**
[0.087]
0.021***
[0.007]
-0.088***
[0.020]
-0.417***
[0.115]
3.906
[2.852]
-1.594**
[0.754]
-0.737*
[0.386]
-4.108
[3.271]
-5.042*
[2.769]
(9)
EMEsOLS
439
0.832
0.617
[1.020]
0.001
[0.004]
0.023
[0.019]
0.017***
[0.004]
-0.024**
[0.012]
-0.050
[0.063]
(13)
ADVOLS
439
0.835
-0.632***
[0.219]
0.001
[0.004]
0.024
[0.019]
0.017***
[0.004]
-0.024**
[0.012]
-0.058
[0.063]
(14)
ADVOLS
439
0.835
0.609
[1.022]
-0.632***
[0.219]
0.001
[0.004]
0.024
[0.019]
0.017***
[0.004]
-0.024**
[0.012]
-0.058
[0.063]
(15)
ADVOLS
ADV COUNTRIES
Observations
R-squared
Defacto float * Crisis
Defacto peg * Crisis
Defacto Float
Defacto Peg
Crisis indicator
Real GDP Growth
Net Interntl. Res. to GDP ratio
Debt/GDP (external for EMEs)
1,237
0.704
-0.620
[0.378]
0.019
[0.020]
-0.163**
[0.066]
0.022***
[0.006]
-0.085***
[0.017]
-0.366***
[0.097]
0.705***
[0.203]
-1.542**
[0.734]
Inflation
Gov Bal./GDP
ALL-OLS
VARIABLES
(1)
1,237
0.701
-1.017***
[0.288]
-0.265
[0.306]
0.026
[0.023]
-0.183***
[0.068]
0.023***
[0.007]
-0.081***
[0.017]
-0.362***
[0.102]
0.774***
[0.202]
ALL-OLS
(2)
WHOLE SAMPLE
1,237
0.707
0.020
[0.020]
-0.163**
[0.065]
0.023***
[0.006]
-0.088***
[0.017]
-0.367***
[0.097]
1.223***
[0.272]
-1.240*
[0.727]
-0.088
[0.308]
-1.096**
[0.434]
-1.445***
[0.350]
ALL-OLS
(3)
(7)
EMEsOLS
798
0.644
-0.750
[0.510]
1.013
[0.808]
-0.190**
[0.087]
0.020***
[0.006]
-0.087***
[0.020]
-0.430***
[0.110]
0.793***
[0.290]
-1.203*
[0.691]
28
Panel C: Crises defined as Standard Deviation in Interest Rates
798
0.642
-0.876**
[0.408]
-0.227
[0.391]
1.268
[0.841]
-0.214**
[0.090]
0.021***
[0.007]
-0.085***
[0.021]
-0.427***
[0.115]
0.840***
[0.305]
(8)
EMEsOLS
ONLY EMEs
798
0.646
0.992
[0.812]
-0.191**
[0.087]
0.021***
[0.007]
-0.091***
[0.021]
-0.429***
[0.110]
1.149***
[0.356]
-1.019
[0.696]
-0.121
[0.404]
-1.061*
[0.561]
-1.173**
[0.458]
(9)
EMEsOLS
439
0.855
1.144***
[0.191]
0.002
[0.004]
0.017
[0.019]
0.016***
[0.003]
-0.010
[0.009]
-0.046
[0.058]
-0.302***
[0.114]
1.578*
[0.856]
(13)
ADVOLS
439
0.851
-1.039***
[0.208]
-0.401*
[0.207]
0.001
[0.004]
0.018
[0.019]
0.016***
[0.003]
-0.013
[0.010]
-0.060
[0.060]
0.568***
[0.156]
(14)
ADVOLS
439
0.859
0.001
[0.004]
0.018
[0.018]
0.016***
[0.003]
-0.009
[0.009]
-0.057
[0.059]
-0.064
[0.121]
1.651*
[0.861]
-0.518**
[0.209]
0.909***
[0.180]
-0.417**
[0.187]
(15)
ADVOLS
ADV COUNTRIES