(8)Currency Crises and Institutions

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Currency crises and institutions

Pattama L. Shimpalee a, Janice Boucher Breuer b,*

a Faculty of Economics, Chiang Mai University, Chiang Mai 52000, Thailandb Department of Economics, Moore School of Business, University of South Carolina,

Columbia, SC 29208, USA

Abstract

This study furthers recent literature on currency crises and institutions. The main objective is to re-

evaluate the causes of currency crises by focusing on the role played by a broader array of institutional

factors and crisis episodes than have previously been considered while at the same time controlling for

economic factors. Our dataset consists of over 30 countries covering 13 institutional factors for the period

1984e2002. Two questions are addressed. They are (1) what mix of institutions may contribute to or set

the stage for a currency crisis? and (2) what mix of institutions may affect the depth of currency crises asmeasured by a decline in output? Our findings reveal that institutional as well as economic factors affect

the probability of currency crises and that worse institutions are associated with bigger contractions in out-

put during the crisis. In general, our strongest results regarding institutions show that corruption, a de facto

fixed exchange rate regime, weak government stability, and weak law and order increase the probability of

a currency crisis. We find mixed evidence that deposit insurance, the removal of capital controls, a lack of

central bank independence, financial liberalization, and civil law increase the chance of crisis. We find

a similar set of factors worsens the contraction in output during a crisis except for deposit insurance, which

we find moderates the contraction in output.

2005 Elsevier Ltd. All rights reserved.

JEL classification:F3; F4

Keywords:Currency crises; Institutions

* Corresponding author. Tel.: 1 803 777 7400/7419.E-mail address: [email protected](J.B. Breuer).

0261-5606/$ - see front matter 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jimonfin.2005.10.008

Journal of International Money and Finance 25 (2006) 125e145www.elsevier.com/locate/econbase
mailto:[email protected]://www.elsevier.com/locate/econbasehttp://www.elsevier.com/locate/econbasemailto:[email protected]


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1. Overview of the issue

Are currency crises ordinary events? A review of international monetary history seems to

suggest perhaps so. From 1972e2002, there have been 169 currency crises across 56 countries.1

Currency crises have occurred in all but three of those years and have shocked developing,emerging, and developed countries alike, with the most recent examples being the Argentine

crisis of 2001e2002 and the Turkish crisis of 2001e2002. No country seems immune and in-

deed, the media portrays the United States as poised for its own currency crisis.

By definition, crises are bad outcomes. In economic terms, crises take a toll on countries in

terms of reduced income and increased unemployment. Corporate balance sheets are adversely

affected and bankruptcies may occur. Banks may become illiquid or worse, insolvent. Payments

of households and businesses become onerous and credit becomes circumscribed. Economic

growth is interrupted. As well, political and social unrest may be ignited. All in all, citizens

suffer. The economic, financial, political, and social fallout a currency crisis brings make un-

derstanding their causes critical.

A proliferation of theoretical and empirical papers investigating the causes and consequences

of crises as well as how to guard against them has been sparked by the cluster of currency crises

since the mid-1990s. Indeed, there has been much work devoted to predicting crises.2 Culling

extant studies of currency crises, a shortlist of economic factors contributing to currency crises

emerges. These include real exchange rate overvaluation, a lack of foreign reserve adequacy

relative to short-term debt or broad money, domestic credit growth, current account deficits,

poor export growth, and declining foreign reserves. While these studies show poor macroeco-

nomic fundamentals as causes of currency crises, they leave open the question of the role of

institutions. Weak institutions may contribute to poor macrofundamentals and hence predisposea country to sudden stops of capital. It is also possible that institutions, in addition to macroeco-

nomic fundamentals, may contribute to crises.

In two related papers,Alesina and Wagner (2003)andCalvo and Mishkin (2003) consider

the quality of institutions and exchange rate arrangements. Alesina and Wagner (2003)found

that countries with poor institutional quality related to the business environment and the so-

cio-political environment, have difficulty in maintaining an announced peg and are more likely

to abandon it. Calvo and Mishkin (2003),in re-assessing the debate over fixed versus floating

exchange rates, argue that deeper institutional features relating to fiscal stability, financial sta-

bility, and price stability are more important to macroeconomic stability and the avoidance of

crises than the exchange rate regime, per se.This study furthers the literature on currency crises and institutions. The main objective is to

re-evaluate the causes of currency crises by focusing on the role played by a broader array of

institutional factors and crisis episodes than have previously been considered, while controlling

for economic factors. Since good macroeconomic performance and good institutions often go

hand in hand, it is important to control for economic factors in order to correctly estimate the

1 Calculations based onBordo et al. (2001) and updated by authors.2

Contributions in this vein includeEichengreen et al. (1995), Sachs et al. (1996), Frankel and Rose (1996), Goldfajnand Valdes (1997), International Monetary Fund (1998), Milesi-Ferretti and Razin (1998), Berg and Pattillo (1999),

Bussiere and Mulder (1999), Kaminsky and Reinhart (1999), Rossi (1999), Aziz et al. (2000), Edison (2000), Goldstein

et al. (2000), Hawkins and Klau (2000), Johnson et al. (2000),Osband and Van Rijckeghem (2000), Kamin et al. (2001),

Block (2002), Ghosh and Ghosh (2002), Kumar et al. (2002), Martinez-Peria (2002), andMulder et al. (2002).

126 P.L. Shimpalee, J.B. Breuer / Journal of International Money and Finance 25 (2006) 125e145


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contribution of institutions to currency crises. Our dataset consists of over 30 countries covering

13 institutional factors for the period 1984e2002.

Two questions are addressed. They are (1) what mix of institutions may contribute to or set

the stage for a currency crisis, controlling for macroeconomic factors? and (2) what mix of in-

stitutions may affect the depth of currency crises as measured by a decline in output, controllingfor macroeconomic factors? Along the way, we offer hypotheses relating institutions to currency

crises.

Our findings reveal that institutional as well as economic factors affect the probability of

currency crises and that worse institutions are associated with bigger contractions in output dur-

ing the crisis. In general, our strongest results regarding institutions show that corruption, a de

facto fixed exchange rate regime, weak government stability, and weak law and order increase

the probability of a currency crisis. We find ambiguity over deposit insurance e in some cases,

deposit insurance increases the probability of currency crises and in others, reduces it. We find

mixed evidence that the removal of capital controls, a lack of central bank independence, finan-

cial liberalization, and civil law increase the chance of crisis. We find little evidence that bu-

reaucratic quality, ethnic tensions, external conflict, and internal conflict are important

factors in currency crises. We find a similar set of factors worsens the contraction in output dur-

ing a crisis except for deposit insurance, which we find moderates the contraction in output.

The outline of the paper is as follows: Section2reviews the literature on currency crises,

paying particular attention to the latest work on institutions. Section 3 presents the data and

the estimation methodology. Section4 presents the empirical results. Section5offers conclud-

ing remarks and directions for future work.

2. Background on models of currency crises

The question of what causes crises has been addressed with sequential generations of models

developed largely along historical lines e first, to explain the sovereign debt crisis of Latin

America, next to explain the European and Mexican crises, and on to explain the Asian crisis.3

In first generation models, poor macroeconomic fundamentals incite speculative capital out-

flows, which in turn generate a currency crisis. In second generation models, speculative capital

outflows are triggered when announced policy and the credibility that it can be maintained are

called into question. Third generation models are based on a boom/bust (overlending/overbor-

rowing) cycle and model currency crises as co-terminus with banking crises.

Fourth generation models are relatively new and introduce institutional factors (elsewheretermed social capital, social infrastructure, deep determinants) as determinants of currency

crises. In these models, weak institutions worsen problems associated with risk and uncertainty

and contribute to a misallocation of resources thereby setting the stage for currency crises.Ra-

jan and Zingales (1998) consider contract enforcement and the opportunity for malfeasance;

Bussiere and Mulder (1999)examine, e.g. political factors such as divisive and polarized parlia-

ments;Rossi (1999)considers capital account openness, bank supervision, and depositor safety;

Johnson et al. (2000)consider a number of variables including rule of law, judicial efficiency,

and corruption; Li and Inclan (2001) consider central bank independence, coordinated wage

bargaining, stock-market development, and more;Acemoglu et al. (2002) consider constraints

on the executive branch; Block (2002) considers the strength of the government, Ghosh and

3 SeeBreuer (2004)for a review.

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Ghosh (2002) consider governance, rule of law, creditor and shareholder rights; and Mulder

et al. (2002)consider among other factors, the legal regime, contract enforcement, and account-

ing standards.

Generally speaking, institutions affect currency crises through two causal mechanisms (Li

and Inclan, 2001). First, institutions tend to have an impact and correlate with the health ofthe national economy. Therefore, institutions that lead to bad economic fundamentals may con-

tribute to currency crises whereas institutions that help produce good economic fundamentals

remove one reason for currency crises to occur. Second, institutions are informative. Institutions

signal market agents about future economic fundamentals, and can thereby shape market ex-

pectations. Consequently, institutions that correlate with bad economic fundamentals destabi-

lize market expectations, increase market uncertainty about the likelihood of currency crisis,

and make currency crises motivated by speculative capital outflows more likely. On the other

hand, institutions that correlate with good economic conditions stabilize market expectations,

reduce market uncertainty about the probability of currency crisis, and make speculative capital

outflows less likely.

3. Data, hypotheses, and estimation methodology

We next present a set of hypotheses and an empirical model relating institutions to the prob-

ability of currency crises and the depth of contraction in output suffered during them. We then

discuss the data.

3.1. Hypotheses on institutional variables

Table 1presents the 13 institutional variables considered in our study. The third column of

the table presents the hypothesized (directional) effect of these institutions on the probability of

a currency crisis. We briefly summarize the institutions and offer hypotheses about each.

3.1.1. Bureaucratic quality

Bureaucratic quality measures the strength and quality of civil service and bureaucrats and

how able they are to manage political problems without interruption of services.4 A higher level

of bureaucratic quality means that government services and policies are less likely be interrup-

ted and/or altered and that agencies are less likely to be influenced by political pressure. Con-

sequently, there will be less uncertainty with respect to the conduct of government and lessuncertainty with respect to economic outcomes. With less uncertainty, capital outflows may

not be as subject to panic and herding. Thus, currency crises may be less likely to arise with

greater bureaucratic quality. Since a higher index value indicates greater bureaucratic quality,

the expected sign of bureaucratic quality is negative.

3.1.2. Government stability

A higher level of government stability means that a government is more likely to be able to

continue its announced programs and to stay in office. Factors such as type of governance, the

command of the legislature, and popular approval of policies are considered.4 Government sta-

bility consequently leads to less uncertainty as to what government policy toward businesses

4 Source: International Country Risk Guide. PRS Group.



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(banking and non-banking) will be in the future. With less uncertainty, there is less likely to be

a misallocation of resources and associated inefficiencies. So, government stability strengthens

the economy. Thus, a higher degree of government stability is less likely to lead to capital flightand thus it is less likely that currency crises will arise. Also, with a higher degree of government

stability and therefore less uncertainty, panic selling of currency may be less likely to arise.

Since a higher index value indicates more government stability the expected sign of govern-

ment stability is negative.5

3.1.3. Corruption

Corruption includes the demand for bribes connected with import and export licenses, ex-

change controls, tax assessments, police protection and loans. It also includes nepotism and cro-

nyism.4 Higher levels of corruption are more likely to lead to inefficient economic decisions

Table 1

Institutional variables: hypotheses and data sources

Variable Index value Effect on probability

of currency crisis or

depth of contraction

Data source Frequency

Bureaucratic quality 0e4, Higher values betterbureaucratic quality

Negative International Country

Risk Guide

Monthly

Government stability 0e12 Higher values moregovernment stability


Risk Guide

Monthly

Absence of

corruption

0e6, Higher values lesscorruption


Risk Guide

Monthly

Law and order 0e6, Higher values morelaw and order


Risk Guide

Monthly

Absence of ethnic

tensions

0e6, Higher values lessethnic tensions


Risk Guide

Monthly

Absence of external

conflict

0e12, Higher values lessconflict


Risk Guide

Monthly

Absence of internal

conflict

0e12, Higher values lessconflict


Risk Guide

Monthly

Exchange rate

regime

0, 1 Where 1 fixed rateregime

Ambiguous Reinhart and Rogoffs

(2002) de facto exchange

rate regime

Annual

Capital controls 0, 1 Where 1 capitalcontrols

Ambiguous Annual report on

exchange rate

arrangements and

restrictions, IMF

Annual

Central bank

independence

0e1 Where 1 maximum

independence

Negative Cukierman (1992)and

Cukierman et al. (1992)

By country

Deposit insurance 0, 1 Where 1 depositinsurance

Ambiguous Barth et al. (2001) By country

Financial

liberalization

Proxied by the real interest

rate on deposits

Ambiguous Constructed from

International Financial

Statistics

Monthly

Legal origin 0, 1 Where 1 civil law Ambiguous La Porta et al. (1998) By country

5 Since a higher index value for government stability may also be associated with autocratic regimes and an absence

of democracy and voice, it is possible that the expected sign could be positive.



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and to a greater misallocation of resources. Inefficiencies and misallocation of resources put the

economy at greater risk for suffering poor economic outcomes (be it in the banking sector,

manufacturing sector, government sector, export and import sector, etc.). Since poor economic

outcomes lead to capital flight, higher levels of corruption may increase the likelihood of cur-

rency crises. Corruption can also make more uncertain the outcome of contracts and transac-tions which may increase the propensity for panic selling of assets. Since a higher index

means less corruption, the expected sign of corruption on the probability of currency crises

is negative.

3.1.4. Law and order

Law and order measures the strength and impartiality of the legal system and popular obser-

vance of the law.4 A higher degree of law and order means that not only is there greater ob-

servance of the law by the populace but also that the judicial system is fair and impartial. Thus,

a higher degree of law and order also implies less uncertainty in all types of transactions. Con-

tractual obligations are more likely to be fulfilled according to the terms of the agreement and

the judicial system is more likely to settle cases fairly. By reducing uncertainty in transactions,

there is less likely to be a misallocation of resources and fewer inefficient outcomes. Thus, law

and order strengthens an economy in ways that may not be directly observable in macroeco-

nomic performance and makes it less likely to be subject to capital flight and currency crises.

Since a higher number indicates higher observance of law and order, the expected sign of law

and order is negative.

3.1.5. Ethnic tensions

Ethnic tensions are attributed to racial, nationality, or language division and gauge how in-tolerant groups might be to compromise.4 Ethnic tensions may affect the way in which contrac-

tual agreements between two parties of different ethnic backgrounds are arranged as well as

how likely they are to be abided. Ethnic tensions thus raise the level of uncertainty in the econ-

omy for all types of transactions related to commerce, borrowing and lending, government pol-

icy toward business, and so on, particularly in principal-agent relationships. A higher level of

ethnic tensions may thus increase the level of uncertainty in business and financial transactions.

This may lead to inefficient outcomes and/or outcomes that have a negative impact on the econ-

omy. Thus, capital flight and consequently currency crises may be more likely to arise in coun-

tries with greater ethnic tensions. Since a higher number indicates lower ethnic tensions, the

expected sign of this coefficient is negative.

3.1.6. External conflict

External conflict ranges from trade restrictions and embargoes to geopolitical disputes to in-

cursions, insurgencies, and warfare. A higher level of external conflict means that there is more

uncertainty in international transactions with the domestic country. A higher level of external

conflict thus may make a country more subject to capital flight and more likely to experience

currency crises. Since a higher index means lower external conflict, the expected sign of this

coefficient is negative.

3.1.7. Internal conflictInternal conflict is based on the extent of political violence toward the incumbent. A higher

level of internal conflict reduces the willingness of parties to abide by contracts and heed prop-

erty rights and thus raises the level of uncertainty in all types of transactions. A higher level of



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uncertainty leads to more inefficient outcomes and a greater misallocation of resources which

can weaken the economy. This can cause capital flight. The higher level of uncertainty can also

make capital flight more likely. Thus, currency crises may be more likely the higher the level of

internal conflict. Since a higher index indicates lower internal conflict, the expected sign of this

coefficient is negative.

3.1.8. Exchange rate regime

In the aftermath of the recent financial crises, a view emerged that the exchange rate regime

was in part responsible for the likelihood and the depth of these crises. In other words, a number

of emerging market economies have experienced devastating financial crises and macroeco-

nomic turbulence because they had kept exchange rates fixed. It is possible that an exchange

rate peg may encourage borrowers to ignore exchange rate risk. Thus, a fixed exchange rate

regime may contribute to macroeconomic instability and hence currency crises. A counterargu-

ment is that a fixed exchange rate regime promotes monetary discipline and thus improves long

run macroeconomic performance and helps stabilize expectations. We code fixed exchange rate

regimes with a 1, and 0 otherwise; however, the expected sign on this coefficient is ambiguous.

3.1.9. Capital controls

The classic view such as Greenwald et al. (1984), Quirk and Evans (1995), and Cooper

(1998)argues that free capital mobility enhances a more efficient allocation of resources raising

welfare in the process. Controls on inflows seem to hamper economic performance. Thus, cap-

ital controls may predispose a country to currency crisis. On the other hand,McKinnon and Pill

(1997)argue that it is also possible that the absence of controls encourages overlending and

overborrowing which can put a country at risk for a currency crisis. Thus, the expected signof capital controls is ambiguous. We code controls on capital account transactions, or restric-

tions on capital movements, especially inflows with a 1, and 0 otherwise.

3.1.10. Central bank independence

Central bank independence may improve real economic performance and the avoidance of

currency crises for several reasons. For example, an independent central bank that is free from

political pressure may behave more predictably, promoting economic stability and reducing

a risk premium in real interest rates. Moreover, to the extent that high inflation has adverse ef-

fects on economic performance by creating distortions, encouraging rent seeking activity, or

raising a risk premium, one would expect central bank independence to improve economic per-formance. In other words, central bank independence leads to lower inflation rates and greater

price stability; it contributes to the long run health of national economy and removes one im-

portant cause of speculative attacks against ones currency. Thus, the more independent a central

bank, the lower the probability of currency crises. Therefore, the expected sign of central bank

independence is negative. We use the index of legal central bank independence taken from

Cukierman (1992), andCukierman et al. (1992). It is coded on a scale between 0 and 1 where

0 stands for the minimum level of independence and 1 for the maximum level.

3.1.11. Deposit insurance

The effect of deposit insurance on currency crises is ambiguous. On the one hand, depositinsurance offered to banks reduces the downside risk of depositor losses and hence banking cri-

ses which in turn, decreases the probability of currency crises. On the other hand, deposit in-

surance offered to banks can set the stage for overborrowing/overlending which can lead to



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currency crises. Since we code countries with explicit deposit insurance protection systems with

a 1, and 0 otherwise, the expected sign of deposit insurance is ambiguous.

3.1.12. Financial liberalization

There are different opinions about the role of financial liberalization in currency crises.Some argue that financial liberalization increases the allocation efficiency of the financial sector

laying the foundation for sounder macroeconomic fundamentals. Consequently, financial liber-

alization should reduce the likelihood of currency crises. Others argue that financial liberaliza-

tion, by encouraging competition in the financial sector, can lead to imprudent lending practices

which lower the stability of the banking and corporate sectors and thus make currency crises

more likely. Consequently, the effect of financial liberalization on currency crises is ambiguous.

We use a real interest rate as a proxy for financial liberalization as in Rossi (1999). The real

interest rate can be interpreted a proxy for financial liberalization since real interest rates are

usually lower, or negative, in repressed financial systems (Rossi, 1999).

3.1.13. Legal origin

La Porta et al. (1998) argue that civil law countries afford less protection to creditors and

shareholders and thus increase financial sector instability and the propensity for currency crises.

On the other hand, in civil law countries, judicial decisions are determined according to a cod-

ified set of laws whereas in common law countries, cases are decided on precedent. Thus, civil

law countries may reduce risk and uncertainty and thus indirectly reduce the likelihood of cur-

rency crises. Consequently, the effect of legal origin (civil law) on currency crises is ambigu-

ous. Countries with a civil law system are coded as 1 and common law countries as 0.

The hypotheses relating institutions to the probability of currency crisis are the same regard-ing the depth of the currency crisis. That is, a worse set of institutions should correspond to

larger contractions in output during crisis.

3.2. Estimation methodology

The two questions we raised in Section 1 require different estimation strategies be em-

ployed. To answer the first question relating institutional factors to the probability of a currency

crisis, we use a multivariate probit. The basic equation is:

Pr

Currency Crisisi;t 1FXi;t1b

;

1

where Pr (Currency Crisisi,t) denotes the probability of a currency crisis in monthtin countryi,

and takes a value of 1 at the onset of the crisis and 0 otherwise. Xi, t1is a vector of explanatory

variables (at time t 1) partitioned to include economic factors and institutional factors; F isthe standard cumulative normal distribution. The panel of countries used in estimating Eq.(1)

includes those that experienced one or more crises over the sample period as well as some that

did not experience any crisis over the entire period. Consequently, we control for heteroskedas-

ticity and report heteroskedastic-consistentp-values.

The second question we address is whether economic and institutional factors systematically

affect the depth or severity of a currency crisis. To answer it, a measure of the depth or severityof a currency crisis must be constructed. We measure the depth of the crisis (DC) by an index of

a trend forecast of output (per capita) relative to actual output using annual instead of monthly

data. Increases in DC thus imply an increase in the depth of the crisis and allow us to interpret

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the coefficients on the institutional variables similarly to those hypothesized for the probability

of crisis. The equation we use is:

DCi;t b0 b1X1;i;t1 b2X2;i;t1 wi;t; 2

where DCi,t is the depth of the crisis. The subscripts i and tindicate the country and the crisis num-

ber for that country. For example, there are four crises in Indonesia in November 1978, April 1983,

September 1986, and August 1997. Thus, DCi,4 would indicate the fourth crisis for country i.

Note that since crisis events are not uniformly distributed over time, the tsubscript is not an

indicator of time (e.g. months or years). Thus, the data no longer have a time-series dimension.

Moreover, the panel used in estimating Eq. (2) includes only countries that have suffered a currency

crisis.X1,i,t1is a vector of economic variables and X2,i,t1is a vector of institutional variables.

The set of explanatory variables is the same as the set used for the probit models.

In order to test empirically the connection between institutional variables and the depth of

a currency crisis, controlling for economic factors, we estimate several specifications that differ

in their treatment of the error term. First, we use ordinary least squares (OLS). This is the sim-

plest treatment and does not admit the possibility that there are country-specific effects or that

the error terms across crises may be correlated.

To account for the possibility that the depth of the crises may differ by country, we use two

estimation methodologies that introduce country-specific effects. These are the random effects

estimator (RE) and the between estimator (BE), respectively. We cannot use fixed effect esti-

mation since several of our institutional variables are country-specific.

Third, we re-estimate Eq. (2)using what is best described as pseudo seemingly unrelated

regression (SUR). SUR estimation has the advantage of allowing error terms across observationsto be correlated. In our case, we allow the error terms across crises within a country to be cor-

related, rather than at a point in time as is usual with standard SUR applied to panel data. The

estimation strategy is not a straightforward application of SUR for a few reasons. First, the data

on the depth of the currency crises are neither cross-sectional nor time series. The data are ob-

servations on the size of the contraction in trend forecast of output per capita relative to actual

output per capita during a crisis. Since countries historical experience with crises differs by date

and number, these data do not have a true time-series dimension. Second, in the estimation, we

restrict the coefficient estimates to be identical across countries and crisis events. Standard SUR

with panel data permits the coefficient estimates to differ by cross-sectional unit.

Lastly, because the methods above may suffer from sample selection since only crisis epi-sodes were included in theestimation, we apply Heckmans two-step selection across both cri-

sis and non-crisis episodes.6 Naturally, this dramatically increases the sample size since there

are many years in which no crises are experienced by countries. In all estimations, heteroske-

dasticity has been corrected.

3.3. Data

Our data consist of monthly (and annual) observations from January 1984 to December 2002

for 35 or 44 countries, depending on data availability and the method used to date the crises,

discussed below. The starting date for the sample period was constrained by data availability on

6 We thank Roberto Rigobon for suggesting this.



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some institutions. Institutional variables are drawn from several sources as listed in Table 1.

The economic data are from the IMFs International Financial Statistics CD-ROM.

A few points about the data are worth noting. First, we identify a currency crisis using three

alternative techniques. Currency crises are identified followingEichengreen et al. (1995), Ka-

minsky and Reinhart (1999), and Frankel and Rose (1996). Eichengreen et al. (1995) use anindex computed from a weighted average of exchange rate changes, interest rate changes,

and foreign reserve changes to identify crises whereas Kaminsky and Reinhart (1999) use

a weighted average of exchange rate changes and foreign reserve changes. Frankel and Rose

(1996)use an index based on exchange rate changes only. Each of the techniques requires con-

structing an index of speculative exchange market pressure based on (log) exchange rate

changes, (log) foreign reserve changes, and/or interest rate changes. When the index breaches

a threshold value, the start date of a crisis episode (date) is identified. Since the exact identifi-

cation of crisis start datesis not always the same across techniques, we present results for all

three dating procedures.7

Second, the data on institutions from International Country Risk Guide are index values

where, in all cases, higher index values are associated with better institutions. For example,

the higher the score on corruption, the less corruption there is. The same applies to the data

on ethnic tensions, internal conflict, and external conflict. This is why we have modified these

variable names inTable 1with the term absence of..

Third, the exchange rate regime classification we use is the de facto classification produced

byReinhart and Rogoff (2002)instead of the IMFs de jure classification. The IMF classifica-

tion may misrepresent exchange rate arrangement type because it is based on the arrangement

type countries self-report to the IMF. These may be different from the exchange rate regim e

countries maintain in practice. Reinhart and Rogoffs data attempt to adjust for this problem.

8

Fourth, eight macroeconomic variables are also included in the estimation to control for

macroeconomic factors that may contribute to a currency crisis independent of institutional fac-

tors. The economic control variables included are domestic credit/real GDP, exports/real GDP,

foreign reserves/real GDP, the inflation rate, M2/foreign reserves, the real exchange rate, the

trade balance/real GDP, and the U.S. interest rate.

4. Currency crises and institutions: empirical results

In this section, the effects of institutions are measured in two dimensions e on the proba-

bility of currency crisis, and on the depth of contraction in output during the crisis. We turnto each of these next.

4.1. Institutions and the probability of currency crisis

Tables 2e4report results of the probit analysis applied to Eq. (1)using the three different

techniques for identifying currency crises (discussed in Section3.3). We report results across all

7 UsingEichengreen et al. (1995),we identify 67 crisis episodes across 11 countries in Asia, 15 in Europe, and 9 in

the Latin America; withKaminsky and Reinhart (1999),we identify 68 episodes across 11 countries in Asia, 16 in Eu-rope, and 17 in the Latin America; and withFrankel and Rose (1996), we identify 55 episodes across 11 countries in

Asia, 16 in Europe, and 17 in the Latin America.8 Based on Kaminsky and Reinharts crisis identification method, we identified 55 crisis episodes under a fixed ex-

change rate regime and 13 crisis episodes under a floating rate regime.

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Table 2

Probit estimates of probability of currency crisis, January 1984eDecember 2002 (Eichengreen et al. (1995) method)

Variable Full model

a

Models excluding some variables

a

(1) (2) (3) (4) (5)

Bureaucratic quality 0.115 (0.281) e 0.111 (0.290) 0.094 (0.363) e

Government stability 0.051* (0.067) 0.046* (0.071) 0.053* (0.066) 0.053* (0.066) 0.048* (0.074Absence of corruption 0.074** (0.023) 0.049** (0.045) 0.077** (0.023) 0.101** (0.015) 0.051** (0.04Law and order 0.067** (0.039) 0.055** (0.046) 0.061** (0.044) 0.065** (0.039) 0.051** (0.05Absence of ethnic

tensions

0.023 (0.694) 0.020 (0.743) e 0.031 (0.560) e

Absence of external

conflict

0.036 (0.344) 0.037 (0.321) 0.038 (0.309) 0.032 (0.408) 0.039 (0.296)

Absence of internal

conflict

0.019 (0.331) 0.015 (0.338) 0.012 (0.429) e 0.008 (0.534)

Exchange rate regime 0.301* (0.058) 0.256 (0.071) 0.312* (0.042) 0.240* (0.074) 0.266* (0.068)

Capital controls 0.316* (0.098) 0.326* (0.087) 0.309* (0.103) 0.235 (0.206) 0.320* (0.091Central bank

independence

0.076 (0.209) 0.053 (0.341) 0.079 (0.188) 0.068 (0.260) 0.056 (0.308)

Deposit insurance 0.196* (0.090) 0.195* (0.090) 0.210* (0.087) 0.097* (0.098) 0.123* (0.095Financial liberalization 0.004 (0.521) 0.003 (0.521) 0.002 (0.527) 0.001 (0.547) 0.004 (0.521)Legal origin 0.349* (0.094) 0.311 (0.126) 0.325* (0.104) 0.271 (0.187) 0.293* (0.135)

Number of countries 35 35 35 35 35

Number of crises 67 67 67 67 67

Number of observations 7980 7980 7980 7980 7980

Pseudo R2 0.406 0.393 0.384 0.377 0.382

Note: currency crises are identified using the Eichengreen et al. (1995)method and takes a value of 1 at the start of crises. The tab

by 100. Numbers in parentheses are p-values. A dash (e) denotes the variable was dropped from the analysis.

*Significant at the 10% marginal significance level; **significant at the 5% marginal significance level; ***significant at the 1a Eight macroeconomic control variables (see Section3.3) and year dummies are included in the estimation but not reported
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Table 3

Probit estimates of probability of currency crisis, January 1984eDecember 2002 (Kaminsky and Reinhart (1999)method)

Variable Full modela Models excluding some variablesa

(1) (2) (3) (4) (5)

Bureaucratic quality 0.025 (0.735) e 0.026 (0.731) 0.021 (0.773) e Government stability 0.054** (0.028) 0.056** (0.023) 0.054** (0.029) 0.051** (0.037) 0.056** (0.02Absence of corruption 0.086 (0.162) 0.094* (0.096) 0.082 (0.172) 0.094 (0.123) 0.091* (0.102Law and order 0.037** (0.035) 0.040** (0.030) 0.039** (0.035) 0.029** (0.047) 0.042** (0.03Absence of ethnic

tensions

0.013 (0.750) 0.014 (0.745) e 0.026 (0.527) e

Absence of external

conflict

0.001 (0.981) 0.001 (0.979) 0.000 (0.993) 0.006 (0.809) 0.000 (0.992)

Absence of internal

conflict

0.035 (0.270) 0.034 (0.276) 0.037 (0.219) e 0.037 (0.223)

Exchange rate regime 0.011** (0.052) 0.009* (0.055) 0.011** (0.053) 0.017** (0.050) 0.009* (0.056) Capital controls 0.058 (0.671) 0.047 (0.723) 0.066 (0.623) 0.036 (0.788) 0.056 (0.673)Central bank

independence

0.105* (0.077) 0.112* (0.065) 0.105* (0.077) 0.104* (0.077) 0.121** (0.05

Deposit insurance 0.060** (0.048) 0.068* (0.045) 0.046** (0.052) 0.034* (0.072) 0.053* (0.057Financial liberalization 0.019 (0.879) 0.022 (0.860) 0.010 (0.934) 0.004 (0.957) 0.013 (0.915)Legal origin 0.067* (0.055) 0.054* (0.068) 0.054* (0.068) 0.092** (0.035) 0.040* (0.071)



Number of observations 10,032 10,032 10,032 10,032 10,032

Pseudo R2 0.410 0.409 0.409 0.394 0.407

Note: currency crises are identified using the Kaminsky and Reinhart (1999)method and takes a value of 1 at the start of crisemultiplied by 100. Numbers in parentheses are p-values. A dash (e) denotes the variable was dropped from the analysis.

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Table 4

Probit estimates of probability of currency crisis, January 1984eDecember 2002 (Frankel and Rose (1996) method)

Variable Full modela Model excluding some variablesa

(1) (2) (3) (4) (5)

Bureaucratic quality 0.077 (0.350) e 0.076 (0.352) 0.073 (0.373) e Government stability 0.068** (0.019) 0.072** (0.012) 0.067** (0.020) 0.062** (0.029) 0.071** (0.012)Absence of corruption 0.089** (0.027) 0.113*** (0.012) 0.088** (0.027) 0.085*** (0.010) 0.118*** (0.010Law and order 0.050** (0.023) 0.063** (0.015) 0.052** (0.023) 0.027** (0.042) 0.064** (0.015)

Absence of ethnic

tensions

0.017 (0.713) 0.016 (0.719) e 0.001 (0.986) e

Absence of external

conflict

0.033* (0.077) 0.032 (0.117) 0.029 (0.180) 0.021 (0.183) 0.032* (0.104)

Absence of internal

conflict

0.059 (0.106) 0.059 (0.111) 0.056 (0.114) e 0.056 (0.119)

Exchange rate

regime

0.031* (0.085) 0.011* (0.097) 0.013* (0.094) 0.046* (0.074) 0.023* (0.097)

Capital controls 0.189 (0.226) 0.158 (0.301) 0.178 (0.245) 0.154 (0.317) 0.148 (0.324)Central bank

independence

0.029 (0.533) 0.035 (0.452) 0.030 (0.515) 0.014 (0.759) 0.036 (0.436)

Deposit insurance 0.235** (0.044) 0.209* (0.058) 0.224** (0.051) 0.285** (0.033) 0.199* (0.060)

Financial

liberalization

0.006* (0.057) 0.004* (0.057) 0.007** (0.054) 0.009** (0.052) 0.005* (0.057)

Legal origin 0.243 (0.203) 0.289 (0.120) 0.225 (0.222) 0.206 (0.276) 0.270 (0.129)



Number of

observations

10,032 10,032 10,032 10,032 10,032

Pseudo R2 0.407 0.392 0.404 0.384 0.370

Note: currency crises are identified using the Frankel and Rose (1996)method and takes a value of 1 at the start of crises. The tab

by 100. Numbers in parentheses are p-values. A dash (e) denotes the variable was dropped from the analysis.

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three strategies as a check on robustness. In each, the sample period covers January 1984 eDe-

cember 2002 and the equation is estimated with year dummies and eight economic control var-

iables listed in Section3.3. The macroeconomic variables are not included in the tables since

our focus is on the effect of institutions on the probability of currency crises. 9

Table 2presents the results usingEichengreen et al. (1995)s crisis definition.Table 3reportsresults following Kaminsky and Reinhart (1999)s crisis definition, and Table 4 reports results us-

ingFrankel and Rose (1996)s crisis definition. Country coverage varies by crisis definition with

35 countries in Table 2 and 44 countries in Tables 3 and 4. In each of the tables, results from seven

different specifications are shown. The seven different specifications are each pared down ver-

sions of the full (first) specification where several variables are excluded from the estimation if

they consistently turned out to be statistically insignificant across tables. Model (2) omits bureau-

cratic quality while model (3) omits ethnic tensions and model (4) omits internal conflict. Models

(5), (6) and (7) omit bureaucratic quality and ethnic tensions, bureaucratic quality and internal

conflict, and bureaucratic quality, ethnic tensions, and internal conflict, respectively.

Overall, the results suggest that controlling for economic factors, several institutions are asso-

ciated with an increased probability of currency crises. Looking across Tables 2e4,the results

uniformly show that corruption, a de facto pegged exchange rate regime, a worsening of govern-

ment stability and a worsening of law and order increase the probability of a currency crisis.10

These results are robust across the three techniques used to date crises. The results show some

differences regarding deposit insurance.Tables 2 and 3show that deposit insurance reduces the

likelihood of a crisis andTable 2 also shows some evidence that capital controls reduce the likeli-

hood of crisis although this result is not uniform across the seven specifications. Results fromTa-

ble 3also show that the probability of a currency crisis increases with a decline in central bank

independence and that civil law countries are more prone to crises than common law countries.In contrast toTables 2 and 3,Table 4shows that deposit insurance now raises the probability

of a currency crisis. Results fromTable 4also differ from those inTable 2by adding financial

liberalization as a significant factor in increasing the probability of a currency crisis. However,

the coefficient estimate has the wrong sign. The result may be due to the method used to iden-

tify currency crises. TheFrankel and Rose (1996)estimation method relies only on a criteria

based on exchange rate changes to date currency crisis episodes. Since countries typically em-

ploy interest rate increases to defend a currency in crisis, and because the financial liberaliza-

tion measure is the real interest rate, it is not surprising that the coefficient is significant. The

negative sign suggests that an interest rate defense can work to reduce the probability of crisis.

In all tables and specifications, there is not much support for the hypotheses that weakbureaucratic quality, ethnic tensions, external conflict, and internal conflict increase the prob-

ability of currency crises.

9 InTables 2e4,six of eight economic variables (not reported) are statistically significant at the 10% level or better.

ForTable 2, they are the ratio of domestic credit to real GDP, the ratio of exports to real GDP, the ratio of foreign re-

serves to real GDP, inflation rate, the real exchange rate, and the U.S. interest rate. M2/foreign reserves and the trade

balance/real GDP is not. For Table 3, M2/foreign reserves and the trade balance/real GDP gain significance and ex-

ports/real GDP and the U.S. interest rate lose significance. ForTable 4,exports/real GDP gain significance and the tradebalance/real GDP loses significance.10 Recall that corruption, government stability, and law and order are measured as indexes where increases imply an

improvement or better outcome. For example, an increase in the corruption index implies less corruption and thus

should reduce the probability of a currency crisis. So, the expected coefficient sign is negative.

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4.2. Institutions and the depth of currency crises

Tables 5e7report results from the estimation of Eq.(2), using the three different techniques

for identifying currency crises (discussed in Section3.3). Because construction of the depth of

crisis measure is limited by annual data, results reported in Tables 5e

7are based on annual-ized observations during crisis episodes only. As discussed in Section 3.3, the data are not panel

data. Again, we report results across all three strategies for identifying crises as a check on

robustness. In each, the equation is estimated with the same economic control variables (not

reported).11 We present results for four different estimation methods applied to Eq. (2).

These are OLS, random effects, between effects, and pseudo-SUR which were discussed in

Section3.2.

Table 5presents the results usingEichengreen et al. (1995)s crisis definition.Table 6reports

results followingKaminsky and Reinhart (1999)s crisis definition, andTable 7reports results

usingFrankel and Rose (1996)s crisis definition.Overall, the results suggest that controlling for economic factors, many of the same institu-

tions that increase the probability of a currency crisis also increase the depth of the crisis (as

measured by a loss in output relative to trend). Looking acrossTables 5e7, several institutions

are consistently associated with the depth of the crisis. These are corruption, deposit insurance,

government stability and law and order. More widespread corruption (measured by a decrease

in the corruption index), lower levels of government stability, and lower levels of law and order

increase the depth of the contraction in output during the crisis, whereas deposit insurance re-

duces the size of the contraction. The findings for deposit insurance further the debate over it;

we find that while deposit insurance increases the probability of a currency crisis, it moderates

the loss in output during the crisis.Results fromTables 5 and 6also show that civil law countries suffer bigger declines in out-

put than common law countries.Tables 5e7provide modest evidence that countries with more

independent central banks suffer smaller contractions in output during crisis. There is also mod-

est evidence that de facto fixed exchange rate regimes are likely to experience bigger contrac-

tions in output and that capital controls correspond to a smaller loss in output during a crisis.

Lastly, there is modest evidence that a lessening of ethnic tensions and internal conflict (a rise

in the index value) corresponds to a smaller decline in output.

5. Conclusions and directions for future research

The last two decades have seen a proliferation of currency crises among countries worldwide

with a reinforced interest in them among academicians and policymakers. However, only re-

cently has there been a major shift in focus away from macroeconomic fundamentals toward

understanding the deeper institutional determinants of crises. While crises are directly initi-

ated by large scale capital outflows, it begs the question what causes large scale capital out-

flows? Risk and uncertainty are certainly fundamental but this only further begs the question

what determines risk and uncertainty? The newest research answers with institutions. In-

stitutions are broadly conceived but in general can be framed as economic, financial, political,

legal, or social arrangements, explicit or implicit, that affect expectations, and hence risk and

11 See Section3.3.

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Table 5

Depth of contraction in output during crises (Eichengreen et al. (1995) method)

Variable Full modela

OLS Random effects Between effects Pse

Bureaucratic quality 0.007 (0.606) 0.007 (0.543) 0.024 (0.677) 0.0Government stability 0.037* (0.059) 0.057** (0.017) 0.016** (0.052) 0Absence of corruption 0.018* (0.084) 0.017* (0.074) 0.020* (0.077) 0Law and order 0.015* (0.077) 0.011* (0.090) 0.038* (0.059) 0Absence of ethnic tensions 0.001 (0.995) 0.002 (0.802) 0.004 (0.901) 0Absence of external conflict 0.001 (0.774) 0.005 (0.152) 0.013 (0.345) 0.0Absence of internal conflict 0.006 (0.286) 0.010** (0.040) 0.020 (0.499) 0

Exchange rate regime 0.005 (0.282) 0.019* (0.077) 0.021* (0.069) 0.0Capital controls 0.015** (0.037) 0.020** (0.044) 0.011* (0.080) 0Central bank independence 0.005 (0.954) 0.014* (0.064) 0.017 (0.960) 0Deposit insurance 0.073** (0.016) 0.081*** (0.003) 0.023* (0.079) 0Financial liberalization 0.002 (0.886) 0.003 (0.821) 0.001 (0.610) 0Legal origin 0.064*** (0.003) 0.068*** (0.001) 0.070*** (0.001) 0.0

Number of countries 28 28 28 28

Number of observations 67 67 67 67

R2 0.452 0.477 0.425 0.4

Note: dependent variable is a trend forecast of output per capita divided by actual output per capita during crisis episodes. See Se

estimation. Crisis episodes are identified using the Eichengreen et al. (1995) method.

*Significant at the 10% marginal significance level; **significant at the 5% marginal significance level; ***significant at the 1a Eight macroeconomic control variables (see Section3.3) are included in the estimation but not reported. See also Footnote
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Table 6Depth of contraction in output during crises (Kaminsky and Reinhart (1999)method)


OLS Random effects Between effects Pse

Bureaucratic quality 0.007 (0.300) 0.004 (0.463) 0.013 (0.464) 0Government stability 0.021** (0.052) 0.031* (0.062) 0.025* (0.065) 0Absence of corruption 0.045** (0.024) 0.028** (0.040) 0.057** (0.045) 0Law and order 0.005* (0.091) 0.004* (0.069) 0.013** (0.053) 0Absence of ethnic tensions 0.003 (0.237) 0.004 (0.239) 0.005 (0.343) 0.0

Absence of external conflict 0.003 (0.168) 0.000 (0.970) 0.006 (0.145) 0Absence of internal conflict 0.001 (0.491) 0.002 (0.887) 0.004 (0.915) 0

Exchange rate regime 0.005 (0.282) 0.019* (0.077) 0.021* (0.069) 0.0

Capital controls 0.006* (0.065) 0.003 (0.660) 0.011 (0.695) 0Central bank independence 0.029* (0.083) 0.037* (0.072) 0.016* (0.092) 0Deposit insurance 0.015* (0.083) 0.022** (0.046) 0.048** (0.038) 0Financial liberalization 0.007 (0.253) 0.005 (0.232) 0.003 (0.274) 0Legal origin 0.028** (0.016) 0.033*** (0.013) 0.034*** (0.011) 0.0

Number of countries 38 38 38 38


R2 0.471 0.548 0.490 0.4


estimation. Crisis episodes are identified using the Kaminsky and Reinhart (1999) method.

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Table 7

Depth of contraction in output during crises (Frankel and Rose (1996) method)


OLS Random effects Between effects Pseu

Bureaucratic quality 0.011 (0.421) 0.008 (0.463) 0.020 (0.636) 0.Government stability 0.038* (0.089) 0.045** (0.053) 0.054** (0.041) 0.Absence of corruption 0.012*** (0.011) 0.027** (0.045) 0.032** (0.037) 0.Law and order 0.011* (0.078) 0.015* (0.079) 0.039** (0.047) 0.Absence of ethnic tensions 0.005 (0.923) 0.009 (0.897) 0.003 (0.839) 0.Absence of external conflict 0.002 (0.662) 0.007** (0.047) 0.005 (0.548) 0.Absence of internal conflict 0.008 (0.864) 0.004 (0.305) 0.004 (0.722) 0.

Exchange rate regime 0.018* (0.064) 0.007* (0.073) 0.009* (0.069) 0.01Capital controls 0.002 (0.906) 0.016* (0.061) 0.015* (0.075) 0.00Central bank independence 0.110* (0.096) 0.075 (0.506) 0.169 (0.631) 0.Deposit insurance 0.008* (0.058) 0.018** (0.024) 0.014** (0.029) 0.Financial liberalization 0.006* (0.081) 0.012* (0.085) 0.017* (0.079) 0.00Legal origin 0.011 (0.621) 0.018 (0.446) 0.015 (0.923) 0.03Number of countries 31 31 31 31


R2 0.422 0.441 0.453 0.41


estimation. Crisis episodes are identified using the Frankel and Rose (1996) method.

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uncertainty, in transactions. Their effects may be heightened for international transactions,

making them particularly important to understanding currency crises.

We find the notion institutions matter as plausible and so undertake research that seeks to

determine how so with regard to understanding currency crises. We consider 13 institutional

factors ranging from law and order to legal origin to ethnic tensions to deposit insurance to cen-tral bank independence and investigate their effects on the probability of currency crises. How-

ever, we also control for macroeconomic factors which are widely accepted as playing some

role in the genesis of crises, or may themselves depend on institutions. We also consider

how institutions affect the depth of the contraction in output during a crisis. The set of institu-

tions we consider is longer than in previous studies and we offer hypotheses about each. Our

work also covers a wider set of countries ranging from developing to developed and those

with fixed and floating exchange rates.

Our findings offer evidence that institutions, in addition to macroeconomic factors, do indeed

matter to the onset of currency crises and the contraction in output that ensues. Using a multi-

variate probit analysis, we find consistent support that the following institutions increase the

probability of currency crisis: less stable governments; weak law and order; more widespread

corruption; and a de facto fixed exchange rate regime. There is some ambiguity over the results

regarding deposit insurance which vary with the type of method used to identify crisis episodes.

We find more modest support that capital controls and central bank independence reduce the

likelihood of crises, and that civil law countries are more prone to crises. We find little to no

evidence that bureaucratic quality, ethnic tensions, external conflict, or internal conflict matter

to currency crises. Using panel estimation techniques, we find, for the most part, that these

same factors worsen the contraction in output during the crisis, save for deposit insurance

which reduces the contraction.Several other questions remain open for future research. First, the out-of-sample perfor-

mance of the probit models is not tested in this study. This is left for future research. Second,

we think it is possible that nonlinearities are important. We wonder whether the breaching of

certain threshold levels for institutions, separately or jointly, increases theprobability of crisis.

Another issue that could be explored is the duration of fixed rate systems.12 The importance of

economic and institutional factors to explaining how long a country is able to maintain a fixed

rate may be worthwhile to investigate.

Acknowledgements

Thanks to participants at the JIMF/CRIF/TAFI conference in San Juan, Puerto Rico and

especially to Roberto Rigobon. Research generously supported by a grant from the Center

for International Business Education and Research.

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