Duration of fixed exchange rate regimes in emerging economies

Journal of International Money and Finance 37 (2013) 439–467

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Journal of International Moneyand Finance

journal homepage: www.elsevier .com/locate/ j imf

Duration of fixed exchange rate regimes inemerging economies

Unay Tamgac*

Department of Economics, TOBB Economy and Technology University, Sö�gütözü Cad. No: 43, Sö�gütözü,Ankara 06560, Turkey

JEL classification:E42F31F33

Keywords:Exchange rate regimeSurvival analysisDurationEmerging economies

* Tel.: þ90 312 292 4544; fax: þ90 312 292 410E-mail addresses: [email protected], unaytam

0261-5606/$ – see front matter � 2013 Elsevier Lthttp://dx.doi.org/10.1016/j.jimonfin.2013.06.015

a b s t r a c t

This paper examines the duration of fixed exchange rate regimesand investigates whether there is a certain pattern of timedependence in the survival rate of pegged exchange rate regimesfor emerging economies. We query why some fixed regimes lastlonger and determine the macroeconomic, social and politicalfactors that make a pegged regime more durable. We use survivalanalysis, a technique which accounts for the unobservable cumu-lative effects associated with maintaining a fixed exchange ratethat build up over the duration of a regime. In our model, timeenters as a proxy for these unobserved persistent effects as weinvestigate the relative importance of the fundamentals in theeconomy on regime durability by considering their relationtogether with the effect of time itself. Using the de facto exchangerate regime classification proposed by Reinhart and Rogoff (2004),we find non-monotonic duration dependence and show that timeis a significant factor for the duration of pegged regimes. Open-ness, changes in foreign reserves, growth, real exchange ratemisalignment, claims on government and sociopolitical instabilityare also found to influence the pegged regime duration.

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1. Introduction

Exchange rate regime choice is one of the most important policy issues in international finance. Avast literature analyzes the advantages and disadvantages of one exchange rate arrangement over

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U. Tamgac / Journal of International Money and Finance 37 (2013) 439–467440

another, and whether exchange rate regime choice matters.1 However, when it comes to choosing thebest exchange rate regime there still is no one size fits all answer and exchange rate regime choiceremains a central question (Frankel, 1999). Since the collapse of the Bretton Woods System of FixedExchange Rate Regimes, countries have tried various exchange rate arrangements. Whether a volun-tary decision of a regime change or an inevitable result of external factors which make the regimeunsustainable over time, there have been many switches between fixed and floating regimes over thepast couple of decades. Since 1970 emerging countries on average have experienced the highestnumber of regime switches. They have made many attempts to adopt a fixed exchange rate regime,mostly either to import credibility, fight inflation, or encourage trade. However, in many cases the exitsfrom fixed exchange rate regimes have been dismal with large output losses following currency crises.2

The international financial crisis in the 1990s in Latin American and the East Asian crises havestarted a debate about the sustainability of fixed exchange rate regimes. Contrary to the view thatintermediate regimes are vanishing, today about 40 percent of emerging countries have intermediateregimes. Together with harder peg regimes about 70 percent of the regimes in the emerging economieshave some sort of a fixed regime. This high percentage shows that fixed exchange rate arrangementsstill prevail for emerging economies.

One striking example of fixed exchange rate regimes is that of China, which has been operatingunder an officially pegged exchange rate system where the value of the Renminbi is controlled by theChinese Central Bank since 1994. The Chinese exchange rate policy has been a source of controversyand intense debate in recent years. While the continuation of the policy is in the hands of the Chineseauthorities, the interaction of economic considerations, international politics, and constituent interestwill play a role in the final decision. While China poses a success story for fixed exchange rates, thesustainability of fixed exchange rate regimes is still questionable and emerging economies still face thedecision of whether to float or fix.

In this study, we consider fixed exchange rate regimes from a different angle by analyzing regimesustainability and the factors that affect regime duration (defined as the time that a particular regimehas been in place). Considering the failure of many fixed regimes in the 1980s and 1990s and thesuccess of countries like China and Hong Kong we question the factors that affect the continuation ofpegged regimes. Specifically what are the characteristics that make a pegged regime durable over timeor cause its collapse?

Sustainability of the regime affects not only the regime choice initially, but also the subsequenteconomic outcomes. Husain et al. (2005) find that regime durability is associated with superior eco-nomic performance. Aizenman and Glick (2008), on the other hand, shows that the cost of an exit froma peg increases with the duration of the peg before the crisis. In this regard, the factors that lead to thesustainability of the regime are an important consideration. However, despite this importance, fewstudies analyze the duration of exchange rate regimes or address questions of what factors play a rolein the sustainability of a regime.

This paper intends to shed light on the sustainability of fixed exchange rate regimes for emergingmarket economies. Besides the basic question of why some fixed exchange rates last longer and thedetermination of the factors that make a pegged regime more durable, an important point we addressand that has often been neglected in the literature is whether the duration of the regime is itself asignificant factor influencing its collapse.

Country characteristics, external financial conditions, and macroeconomic factors are importantcandidates that affect regime duration. Specifically some macroeconomic indicators such as deterio-ration in the current account or a large amount of foreign borrowing may force the country to abandona fixed exchange rate regime towards more flexible arrangements. However, besides such funda-mentals some unobservable cumulative effects associated with maintaining a fixed exchange rateregime may be in play. These unobservable characteristics may not affect survival initially, or may notbe critical at a specific time point, but can cumulate over time and eventually cause a fundamental

1 See Bordo (2003).2 To name only the most spectacular cases, Mexico in 1994; Thailand, Republic of Korea, and Indonesia in 1997; Russia in

1998; Brazil in 1999; and Turkey and Argentina in 2001.

U. Tamgac / Journal of International Money and Finance 37 (2013) 439–467 441

imbalance that affects sustainability. Regime credibility is one such factor that is hard to measure, thatvaries over the lifetime of the regime, and may have significant effect over regime duration.

The existence of such unobservable factors poses a challenge in modeling regime duration. Anyanalysis that does not consider them as a separate covariate would be biased. Since these persistenteffects build up over time, their effect should be proportional to the time the regime has been effective.Time, specifically regime duration, can be used to account for these unobserved factors. Thus, in ouranalysis we measure the effects of economic, social, and political factors on regime durability byconsidering their relation together with the effect of time itself.

Time within a particular exchange rate regime could be important for eventually undermining theregime’s stability for several reasons. As Husain et al. (2005) point out, regimes that last longer pre-sumably do so because macroeconomic policies are maintained in a consistent manner over time. Inthat regard, the regime’s duration can be seen as a proxy for the consistency of the macroeconomicstance with respect to the exchange rate regime. If consistency is a factor that increases the sustain-ability of the regime, long lasting regimes should be associated with higher survival rates. On the otherhand, Drazen and Masson (1994) show that there may be some persistent effects, associated withmaintaining an announced policy over time, which may reduce the likelihood to carry the announcedpolicy further. The longer a pegged regime has been effective, the more will be the accumulation ofthese persistent effects, so that it becomes harder to sustain the regime. In such a setting, long lastingregimes should increase the probability of abandoning the regime, and we should expect a negativeassociation between the time the regime has been effective and the subsequent foreseen duration.

In our analysis, besides determining the factors that affect regime duration, we test the significanceof these unobservable cumulative effects by analyzing duration dependence. In particular, we testwhether there is a certain time dependence of the durability of fixed exchange rate regimes; i.e.whether the time spent on the regime, after controlling for explanatory variables, plays a significantrole for the continuation of the regime.

The specific nature of time makes it problematic to use standard regression techniques to estimatethe effect of time and some other time varying covariates on the duration of exchange rate regimes. Weaccount for this problem using a specific technique, namely survival analysiswhose strength lies in theexplicit modeling of time-dependence, along with the inclusion of explanatory variables that changeover time.3

Every model in survival analysis assigns a role to time. By doing so, time is used as a proxy for theeffects that cannot be fully understood, cannot be measured, or are unknown (Cleves et al., 2008). Inour analysis, time enters as a proxy for unobserved regime characteristics that affect regime durabilityover time. Survival analysis allows us to jointly look at the underlying factors that affect regimedurability and the time the regime has been effective. Using survival analysis we test to what extendthe regime duration depends on the fundamentals in the economy and how regime sustainabilityevolves after accounting for explanatory factors that affect regime duration.

We first analyze how the survival experience differs across economies, and whether there is certainduration dependence in the survival rate. The Reinhart and Rogoff (2004) de facto exchange rateclassification is used to analyze the exchange rate regime duration of different countries. Then wemodel the pegged regime sustainability for emerging economies using economic and financial vari-ables via a Cox (1972) proportional hazards estimate. Our analysis reveals the existence of non-monotone duration dependence for emerging economies in which the sustainability of a peggedregime initially decreases and then increases by time.4

We start with a literature review in Section 2, and follow with a discussion on the different ex-change rate regime classifications in Section 3. Following the analysis of exchange rate regime durationin Section 4, there is a methodological discussion on survival analysis in Section 5 and a discussion of

3 Survival analysis and duration analysis will be used interchangeably throughout the paper.4 Duration dependence shows how the probability of abandoning a fixed exchange rate regime is affected by the duration of

the peg. Positive (negative) duration dependence means that probability of abandoning a regime increases (decreases) with theduration of the peg. Non-monotone duration dependence means the probability alternates between positive and negativeduration dependence.


the explanatory variables in Section 6. After the estimation results in Section 7, the paper concludes inSection 8.

2. Literature review

Klein andMarion (1997) are the first to introduce the duration of the exchange rate peg as a factor toaffect the probability of devaluation. In their study of exchange-rate pegs in 16 Latin Americancountries and Jamaica for the period 1957–1991, they find some evidence of duration dependence withthe likelihood of devaluation first rising and then falling during the first year of a peg. In another study,Wong and Leung (2005) analyze how the duration of exchange rate pegs can be explained for Asiancountries. Using the Reinhart and Rogoff (2004) de-facto regime classification for a large sample ofcountries over 1980–2001, Detragiache et al. (2005) find some evidence that less well-establishedregimes are more likely to be abandoned.

These previous studies have employed probit/logit analysis to estimate the impact of differentfactors on the probability of exit from a regime. The problem with logit and probit models is theimplicit assumption that the probability of leaving the peg stays constant over time. To avoid thislimitation, some studies have used time dummies to account for duration dependence. However,another problem is that the predictor variables are usually measured at a specific time point, andthere may be cases when the event occurs while the subject is not under observation, which isreferred to as “censoring”. The studies that use the standard regression techniques estimate theregime duration considering only the regimes that are terminated by the end of the study period, i.e.excluding the right censored regime spells. This creates a bias towards obtaining shorter peg du-rations, and makes it impossible to obtain unbiased estimates using the standard techniques.Because of such censoring, even the simplest analysis will break down when applied to survival data(Cleves et al., 2008).

This problem has been addressed in the literature by the use of a specific technique, namely survivalanalysis whose strength lies in the explicit modeling of time-dependence, and the inclusion ofexplanatory variables that change over time. Survival analysis is capable of dealing with the censoringproblem, which is frequently observed with regime spells. Another basic reason for the use of survivalanalysis is that the distributions for time to an event are almost certainly nonsymmetrical, and linearregressions are not robust for these violations. The regular OLS estimates rely on the normalityassumption of the residuals. When time is the independent variable, the normality assumption re-quires time conditional on the explanatory variables to follow a normal distribution.5 This assumptionof normality of time to an event is unreasonable for many events (Cleves et al., 2008).

A related study by Tudela (2004) uses survival analysis to explain the origins of currency crises byrelating their occurrence to realizations of explanatory variables and to the duration pattern of the non-crisis periods.6 Blomberg et al. (2005) is one of the initial studies that apply survival analysis to test fortime dependence in exchange rate arrangements. They find substantial evidence for negative durationdependence for Latin American pegs after a few months the regime has been effective. Setzer (2005)focuses on the political and institutional determinants of pegged regime duration. He studies a sampleof developing countries where the exit from a peg is defined according to the Levy-Yeyati andSturzenegger (2004) regime classification and by a speculative attack.

In another study, Wälti (2005) finds that developed economies exhibit almost no durationdependence, while emerging economies have a non-monotonic behavior of duration dependence, witha higher hazard rate. However, excluding censored observations, he finds positive duration depen-dence for both country groups, though more pronounced for developed economies. In a more recentstudy, Klein and Shambaugh (2008) find significant negative duration dependence for both peggedand floating regime spells, however they do not consider the factors that affect regime duration.

5 In an OLS model, where time is the dependent variable and xj the vector of explanatory variables, the estimated model istimej ¼ b0 þ b1xj þ εj, εj w N(0, s2) and the implicit assumption is that timej w N (b0 þ b1xj, s2).

6 The analysis applied for OECD countries for the period 1970–1997 shows the existence of highly significant negativeduration dependence.


The Weibull model in their study implicitly assumes monotone hazard rates and this contradicts theprevious findings of non-monotone hazard rates for exchange rate regime duration.

Previous studies not only differ in terms of country coverage andmethodology but they also havemixedresults on regime duration. Among the fewstudies that have analyzed regimeduration, nonehas a focus onemerging economies. Wälti (2005), analyses the regime duration for emerging and advanced economies,whereas Setzer (2005) looks for the duration of pegged regimes for developing and emerging countries.

Our analysis is focused on emerging economies because they have similar economic structures,similar challenges in the conduct of monetary policy, and who as a group have experienced manyregime switches. In the past decades, different from other groups, emerging economies have facedextreme episodes of monetary instability, ranging from very high inflations, to extensive capital flight,to financial system collapses. Husain et al. (2005) point out that exchange rate regimes have steadilybecome less durable since the mid-1970s, and that emerging markets experience the most instability.In advanced economies however, durability has increased. Thus, these two country groups representdifferent patterns of exchange rate durability and have to be treated separately.

In estimating the factors that affect regime duration, the neglect of the differences between countrygroupswouldbias the results. Countrygroupsdiffer in terms of their levels of institutional andeconomicdevelopment, the depth of their financial markets as well as the stability of their macroeconomicenvironment. As the country characteristics and level of development differs among country groups, sodo their objectives and challenges in the conduct of monetary policy (Mishkin and Savastano, 2002).

In that regard the focus of this study is explicitly on emerging economies, the group of countriesthat have experienced the highest number of regime switches, and for which the regime durabilityposes an important challenge in the conduct of monetary policy. We did not consider other lessdeveloped countries because they have not yet reached the same stage of financial and economicdevelopment as the emerging economies. Another reason is that emerging economies have a distinctshape of the distribution of regime duration. There are many pegs that last for less than five years andfew peg spells that survived more than fifteen years. We do not observe this pattern in other countrygroups; instead, we observe more evenly distributed regime durations. We did not look into specificcountry groups like the CFA countries for the same reason.7

Another significant difference between the previous studies and our analysis is that instead oflooking at the behavior of the exchange rate itself we are considering the long-term behavior of theexchange rate regimes from a policy standpoint. Our intention is to determine how time plays a role inthe continuation of a fixed exchange rate regime policy. The question of interest is whether thecontinuation of the regime is a signal for its durability or whether longer lasting regimes become morefragile. One time realignments or changes in the value of the peg do not necessarily constitute a policychange. Therefore, we focus on the general de facto exchange rate policy of the country rather than acertain path of the exchange rate. In that regard the determination of the appropriate exchange rateregime classification becomes an important consideration and will be explained below in Section 3.

3. Exchange rate regime classification

Our interest is on the actual behavior of the monetary authority and the foreign exchange market.Therefore, the first consideration is to use a de facto measure of exchange rate behavior rather than thede jure classification. A second consideration is to look for the long-term behavior of the exchange ratefrom a policy perspective.

The most commonly used de facto classifications are by Ghosh et al. (2003), Levy-Yeyati andSturzenegger (2004) (henceforth, LYS), Reinhart and Rogoff (2004) (henceforth, RR), and more recentlyby Klein and Shambaugh (2008). There is considerable variation among these measures, which hasbeen the subject of several studies. While Husain et al. (2005) study the performance of differentexchange rate regimes; Klein and Shambaugh (2008) also provide a comparison of de facto classifi-cation schemes. As they state, the differences in the classifications schemes is not simply a difference in

7 In addition, the CFA countries in particular have not experienced exits from fixed exchange rate regimes. They have haddevaluations but not regime switches to a floating regime.


measure pegs, but it is due to their efforts to address different questions. Thus, the determination of theright de facto classification depends on the question being posed.

Ghosh et al. (2003), develop a hybrid classification using the intersection of the de jure and a defacto classification based on the observed exchange rate behavior.8 The LYS classification discards thede jure classification altogether, and uses cluster analysis to determine the de facto flexibility of ex-change rate regimes according to the behavior the changes in the nominal exchange rate, the volatilityof these changes, and the volatility of reserves one year at a time.9 The Klein and Shambaugh (2008)classification considers a country on a fixed regime if the end of month official bilateral exchangerate stays within the same �2 percent band for the entire year.

One problem with classifications that rely on the actual behavior of the exchange rate is the pos-sibility of classifying a floating regime as pegged due to an absence of shocks. Also in most of theclassifications, a one-time discrete devaluation (or a repeg) is considered a change in the regime whenin effect there is no direct change in the exchange rate policy.10 The RR classification is the only clas-sification to distinguish between exchange rate stability due to the absence of economic or politicalshocks, and stability as a result of the planned policy. The RR classification views themonthly exchangerate behavior as part of a larger, continual regime and uses a volatility measure based on a five-yearmoving horizon supplemented by country chronologies to classify regime.

The use of a five-year window to measure the true flexibility of the regime helps to distinguishbetween longer-term “regimes” and shorter term “spells” within a regime, such as the widening of ahorizontal band or a one-time devaluation followed by a re-peg (Husain et al., 2005). It avoidsrecording exchange rate movements within a year due to one-time events (a one time devaluation orrealignment) or a political shock as a regime change. As Reinhart and Rogoff (2004) note, it is unlikelyto have no shocks over a five-year span and if the exchange rate is treated as stable in the RR classi-fication, it is typically not due to an absence of shocks.

Another basic distinction of the RR classification is the inclusion of market determined parallelexchange rate data, which prove to be a better indicator of the monetary policy stance than the officialrate, and are an important indicator of the prices underlying a significant share of economic trans-actions (Reinhart and Rogoff, 2004). By considering the parallel market for exchange rates, the RRclassification captures the real prices in the economy and provides an adequate de facto measure forthe study of exchange rate regimes.

Developed to study the evolution of policy regimes, RR classification best serves our purpose ofstudying regime duration fromapolicy standpoint. It is also the only classification available at amonthlyfrequency. High frequency data allows us to capture regime changes that occur within a year, whichcannot be detected at an annual frequency. However, because of limited availability of some variables atamonthly frequency, weuse the quarterly RR classification for the analysis of pegged regime duration.11

4. Survival of fixed exchange rate regimes

In the RR classification, two classifications are provided: the fine classification that classifies coun-tries into fifteen categories and the coarse classification that is aggregated into six boarder categories(Table 1).

8 They construct the de facto classification by ranking countries on an annual measure based on the mean and the volatilityof the monthly nominal exchange rate changes, and mapping this continuous measure into three categories as pegged, in-termediate and floating regimes, with the same proportional distribution as the de jure classification for that year. Observationsfor which the de jure does not match with the de facto classification are dropped out.

9 One handicap of the cluster analysis techniques used in the LYS classifications is the breaks in the regime series due to theinconclusive categories in the exchange rate classification.10 For example in the Klein and Shambaugh (2008) classification one time discrete devaluations (one month with a change inthe exchange rate greater than 2 percent, but the following 11 months exactly equal to zero) are not considered as pegs.11 Another characteristic of the RR classification is the introduction of “freely falling” as a new category. Reinhart and Rogoff(2004) state that “.regimes associated with an utter lack of monetary control and the attendant very high inflation should not beautomatically lumped under the same exchange rate arrangement as low inflation floating regimes.” Thus cases where thetwelve month inflation rate is equal to or exceeds 40 percent per annum are classified as “freely falling”.

Table 1RR classification scheme.

Fine classification Coarse classification

Code Description Code Description

1 No separate legal tender 1 Peg2 Pre announced peg or currency board arrangement 1 Peg3 Pre announced horizontal band narrower than or equal to �2% 1 Peg4 De facto peg 1 Peg5 Pre announced crawling peg 2 Limited flexibility6 Pre announced crawling band narrower than or equal to �2% 2 Limited flexibility7 De factor crawling peg 2 Limited flexibility8 De facto crawling band narrower than or equal to �2% 2 Limited flexibility9 Pre announced crawling band wider than or equal to �2% 2 Limited flexibility10 De facto crawling band narrower than or equal to �5% 3 Managed floating11 Moving band narrower than or equal to �2% (allows for both

appreciation and depreciation over time)3 Managed floating

12 Managed floating 3 Managed floating13 Freely floating 4 Freely floating14 Freely falling 5 Freely falling15 Dual market in which parallel market data is missing. 6 Dual market


Although in the 1950s and 1960s, the world was dominated by fixed exchange rate regimes, sincethe collapse of the Bretton Woods System, countries moved towards more flexible arrangementsthrough the 1970s and 1980s. Since the beginning of the 1990s, there has been a noticeable increase infixed regimes (coarse categories 1 and 2) along with a decline in the freely falling regimes (coarsecategory 5).12 Limited flexibility (coarse category 2) andmanaged float regimes (coarse category 3) sawrise in the 1970s and since 1975, 20–28 percent of the countries have fallen into one of these twocategories. Besides these common trends, there is great heterogeneity of the exchange rate regimesacross decades and across economies.

Since 1970, in emerging countries faced the highest number of regime switches. The highestnumber of regime switches was in the 90s for all country groups except advanced countries thatexperienced the most number of switches in the 70s. For the period of 1970–2007, emerging econo-mies adopted half as many more different regimes on average than advanced economies.13 The dif-ference is most pronounced in the 80s when on average an emerging economy had 1.74 differentregime arrangements compared to 0.55 for advanced economies, and 1.35 for developing economies.This means that emerging economies adopting a different regime every 6–7 years and emphasizes thatit is the emerging economies who struggle most with the regime choice.

We study the fixed exchange rate regimes in emerging economies for the period 1970 to 2007.14 Thestart of 1970 is chosen since it marks the period of financial liberalization for many emerging econo-mies. This is the period when most emerging economies started to have open capital accounts. It alsomarks the collapse of the Bretton Woods System. Starting with the 1970s emerging countries expe-rienced a decline in fixed regimes and a steady increase towards more flexible regimes, especially anincrease in limited flexibility regimes (Fig. 1). This increase continued up until the 1990s after whichthere was a move towards more fixed regimes. Contrary to the bipolar hypothesis for the emergingeconomies, intermediate regimes remain prevalent.15 Although there has been a slight decline sincethe mid 1990s, limited flexibility regimes still account for about 40 percent of the regimes in emergingmarkets of which crawling band is the most common one.

12 For a detailed analysis on the evolution of exchange rate regimes, see Levy-Yeyati and Sturzenegger (2004).13 Our calculations indicate that advance, developing and emerging economies experienced on average 0.93; 1.22 and 1.43regime switches per decade respectively for the period 1970–2007.14 The emerging economies used in the study are: Argentina, Brazil, Chile, China, Colombia, Costa Rica, Dominican Republic,Egypt, Hong Kong, India, Indonesia, Israel, Jordan, Korea, Malaysia, Mexico, Morocco, Peru, Philippines, South Africa, Thailand,Turkey, Uruguay, Venezuela. These are the countries included in the 2005 Morgan Stanley Capital International (MSCI) indexwith the exception of Costa Rica, Dominican Republic, and Uruguay.15 For the “two corners” or “bipolar” hypothesis see Eichengreen (1994), Fischer (2001) and recently Eichengreen (2008).

Fig. 1. Percentage of exchange rate regimes for emerging economies by decade: RR fine classification.


4.1. Regime duration

We aggregate the RR fine classification into two broad categories as fixed and float. All regimecategories that have some sort of rigidity in the regime are considered fixed. The remaining categories(managed floating, freely floating, and freely falling regimes in the fine classification) are the floatingregimes.16 Fixed regimes account on average for around 70 percent of the regime arrangements inemerging economies in our sample (see Fig. 2).

An exit (or failure) defines as a move from any of the fixed exchange regime categories to any of thefloating categories or to a regime category that is identified as a dual market of exchange rates (RRclassification category 15). The spell length is the duration of the fixed regime from its beginning untilthe occurrence of an exit.17

Themajority of exits were in the 1970swith the abandonment of the BrettonWoods System of FixedExchange Rate Regime, and the majority of the regime changes were in the 1990s. Advanced countriesexperienced the most number of regime changes in the 1970s and did not experience many regimechanges thereafter. Emerging economies experienced the most number of regime changes in the 1980swhile most number of exits from fixed regimes occurred in the 1990s. These are in line with theincreased regime durability for advanced economies since 1970s who have managed to avoid imbal-ances of the sort that caused the collapse of the Bretton Woods system; and the decreased regimedurability for emerging economies due to the failures associated with increased exposure to volatileinternational capital flows (Husain et al., 2005).

There are 35 spells for emerging economies, 21 terminated by an exit (10 in the 1980s, 6 in the1990s, and 5 in the 2000s) and 14 censored observations. The exit ratio, defined as the number ofexits per regime spell is 60 percent for emerging economies, which is higher than that of advancedand developing economies. Parallel to this developing and advanced economies have longerregime durations (12.52 and 11.92 years respectively) compared to the emerging and fragileeconomies (9.59 and 9.15 years respectively) while average regime duration for all economies is10.66.18

16 The regime categories 1 to 11 in the RR fine classification are referred to as fixed; categories 12, 13 and 14 as floatingregimes.17 In the survival analysis literature, the failure or death is called the “event”, and therefore these models of failure or deathare generically termed time-to-event-models. Since the interest here is on the duration of fixed exchange rate regimes, the“event” corresponds to the abandonment of the exchange rate regime and a “spell” is the time length for which the fixedregime has been effective.18 When only the regimes that have terminated are considered fixed regime duration decreases to 6.27 in developing, 5.89 inadvanced, 5.55 in fragile and 5.86 in emerging with an average of 5.73 for all economies.

0

20

40

60

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1950q1 1960q1 1970q1 1980q1 1990q1 2000q1 2010q1Time

percent_fix percent_flex

Fig. 2. Fixed and flexible exchange rate regimes for emerging economies.


Previous studies report different results for the mean and median pegged regime duration. Severalstudies find low regime persistence (Klein and Marion, 1997; Setzer, 2005; Klein and Shambaugh,2008).19 The difference in the average regime duration reported in the studies is partially due todifferences in the exchange rate classification schemes and partly due to the country choice. Exclusionof the long duration countries will decrease the mean regime duration. Regime duration also differsdepending on how the regime is defined. Most of the previous studies focus on currency devaluationsor peg spells that end by a speculative attack.20 In line with our focus on policy regimes, we are usingthe RR regime classification, which focuses on long term the exchange rate regime behavior. The use ofa five-year volatility window for the exchange rate, and allowance for one time discrete devaluationswithin the regime, yields fewer peg spells and longer spell duration compared to the other classifi-cation schemes. Therefore the finding of longer regime duration is common for the studies that usethe RR regime classification (examples include Detragiache et al., 2005; Wälti, 2005; Husain et al.,2005).21

Besides the low regime duration and high exit ratio, another specific feature of fixed regimes inemerging economies is the distinct shape of the distribution of regime duration. Fig. 3 plots the fre-quency of peg spell durations for different economies. Many pegs last for less than five years and fewerpegs have longer durations. Less than a dozen peg spells survived more than fifteen years. We do notobserve this pattern in other country groups; instead, we observe more evenly distributed regimedurations.

This observation brings us back to the initial question of whether there is certain time dependencefor regime duration in emerging economies. Specifically is this low regime persistence a result ofmacroeconomic and financial conditions, or is there a certain time dependence that makes fixed re-gimes less durable over time. If so, what are the factors that affect regime durability?

19 Klein and Marion (1997) report an average duration for a dollar peg of 32 months (2.6 years), with a median duration of 10months. Setzer (2005) finds average currency peg duration of 3.97 years. Klein and Shambaugh (2008) report a medianduration of 2 years for pegs and floats and a mean of 5.21 and 4.67 years respectively.20 Klein and Marion (1997) use the end-of-the-month exchange rate data published by IFS and define a currency as peggedwhen there is a particular fixed value of a country’s currency with respect to the US dollar. One time devaluations areconsidered as an exit from peg. The LYS classification used by Setzer (2005) also considers the exchange rate behavior one yearat a time and results in fewer spells with shorter duration than the RR classification.21 Detragiache et al. (2005) report regime duration of 16.6 years. Wälti (2005) reports the mean regime duration of 11.6 years,15.6 years for developed and 9.25 for emerging countries. Husain et al. (2005) find average regime duration of about 8.5 year, 10years for emerging markets, 14 years for advanced economies and 16 years for developing countries.

3 2 31 2 1 1 1 1

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Distribution of Peg Duration (1970-2007)

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Distribution of Peg Duration (1970-2007), Exits Only

a. Pegs that start after 1970 b. Pegs that start after 1970, Exits Only

Fig. 3. Distribution of peg durations. a. Pegs that start after 1970 b. Pegs that start after 1970, exits only.

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5. Methodological discussion

5.1. Survival analysis background

Given the nature of the role that time plays, much of survival analysis is concerned with the esti-mation of, and inference for the functions that characterize the distribution of the survival time (Cleveset al., 2008). Before the details of our analysis, below is a brief discussion of these functions of timewhich form the basis of our analysis.

Survival function, S, is the probability that the time until the event, T, is later than some specifiedtime t:

SðtÞ ¼ PðT > tÞ; (1)

where t is time, T is the nonnegative random variable that represents the time until the event underexamination (or duration until the event occurs), X is the vector of covariates associated with T, and Pstands for probability.22 It is termed survival function because it gives the probability of survivingbeyond time t. The cumulative distribution of duration F(t) and the corresponding density function f(t),can both be obtained from S(t):23

FðtÞ ¼ PðT � tÞ ¼ 1� SðtÞ; and (2)

f ðtÞ ¼ dFðtÞdt

¼ ddt

f1� SðtÞg ¼ �S0ðtÞ: (3)

A related term is the hazard rate, also known as the hazard function h(t), which is defined as theevent rate at time t conditional on survival until time t or later.24 It is calculated as the limitingprobability that the failure event occurs in a given interval, conditional upon the subject having sur-vived to the beginning of that interval, divided by the width if that interval (Kiefer, 1988).25

hðtÞ ¼ limDt/0

Pðt < T < t þ DtjT > tÞDt

¼ f ðtÞSðtÞ (4)

The hazard rate is the negative time derivative of the survival function S(t) and this relationship isexpressed as:

hðtÞ ¼ f ðtÞSðtÞ ¼ dFðtÞ=dt

SðtÞ ¼ �dSðtÞ=dtSðtÞ ¼ �S0ðtÞ

SðtÞ ¼ �dlnSðtÞdt

(5)

The cumulative hazard function H(t) measures the total amount of risk that has been accumulated upto time t26:

HðtÞ ¼Z t

0hðuÞdu (6)

The theoretical relationship between the cumulative hazard function H(t) and the survival function,S(t), can be obtained from Equations (5) and (6) such that:

22 In our study T will represent the duration of the exchange rate regime, i.e. the time until the termination of the peggedregime and X will be the fundamentals in the economy that are related with the survival of the fixed exchange rate regime.23 Note that the survival function, S, is the reverse cumulative distribution function, i.e. the probability that the randomvariable T will equal or exceed the value t meaning there is no failure event prior to t. S(t) ¼ 1 � F(t) ¼ P(T > t).24 The conditional failure rate, the intensity function, the age specific failure rate, the inverse of the Miller’s ratio, and the forceof mortality are some other terms used for the hazard rate.25 The hazard function can also be defined as the conditional probability that the spell will terminate at time T is equal to t,given that it has survived until time t.26 The cumulative hazard function H(t) can also be interpreted as the total number of expected failures in (0,t) for a subject, iffailure were a repeatable process.


HðtÞ ¼Z t

0

f ðuÞSðuÞdu ¼ �

Z t

0

1SðuÞ

�ddu

SðuÞ�du ¼ �lnfSðtÞg; (7)

In non-parametric analysis it is common to use the Kaplan and Meier (1958) or product limit esti-mate of the survivor function S(t). For a dataset of observed failure times t1,., tk, where k is the numberof distinct failure times, nj the number of individuals at risk at time tj and dj the number of failures attime tj, the Kaplan–Meier estimate at anytime t is calculated as the product of survival probabilities inperiod t, and the preceding periods:

bSðtÞ ¼Yjjtj�t

nj � dj

nj

!; (8)

In non-parametric analysis the cumulative hazard function is estimated via the Nelson (1972) Aalen(1978) estimator, which is as a summation overall distinct failure times less than or equal to t:

bHðtÞ ¼Xjjtj�t

djnj; (9)

Alternatively an estimate for the cumulative hazard function can be derived from the relationship inEquation (7) using the Kaplan–Meier estimator for S(t)27:

bHðtÞ ¼Z t

0

f ðuÞbSðuÞdu ¼ �Z t

0

1bSðuÞ�

ddubSðuÞ�du ¼ �ln

nbSðtÞo; (10)

5.2. Non-parametric analysis

First, to study how the survival experience of fixed regimes differs among economies, we usenonparametric methods of survival analysis.28 In our study, the hazard rate, h(t), defines the rate atwhich the fixed exchange rate regime will terminate at some time t, given that it has lasted to time t;and the survival function, S, gives the probability that the fixed exchange rate regime will survivebeyond time t.

To show that the survival experience varies across economies we estimate the survivor functionseparately for different country groups via the Kaplan and Meier (1958) estimate.29 The variation ofthe survival experience across economies is evident from the plot of the Kaplan–Meier survival es-timate, the Nelson–Aalen cumulative hazard estimate, and the smoothed hazard estimate acrossdifferent economies in Fig. 4 panels (a), (b) and (c) respectively. As can be seen that the hazardfunction varies across economies. Emerging economies have the lowest survival estimates, followedby developing economies. Advanced economies have a slightly higher chance of survival than thefragile economies. The high hazard rate for the emerging countries also accounts for their shortregime spells. Fragile countries have the lowest hazard rate, which can explain their long regimespells.

As a formal test for the equality of the survivor functions, a log rank test is applied for differentcountry groups. The equality of survivor functions is rejected for country type at the 10 percent level

27 The theoretical relationship between S(t) and H(t), where S(t) ¼ exp{�H(t)} can be used to convert one estimate to theother. Both the Kaplan–Meier estimator and the Nelson–Aalen estimator are consistent estimates of each, and the statistics areasymptotically equivalent. In small samples however, the Kaplan–Meier estimator is superior when estimating the survivorfunction and the Nelson–Aalen estimator is superior when estimating the cumulative hazard function (Klein and Moeschberger,2003).28 There are three approaches of modeling in survival analysis: parametric, non-parametric and semi-parametric. The para-metric models require explicit assumptions about the distribution of the failure time. Parametric models are highly structured,and require specifying the form of the baseline hazard. Since we do not want to impose any restriction on the hazard function,we limit our study with non-parametric and semi-parametric analysis.29 The estimates are not provided for brevity but can be obtained from the author upon request.

0.00

0.25

0.50

0.75

1.00

0 50 100 150 200 250analysis time

advanced developingemerging fragile

1950-2007Kaplan-Meier survival estimates

0.00

0.50

1.00

1.50

2.00

0 50 100 150 200 250analysis time


1950-2007Nelson-Aalen cumulative hazard estimates

a. Kaplan-Meier survival estimate b. Nelson-Aalen cum. hazard estimate

c. Smoothed hazard estimate

0 20 40 60 80 100analysis time


1970-2007Smoothed hazard estimates

Fig. 4. Survival experience across economies. a. Kaplan–Meier survival estimate b. Nelson–Aalen cum. ha rd estimate c. Smoothed hazard estimate.

U.Tam

gac/Journal

ofInternational

Money

andFinance

37(2013)

439–467

451

0500.

.01

510.

.02

za


using the log rank test while it cannot be rejected for geographic region or for different decades.30 Thus,the pattern of duration dependence is specific to the country groups and any analysis on regimeduration should be country group specific as well.

The behavior of the hazard rate over time provides a convenient definition of duration dependence.Positive (negative) duration dependence at t* occurs when the hazard rate increases (decreases) as thespell continues. This implies that the regime is more (less) likely to end, the longer a country has beenin the regime.31 Positive duration dependence requires dhðtÞ=dt > 0 at t ¼ t� while negative durationdependence requires dhðtÞ=dt < 0 at t ¼ t�.

The hazard estimate for emerging economies is the highest and most volatile (Fig. 4 panel c). Thissuggests that the probability of exit from a peg varies considerably by time and that durationdependence is most evident for emerging economies. This also supports our argument that time is animportant factor for the sustainability of the pegged regime for emerging economies, more so than theremaining country groups.

Our estimate of the smoothed hazard function for pegged regimes is consistent with the findings ofSetzer (2005): initially increasing followed by a decreasing hazard. However, we find no evidence forpositive duration dependence in either group. Both advanced and emerging economies have a non-monotonic hazard rate. For both country groups the shape of the hazard function is similar:increasing initially, then decreasing, after which it starts to increase again (panel a).When the censoredobservations are excluded the same shape is preserved and the hazard rate is still much higher foremerging economies. This contradicts Wälti (2005) who finds almost no duration dependence foradvanced economies and positive duration dependence when censored observations are excluded forboth country groups, though more pronounced for developed economies.

The non-monotonic behavior of the hazard function implies that the hazard rate alternates betweenpositive and negative duration dependence. Initially as the regime becomes effective there is positiveduration dependence where the hazard increases up until the first ten years. During that period theregime is fragile. After around ten years, the hazard rate starts to decline indicating negative durationdependence. This suggests that for pegged regimes that have survived beyond this threshold level theprobability to exit declines and the regime becomes more durable afterward. However around 75% ofthe regimes fail before this ten year threshold. It is only a small fraction of regimes that survive beyondthe threshold point. For such regimes, duration becomes an advantage for sustainability: longer theregime has been effective the less is the likelihood that it will be terminated. This is why there aremanyshort lived fixed exchange rate regimes and only a few regimes that last more than 10 years.

5.3. Semi-parametric analysis

To determine the factors that affect the pegged spell duration in emerging economies we assume aproportional hazards model. In this type of models, the hazard function of subject j is some function ofthe hazard everyone faces modified by xj which are the predictors specific to subject j. Specifically, thehazard rate for the exit from a fixed regime for subject j with k covariates is defined as:

hjðtÞ ¼ h0ðtÞexp�xjß�; where (11)

exp�xjß� ¼ exp

�xj1ß1 þ xj2ß2 þ..þ xjkßk

�; (12)

where xj is the vector of covariates for subject j, b is the vector of regression coefficients and h0(t) is thebaseline hazard which is the overall hazard when all covariates are equal to their base values, i.e.x ¼ 0.

30 The null hypothesis of equality of the survivor functions across different economies that testsH0 ¼ hadvanced(t) ¼ hemerging(t) ¼ hdeveloping(t) ¼ htransition(t) ¼ hfragile(t) is also strongly rejected using different tests (Wilcoxon,Tarone-Ware and Peto-Peto-Prentice). The test results can be provided by the author upon request.31 In a memory-less system the hazard rate is constant over time meaning that the likelihood of a regime termination does notdepend on how long the regime has been effective: dh(t)/dt¼ 0 for every t; while non-monotonic duration dependence implies thatthe hazard function evolves with time in a fashion alternating between positive and negative duration dependence over time.


Not to impose any restrictions onto the hazard function we use the Semi-parametric Cox Propor-tional Hazards Regression Model (Cox, 1972) to estimate the covariates.32 As the name “Semi-para-metric” implies, rather than specifying a functional form, h0(t) is left unspecified. There is noassumption for the shape of the hazard over time either. It can be monotonically increasing, decreasingor any other non-monotonic form. The assumption is that whatever the general shape, it is the same foreveryone and the covariates are assumed to shift the baseline hazard function multiplicatively (Cleveset al., 2008). In other words no parametric form of the survivor function is specified, but the effects ofthe covariates are parameterized to alter the baseline hazard function. In that sense, semi-parametricmodels fall between the parametric and non-parametric models. They are non-parametric as far astime is concerned, but the effect of the covariates is assumed to take a certain form. By not assumingany parametric form of the hazard function and instead leaving the specific form unestimated, the Coxmodel provides considerable flexibility. The only assumption is the proportional hazard assumption(p.h.a.), i.e. that the covariates shift the baseline hazard function multiplicatively.

The p.h.a. implies that the ratios of hazard functions for two subjects j and m, with different valuesof independent variables, are multiplicative replicas of one another and do not depend on time. Thismeans that subjects with equal time, share the same baseline hazard and their overall hazard ratedepends on their x values. That is:

hjðtÞhmðtÞ

¼ exp�xjb�

expðxmbÞ : (13)

The likelihood function L(b) of the data for k distinct failure times, where ti is the ith failure time is:

LðßÞ ¼ Lfðßjðt1; x1Þ; ðt2; x2Þ;..; ðtn; xnÞg ¼ f ðt1jß; x1Þ..f ðtnjß; xnÞ (14)

The estimate of b, the vector of regression coefficients, is found bymaximizing the natural logarithmof the likelihood function, L(b). In the Cox model the log likelihood function is calculated over separatebinary outcome analyses. Because of the specific formof the hazard function in the semi-parametric Coxmodel the h0(t) term cancels out from the calculations and likelihood function reduces to:

LðbxÞ ¼Ykj¼1

8><>: h0ðtÞexp�b0 þ xjbx

�Pi˛Rj

h0ðtÞexpðb0 þ xibxÞ

9>=>; ¼Ykj¼1

8><>: exp�xjbx

�Pi˛Rj

expðxibxÞ

9>=>;; (15)

where k is the number of distinct failure times and xi the vector of explanatory variables for subject i.This way the regression coefficients can be estimated from the data via partial-likelihood approach(Kiefer, 1988).

6. Explanatory variables

We fit a Coxmodel for the duration data and estimate the vector of regression coefficients, b, for themodel:

LRH ¼ xjß ¼ x1ß1 þ x2ß2 þ..þ xkßk: (16)

where the dependent variable is the log relative hazard (LRH) for subject j. The vector of covariates xj,represents the factors that are likely to affect the survival probability of fixed exchange rate pegs.

The hazard of the risk changes the instant the covariates change values. Since our analysis time isquarters, the covariates take on different values at each quarter, and we have a different risk pool

32 Note that the Cox model does not have an intercept. Even if we add an intercept to the model it will be subsumed into thebaseline hazard (only change the h0(t) term) which is not estimated. Thus the intercept is unidentifiable from the data since anyvalue would work as well. The model with an intercept hj(t) ¼ h0(t) exp(b0 þ xj bx) reduces to hj(t) ¼ [h0(t) exp(b0)]exp(xj$bx) ¼ h00(t) exp (xj$bx).


(subjects at risk) for every quarter. Therefore the data is constructed such that each time period (i.e.quarter) in one of the 35 spells in the sample constitutes one observation.

We test themodel for awide range of covariates, which are likely to affect the survival probability offixed exchange rate regimes. These covariates are grouped into two categories: the economic factorsand the socio-political factors, and are discussed separately in the following section.

6.1. Economic factors of regime duration

The economic factors that affect regime duration are related to those studied in the exchange rateregime choice and currency crisis literature.

6.1.1. Optimum currency area literatureTheories of optimum currency area (OCA) argue that the regime choice depends on the structural

characteristics of the national economy. The same factors pointed out by the OCA literature for a fixedregime choice are also likely to contribute for the continuation of the regime. A change in theseconsiderations/factors or poor economic performance might call for a regime change.

Foreign borrowing: According to the regime choice literature, and empirical findings, countries thatrely heavily on foreign borrowing (high liability dollarization) benefit more from pegged regimes(Calvo and Reinhart, 2002). A pegged exchange rate will insulate these countries from the adverseeffects of currency fluctuations and balance sheet effects.33 However, it can also create a moral hazardproblem where agents in the economy increase their foreign borrowings. In such a case, abandoningthe pegged regime would reduce the moral hazard effect. To analyze which effect dominates we useforeign liabilities over reserves as a proxy for liability dollarization.

Openness: OCA theories predict that the more open the economy, the more is the trade-enhancingeffect of fixed exchange rates, indicating a pegged regime for open economies (McKinnon, 1963). Onthe other hand, open economies are more exposed to external real shocks, and would benefit morefrom the isolating properties of flexible regimes. Hence, the relationship between trade openness andregime choice is twofold. We are using the standard measure of exports plus imports over GDP as aproxy for trade openness to measure these effects.

Terms-of-trade shocks: Devarajan and Rodrik (1991) show that the variance of the terms-of-tradeshock and the openness of the economy are an important consideration for the regime choice.34

Countries faced with frequent shocks in their terms of trade would benefit by having flexible ex-change rates. The affect of the terms of trade shocks on the economy depends on the openness of theeconomy. Therefore, the variation in terms of trade and the variation interacted with openness isincluded in themodel to test for these effects. Export growth, import growth, and the trade balance as ashare of GDP are used to measure real economic conditions and trade performance.

Size: Economic size may affect the exchange rate regime choice through different channels. Smallcountries are more dependent on external financing whichmakes themmore vulnerable to fluctuationin global capital markets. In addition, the negative effect of exchange rate volatility on trade is higherfor smaller countries. Thus, for small countries, the benefits of stabilizing the exchange rate would behigher. As a proxy for economic size, we use real GDP relative to US real GDP.

Inflation: Pegged regimes are often adopted by countries struggling with high inflation as a signal ofcommitment to fight inflation. Carmignani et al. (2008) view the relationship between inflation andthe regime choice from a credibility-consistency dilemma. In the credibility argument, the adoption of afixed exchange rate system can provide relief for countries by lowering inflationary expectations. Thus,the credibility argument calls for a positive association between inflation and pegged regimes. How-ever, the peg can serve as a pre commitment device only as long as the costs of abandoning the pegexceed the benefits of creating surprise inflation. If the credibility of the peg is eroded, inflationaryexpectations rise, and the pegged regime becomes unsustainable. In that case, high inflation raises the

33 See Chang and Velasco (2000).34 Their analysis shows that flexible exchange rate regimes would be more suitable for the CFA Zone countries that are subjectto large swings in their terms of trade.


probability of exiting the pegged regime. Thus, the consistency argument calls for a negative rela-tionship between inflation and the continuation of a pegged regime.

6.1.2. Crises literatureLast decades have shown that fixed exchange rate regimes are prone to crises and hence costly

devaluations. Authorities who fear that devaluation is likely may opt for a voluntary regime change orsometimes the pressure to devalue may be so high that a regime change inevitable. Although deval-uation does not necessarily indicate a regime change, and the authorities may maintain the fixedregime after some realignment in the exchange rate, it is likely that the factors that contribute to theabandonment of a currency peg, i.e. currency crisis, may contribute to the abandonment of the regime.

The first-generation models analyze balance of payments crises whose occurrence can be predictedby imbalances in macroeconomic fundamentals. The usual signals for devaluation are large andgrowing budget deficits, matched by large and growing current account deficits, decreases in foreignexchange reserves, and misalignments in the real exchange rate. The crisis literature also points out tothe possibility of self fulfilling-crisis where depending on agents’ expectations occurrence of crisisexhibits multiple equilibria. The second-generation crisis theory points to domestic economic or po-litical vulnerabilities that may discourage authorities from upholding a pegged regime, especially infront of speculative activity. The literature has focused on unemployment, inflation differential as wellas political factors such as upcoming elections, weak governments, possibly non-democratic or corruptregimes (see Alesina and Wagner (2006), Frieden (2008) and Levy-Yeyati and Sturzenegger (2004)).Finally, the third-generation crisis theory is focuses on financial sector vulnerabilities.

Abroadsetof variablesderived fromthe theoriesof thecrisis literaturehavebeen identifiedandstudiedas crisis indicators in the empirical literature.35 These indicators include generalmacroeconomic variablesrelated to the real, financial and government sectors, as well as political and institutional variables.

Crisis indicators: We are using several variables from the currency crisis literature expected toimpact the duration of the exchange rate regime. These are government borrowing from the centralbank, debt to GDP ratio, and fiscal surplus to GDP ratio as indicators of the fiscal imbalances; currentaccount to GDP ratio to measure the external position of the country; changes in foreign exchangereserves and reserves over M2 as indicators of pressures on the parity. The misalignments in the realexchange rate may contribute to imbalances in the external accounts. As these imbalances grow, itbecomes harder to maintain a fixed parity. We proxy the misalignment in the real exchange rate withthe deviation of the real exchange rate from a moving trend over the previous 5 years.

It has been shown that an excessive credit creation, over-borrowing and boom-bust cycles are evidentin the periods that lead to a currency crisis.36 Real GDP growth, domestic credit growth, the annualizedgrowth rate of ratio ofM2 to reserves, growth rate of bank deposits toM2 ratio, and short-term debt overreserves are used asmeasures to account for these effects. The government’s decision to defend the paritydependsonthecosts andbenefitsofkeepingorabandoning the regime.Thus, theadverseconsequencesofpolicies needed tomaintain theparityare important factors for thedurability of the regime. In that regard,the level of unemployment and nominal interest rates are analyzed as factors that affect regime duration.

Monetary autonomy, and the ability to control the economy, is another consideration in the ex-change rate regime choice. The “impossible trinity” argument states that fixed exchange rates, freecapital movement and an independent monetary policy together are not possible. If a country wants tomaintain its capital account free, it has to choose between running a stabilizing monetary policy andreducing currency volatility, it cannot do both.37 Countries cutoff fromworld capital markets via capitalcontrols will be insulated from the negative consequences of capital out flows and should have higherregime sustainability. Empirical studies however suggest that capital controls are ineffective, and may

35 For the crisis literature see Krugman (1979), Obstfeld (1996), Jeanne (1997) among others. For a review of the indicators inthe crisis literature see Abiad (2003) and Kaminsky (2006) among others.36 See Aghion et al. (2001), McKinnon and Pill (1997) and Mishkin (1999) among others.37 Choosing a fixed exchange rate regime means loss of monetary autonomy given an open capital account. However in to-day’s environment with the growth of trade in goods and services, capital controls are easily evaded, i.e. there is no idealcountry with complete capital controls in the modern world.


lead to economic instability (Edwards, 1999a,b). Grilli and Milesi-Ferretti (1995) find a negative effectof capital controls on foreign borrowing while Glick and Hutchison (2005) find that countries with lessrestrictive capital controls are less prone to speculative attacks. As a measure of country’s degree ofcapital account openness, we are using the updated version of the Chinn and Ito (2008) FinancialOpenness Index.

Global economy: Global economic conditions also affect regime durability. Especially world interestrates are important for global capital movements. U.S. real interest rate is used to account for changes inglobal financial conditions. The country versus US interest rate differential and the volatility of the USinterest rate are used as other measures. Finally, several time and regional dummies are used to ac-count for contagion. The main source of the macroeconomic data is the IMF International FinancialStatistics and World Development Indicators database.38

6.2. Socio-political factors of regime duration

Political and institutional factors have an important role in economic policymaking. Frieden (2008)points out “Those who ignore the political economy of currency policy will make mistakes in developingfeasible exchange rate policies. Both analysts and policy-makers would be well advised to pay concentratedattention to political economy factors in exchange rate policy-making” Same is true for the exchange ratepolicy which is shown to be affected by political and institutional factors.

Economic considerations, while vital for this decision, do not fully determine exchange rate policy.The complication arises because of the wide distributional effects of exchange rate policy, and becausethe costs and benefits might fall onto different parties in the economy. Rather than acting as a socialplanner purely considering economic objectives, the decision-making authority might have severalpolitical or ideological considerations. Therefore to fully understand the factors that affect regimeduration requires an understanding of the political incentives (Steinberg and Walter, 2013).

There is a large literature that analyses the political choice of exchange rate regimes. Governmentideology, timing of elections, state of democracy, decision-making power, central bank independence,and socio-political stability are some variables that have been studies in this context (see Alesina andWagner, 2006; Frieden and Broz, 2006)

Elections: Termination of a policy can be regarded negatively by the public as an inability to keepthe course of action, hence governments which abandons a pegged regime are likely to lose credibilityat least for the near term. If elections are near such a government may be punished by the public andmay not be reelected. Consequently, a government is likely to avoid a policy change until the electionsare over. Hence, timing of elections is also a candidate that may affect regime duration. We use currentnumber of years for chief executive and a dummy if there was a legislative election in that year, bothtaken from the Database of Political Institutions (DPI) to account for how electoral cycle effects regimeduration (Beck et al., 2001).

Government ideology: The economic political literature has pointed out to different policy con-siderations between left and right wing governments, with the former placing more weight onemployment and the latter on price stability. Hence, considering partisan interests, shorter peggedregime duration is expected under left governments who are more inclined to use expansionarymomentary policy or increase government spending than right governments. However, it may also bepossible for left wing governments to try harder to maintain a fixed exchange rate policy to overcomethe credibility problems they are associated with. Right wing governments, known to be concernedwith inflation, may not be worried that the public will punish them once a fixed regime is terminated,because such a policy is likely to be viewed as inevitable rather than a choice of partisan behavior.Dummy variables to represent executive party ideology taken from DPI are used to test which effectprevails.

Announcement effect: There are various reasons why a government may not publicly announce theexchange rate policy. Announcements of the peg can act as a coordinating device and invite speculative

38 Quarterly data is used. For some of the variables that are available only at an annual frequency their quarterly interpolatedvalues are used.


attacks (Reinhart and Rogoff, 2004). The insurance of the peg against exchange rate variations maycreate an incentive to increase foreign borrowing. Thus, announcement of a peg may create a moralhazard through the worsening of the private sector balance sheet.39 A dummy for the de jure exchangerate regime accounts for this announcement effect.

Socio-political unrest: As with any policy change, a change from a fixed to a flexible regime may beagainst the interest of several groups and is likely to cause some political and social discontent. A policychange requires considerations for the right timing of the authorities. One argument is that politicalcosts of abandoning a peg increasewith socio-political instability (Edwards,1996). A contrary argumentis that governments under socio-political instability are fragile and do not expect to remain in seat forlong. They heavily discount future benefits and are less likely to bear the costs of maintaining a peggedregime. The followingmeasures of socio-political instability are used to seewhich effect dominates: theindex of civil liberties from the FreedomHouse, weighted conflict index, number of riots, and number ofrevolutions from Arthur Banks Cross-National Time-Series Database (CNST) (Banks and Wilson, 2013).

Central bank independence: The literature identifies independent central banks and fixed exchangerates as the main institutional mechanisms governments use to gain anti-inflationary credibility. Inde-pendent central banks reduce inflation because they tend to take a longer view of the policy process andare, on average, more conservative about price stability than elected politicians do. While Broz (2002)argues that independent central banks and fixed rates are alternative monetary commitments that varyin transparency, Bodea (2010) suggests that these two commitments can be complements or substitutesdepending on the conditions. These two mechanisms can act as complements where increased centralbank independence would increase regime sustainability by lowering inflationary expectations and alsobecause a central bankerwith lower timepreference rate (toughcentral banker) ismore likely tomaintaina fixed regime upon exogenous shocks. However, it is also likely for governments to substitute a fixedexchange rate regime with central bank independence, to avoid the risk of severe misalignment andcurrency devaluations associated with fixed regimes. We use central bank independencemeasures fromCukierman et al. (1992) and complement it with the measures from Crowe and Meade (2007).

7. Estimation results

Considering that the Bretton Woods system collapsed in 1971, and that all major currencies werefloating by 1976, we choose 1970 as the cutoff point. We estimate the hazard function for fixed regimesin emerging economies that have started after 1970 via the semi-parametric Cox model discussed inSection 5.3. The linear predictor is the log-relative hazard in Equation (16) and coefficients b are esti-mated for the different covariates discussed above.40

The hazard function gives an estimate of the probability that the pegged regime will terminate attime t, given that it has survived until that time. While the predicted coefficients give an estimate ofhow the macroeconomic factors affect the hazard function, the evolution of the baseline hazardfunction gives an estimate on how regime durability varies over time. After obtaining the modelpredictions, we are able to retrieve the shape of the hazard function for different covariates anddetermine how time influences the survivability of pegged regimes.

Before using the Coxmodel, we first verify that the basic assumption of proportional hazards (p.h.a.)is not violated. One way to test for the p.h.a. is to retrieve the Schoenfeld (1982) residuals. Under thenull hypothesis of p.h.a. the smoothed curve of the residuals over time should have a zero slope.41 Foreach model specification, we retrieve the Schoenfeld residuals for all the variables in the model, andverify that the p.h.a. is not violated. The test results for the baseline specification show that there is noevidence that the specification violates the p.h.a.42

39 For a discussion on how an announced exchange rate peg can increase liability dollarization see Carmignani et al. (2008).40 Regarding multi collinearity, the correlation matrix for the explanatory variables shows no significant correlation amongthe variables included in the same regression.41 A detailed discussion for testing the proportional hazards assumption is provided in Grambsch and Therneau (1994).42 A visual inspection of the smoothed function of the Schoenfeld residuals, which are not provided for brevity, also confirmthe result that the null hypothesis of zero slopes, i.e. of proportional hazards assumption, cannot be rejected.


The overall models’ fit are tested using different specification tests. As a model specification, alinktest verifies that the coefficient on the squared linear predictor is insignificant. To test for specifi-cation, we refit the entire model with time interaction, and test that the coefficient on the timeinteracted term is insignificant. Finally, the overall model fit is assessed through an analysis of the Cox-Snell residuals (Cox and Snell, 1968).

7.1. Baseline specification results

We estimate the hazard function for emerging economies using several explanatory variables.Through a one by one elimination of the insignificant variables, and the assessment of the model fitfrom Cox-Snell residuals, we find the baseline specification that provides the best estimate foremerging market economies. The variables that are in the baseline specification are: openness, thegrowth rate of international reserves, real GDP growth, deviation of the real exchange rate from a fiveyear moving trend, claims on government as a share of GDP, net foreign assets over M2, foreign lia-bilities over reserves, and the current account to GDP ratio. For each of the covariates, x, the estimatedcoefficients bx and the robust z statistics are reported in Table 2. The estimates for the hazard ratios canbe obtained by exponentiating the coefficient estimates, i.e. by calculating exp(bx). These expo-nentiated coefficients give us the hazard ratio for a one-unit change in the corresponding variable.

TheCox-Snell residuals test andAICareused to chooseamongthespecifications inTable2. If themodelfits well, the Cox Snell residuals should have a standard exponential distribution with hazard functionequal to 1 for all t. Then the cumulative of the Cox-Snell residuals should be a 45� line. Avisual inspectionof the residuals reveals that themodel B3 has the best fit among the baseline specifications. This result isverified by the Akaike Information Criterion (AIC) since model B3 has the lowest AIC in Table 2.

Reserve growth and current account to GDP ratio are both significant with a negative sign in all thebaseline specifications. Higher foreign reserves and current account surplus increases the regimesustainability. This is consistent with both theoretical and empirical findings of the currency crisisliterature, that current account deficits are high for the periods before devaluation. A worseningexternal position makes the regime unsustainable, while a decline in reserves increase the pressure fora government to defend the regime.

The sign on openness is significant and negative, meaning that for economies that are more open,the exit of the peg is less likely. This shows that the trade enhancing effects of fixed exchange ratesdominate, and supports the OCA view that open economies benefit more by applying a pegged regime.

The model is estimated using several measures of exchange rate misalignment. We find that theabsolute deviation of the real exchange rate increases the hazard rate and has a higher model fitcompared to the other measures. This indicates that whether the exchange rate is overvalued orundervalued is not indicative of the probability of an exit. It is the magnitude of the misalignment thatmatters. To verify this argument we have constructed two measures: the misalignment when thecurrency is overly appreciated and themisalignment when the currency is underappreciated. Althoughboth measures have the expected signs, only over appreciation is significant. This shows that overappreciation is a better indicator for the pegged regime duration. An undervalued exchange rate doesnot pose as much threat for the durability of the regime as does an over appreciated one.

Annual real GDP growth is highly significant in the baseline specification. A regime termination isunlikely while the economy is growing. Countries who benefit from a pegged regime through higherGDP growth will sustain the regime while a decline in economic growth economy increases the likeli-hoodof an exit.Net foreign assets overM2 is highly significant,with a negative sign. A strong foreign assetposition makes the regime more sustainable and is an indication for longer regime durability. Thecurrent account however looses significancewhen net foreign assets are included into the specification.Foreign liabilities over reserves are also significant in the baseline specificationwith a positive. Increasedliability dollarization is one of the indicators that a pegged regime will be terminated.

Another variable that is significant in the baseline specification is claims on government as a share ofGDP. The positive sign indicates that the hazard rate of the peg increases as the liabilities of the gov-ernment increase. This is in line with the speculative attack literature where increased governmentborrowing and the monetary financing of the public debt leads to the eventual collapse of a fixedexchange rate regime (Aghion et al., 2001). Higher claims on the government mean increased

Table 2Baseline model specification.

B1 B2 B3 B4 B5 B6

Openness �7.067(2.11)**

�6.632(2.41)**

�1.424 �0.735 �2.221 �2.137�1.09 �0.44 �1.56 �1.49

Current account/GDP �9.458(1.77)*

�8.741(1.78)*

�3.658 �1.03�0.6 �0.25

Reserves growth �5.104(2.82)***

�4.974(3.01)***

�5.023(2.56)**

�4977(2.64)***

�2.646(2.67)***

�2.62(2.35)**

Real GDP growth �7.294(3.27)***

�7.591(4.68)***

�5.13(2.03)**

�7687(2.13)**

�6.898(2.70)***

�5.475(2.85)***

Claims on gov/GDP 12.831(4.42)***

12.531(4.05)***

7.094(3.52)***

23.924(3.79)***

4.859(1.80)*

17.989�1.63

Absolute deviationof ln exchange rate

12.193(2.86)***

17.111(3.86)***

10.162(2.09)**

Net foreign assets/M2 �2.098(2.59)***

�2.458(2.22)**

�1.313(2.04)**

�1.376(2.69)***

Positive deviation ofln exchange rate

14.706 6.329 1.226�1.2 �0.74 �0.14

Negative deviation ofln exchange rate

�12.029(2.95)***

�18485(3.32)***

�10.812(2.57)**

Foreign liabilities/reserves

0.235(2.32)**

0.237(2.26)**

No. of observations 772 772 777 777 1049 1049No. of subjects 26 26 26 26 29 29r2p 0.63 0.63 0.67 0.67 0.52 0.52Log pseudo likelihood �12.13 �12.15 �10.89 �10.88 �21.96 �21.98Degrees of freedom 8 7 7 8 7 6Akaike Information

Criterion (AIC)40.26 38.3 35.78 37.76 57.92 55.96

Notes: Robust z statistics in parentheses.* indicates significance at 10%, ** indicates significance at 5%, *** indicates significance at 1%.


government borrowing. Thus, aworsening of the fiscal condition is an indicator of increased risk for thecontinuation of a peg.

It would be interesting to investigate how the results would differ when a pegged regime wouldend into a floating or freely falling regime. However, majority of the exits (16 out of 21) havebeen into a freely falling category. Hence there are not enough observations into floating regimesto make such a separate estimation and we treat exits into any of the floating categoriessimilarly.43

One point of concern is the possibility that the factors to explain regime duration might depend onthe stability of the peg. To overcome this issue we have re-estimated the model with lagged variables.Results with lagged variables are almost identical to those presented here. However, we still interpretour results cautiously.44

7.2. Alternative specification results

After estimating the baseline specification, we extend the model to include several other explan-atory variables. The estimation results of the alternative specifications, for the significant variablesonly, are provided in Table 3,

43 Periods where the twelve-month rate of inflation equals or exceeds 40 percent and the six months immediately following acurrency crisis (but only for those cases where the crisis marks a transition from a fixed or quasi-fixed to a managed orindependently floating regime) are classified as freely falling.44 The results are available upon request.


As a measure of the effectiveness of capital controls on the sustainability of pegged regimes, themodel does not give significant results for the Chinn and Ito (2008) financial openness index. Thisfinding is in line with the previous findings on the ineffectiveness of capital controls and shows thatthere is no significant difference in the failure rates of pegged regimes for countries with differentdegrees of exposure to financial markets.

Among the region dummies, we get a significant coefficient only for Latin America. As expected, thesign is positive, indicating a higher probability of exit from a regime for Latin American countries.Among time dummies, we get a significant and negative sign for the 1990s and 1970s, meaning that theprobability to continue the regime is higher in those periods.

The coefficient for inflation is not significant. We do not get significant results for money growth,inflation volatility, hyperinflation dummy or inflation differential with respect to US either.45 Regardingtrade variables, both import and export growth are insignificant, but the standard deviation of exportgrowth is significant with a negative sign. Economic size, measured as log of real GDP, is also not signif-icant. However, with the inclusion of economic size, exchange rate misalignment becomes insignificant.

Many countries adopt exchange rate regimes that are not consistent with their announced policies.We estimate how the announcement of the exchange rate regime would affect the regime duration bythe inclusion of dummy variables for de jure regimes. A de jure floating peg would mean that thecountry has a fear of floating.46 We get a positive coefficient for the de jure floating dummy and anegative coefficient for the de jure peg dummy: the hazard rate increases for a de jure float and de-creases for a de jure peg. This means that a de facto peg that is not announced is under greater pressureto fail. This result can have an important policy implication since the announcement of a peg, i.e. theconsistency of the de facto and de jure regime makes the peg more durable.

7.3. Socio-political specification results

The estimation results for the significant socio-political factors of regime duration are presented inTable 4. Measures of conflict, riots, purges, and assassinations are all significant with a negative sign.Regimes are expected to survive more in socio-politically unstable environments. One explanationmight be that due to sociopolitical unrest, and security problems, governments may not choose tomake a regime change and instead continue to maintain the pegged regime. Another explanation canbe that countries already in conflict will avoid the extra distress of a regime change, and the costs tomaintain the peg might be tolerated more by the public as compared to in peaceful societies. Index ofcivil liberties does not change the estimates, and is insignificant.

Another finding is that the hazard rate is higher under democracies. For a democratic government,the costs tomaintain the pegmay be higher than for an autocratic one due to the fear of losing power inthe coming elections. The concentration of authority also increases the probability of an exit: regimechange is more likely in presidential than in an assembly elected president and then in a parliamentarysystem. Both years left in the office and the elections dummy are insignificant. Hence, we do not findevidence for the importance of elections on regime duration. Nor do we find that government ideologyinfluences survival probability. One significant variable is socio-political stability.

Independent central banks have been argued to reduce inflation because they tend to take a longerview of the policy process and are, on average, be more conservative about price stability than electedpoliticians. Our measure of central bank independence is significant with a positive sign. Hence, ourfindings support the substitutability argument between central bank independence and fixed regimes,i.e. that the likelihood exit from a pegged regime increases with central bank independence.

7.4. Duration dependence

After the estimation, to compare the results of the model with the observed data, the smoothedhazard function for emerging economies over the period 1970–2007 is plotted together with the

45 Hyperinflation is defined as an annual value of inflation exceeding 40 percent.46 See Calvo and Reinhart, 2002; Hausmann et al., 2001.

Table 3Alternative model specifications.

A1 A2 A3 A4 A5 A6 A7

Openness �1.186 �0.462 �1.078 �0.921 14.246 �0.388 �2.206�0.72 �0.35 �0.99 �0.42 �0.73 �0.25 �1.56

CA_GDP �1.684 �7.511 �4.832�0.34 �1.22 �1.16

Reserves_gr �1.823(1.98)**

�2.704(3.13)***

�3.018(4.43)***

�5.186(3.06)***

�5.097(2.91)***

�5.623(3.52)***

�2.623(2.62)***

GDP_real_growth4 �8.32(3.27)***

�9.564(3.24)***

�10.731(6.15)***

�9.231(2.36)**

�10.64(2.64)***

�9.381(2.64)***

�6.739(2.56)**

Claims_on_Gov_GDP 6.907(2.81)***

6.099(3.80)***

7.069(3.29)***

9.612(3.26)***

16.178(3.53)***

7.436(4.67)***

4.809(1.84)*

abs_dev_lnerateFA_net_M2 �1.632

(2.65)***�1.596(2.55)**

�1921(2.69)***

�1998(2.18)**

�3.509(3.08)***

�2.256(2.44)**

�1.303(2.07)**

pos_dev_lnerate �5.187 �3.681 �3.223 1.174 6.784 1.646 1.593�0.33 �0.39 �0.32 �0.14 �0.68 �0.17 �0.18

neg_dev_lnerate �8.776(2.52)**

�11.788(3.42)***

�21.826(2.71)***

�20.214(2.87)***

�24.947(3.67)***

�18.527(3.36)***

�10.725(2.58)***

Inflation differentialDummy – 1970 �41.808

(3.97)***Dummy – 1990 �2.503

(3.04)***�1.66(1.98)**

Dummy Latin_America 3.653(2.57)**

Interest rate differential 0.00029(1.96)**

US inflation rate �0.566(3.28)***

Standard deviation ofexport growth

�1.265(2.10)**

�1.354(2.46)**

Dummy – De jure float 1.429(2.50)**

No. of observations 818 1212 1212 953 953 953 1212No. of subjects 27 31 31 28 28 28 31r2p 0.39 0.47 0.57 0.62 0.67 0.63 0.44Log pseudo likelihood �18.73 �25.44 �21.01 �13.39 �11.82 �12.98 �27.13Degrees of freedom 9 8 9 9 9 9 7Akaike Information

criterion (AIC)47.84 59.26 51.74 38.12 37.96 35.74 59.64



estimated hazard function from Cox regression for baseline specification B3 in Fig. 5 panel (a) and (b)respectively. The estimated hazard function from the semi-parametric Cox model closely matches thesmoothed hazard estimate. The non-monotonic duration dependence for emerging market exchangerate pegs is evident from these figures. After controlling for explanatory factors, we are able to retrievethe shape of the baseline hazard function, where the hazard function increases initially until the end ofthe first ten years of the pegged regime.

The rising hazard function indicates greater likelihood of a collapse in the pegged exchange rateregime over time until a critical threshold point is reached. We offer three possible interpretations ofthis empirical result based on the theoretical literature. First, over the course of their lifetime, peggedregimes are hit with domestic and external shocks that may be persistent and lead to imbalances, notall of which are observed by the econometrician. There can be growing imbalances that make theregime unsustainable such as deteriorating macroeconomic conditions or unfavorable changes in theexternal environment that force the abandonment of a peg. There can also be some favorable eco-nomic conditions, such as a growing economy, or increasing currency account surplus, which indi-cate that the pegged regime delivers good outcomes; that a regime change is not required whichsignal for increased durability. The effect of these factors is captured by the fundamentals in our

Table 4Socio-political model specifications.

P1 P2 P3 P4 P4 P5 P6 P7

Openness �7521(2.23)**

�21588(3.81)***

�7842(2.68)***

�11535(1.75)*

�6.89(2.04)**

�7934(2.59)***

�7247(2.10)**

�9597(2.71)***

Current account/GDP �8197 �10631 �9016 �15114 �9.12(1.71)*

�14008(1.84)*

�20461(1.95)*

�6004�1.32 �0.79 �1.17 �1.51 �0.84

Reserves growth �5723(2.23)**

�15705(1.74)*

�8087(3.93)***

�8652(2.06)**

�4925(2.75)***

�4076(2.87)***

�3687(3.02)***

�7168(2.54)**

Real GDP growth �6698(3.58)***

�21651(3.39)***

�9458(3.25)***

�8588(3.38)***

�7037(3.16)***

�8421(3.31)***

�9881(2.81)***

�7482(3.85)***

Claims on gov/GDP 14,521(4.02)***

37,897(4.35)***

18,699(4.13)***

20,039(2.39)**

12,477(4.29)***

13,338(3.41)***

14,651(2.81)***

19,459(4.89)***

Positive deviation ofln exchange rate

18,193 36,192 7338 25,054 14,428 29,918(1.96)**

29,104 24,198(1.78)*�1.2 �0.12 �0.52 �1.24 �1.17 �1.53

Negative deviation ofln exchange rate

�13421(2.55)**

�46.89(3.72)***

�10959(2.55)**

�15987(1.95)*

�11603(2.79)***

�20284(2.53)**

�17176(1.77)*

�16.13(3.10)***

Foreign liabilities/reserves

0.206(1.81)*

0.005 0.275(2.27)**

0.298(1.71)*

0.227(2.33)**

0.395(2.94)***

0.513(2.56)**

0.177�0.02 �0.94

Weighted conflictindex

�0.00014(2.85)***

Type of democraticor autocratic regime

�4512(2.05)**

Institutionalizeddemocracy score

0.448(2.12)**

Purges �32479(25.88)***

Assassinations �0.978(1.69)*

Political system �1226(1.75)*

Central bankindependence

20,461(2.28)**

Riots �0.272(2.08)**

No. of observations 693 639 839 693 693 839 642 693No. of subjects 26 22 27 26 26 27 25 26r2p 0.61 0.73 0.66 0.65 0.59 0.67 0.65 0.64Log pseudo likelihood �12 �7.03 �11.49 �10.58 �12.49 �11.15 �9.93 �11.14Degrees of freedom 9 9 9 9 9 9 9 9



analysis. Using survival analysis, we were able to quantify the effect of these fundamentals on thehazard rate.

More importantly, we have shown the existence of unobservable persistent effects that accumu-late over time with different implications for the sustainability of the peg over the duration of thepeg. The baseline hazard function shows how the hazard function varies by time when all thecovariates are set to zero. A time varying baseline hazard confirms that, even after controlling for thefundamentals in the economy, time is a significant factor for the survivability of exchange rate pegsin emerging economies. The gradual increase in the baseline hazard for the initial 10 years of the pegsuggests that persistent effects dominate. This is the period in which commitment to the regime isnot well established and the benefits of the peg have not yet been fully realized. Besides the expectedadvantages of price stability, pegged regime constraints the government’s policy choices. Monetarypolicy looses power as a stabilizing policy and governments can no longer rely on seignorage rev-enue. While the benefits of a pegged regime can be reaped in the long term (through enhanced tradeand lower inflation), the tradeoffs in terms of policy flexibility may be evident. The increasing hazardrate can be interpreted as the pressure on the government for a switch to greater exchange rateflexibility.

a. Smoothed Hazard Function b. Estimated Hazard F nction from Cox Regression for Basel e Specification B3

.005

.01

.015

.02

Smoo

thed

haz

ard

func

tion

20 0 60 80analysis time

Cox Propo tional Hazards Regression

0500.

.01

510..0

2

0 20 40 60 80 100analysis time

Emerging Economies: 1970-2007Smoothed hazard estimate

Fig. 5. Hazard functions. a. Smoothed hazard function b. Estimated hazard function from Cox reg ssion for baseline specification B3.

U.Tam

gac/Journal

ofInternational

Money

andFinance

37(2013)

439–467

463

uin

4

r

re


A second, related, interpretation is that time accounts for market expectations. In the traditional Barroand Gordon (1983) setting, the time preference of the government is not known to public, and thecommitment into the anti-inflationary program signals a tough policymaker with low time preference.Hence the longer the regime prevails the higher would be the credibility for the regime. However, inour setting, the initial expectation is that a pegged regime is prone to be abandoned and even a toughpolicy maker (under unfavorable circumstances) will be left with no choice but to renege. Hence, astime goes by markets’ expectation for a regime change increases. The longer the regime is maintained,the harder it will be in the future tomaintain the regime. Thesemarket expectations can be reflected ininflationary expectations or rising interest rates, which constrain the government policy further.47,48

Third, the initial period of increased hazard may be interpreted as the hurdle of a “learning period”in which the agents in the economy, including the government, the public, as well as the othercountries (these can be the trade partners or investors in other countries) learn how to play with thenew rules. It will take time for the agents in an economy to revise their inflation expectations, as it willtake time for the investor confidence to be established or for the governments and the economic in-stitutions to adapt to the new regime. During the learning period, as the agents are confronted withdifferent challenges, the costs of maintaining the regime hence the likelihood of a regime changeincrease.

After the regime has been effective for around ten years, the baseline hazard starts to decline. A pegthat survives beyond this critical threshold indicates that consistent macroeconomic policies aremaintained and unobservable imbalances are not prominent. Such a peg qualifies as “credible” and itbecomes easier to maintain the peg the longer the regime is in effect.49 This non-monotonic behaviorshows us that credibility is hard to build, mostly because of the build up of unobservable persistenteffects; but once it is established, it becomes easier to sustain. This result can be applied to Hong Kong,which has successfully maintained its pegged regime since 1994. The regime has passed the criticalthreshold level and its continuation signals for increased durability. Unless there is a deterioration ofthe fundamentals in the economy, the probability that the regime will survive is increasing.

8. Conclusion

The choice of the exchange rate regime is an important policy decision facing emerging economies.Despite frequent collapses, many emerging economies continue to follow some sort of a de facto fixedexchange rate regime. Regime switches between fixed and floating regimes are common, and there arefew emerging economies that have managed to maintain a fixed regime for more than 10 years. Theexpected duration of pegged regimes and the factors that affect their sustainability is still a centralquestion for economists and policy makers. Is this low regime persistence a result of macroeconomicand financial conditions, or is there a certain time dependence that makes fixed regimes less durableover time. Specifically, what are the factors that affect regime durability?

One problem in measuring the effect of the fundamentals on regime duration is the possibility ofsome unobservable cumulative effects associated with maintaining a fixed regime that build up overthe regime duration. These are the effects for which there is no direct measure, but their impact affectsregime sustainability. Example of such an effect is the build up of a regime’s credibility. In such a case

47 One may infer market expectation using the price differential between the spot and future contracts as Bernoth et al.,(2012). The presence of huge price differential implies that market does not have much confidence on the regime sustain-ability. However, for our set of emerging economies futures markets are not operative which precludes inclusion of thismeasure into our model. A similar argument, however, applies to the interest rate differential. We have used the interest ratedifferential relative to U.S. as one of the macroeconomic fundamentals in our duration estimates and found a significant effect.The non-monotonic duration dependence on the hazard rate persists even when the interest differential is included in theanalysis.48 We thank an anonymous referee for raising this issue.49 It can be viewed that the difficulties associated with the adoption to the new policy, i.e. the learning period, are overcome.By staying “firm enough” the government has signaled its commitments and trust towards the regime is established. Long termbenefits of the peg are to be realized as the economy has adopted to the new exchange rate policy, and the government is lesslikely to renege on a commitment of a regime that has already established credibility.


the expectation would be for a regime to become more sustainable the longer it has been effective.However, it is also possible that some negative factors that affect the continuation of the regimeadversely will build up over time and make the regime less sustainable.

The effect of such unobservable persistent effects has not been studied in the literature before. Ifsuch unobservable effects were present, any analysis that does not consider themwould be biased. Weovercome this problem by using a specific technique, survival analysis, which allows for explicitmodeling of time dependence, and the inclusion of explanatory variables that change over time. In ouranalysis, time enters as a proxy for the unobserved cumulating effects that affect regime duration. Thisallows us to investigate the relative importance of the factors on regime durability by considering theirrelation together with the effect of time itself.

Using a large sample of countries, we have shown that the survival experience of pegged regimesdiffers across economies, and that emerging economies have the highest hazard rate compared to othercountry groups. The hazard rate of the emerging economies is also the most volatile, suggesting thattime plays a more important role in the sustainability of pegged regimes in these economies. Inaddition, we estimate the hazard rate using a Semi-parametric Coxmodel with time varying covariates.Themodel gives us an estimate of sustainability as a function of macroeconomic and financial variables,and as a function of the duration of the pegged regime itself.

The results show that misalignment in the exchange rate, an increase in liability dollarization andclaims on the government reduce the lifetime of a pegged regime. On the other hand, economicgrowth, an increase in foreign reserves, improvement in the current account and a strong foreign assetposition increase regime duration. Open economies have a lower hazard rate, suggesting that the costof abandoning the peg is higher for open economies. Another important finding is that capital controlsare not effective in increasing regime duration.

Among the socio-political factors of regime duration, we find that the probability of an exit from afixed regime is less likely under social conflict and autocratic regimes. Electoral cycles and governmentideology are not found to have a significant effect on regime duration. Regime change is more likely inpresidential than in an assembly elected president and than in a parliamentary system. We also findthat the likelihood exit from a pegged regime increases with central bank independence, supportingthe argument that central bank independence and fixed regimes are exercised as alternative monetarycommitments. Even after controlling for macroeconomic and socio-political factors, emerging econ-omies exhibit non-monotonic duration dependence, where the probability of abandoning the peggedregime increases initially and then declines. This suggests that time, which captures the cumulatingunobservable effects, is an important factor for the regime duration, and also explains why fewcountries manage to sustain a pegged regime for more than a decade.

An extension of the analysis for future work would be to explore the survival experience for a finerclassification of intermediate regimes, and analyze how the degree of flexibility affects regime dura-tion. It would also be interesting to look at regime duration for alternative de facto classificationmeasures of exchange rate regimes, specifically, those that do not allow one-time devaluations withinthe same regime.

So far, the literature has focused on the external factors of regime duration. However, this paper haspresented that policy makers also need to consider the time dependence of regime duration. Knowl-edge of the circumstances that make the regime unsustainable can work as early warning signals forauthorities whowant tomaintain a pegged regime or guide them on the timing of a regime change andwhen to follow tough policies. Our findings suggests that pegged regimes that have survivedmore than10 years, such as that of Hong Kong, have passed the threshold point and, unless there are unfavorablechanges in the macroeconomic and financial environment, the regime will become even more durableas time goes by.

Acknowledgments

This paper is part of my dissertation at UC Santa Cruz. I am grateful to my adviser, MichaelHutchison, and my dissertation committee members, Joshua Aizenman and Michael Dooley for theiradvice and guidance. Comments and suggestions from the seminar participants at UCSC, especially


Kenneth Kletzer, David Kaun, Aspen Gorry, Ryan Oprea, Nirvikar Singh, Ai-Ru Cheng is thankfullyacknowledged. All errors are mine.

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Duration of fixed exchange rate regimes in emerging economies

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