EC331 Submission 1306509 (1)

EC331 – Research in Applied Economics 1306509
1
The Effects of Exchange Rate
Regimes on the Probability of Crises
Wilson Kong1
Student ID: 1306509
Department of Economics
University of Warwick
Coventry, United Kingdom
Email: W.Kong@warwick.ac.uk
Word Count: 4962
Abstract
This paper extends the findings of Domac and Peria (2003) and investigates the effects of
exchange rate regimes on the probability of crises, and whether these effects vary with the
development status of a country. Using a comprehensive dataset covering 189 countries over the
period 1999-2012, I find that the bipolar view applies to developing countries, and free-floating
regimes are least crisis-prone regardless of a country’s development status. These findings are
robust to alternative binary estimation method.
1 I would like to extend my greatest gratitude to Dr. Pedro Serodio for his supervision and encouragement
throughout this project. I also thank Dr. Gianna Boero and Dr. Claire Crawford for organising the RAE module
and the informative lectures. Finally, I am grateful to Ms Helen Riley for her assistance in data sourcing.

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Table of Contents
1 Introduction 3
2 Literature Review 5
3 Methodology and Data 8
3.1 Econometric specification and estimation strategies 8
3.2 Data 8
3.2.1 Definition of crises 8
3.2.2 Definition of exchange rate regimes 9
3.2.3 Definition of development status 11
3.2.4 Control variables 11
4 Results 15
5 Robustness analysis 20
6 Conclusion, Limitations and Potential for Future Research 22
7 Bibliography 24
8 Appendix 28

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1. Introduction
Following the Great Recession and European Sovereign Debt Crisis, there has been
renewed interest in the determinants of financial crises. In particular, opinions have been divided
as to whether the Euro culminated the European Crisis. Krugman (2012) and Cleppe (2015) argue
that by joining the Euro, countries such as Greece and Spain gained access to a central bank backed
by Germany’s creditworthiness, resulting in the perception that investments in these countries are
safer than what they really were. This drove down borrowing rates, causing great amounts of
“cheap money” inflows and governments to accumulate unsustainable levels of debt. This
contradicts Volz (2013) and Hatzigeorgiou (2014), who assert that it would be “a mistake to
conclude that European monetary unification was a fundamentally flawed idea”, and that it is
“likely that Greece, even without the Euro, would have found itself in an economic crisis”
respectively. This ongoing debate indicates that no consensus has been reached regarding the
significance of exchange rate regimes in the recent crises, hence prompting me to further
investigate the underlying relationship between exchange rate regimes and financial crises.
The main hypothesis of this paper is therefore to test if exchange rate regimes have any
significant effects on the probability of financial crises, and if these effects differ across developed
and non-developed countries. Furthermore, considering that many previous literature have focused
their analysis on the bipolar or two-corner solution view of exchange rates, which asserts that hard
pegs and free floats are more viable than intermediate regimes (Mussa et al., 2000), this paper will
also assess the validity of the bipolar view. This paper aims to contribute to the existing literature
by considering two different types of crises, namely systemic banking and currency crises, using
an updated database which spans from 1999-2012. Also, this paper will further the analysis by
considering, for the first time to the best of my knowledge, fine classifications of exchange rate
regimes (Appendix 1), instead of the usual coarse classifications, which comprise of only pegged,
intermediate and floating regimes.

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Adopting the logit framework developed by Domac and Peria (2003), this paper finds
significant evidence to prove that the bipolar view holds for developing countries, but not for the
overall sample of countries. After further decomposing the exchange rate regimes into fine
classifications, results show that allowing the currency to float freely leads to the lowest probability
of crises across all countries. Finally, I show that the results are robust even when the estimations
are performed using probit analysis.
The remainder of this paper is organised as follows. Section 2 covers existing literature on
the relationship between exchange rate regimes and financial crises. Section 3 describes the data
and methodology adopted in this study. Section 4 discusses the empirical results, followed by
Section 5, which covers the robustness test. Finally, Section 6 concludes with remarks on
limitations and potential for future research.

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2. Literature Review
As underpinned by the impossible trinity (Fleming and Mudell, 1964), exchange rate
regime is among the most important monetary decisions central banks need to make. This was the
first major study conducted on exchange rate regimes, and much efforts have since been devoted
into studying the relationship between exchange rate regimes and financial crises. However, these
studies differ substantially in terms of methodology, data and results.
Combes et al. (2013) neatly summarized some of these studies in Table 1, where literatures
are divided based on whether they agree with the bipolar view. In addition to the literature stated
in Table 1, Esaka (2010) and Husain et al. (2005) establish that the bipolar view applies to currency
crises as well, showing that pegged regimes have the lowest probability of currency crises, whereas
those adopting a managed floating regime have the highest probability. With the exception of
Domac and Peria (2003), who examine the effects of exchange rate regimes on the likelihood, cost
and duration of crises, all the aforementioned literature only examine the effects on the likelihood.
(ibid.) find that conditioned on a crisis occurring, the real cost of the crisis is higher for pegged
regimes, while duration is independent of regime.
However, it is worth noting that the dataset used in this study (ibid.) only covers 1980-1997,
which necessarily implies that the effect of a monetary union was not considered, since the Euro
was only introduced in 1999. Miller and Vallee (2010) further the research on exchange rate
regimes and cost of crises, concluding that in credible fixed exchange rate regimes, the size of the
crisis increases with the level of central bank foreign exchange reserves. While Combes et al.
(2013) examined the validity of the bipolar view using a dataset which spans from 1980-2009 and
assert that the bipolar view does not hold for banking, currency and debt crises, they fail to
distinguish between developing and developed countries and hence whether the findings differ
across these categories.

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Besides results, two other areas of contention are the classifications of exchange rate
regimes and the choice of crises database. Earlier papers used de jure classifications, but this has
since been deemed inappropriate and replaced by de facto classifications (Bubula and Ötker-Robe,
2002; Reinhart and Rogoff, 2004; Levy-Yeyati and Sturzenegger, 2005) due to countries being
unable to maintain announced pegs (Alesina and Wagner, 2006) or exhibiting fear of floating
(Calvo and Reinhart, 2002). Existing crises databases can be divided by the way in which they
identify crises, namely Money Market Pressure Index (Von Hagen and Ho, 2007; Jing et al., 2015)
and Event-based Identification (Laeven and Valencia, 2008, 2010, 2013; Demirgüç-Kunt and
Detragiache, 1998, 2002, 2005).

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Table 1: The Literature on Crises and Exchange Rate Regimes (Combes et al., 2013)
Authors Type of crisis Data features Results Analysis
The proponents of the bipolar view
Eichengreen et al.
(1994)
Speculative attacks
1967-1992, 22 countries,
mostly OECD
Intermediate regimes are more prone to banking crises Empirical
Domac and Peria
(2003)
Banking crisis with
dummy
1980-1997, developed and
developing countries
Fixed regimes diminish the likelihood of crisis Empirical
Mendis (2002)
Banking crisis with
crisis dummy
Developing economies Flexible regimes reduce the likelihood of banking crises
Theoretical
Empirical
Bubula and Otker
Robe (2003)
Currency crisis with
EMPI
1990-2001 Intermediate regimes are more crisis prone Empirical
Angkinand and
Willet (2006)
Banking crisis with
dummy
1990-2003
Soft peg and Intermediate regimes are associated with higher
probabilities of financial crises
Empirical
The critics of the bipolar view
Corsetti et al.
(1998)
Asian crises using
crisis index
Expectations of inflationary financing cause the collapse of the
currency
Theoretical
Empirical
Eichengreen and
Hausman (1999)
Pegged regimes are crisis-prone due to a moral hazard problem Theoretical
Chang and
Velasco (2000)
Banking crisis
Pegged regimes are more prone to banking crises. Flexible rates
eliminate (bank) runs with appropriate policy
Theoretical
Fisher (2001) Currency crises
1991-1999, developed and
emerging markets
Softly-pegged ER regimes are crisis prone and not sustainable over the
long period
Theoretical
Daniel (2001) Currency crises
Pegged regimes are inevitably crisis-prone due to unsustainable fiscal
policy
Theoretical
Mc Kinnon (2002) Currency crises
Emerging market
economies
Floating regimes increase nations' vulnerability to currency crises
through higher exchange rate volatility
Theoretical
Burnside et al.
(2004)
Banking and
Currency crises
Government guarantees of the monetary regimes lead to self-fulfilling
banking and currency crises
Theoretical
Rogoff (2005) Debt crises Developing Countries
Rigid regimes or excessive borrowing lead to debt problems under any
system
Theoretical

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3. Methodology and Data
3.1. Econometric specification and estimation strategies
To estimate the probability of crises, I will be adopting the specification developed by
Domac and Peria (2003), which I believe is the most comprehensive specification in terms of
control variables. Furthermore, given that the study (ibid.) only covers crises from 1980-1997, this
paper serves to test if the results presented previously are robust to a more updated crises database.
Modelling after (ibid.), we assume a logistic distribution, and by logit analysis, the
probability of a crisis at time t can be expressed as:
� �� = /��−1 =
� �′��−1
+ � �′��−1
In the same vein, the probability of no crisis at time t is:
� �� (� � � � =
��−1
) =
+ � �′��−1
The dependent variable in this logit analysis is a crisis dummy variable coded 1 for
countries and years during which either a systemic banking or currency or both crises occurred,
and 0 otherwise. X is a matrix of determinants of crises, which serve as control variables in this
analysis. Given that an ongoing crisis is likely to affect the movement of control variables on the
RHS of the equation, only the first year of a crisis is coded 1 in order to prevent the possible
endogeneity. Besides, all determinants of crises are lagged one period to reduce the simultaneity
problem (ibid.).
3.2. Data
3.2.1. Definition of crises
According to data availability, this study is conducted for 189 countries over the period of
1999-2012. In this paper, I will be using the Laeven and Valencia (2013) crises database, as it is

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the most updated Event-based database available. Furthermore, the aforementioned database
records both systemic banking and currency crises, which is required to examine the effects of
exchange rate regimes on both types of crises. According to (ibid.), a banking crisis is defined as
an event which meets the following two conditions:
(1) Significant signs of financial distress in the banking system (as indicated by significant
bank runs, losses in the banking system, and/or bank liquidations).
(2) Significant banking policy intervention measures in response to significant losses in the
banking system.
A banking crisis is considered systemic during the first year when both criteria are met. A currency
crisis, on the other hand, is observed should there be a nominal depreciation of the currency vis-a-
vis the U.S. dollar of at least 30 percent that is also at least 10 percentage points higher than the
rate of depreciation in the year before. During the period of this study, (ibid.) recorded a total of
62 instances where a country was facing a systemic banking or currency or both crises.
3.2.2. Definition of exchange rate regimes
The variable of interest is the de facto exchange rate regime. This paper will use IMF’s
latest Annual Report on Exchange Rate Arrangements and Exchange Rate Restrictions (AREAER)
to capture each country’s de facto exchange rate regime. This is mainly because the AREAER is
the only classification that is sufficiently up-to-date to cover all the crises in (ibid.) database, and
“by combining (often confidential) information on the central bank’s intervention policy with
actual exchange rate volatility, it avoids the occasional anomalies from which purely mechanical
algorithms to classify regimes (as in other classifications) inevitably suffer” (Ghosh et al., 2014).
The IMF first published the AREAER in 1999, and has since revised its classification
system in 2008. For the purpose of this study, I have recoded the classifications accordingly to
ensure consistency and constructed a variable, imfcoarse, denoting each country’s coarse exchange

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rate regime and another variable, imffine, for its fine exchange rate regime. The process of recoding
and detailed classifications for both imfcoarse and imffine are explained in Appendix 1. In order to
test the validity of the bipolar view, a bipolar dummy variable coded 1 for pegged and floating
regimes, and 0 otherwise, has been constructed. If the bipolar view is valid, the coefficient on this
bipolar dummy variable is expected to be significantly negative. Figure 1 displays the distribution
of crises by coarse exchange rate regimes. Given that the percentages of crises for both intermediate
and floating regimes are similar, I am unable to identify if the bipolar view holds. It appears,
however, that pegged regimes experience significantly fewer crises, in line with the findings of
Domac and Peria (2003).
Fgure 1: Percentage of crises across coarse exchange rate classifications
Figure 2: Percentage of crises across development status

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3.2.3. Definition of development status
In this paper, the World Bank Analytical Classifications presented in World Development
Indicators (World Bank) serve as a proxy for a country’s development status. The classifications
divide countries into low (L), lower middle (LM), upper middle (UM) and high (H) income
countries, based on the countries’ GNI per capita and a set of annually-updated income group
thresholds. As suggested by World Bank, low and middle income countries are classified as
developing countries, and high income as developed. Calvo and Reinhart (2002) assert that
developing countries tend to experience the ‘fear of floating’, due to their lack of credibility and
high exposure to exchange rate fluctuation. Domac and Peria (2003) further added that developing
countries’ high levels of foreign currency denominated debts and trade imply that the choice of
exchange rate regime should have a greater impact on developing countries. As such, we expect
the coefficients to be more significant for the reduced sample of developing countries. Figure 2
displays the distribution of crises by development status. It is evident that most crises happened in
developing countries, hence necessitating additional analysis on the effects of exchange rate
regimes on the probability of crises in these countries.
3.2.4. Control variables
For the remaining determinants of crises, this paper will follow (ibid.) and divide them into
domestic-macroeconomic, external and financial variables. All variables are obtained from the
International Financial Statistic (IMF) and World Development Indicators (World Bank). A full
list of variables and their respective sources can be found in Appendix 2. The domestic-
macroeconomic variables included are inflation rate, real interest rate, the level of real GDP per
capita, real GDP growth, volatility of real GDP growth and the government surplus to GDP ratio.
The external variables are terms of trade change, volatility of terms of trade and change in real
exchange rate. Finally, for financial variables, we include the m2 to reserves ratio, domestic credit
to private sector to GDP ratio, private credit growth, private credit volatility and banks’ cash to

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assets ratio. In addition to these variables, I have constructed a financial liberalization dummy
using the Chinn-Ito index (Chinn and Ito, 2006) to capture the different effects real interest rate
and private credit growth have on the probability of crises between liberalized and non-liberalized
financial markets. This dummy is coded 1 for fully liberalized markets (KAOPEN=2.39), and 0
otherwise.
Summarizing the intuition and economic theory presented by Domac and Peria (2003) and
Demirguc-Kunt and Detragiache (1998), the expected signs on the coefficients of the
abovementioned variables and the rationales behind these expectations are as follows:
Variable
Expected
Sign
Rationale
Inflation rate Positive
High inflation leads to high nominal interest rate, which is a
proxy for poor macroeconomic management. Also, high
inflation erodes real profits, leading to difficulties in accurately
assessing credit quality and hence a deteriorating lending
portfolio.
Real interest rate Positive
High real interest rates worsens the adverse selection problem,
where only high risk projects get financed.
Real interest
rate*financial
liberalization
Positive
The abovementioned effect is amplified as real interest rates are
now determined solely by market forces.
Real GDP/capita Negative
Rich countries typically have better institutions (efficient legal
systems, property rights, strong contract enforcement, prudent
regulators), hence reducing the opportunities for moral hazard.
Real GDP
growth
Negative
Share of non-performing loans and probability of default is
lower during periods of high economic growth.
Volatility of real
GDP growth
Positive
High output volatility implies high real profits volatility, which
affects borrowers’ abilities to repay their loans and to predict
future profits, leading to a deteriorating lending portfolio.

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Variable
Expected
Sign
Rationale
Government
surplus/GDP
Negative
This captures the financing needs of the central government.
Faced with financing problems, governments are less likely to
improve banks’ balance sheets, allowing small problems to
grow quickly into major systemic crises. Also, financially
strapped governments lack credibility when they announce
measures to improve the economy, hence increasing the
probability of bank runs once the public realises any problem
in the banking system. Finally, a low or negative government
surplus to GDP ratio is likely due to expansionary fiscal
policies, which might fuel lending booms and worsen banks’
lending portfolios.
Terms of trade
change
Negative
A worsening terms of trade implies that export prices are
decreasing relative to import prices, therefore reducing the
ability of borrowers, especially those in the tradable sector, to
repay their loans.
Volatility of
terms of trade
Positive
High volatility implies high volatility of income, especially
those in the tradable sector, hence increasing the probability of
default.
Real exchange
rate change
Negative
A worsening real exchange rate has the similar implications as
a worsening terms of trade, hence reducing borrowers’ ability
to repay their loans.
M2/reserves Positive
High M2 to foreign exchange reserves ratio means banks are
more likely to face bank runs in the event of sudden capital
outflows.
Cash/asset held
by banks
Negative
Banks with a high cash to asset ratio can better deal with
potential bank runs and hence avoid insolvency.

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Variable
Expected
Sign
Rationale
Private
credit/GDP
Positive
High private credit to GDP ratio could be a result of excessive
risk-taking, and if not properly regulated, might lead to frauds.
These indicate an unhealthy banking sector.
Private credit
growth
Positive
High levels of private credit growth (lending booms) worsens
banks’ lending portfolios and is a precursor to many banking
crises.
Private credit
growth
*financial
liberalization
Positive
High levels of private credit growth might not be possible in
non-liberalized economies (causing it to be insignificant).
Interacting it with the financial liberalization dummy will
therefore strip off the effects of these controls.
Private credit
volatility
Positive
High private credit volatility could be due to an unstable
banking sector.

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4. Results
Table 2 presents the main results of this paper – whether the bipolar view holds and if this
effect differs across developed and developing countries. All estimates are corrected for
heteroscedasticity using robust standard errors. In the same vein as Domac and Peria (2003), three
separate specifications are estimated for each sample of countries. The first specification (Table 2,
columns (2.1) and (2.4)) includes the domestic-macroeconomic, external and financial variables as
described above, along with the lag of the bipolar dummy. The second specification (columns (2.2)
and (2.5)) isolates the effects of financial liberalization on the probability of crises, through
interacting real interest rate and private credit growth with the financial liberalization dummy.
The final specification (columns (2.3) and (2.6)) includes interaction terms of the bipolar dummy
with terms of trade change, M2 to reserves ratio, cash to assets ratio held by banks and real
exchange rate change, in order to capture the indirect effects of the exchange rate regime on the
probability of crises.
Results indicate that higher inflation increases the likelihood of crises for the overall sample
of countries, but this effect is reversed if we focus only on developing countries. The former result
is consistent with my hypothesis, but the paradoxical latter could be because higher inflation makes
it easier for wages to adjust. Considering that a larger proportion of population in developing
countries earns the minimum wage or just enough to repay their debts, higher inflation allows
employers to freeze or reduce real wage without having to cut nominal wage, hence preventing real
wage unemployment and reducing probability of default.
Contrary to my hypothesis, higher real interest rate reduces the likelihood of crises in
developing countries, and this effect is amplified if their financial markets are non-liberalized. With
lower earnings, people in developing countries might be more prudent with their spending. When
faced with higher interest rates, instead of increasing borrowing and contributing to the adverse

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Table 2: Assessment of the bipolar view
All countries Developing countries
(2.1) (2.2) (2.3) (2.4) (2.5) (2.6)
Lag (inflation) 0.111 0.081 0.103 -0.005 -0.449 -0.647
(0.048)** (0.101) (0.103) (0.053) (0.235)* (0.348)*
Lag (real interest rate) 0.018 -0.022 -0.005 -0.073 -0.927 -1.546
(0.023) (0.126) (0.122) (0.024)*** (0.296)*** (0.766)**
Lag (GDP/capita) -0.000 -0.000 0.000 0.000 -0.000 -0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)*
Lag (GDP growth) -0.005 -0.010 -0.024 -0.062 -0.293 -0.269
(0.043) (0.044) (0.052) (0.072) (0.132)** (0.156)*
3-year volatility of GDP growth 0.211 0.232 0.250 0.267 0.617 0.731
(0.078)*** (0.090)** (0.086)*** (0.141)* (0.328)* (0.470)
Lag (govt surplus/GDP) 0.050 0.057 0.059 0.148 0.317 0.320
(0.085) (0.081) (0.078) (0.191) (0.227) (0.235)
Lag (ToT change) -1.580 -1.754 -0.464 -2.349 -5.126 -5.312
(1.827) (1.789) (2.038) (2.099) (1.971)*** (2.167)**
3-year volatility of ToT 3.100 2.789 2.361 2.204 9.915 15.037
(2.247) (2.222) (2.017) (2.020) (6.729) (9.786)
Lag (real exchange rate change) 0.001 0.003 0.120 -0.001 0.056 0.225
(0.002) (0.010) (0.048)** (0.028) (0.082) (0.082)***
Lag (M2/reserves) -0.026 -0.035 0.209 0.351 1.532 2.255
(0.109) (0.109) (0.209) (0.103)*** (0.563)*** (1.736)
Lag (banks’ cash/assets) -0.062 -0.059 -0.086 -0.027 -0.115 -0.256
(0.034)* (0.037) (0.040)** (0.019) (0.043)*** (0.251)
Lag (credit/GDP) -0.004 -0.008 -0.008 -0.157 -0.302 -0.424
(0.011) (0.009) (0.011) (0.045)*** (0.077)*** (0.170)**
Lag (real credit growth) -0.025 -0.050 -0.047 0.238 0.581 0.956
(0.028) (0.033) (0.033) (0.080)*** (0.155)*** (0.391)**
3-year volatility of real credit growth 0.062 0.099 0.082 0.474 0.529 0.616
(0.062) (0.072) (0.070) (0.120)*** (0.169)*** (0.219)***
Lag (bipolar dummy) -0.057 -0.029 0.200 -0.070 -2.445 -6.985
(0.882) (0.843) (1.529) (1.267) (1.320)* (3.786)*
Lag (real interest rate*financial lib. dummy) 0.034 0.023 0.865 1.475
(0.115) (0.113) (0.295)*** (0.792)*
Lag (real credit growth*financial lib. dummy) 0.054 0.055 -0.633 -0.908
(0.028)* (0.028)* (0.329)* (0.803)
Lag (bipolar*ToT change) -3.147 -2.145
(2.354) (3.517)
Lag (bipolar*M2/reserves) -0.236 0.060
(0.220) (0.760)
Lag (bipolar*banks’ cash/assets) 0.027 0.113
(0.062) (0.153)
Lag (bipolar*real exchange rate change) -0.120 -0.242
(0.052)** (0.142)*
Number of observations 443 443 443 356 356 356
*p<0.1; **p<0.05; ***p<0.01

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selection problem, they save more and reduce spending. This precautionary saving enables them to
weather adverse economic conditions and prevents widespread default. This effect, however, is
significantly smaller for liberalized financial markets. Higher interest rates attract large capital
inflows from foreign investors, who are inclined to withdraw their funds during economic downturns
or when interest rates fall, leading to capital flight, which is inherently destabilizing.
Real GDP growth, terms of trade change, ratio of M2 to reserves and volatility of private
credit growth are significant in affecting the likelihood of crises in developing countries, while
volatility of real GDP growth and banks’ cash to assets ratio have a significant impact across the full
sample of countries. In addition, the direction of these impacts support my previous hypothesis. For
developing countries, higher private credit growth also leads to higher probability of crises, but this
effect diminishes in liberalized financial markets. This could be because liberalized markets serve as
a proxy for better-developed markets, and hence are better able to monitor higher private credit
growth and prevent this lending boom from collapsing the economy. Finally, results show that the
main variable of interest, bipolar, is significant only for developing countries. Having stripped off
the effects of financial liberalization and the indirect effects of the exchange rate regime, the
probability of crises is almost 7 percentage points lower for countries that adopt a bipolar exchange
rate regime (pegged or floating), holding all else constant. This finding therefore suggests that the
bipolar view holds for developing countries.
In addition to assessing the validity of the bipolar view, this paper also aims to further examine
the impacts of different exchange rate regimes by including fine classifications of exchange rate
regime (Table 3). Columns (3.1) and (3.3) present the results according to the first specification,
whereas columns (3.2) and (3.2) correspond to the second specification. When the fine classifications
are included, I am unable to regress based on the third specification as the indirect effects have a
covariate pattern with only one outcome, leading to non-convergence. The default category for all
specifications is free-floating.

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Except for a few notable exceptions, the results here are largely similar to the bipolar case.
Unlike the previous results, volatility of terms of trade is now significant and positive for both the
overall sample and developing countries, supporting my initial hypothesis. More importantly, Table
3 shows that countries that adopt conventional peg or crawling band face higher probabilities of crises
than those that allow their currencies to float freely. The first result only applies to developing
countries, while the latter applies to both the overall sample and developing countries. In addition to
substantiating the bipolar view, this result seems to indicate that free-floating has additional merits
over pegged regimes and hence leads to the lowest probability of crises.

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Table 3: Estimations of probability of crises using fine classifications
*p<0.1; **p<0.05; ***p<0.01
(3.1) (3.2) (3.3) (3.4)
Lag (inflation) 0.113 0.092 -0.015 -0.406
(0.047)** (0.077) (0.045) (0.173)**
Lag (real interest rate) 0.002 -0.028 -0.088 -0.903
(0.020) (0.078) (0.026)*** (0.350)***
Lag (GDP/capita) -0.000 -0.000 0.000 -0.000
(0.000) (0.000) (0.000) (0.000)
Lag (GDP growth) -0.012 -0.010 -0.083 -0.273
(0.050) (0.049) (0.093) (0.156)*
3-year volatility of GDP
growth
0.237 0.249 0.249 0.614
(0.086)*** (0.086)*** (0.126)** (0.353)*
Lag (govt surplus/GDP) 0.083 0.089 0.092 0.218
(0.100) (0.096) (0.190) (0.116)*
Lag (ToT change) -1.286 -1.594 -1.852 -4.110
(1.981) (1.969) (1.820) (1.267)***
3-year volatility of ToT 3.974 3.714 3.343 11.300
(2.159)* (2.145)* (2.397) (6.653)*
Lag (real exchange rate
change)
0.002 0.004 0.003 0.068
(0.002) (0.005) (0.016) (0.025)***
Lag (M2/reserves) -0.031 -0.030 0.455 1.591
(0.089) (0.084) (0.197)** (0.619)**
Lag (banks’ cash/assets) -0.086 -0.086 -0.039 -0.128
(0.044)** (0.050)* (0.022)* (0.091)
Lag (credit/GDP) -0.004 -0.008 -0.135 -0.285
(0.008) (0.007) (0.029)*** (0.086)***
Lag (real credit growth) -0.022 -0.041 0.234 0.625
(0.023) (0.024)* (0.077)*** (0.244)**
3-year volatility of real credit
growth
0.044 0.064 0.401 0.397
(0.040) (0.044) (0.090)*** (0.127)***
Lag (conventional peg
dummy)
-1.800 -1.802 0.912 3.725
(1.440) (1.367) (2.106) (1.896)**
Lag (peg within horizontal
band dummy)
-0.983 -0.948 -0.091 2.006
(1.119) (1.129) (2.205) (1.671)
Lag (crawling band dummy) 2.514 2.437 2.899 8.067
(1.199)** (1.166)** (1.714)* (4.281)*
Lag (managed float dummy) -0.936 -0.968 0.980 0.781
(1.181) (1.144) (2.489) (1.783)
Lag(real interest rate*financial
lib. dummy)
0.029 0.856
(0.070) (0.390)**
Lag(real credit
growth*financial lib. dummy)
0.047 -0.739
(0.023)** (0.582)
Number of observations 349 349 270 270

20
5. Robustness Analysis
To test the robustness of my results, I repeated the estimations of the effects of exchange rate
regimes on the probability of crises using probit analysis. For clearer comparison purposes, only the
variables of interest are presented (full results in Appendices 3 and 4).
Table 4: Probability of crises for all countries: Alternative binary estimation method
Tables 4 and 5 show the relevant coefficient estimates using both estimation methods for the
overall sample and developing countries respectively. The first row corresponds to the assessment of
the bipolar view (Table 2), whereas the subsequent 4 rows correspond to the estimation using fine
classifications (Table 3). Both tables indicate that the use of probit analysis has no qualitative effects
on the previous results, as both the significance and signs of the variables of interest remain
unchanged. Therefore, my previous findings that the bipolar view applies to developing countries,
and that free-floating leads to the lowest probability of crises are robust to alternative estimation
method.
All countries
(4.1) (4.2) (4.3)
Logit Probit Logit Probit Logit Probit
Lag (bipolar
dummy)
-0.057 0.020 -0.029 0.037 0.200 0.112
(0.882) (0.352) (0.843) (0.350) (1.529) (0.630)
Lag (conventional
peg dummy)
-1.800 -0.843 -1.802 -0.870
(1.440) (0.587) (1.367) (0.575)
Lag (peg within
horizontal band
dummy)
-0.983 -0.424 -0.948 -0.399
(1.119) (0.519) (1.129) (0.521)
Lag (crawling
band dummy)
2.514 1.120 2.437 1.136
(1.199)** (0.562)** (1.166)** (0.533)**
Lag (managed
float dummy)
-0.936 -0.329 -0.968 -0.373
(1.181) (0.462) (1.144) (0.460)
*p<0.1; **p<0.05; ***p<0.01

21
Table 5: Probability of crises for developing countries: Alternative binary estimation
Developing countries
(5.1) (5.2) (5.3)
Logit Probit Logit Probit Logit Probit
Lag (bipolar
dummy)
-0.070 -0.010 -2.445 -1.096 -6.985 -3.092
(1.267) (0.435) (1.320)* (0.627)* (3.786)* (1.685)*
Lag (conventional
peg dummy)
0.912 0.506 3.725 1.823
(2.106) (0.863) (1.896)** (0.943)*
Lag (peg within
horizontal band
dummy)
-0.091 -0.036 2.006 0.912
(2.205) (0.914) (1.671) (0.858)
Lag (crawling
band dummy)
2.899 1.547 8.067 3.842
(1.714)* (0.765)** (4.281)* (1.395)***
Lag (managed
float dummy)
0.980 0.607 0.781 0.600
(2.489) (0.835) (1.783) (0.971)
*p<0.1; **p<0.05; ***p<0.01

22
6. Conclusion, Limitations and Potential for Future Research
This paper investigated the relationship between exchange rate regimes and the probability of
crises, with the objective of identifying the least crisis-prone regime policymakers can adopt to avoid
financial crises, and hopefully prevent the reoccurrence of a devastating global crisis like the Great
Recession. Studying a panel of 189 developing and developed countries over the period 1999-2012,
I found that in the context of systemic banking and currency crises, there is significant evidence to
prove that the bipolar view applies to developing countries. Exploring further into the fine
classifications of exchange rate regimes, results show that regardless of development status, countries
which adopt a free-floating regime face the lowest probability of crises. Recalling the impossible
trinity (Fleming and Mundell, 1964), these findings imply that countries with the intention of avoiding
crises should forgo fixed exchange rates and adopt free capital flow and independent monetary policy.
These results are also robust to alternative binary estimation method.
The main limitation of this paper is data inadequacy. Despite being the most comprehensive
macroeconomic datasets currently available, both IFS and WDI still suffer from the inevitable data
gaps, especially for the most underdeveloped and internationally uncooperative countries. This
greatly reduced the sample size of this study, hence impairing the accuracy of its results. Furthermore,
while I strived to select the best crises and exchange rate regime datasets, it is undeniable that all
datasets have their own pros and cons. Due to time constraints, I am unable to further test the
robustness of my results using all available datasets. Finally, considering that the European Sovereign
Debt Crisis is still ongoing, I am unable to fully account for the impacts of this crisis on my results.
This, combined with the fact that a sizeable proportion of crises that occurred during the period of
this study is attributable to the Great Recession and the European Sovereign Debt Crisis, implies that
the findings of this paper could potentially be biased. Therefore, further research can be conducted
after the conclusion of this crisis to obtain more unbiased results.

23
Having said that, I still believe that the findings of this paper should not be overlooked. Since
it has been established that exchange rate regimes possess policy significance, policymakers must
recognize its importance and effectively use exchange rate regimes to prevent future crises.

24
7. Bibliography
 Alesina, A. and Wagner, A. (2006). Choosing (and Reneging on) Exchange Rate Regimes.
Journal of the European Economic Association, 4(4), pp.770-799.
 Angkinand, A. and Willett, T., (2006) “Moral hazard, Financial Crises and the Choice of
Exchange Rate Regimes”, unpublished manuscript.
 Bubula, A. and Ötker, I. (2002). The Evolution of Exchange Rate Regimes Since 1990: Evidence
From De Facto Policies. IMF Working Papers, 02(155), p.1.
 Bubula, A., Otker-Robe. I., (2003) “Are Pegged and Intermediate Exchange Rate Regimes More
Crisis Prone” IMF WP/20/03/223.
 Burnside, C., Eichenbaum, M., Rebelo, S., (2004) “Government guarantees and selffulfilling
speculative attacks” Journal of Economic Theory 119, 31-63.
 Calvo, G. and Reinhart, C. (2002). Fear of Floating. The Quarterly Journal of Economics, Oxford
University Press, 117(2), pp.379-408.
 Chang, R., Velasco, A., (2000) “Financial Fragility and the Exchange Rate Regime” Journal of
Economic Theory 92, 1-34.
 Cleppe, P. (2015). Germany is not to blame for the Greek crisis - CapX. [online] CapX. Available
at: http://www.capx.co/germany-is-not-to-blame-for-the-greek-crisis/ [Accessed 7 Dec. 2015].
 Chinn, Menzie D. and Hiro Ito (2006). "What Matters for Financial Development? Capital
Controls, Institutions, and Interactions," Journal of Development Economics, Volume 81, Issue
1, Pages 163-192 (October).
 COMBES, J., MINEA, A. and SOW, M. (2013). Crises and Exchange Rate Regimes: Time to
break down the bipolar view?. In: SERIE ETUDES ET DOCUMENTS DU CERDI. Clermont-
Ferrand: CERDI.

25
 Corsetti, G., Pesenti, P., Roubini, N., (1998) “Paper Tigers? A model of the Asian crisis” NBER
WP 6783.
 Daniel, B.C., (2001) “A Fiscal Theory of Currency Crises,” International Economic Review 42,
969-988.
 Demirguc-Kunt, A. and Detragiache, E. (1998). The Determinants of Banking Crises in
Developing and Developed Countries. Staff Papers - International Monetary Fund, 45(1), p.81.
 Demirgüç-Kunt, A. and Detragiache, E. (2002). Does deposit insurance increase banking system
stability? An empirical investigation. Journal of Monetary Economics, 49(7), pp.1373-1406.
 Demirguc-Kunt, A. (2005). Cross-Country Empirical Studies of Systemic Bank Distress: A
Survey. National Institute Economic Review, 192(1), pp.68-83.
 Domaç, I. and Martinez Peria, M. (2003). Banking crises and exchange rate regimes: is there a
link?. Journal of International Economics, 61(1), pp.41-72.
 Eichengreen, B., Hausman, R., (1999) “Exchange Rate and Financial fragility”, NBER WP 7418.
 Eichengreen, B., Rose, A., Wyplosz, C., (1994) “Speculative attacks on Pegged Exchange Rate:
An empirical exploration with special reference to the European Monetary System”, NBER WP
4898.
 Esaka, T. (2010). De facto exchange rate regimes and currency crises: Are pegged regimes with
capital account liberalization really more prone to speculative attacks?. Journal of Banking &
Finance, 34(6), pp.1109-1128.
 Fisher, S., (2001) “Exchange Rate Regime: Is the Bipolar view correct” Journal of Economic
Perspectives 15, 3-24.
 Fleming, J. and Mundell, R. (1964). Official Intervention on the Forward Exchange Market: A
Simplified Analysis (Analyse simplifiee de l'intervention officielle sur le marche de change a
terme) (Analisis simplificado de la intervencion oficial en el mercado de cambio a termino). Staff
Papers - International Monetary Fund, 11(1), p.1.

26
 Ghosh, A., Ostry, J. and Qureshi, M. (2014). Exchange Rate Management and Crisis
Susceptibility: A Reassessment. IMF Working Papers, 14(11), p.1.
 Hatzigeorgiou, A. (2014). The Greek Economic Crisis – is the Euro to Blame?. World
Economics, 15(3), pp.143-162.
 Husain, A., Mody, A. and Rogoff, K. (2005). Exchange rate regime durability and performance
in developing versus advanced economies. Journal of Monetary Economics, 52(1), pp.35-64.
 Jing, Z., de Haan, J., Jacobs, J. and Yang, H. (2015). Identifying banking crises using money
market pressure: New evidence for a large set of countries. Journal of Macroeconomics, 43, pp.1-
20.
 Krugman, P. (2012). Greece As Victim. The New York Times, [online] p.A23. Available at:
http://www.nytimes.com/2012/06/18/opinion/krugman-greece-as-victim.html?_r=1 [Accessed 7
Dec. 2015].
 Laeven, L. and Valencia, F. (2008). Systemic Banking Crises: A New Database. IMF Working
Papers, 08(224), p.1.
 Laeven, L. and Valencia, F. (2010). Resolution of Banking Crises: The Good, the Bad, and the
Ugly. IMF Working Papers, 10(146), p.1.
 Laeven, L. and Valencia, F. (2013). Systemic Banking Crises Database. IMF Economic Review,
61(2), pp.225-270.
 Levy-Yeyati, E. and Sturzenegger, F. (2005). Classifying exchange rate regimes: Deeds vs.
words. European Economic Review, 49(6), pp.1603-1635.
 McKinnon, R., (2002) “Limiting Moral hazard and Reducing Risk in International Capital Flows:
The Choice of an Exchange Rate Regime” Annals of the American Academy of Political and
Social Science 579, Exchange Rate Regime and Capital Flows, 200-218.
 Mendis, C., (2002) “External Shocks and Banking Crises in Developing Countries: Does the
Exchange Rate Regime Matter?” CESifo Working Paper Series No. 759.

27
 Miller, V. and Vallée, L. (2010). The size of banking crises in credible fixed exchange rate
regimes. Journal of International Money and Finance, 29(7), pp.1226-1236.
 Mussa, M., Masson, P., Swoboda, A., Jadresic, E., Mauro, P. and Berg, A. (2000). Exchange
Rate Regimes in an Increasingly Integrated World Economy. IMF, 193.
 Reinhart, C. and Rogoff, K. (2004). The Modern History of Exchange Rate Arrangements: A
Reinterpretation. The Quarterly Journal of Economics, 119(1), pp.1-48.
 Rogoff, K., (2005) “Fiscal Conservatism, Exchange Rate Flexibility and the Next Generation of
Debt Crises” Cato Journal 25, 33-39.
 Volz, U. (2013). Lessons of the European crisis for regional monetary and financial integration
in East Asia. Asia Eur J, 11(4), pp.355-376.
 VON HAGEN, J. and HO, T. (2007). Money Market Pressure and the Determinants of Banking
Crises. Journal of Money, Credit and Banking, 39(5), pp.1037-1066.

28
8. Appendix
Appendix 1: IMF classification of exchange rate regimes
List of 1998 Exchange Rate Arrangement Classifications: Exchange arrangement with no separate legal
tender, Currency board arrangement, Conventional pegged arrangement, Pegged exchange rate within
horizontal bands, Crawling peg, Crawling band, Managed floating with no predetermined path for the
exchange rate, Independently floating
List of 2008 Exchange Rate Arrangement Classifications: Exchange arrangement with no separate legal
tender, Currency board arrangement, Conventional pegged arrangement, Stabilized arrangement, Crawling
peg, Crawl-like arrangement, Pegged exchange rate within horizontal bands, Floating, Free floating, Other
managed arrangement
2008 Classifications Revised fine classification Revised coarse classification
No separate legal tender No separate legal tender
Hard peg
Currency board arrangement Currency board arrangement
Conventional pegged arrangement
Conventional pegged arrangement
Intermediate
Stabilized arrangement
Pegged within horizontal bands
Pegged within horizontal bands
Other managed arrangement
Crawling peg Crawling peg
Crawl-like arrangement Crawling band
Floating Managed floating
Floating
Free floating Independently floating

29
Appendix 2: List of control variables and sources used
 Inflation rate: percentage change in CPI. Source: International Monetary Fund (IMF),
International Financial Statistics (IFS)
 Real interest rate: nominal lending rate minus inflations. Source: IMF, IFS
 Real GDP/capita: Source: The World Bank, World Development Indicators (WDI)
 Real GDP growth: Source: WDI
 Volatility of real GDP growth: 3-year standards deviations of real GDP growth. Source: WDI
 Government surplus/GDP: Source: WDI
 Terms of trade change: change in the values of exports over imports. Source: WDI
 Volatility of terms of trade: 3-year standards deviations of terms of trade change. Source: WDI
 Real exchange rate change: Source: IMF, IFS
 M2/reserves: Money and quasi money (M2) to total reserves ratio. Source: WDI
 Cash/asset held by banks: Bank liquid reserves to bank assets ratio. Source: WDI
 Private credit/GDP: Domestic credit to private sector (% of GDP). Source: WDI
 Private credit growth: Source: WDI
 Private credit volatility: 3-year standards deviations of private credit growth. Source: WDI

30
Appendix 3: Assessment of the bipolar view using probit analysis
(2.1) (2.2) (2.3) (2.4) (2.5) (2.6)
Lag (inflation) 0.056 0.049 0.059 -0.008 -0.216 -0.282
(0.020)*** (0.027)* (0.028)** (0.028) (0.060)*** (0.119)**
Lag (real interest rate) 0.009 -0.004 0.001 -0.040 -0.454 -0.684
(0.011) (0.028) (0.027) (0.013)*** (0.103)*** (0.261)***
Lag (GDP/capita) -0.000 -0.000 0.000 0.000 -0.000 -0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Lag (GDP growth) -0.004 -0.004 -0.013 -0.036 -0.134 -0.113
(0.021) (0.020) (0.023) (0.028) (0.060)** (0.049)**
3-year volatility of GDP growth 0.104 0.109 0.122 0.127 0.285 0.309
(0.037)*** (0.038)*** (0.040)*** (0.052)** (0.114)** (0.148)**
Lag (govt surplus/GDP) 0.015 0.020 0.022 0.054 0.136 0.128
(0.031) (0.031) (0.032) (0.056) (0.063)** (0.091)
Lag (ToT change) -0.746 -0.851 -0.162 -1.218 -2.525 -2.642
(0.792) (0.796) (0.945) (0.913) (0.957)*** (1.352)*
3-year volatility of ToT 1.583 1.511 1.171 1.087 4.437 5.985
(0.976) (0.958) (0.884) (0.972) (1.873)** (3.215)*
Lag (real exchange rate change) 0.000 0.001 0.066 0.000 0.034 0.106
(0.001) (0.002) (0.024)*** (0.020) (0.010)*** (0.033)***
Lag (M2/reserves) -0.009 -0.008 0.105 0.192 0.748 0.861
(0.034) (0.028) (0.084) (0.043)*** (0.158)*** (0.375)**
Lag (banks’ cash/assets) -0.029 -0.030 -0.048 -0.014 -0.055 -0.086
(0.014)** (0.015)** (0.020)** (0.008)* (0.022)** (0.040)**
Lag (credit/GDP) -0.003 -0.005 -0.005 -0.080 -0.153 -0.203
(0.005) (0.004) (0.005) (0.020)*** (0.038)*** (0.068)***
Lag (real credit growth) -0.011 -0.022 -0.022 0.125 0.294 0.449
(0.010) (0.013)* (0.013)* (0.043)*** (0.071)*** (0.181)**
3-year volatility of real credit growth 0.032 0.044 0.037 0.241 0.279 0.318
(0.023) (0.024)* (0.024) (0.059)*** (0.074)*** (0.100)***
Lag (bipolar dummy) 0.020 0.037 0.112 -0.010 -1.096 -3.092
(0.352) (0.350) (0.630) (0.435) (0.627)* (1.685)*
Lag (real interest rate*financial lib. dummy) 0.012 0.009 0.413 0.635
(0.026) (0.026) (0.112)*** (0.260)**
Lag (real credit growth*financial lib. dummy) 0.027 0.028 -0.269 -0.321
(0.013)** (0.013)** (0.172) (0.244)
Lag (bipolar*ToT change) -1.539 -1.072
(1.119) (1.868)
Lag (bipolar*M2/reserves) -0.112 0.175
(0.088) (0.239)
Lag (bipolar*banks’ cash/assets) 0.019 0.031
(0.028) (0.039)
Lag (bipolar*real exchange rate change) -0.066 -0.102
(0.025)*** (0.059)*
Number of observations 443 443 443 356 356 356
* p<0.1; ** p<0.05; *** p<0.01

31
Appendix 4: Probit estimations of probability of crises using fine classifications
(3.1) (3.2) (3.3) (3.4)
Lag (inflation) 0.054 0.048 -0.009 -0.199
(0.021)*** (0.025)* (0.020) (0.060)***
Lag (real interest rate) -0.000 -0.012 -0.048 -0.445
(0.010) (0.021) (0.012)*** (0.118)***
Lag (GDP/capita) -0.000 -0.000 0.000 -0.000
(0.000) (0.000) (0.000) (0.000)
Lag (GDP growth) -0.007 -0.004 -0.047 -0.125
(0.025) (0.024) (0.034) (0.062)**
3-year volatility of GDP
growth
0.119 0.124 0.123 0.281
(0.041)*** (0.041)*** (0.046)*** (0.119)**
Lag (govt surplus/GDP) 0.030 0.036 0.036 0.106
(0.037) (0.036) (0.056) (0.055)*
Lag (ToT change) -0.662 -0.814 -1.104 -2.118
(0.814) (0.824) (0.909) (0.803)***
3-year volatility of ToT 1.925 1.850 1.744 5.212
(0.940)** (0.930)** (1.059)* (2.194)**
Lag (real exchange rate
change)
0.001 0.002 0.002 0.034
(0.001) (0.001) (0.003) (0.010)***
Lag (M2/reserves) -0.010 -0.008 0.253 0.781
(0.030) (0.026) (0.073)*** (0.162)***
Lag (banks’ cash/assets) -0.038 -0.040 -0.021 -0.057
(0.018)** (0.019)** (0.011)** (0.029)**
Lag (credit/GDP) -0.003 -0.004 -0.074 -0.148
(0.004) (0.003) (0.016)*** (0.033)***
Lag (real credit growth) -0.010 -0.021 0.126 0.313
(0.010) (0.012)* (0.042)*** (0.081)***
3-year volatility of real credit
growth
0.023 0.030 0.213 0.232
(0.017) (0.017)* (0.045)*** (0.077)***
Lag (conventional peg dummy) -0.843 -0.870 0.506 1.823
(0.587) (0.575) (0.863) (0.943)*
Lag (peg within horizontal
band dummy)
-0.424 -0.399 -0.036 0.912
(0.519) (0.521) (0.914) (0.858)
Lag (crawling band dummy) 1.120 1.136 1.547 3.842
(0.562)** (0.533)** (0.765)** (1.395)***
Lag (managed float dummy) -0.329 -0.373 0.607 0.600
(0.462) (0.460) (0.835) (0.971)
Lag(real interest rate*financial
lib. dummy)
0.014 0.409
(0.020) (0.133)***
Lag(real credit
growth*financial lib. dummy)
0.025 -0.310
(0.013)** (0.224)
Number of observations 349 349 270 270
* p<0.1; ** p<0.05; *** p<0.01

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