EC331 Submission 1306509 (1)

EC331 – Research in Applied Economics 1306509

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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: [email protected]

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.

mailto:[email protected]


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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


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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

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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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