THE DETERMINANTS OF ORDERLY EXITS FROM FIXED AND INTERMEDIATE EXCHANGE RATE REGIMES : A
BINARY CHOICE MODEL
Irene Andreou, GATE (CNRS, Université Lyon 2)1
February 2009
Abstract
This study is aimed at the identification of the factors that influence the conditions of exit from a fixed or intermediate exchange rate regime, to a more flexible arrangement. More specifically, we try to identify the economic variables that exercise a significant influence on the probability of an orderly exit. In order to do this, we employ a binary probit estimation procedure, applied to a large number of developed and emerging economies who have exited from fixed and intermediate exchange rate arrangements since 1980. We find that the significant variables are those representing the growth rate, the evolution of domestic credit, the interest rate, the duration of the initial exchange rate regime and the incidence of exits over the years preceding and following the year during which the exit episode under consideration takes place. These results point strongly towards possible contagion and duration-dependence effects, but also to a strong influence of certain economic fundamentals.
JEL Classification : F31, F47.
Keywords : Exchange Rate Regimes, Currency Crisis, Orderly/Disorderly Exits.
1 GATE-CNRS/ENS LSH, Univ. Lyon 2, 15, Parvis René Descartes BP 7000, 96342 Lyon Cedex 07. Tél. :
1. INTRODUCTION
The collapse of the Bretton Woods System of fixed exchange rates in 1973 marked the beginning of some profound modifications in the world economic landscape, not the least of these being changes concerning countries’ exchange rate regimes. Though right after the collapse of the system most developing and emerging market countries continued to peg the value of their currencies to the currency of their major trading partners, the wave of financial liberalisation and capital movements which swept the world during the 1980s and 1990s has given some countries cause to review and adapt their exchange rate policies.
More specifically, the widespread occurrence of financial and currency crises that has accompanied liberalisation, and that has adversely affected both developed and developing countries during the last decade has brought the issue of optimal exchange rate policy to the centre of discussions in international macroeconomics. And the main hypothesis that has emerged from this wave of research, following the publication of several notable works, is the
“two corners strategy”, which supports the idea that in the current economic context only two exchange rate regimes are viable : either hard pegs such as currency boards or dollarisation, or completely flexible exchange rates. According to this hypothesis, any other form of intermediate exchange rate regime such as adjustable pegs and crawling bands is unsustainable, for it soon becomes the target of severe pressures and speculative attacks.
This view, however, remains open to discussion. Indeed, empirical observation has revealed that while countries may accept the theoretical merits of this hypothesis, they do not always follow its recommendations in practice. In the relative literature, this has been attributed to “fear of floating” (Calvo and Reinhart, 2002) or “fear of pegging” behaviours.
Going one step further, some authors have even sought to question the proposition that intermediate exchange rate regimes are more crisis-prone than the corner solutions (see, for example, Bubula and Otker-Robe, 2003).
This debate may rage on, but empirically, it remains true that in recent years an increasing number of countries have sought to exit rigid exchange rate arrangements towards the more flexible end of the spectrum, either voluntarily or after having been forced to do so by speculative attacks and financial or currency crises. And while an important part of the literature on exchange rates has focused on the debate concerning optimal exchange rate policy, fewer authors have sought to address the question of moving towards greater exchange rate flexibility for countries that have decided to abandon their fixed exchange rates, and at
the same time avoiding the crises that can be devastating for the economy of a country. In other words : the question of orderly exits from fixed and intermediate exchange rate regimes.
This is precisely the subject matter of the present study. The objective here is not to determine the optimal exchange rate regime, or the reasons that might prompt a country to move towards a more flexible exchange rate, but rather, to determine the factors that might facilitate this transition once a country has decided to undertake it. More specifically, the purpose is to identify the macroeconomic variables that increase the probability of an orderly exit from a fixed or intermediate exchange rate regime, in other words, an exit which is not accompanied by any sort of major crisis. When these variables have been identified, it might then be possible for a country to plan and time its exit in such a way as to increase its chances of it being orderly. This will depend on the evolution of these significant variables.
So to identify these important variables, we take the sample of exits that have occurred in developed, emerging and developing economies since 1980, and adopt a binary choice approach to identify the determinants of an orderly exit among a set of macroeconomic variables, an orderly exit being defined as a calm transition, and a disorderly exit being one that is accompanied by marked exchange rate depreciation. Two points are worth noting here.
First, the definition of an exit itself has long been one of the major problems plaguing this particular strand of literature, the reason for this being simply the fact that countries do not always actually do what they say. Therefore, relying on a de jure classification of exchange rate regimes to define an exit would obviously be insufficient. It is for this reason that we choose to adopt the Reinhart and Rogoff de facto classification scheme (2002), to help identify exits. Second, the explanatory variables considered in this paper as plausible determinants of orderly exits are, for the most part, quite classic, in that they stem from first, second and third generation crisis model considerations. The innovative variables used here, if they might be called thus, are the variable taking into account the duration of the peg itself as a determinant of the conditions of exit, and the one accounting for possible contagion effects, in the form of the incidence of exits over the years preceding and following the year during which the exit episode under consideration takes place. All the explanatory variables are incorporated in a probit specification to identify the ones influencing significantly the conditions of exit.
The next section of this paper will present a brief overview of the literature that has concerned itself with the question of exits from fixed and intermediate exchange rate regimes.
Section 3 will present the methodology in more detail, including the definition of exits, the
model, considered as plausible determinants of the conditions of exit. The main results will be presented in Section 4, and Section 5 will conclude.
2. LITERATURE REVIEW
The literature focusing on the specific question of exits from fixed and intermediate exchange rate regimes to more flexible arrangements has not been precisely abundant. The first authors to address the issue of exits were Klein and Marion (1994, 1997), who carried out an empirical investigation of the duration of exchange-rate pegs in 16 Latin American Countries and Jamaica, trying to identify the factors that influence peg duration using logit analysis. Their approach is interesting, in that they include in their analysis a variable on peg duration itself as a determinant of devaluation, as well as structural and political variables.
However, they only examine the determinants of de-pegging, or devaluation, which includes both realignments within an exhange rate regime and exits to a more flexible regime, without making any distinction between the two categories. Moreover, they do not examine the conditions under which de-pegging is orderly or not.
In much the same spirit, Collins (1996) also employs a logit specification to examine the determinants of exchange rate regime choice and shifts to more flexible exchange rates in 24 Latin American and Caribbean countries. The author develops a stylised model of regime choice based on the perceived losses from exchange rate misalignment incurred under fixed versus more flexible regimes, and finds that misalignment indicators (current account deficits, inflation, etc) have often been associated with moves to more flexible exchange rates, especially in the period 1978-1986. Again, no distinction is made between orderly and disorderly transitions.
Another closely related paper is that by Masson and Ruge-Murcia (2003), which also focuses on the determinants of exits, and tries to explain the transitions between exchange rate regimes using a Markov chain model with time varying transition probabilities, these being parameterised as non-linear functions of the various explanatory variables suggested by the currency crisis and OCA (Optimal Currency Area) literature. This is an interesting approach, and the transition probabilities are also used to describe the long-run distribution of exchange rate regimes.
Coming closer to the question of the conditions of exit, Eichengreen and Masson (1998), and Eichengreen, Masson, Savastano and Sharma (1999), emphasize that disorderly
exits are usually those undertaken in the context of macroeconomic pressure, the problem being that countries hesitate to leave a peg while it is still working well, and only consider the exit option when the peg comes under speculative pressure. By then, it is often too late.
Similarly, Rebelo and Végh (2002) study the optimal time for abandoning a fixed exchange rate when a fiscal shock occurs that renders the peg unsustainable. Their approach differs from other papers considered here in that they use an optimising small-economy model, but the message is essentially the same : the sooner the peg is abandoned after the shock, the better.
Two more closely related papers are Duttagupta and Otker-Robe (2003) and Detragiache, Mody and Okada (2005). Both use multinomial logits. The first paper examines the conditions of exits both to and from fixed exchange rate regimes, and supports the view that disorderly exits are usually preceded by deteriorating economic fundamentals ; economic and financial conditions, as well as regime duration do exercise an influence on exit conditions. The second paper imparts quite different conclusions : the authors here attempt to estimate the difference between orderly and disorderly exits, also incorporating a variable on duration, using the no exit case as the reference situation. Overall, their model indicates that circumstances of orderly and disorderly exits have been broadly similar. This conclusion is also more or less supported by Asiçi, Ivanova and Wyplosz (2005), who find no evidence that samples of orderly and disorderly exits come from different populations. It is worth noting here that this paper also tries to deal with the self-selection problem by employing the Heckman two-step procedure, allowing for the estimation of both the probability of exit and the effects of the exit.
The closest to the present paper is the one by Asiçi and Wyplosz (2003), which uses a logit specification to estimate the effects of different macroeconomic variables on the conditions of exits from fixed and intermediate exchange rate regimes. The main differences are twofold. First, the present paper adopts a different definition of exits which is less restrictive, thereby allowing for the identification of more exit episodes that may potentially carry useful information. Second, it introduces new explanatory variables accounting for duration of the peg itself and contagion effects, as measured by the incidence of exits over the years preceding and following the year during which the exit episode under consideration takes place.
The next section delves deeper into the question of the determinants of the conditions of exit, presenting the methodology and data that will be used in the estimation.
3. METHODOLOGY AND DATA
Definition of an exit
The first step in carrying out the analysis on the determinants of orderly exits is, naturally, the definition of an exit episode. This implies the need of having data on countries’
exchange rate regimes at different points in time. As mentioned earlier in the introduction, what countries say usually differs from what countries actually do, sometimes quite substantially. And this is the reason that relying on the IMF’s de jure classification of exchange rate regimes would be insufficient for the purposes of this study. Luckily, apart from the IMF’s de facto classification, several authors have also undertaken the construction of their own de facto classification schemes, and here we will use one of these, namely, Reinhart and Rogoff’s (2002) natural classification of exchange rate regimes. This classification has the advantage of relying on market-determined exchange rates to identify the actual exchange rate regime, and is given in Table 1, in order of increasing flexibility.
Table 1 : R&R De Facto classification of Exchange Rate Regimes
Category Characteristics
1 No separate legal tender
2 Pre-announced peg or currency board
3 Pre-announced horizontal band that is narrower than or equal to ±2%
4 De facto peg
5 Pre-announced crawling peg
6 Pre-announced crawling band that is narrower than or equal to ±2%
7 De facto crawling peg
8 De facto crawling band that is narrower than or equal to ±2%
9 Pre-announced crawling band that is narrower than or equal to ±2%
10 De facto crawling band that is narrower than or equal to ±5%
11 Moving band that is narrower than or equal to ±2%
12 Managed floating 13 Freely floating 14 Freely Falling 1
1 The “freely falling” category corresponds to an annual inflation rate of 40%.
Source : Reinhart and Rogoff, 2002.
Following this classification scheme, here we will follow the definition of an exit proposed in Asiçi and Wyplosz (2003), which considers that an exit episode occurs when a country shifts from categories 1 to 11 to categories 12 to 14. But with a twist. Whereas Asiçi and Wyplosz adopt a three-year window, including at least two years in the former regime and one year in the latter, we do away with this window, which might be considered a little restrictive, thus overlooking certain interesting exit episodes. Instead, we require a country to have spent only one year in the fixed regime and at least six months in the flexible one to consider that it is an exit episode. Other definitions are, of course, possible, but the justification given for the adoption of this specific definition is that a shift to categories 12, 13 or 14 represents the abandonment of any systematic and explicit commitment by the monetary authorities to maintain the exchange rate at a specific level or within a band, however narrow or wide it might be. As for the time window used, its choice might be considered arbitrary, but a more detailed study of the economic history of the countries considered, confirms that the one used here is highly pertinent to the purposes of this study.
The distinction between orderly and disorderly exits
In the probit specification that is adopted in this paper, the dependent variable are the conditions of exit (COND), which can be orderly, ie. calm, or disorderly, that is to say, accompanied by a currency crisis. The conditions of exit are in turn influenced by a number of explanatory variables, to be defined in the next sub-section. But first, we need to adopt a criterion that permits the distinction between orderly and disorderly exits.
The criterion that is usually adopted in the literature on exchange rate regimes is the evolution of the market-determined exchange rate. More specifically, an exit is considered as peaceful if the depreciation of the market-determined exchange rate over the six months preceding the exit and the six months following it does not exceed 25%. It is important to consider the evolution of the exchange rate before and after the actual exit date because usually exchange rates start depreciating well in advance, and this depreciation needs to be captured in order to correctly distinguish between orderly and disorderly exits.
This definition, combined with the earlier definition of an exit, leads to the identification of 64 exit episodes in developed, emerging, and developing countries between 1980 and 2007, of which 20 are orderly and 44 are disorderly. The peaceful exits are coded 1, and the troubled ones 0. Therefore :
COND = 1 if (ERt+6 – ERt-6)/ERt-6 * 100 < 25%
= 0 otherwise (1)2
Two points are worth making here. First, while this is the most widely used technique for determining whether an exit is orderly or not, it is not the only one. More specifically, the idea underlying this approach is that an exit is disorderly when a currency crisis follows it.
But this means disregarding to a certain extent other macroeconomic variables. It is entirely possible that the currency might not experience an excessive depreciation but that the evolution of other macroeconomic variables might justify the characterisation of the exit episode as a disorderly one. To put these concerns to rest, we try an alternative technique to distinguish between orderly and disorderly exits. Namely, taking the Reinhart-Rogoff classification mentioned previously, we consider that an exit is orderly when the new regime is either category 12 or 13, and disorderly when the new regime is category 14. Why?
Because this latter category is accompanied by inflation rates exceeding 40%, thus precluding any definition of this type of exit as orderly. Comparing the two lists of exit episodes thus obtained3, we observe that they are almost identical, with the exception of a couple of cases.
These cases are classified as disorderly by the exchange rate technique, but orderly by the alternative one, meaning that the inflation rate did not exceed 40%. We therefore delve deeper into the history of these two particular cases and discover that given the evolution of other variables, logic dictates that they be classified as disorderly. It is therefore possible to conclude that the exchange rate technique does very well with the distinction between orderly and disorderly exits, and moreover, it avoids the problem of using similar variables in the definition of both the dependent and explanatory variables.
The second point concerns the number of orderly and disorderly exits identified. The division of the sample into these two categories is arguably not very balanced, but it might also be argued that this is only natural. Indeed, many studies, some of which have been mentioned earlier, point out, in no uncertain terms, the fact that many countries might only decide to abandon their peg when they are forced to do so. It is counter-intuititive to leave a peg while it is working well. But it might also be economically rational, since this is usually the time which maximises the chances of the exit being orderly. Therefore, since few
2 In the calculation of the depreciation of the exchange rate we take (ERt+6 – ERt-6)/ERt-6 instead of (ERt-6 – ERt+6)/ERt-6 because, the exchange rate being in uncertain quotation (a quotation technique which consists in expressing the value of one foreign unit against a variable value of national units), its level needs to rise in order for it to depreciate.
3 Not included here, but available on request to the author.
countries have followed this logic, it becomes clear why the exits in the sample are, in majority, disorderly.
Table 2 summarises the exit episodes identified by our definitions, specifying the name of the country concerned for each exit, as well as the month and the year of its occurrence.
Table 2 : The Incidence of Exits
COUNTRY DATE CONDITIONS COUNTRY DATE CONDITIONS
Algeria January 1988 1 Lao January 1997 0
Argentina December 2001 0 Madagascar July 1985 1
Australia November 1982 1 Malawi August 1997 0
Brazil February 1999 0 Malaysia August 1997 0
Burundi May 1996 1 Mexico February 1982 0
Chile June 1982 0 Mexico January 1995 0
Chile September 1999 1 Moldova June 1998 0
Colombia October 1983 1 Myanmar May 1983 1
Costa Rica September 1980 0 Myanmar April 1988 0
Czech Republic May 1997 0 Myanmar January 1993 0
Dominican Rep. September 1982 1 Myanmar August 1996 0
Dominican Rep. July 1987 0 New Zealand March 1985 1
Dominican Rep. November 2003 0 Paraguay September 1981 1
Ecuador March 1982 0 Paraguay March 1989 0
Ecuador October 1997 0 Philippines October 1983 0
El Salvador August 1982 1 Philippines July 1997 0
Finland September 1992 0 Poland June 1991 0
Greece July 1981 0 Poland April 2000 1
Guatemala December 1984 0 Singapore December 1998 1
Guatemala June 1989 0 Suriname May 1982 1
Guinea May 2000 1 Suriname February 1998 0
Guinea-Bissau January 1993 0 Sweden December 1992 1
Haiti May 1993 0 Syria June 1982 1
Honduras March 1990 0 Thailand July 1997 0
Iceland October 2000 1 Turkey February 2001 0
Indonesia August 1997 0 UK September 1992 1
Israel September 1986 0 Uganda October 1989 0
Italy September 1992 0 Uruguay November 1982 0
Jamaica October 1990 0 Uruguay December 1991 0
Jordan October 1988 0 Uruguay January 2002 0
Kenya January 1987 1 Venezuela February 1983 0
Korea December 1997 0 Venezuela February 2003 0
Source : Author’s calculations.
Explanatory Variables
Now let us define the exogenous explanatory variables that are thought to influence the conditions of exit. These variables, eight in all, are quite classic in that they stem from first, second and third generation crisis model considerations, the two notable additions being a variable measuring the duration, in months, of the peg, and a variable attempting to take into account possible contagion effects, by measuring the incidence of exits over the years preceding and following the year during which the exit episode under consideration takes place.
• Current Account (BAL) : Measured as the percentage of (Exports – Imports) / GDP on the month (or, if monthly data is not available, on the quarter) preceding the exit date, this variable gives a snapshot view of the country’s external position. Given that a weak external sector is usually associated with currency crises, we would expect the coefficient of this variable to have a positive sign, that is to say, that it increases the probability of an orderly exit.
• GDP Growth (GRW) : This is measured as the percentage change in GDP over the year preceding the exit and, naturally, should enter the equation with a positive sign, since recessions are often associates with crises.
• Inflation (INFL) : The inflation rate is calculated as the annual percentage change in consumer prices over the year preceding the exit and should normally carry a negative sign in the estimation output, since high inflation rates increase the probability of a disorderly exit.
This is because high inflation rates are often associated with a loss in a currency’s value.
Concerning this variable, given the fact that inflation rates are extremely variable in the sample, it has been transformed into a dummy variable taking the value of 1 if the inflation rate is less than 5% and 0 other wise. This transformation seems justified given the nature of the data and enhances the performance of the indicator. More information on this in the next section.
• Variation of Foreign Reserves (RES) : This is calculated as the annual percentage change in reserves over the year before the exit, and should logically carry a positive sign.
The reason for this is that a negative variation, that is, a depletion of a country’s foreign reserves is indicative of a delicate position, such as an attempt to fend off a speculative attack on its currency. In any case, a deterioration of this variable is usually a sign of a forthcoming currency crisis.
• Interest Rate on the month before the exit (INT) : This variable is self-explanatory, and should enter the equation with a negative sign, since high interest rates are often indicative of a morose economic climate. They could also mean that the country is attempting to uphold its currency in the face of a speculative attack, or preclude an anticipated depreciation.
• Domestic Credit (CRED) : This variable is calculated as the annual percentage change in domestic credit during the year preceding the exit. It is expected to have a negative sign since, as is highlighted in third generation currency crises models, it tends to increase prior to a crisis and this rapid expansion is transformed into a contraction as the crisis unfolds.
A rapid credit expansion is generally a negative sign for an economy (exceptions always exist), and might therefore be a sign of a forthcoming crisis, currency or otherwise.
• Duration (DUR) : This is calculated as the duration of the fixed or intermediate exchange rate regime, in months, since its establishment and up to the exit date. The reason for including this is an attempt to capture any possible duration dependence effects of the exit conditions. The sign here should be positive, since longer-lived regimes are less likely to be associated with a currency crisis at the time of exit. This, however, might not always be the case.
• Incidence of Exits (EXIT) : This final variable is calculated as the number of other exits that occurred among the countries in our sample over the years preceding and following the year during which the exit episode under consideration takes place (twelve months before and twelve months after the exit date). By including this variable we are attempting to capture possible contagion effects, so the coefficient here should carry a negative sign. This might not be true if orderly exits are grouped in a relatively limited period of time, but this does not seem to be the case in our sample.
All variables are calculated using the IMF’s Financial Statistics, with a monthly frequency wherever possible, and quarterly frequency otherwise. Table 3 gives some preliminary descriptive statistics on the explanatory variables used in this study. Overall, these results seem to support what theory would lead us to expect, but these observations need to be supported by the estimation results, presented in the next section.
Table 3 : Descriptive Statistics for the Macroeconomic Explanatory Variables
VARIABLE Orderly Disorderly
(EXP-IMP)/GDP (%, value on quarter/year closest to exit)
Average -0,0346 -6,1949
Standard Deviation 8,6262 7,5794
GDP Growth (%, annual) Average 9,9016 3,7578
Standard Deviation 10,2267 16,2380
Inflation (annual % change in consumer prices, during year before exit )
Average 8,4619 19,5449
Standard Deviation 6,5980 23,8094
Variation of Reserves (annual % change during year before exit)
Average 4,3165 -9,1719
Standard Deviation 59,3839 40,7269
Interest Rate on month before Exit Average 10,0106 25,1386
Standard Deviation 7,0777 14,1446
Domestic Credit (annual % change) Average 14,6895 32,5216
Standard Deviation 16,5591 30,4243
Duration (in months) Average 159,9 58,7727
Standard Deviation 57,2537 53,6751
Incidence of Exits (contagion) Average 4,55 6,8636
Standard Deviation 2,1637 3,3866
Source : Author’s calculations
4. MAIN RESULTS
This section is dedicated to the presentation of the main results of the estimation of a probit model designed to test whether the probability of an orderly exit is statistically associated with the variables just examined, followed by some brief tests on the model’s pertinence.
Estimation Results
Tables 4 and 5 present the estimation outputs of the model under two different specifications. The first specification includes all of the explanatory variables considered, while the second specification includes only those found to be significant. The reason for including fewer variables in the second specification is to avoid the limitation of the degrees of freedom and therefore the less satisfactory results that would arise from the inclusion of all variables at once.
Table 4 : Results for Specification 1
Dependent Variable: COND
Method: ML - Binary Probit (Quadratic hill climbing) Sample: 1 64
Included observations: 64
Variable Coefficient Std. Error z-Statistic Prob.
C 1.869006 1.406090 1.329223 0.1838
BAL 0.022283 0.034951 0.637537 0.5238 GRW 0.044539 0.016292 2.733820 0.0063 INFL 0.002378 0.015895 0.149614 0.8811 INT -0.119078 0.044384 -2.682876 0.0073 RES 0.002477 0.004700 0.527010 0.5982 CRED -0.039996 0.022189 -1.802488 0.0715 DUR 0.010198 0.003735 2.730402 0.0063 EXIT -0.208478 0.120053 -1.736545 0.0825
Mean dependent var 0.312500 S.D. dependent var 0.467177 S.E. of regression 0.276648 Akaike info criterion 0.704548 Sum squared resid 4.209380 Schwarz criterion 1.008141 Log likelihood -13.54553 Hannan-Quinn criter. 0.824148 Restr. log likelihood -39.74953 Avg. log likelihood -0.211649 LR statistic (8 df) 52.40800 McFadden R-squared 0.659228 Probability(LR stat) 1.40E-08
Obs with Dep=0 44 Total obs 64
Obs with Dep=1 20
As it has already been mentioned, the first specification includes all of the considered variables. As Table 4 shows, the results are mitigated, but not disappointing. Indeed, all of the
with a positive coefficient increases the probability of an orderly exit, while an increase in those with a negative coefficient decreases this probability. In theory, this would have to be the case of the inflation, interest rate, domestic credit and exit variables. However, even though most of the variables carry the expected signs, not all of them are significant. The
“Prob.” Values indicate that the null hypothesis that each of these coefficients is equal to 0 would not be rejected at any conventional significance level for three of the eight variables : the current account, inflation and foreign reserves. As far as the current account is concerned, this result is hardly surprising. Indeed, this variable is rarely found to play a significant role in the occurrence of orderly exits in the relevant literature. A similar observation could be made about the variation of foreign reserves. We have assumed here that a decrease in foreign reserves diminishes the probability of an orderly exit mainly because a decrease in this variable might imply that the monetary authorities have used it in an effort to fend off speculative attacks on the currency. However, such an attempt might fail, resulting in a disorderly exit, or succeed, which would result in an orderly one. So the effect of a variation in foreign reserves might go either way. This is why the role of this variable in the occurrence of orderly or disorderly exits is not very clear-cut. But while the results for these two variables are not surprising, the result for inflation is. Theory would lead us to expect that a high inflation rate decreases the probability of a disorderly exit since high inflation rates are associated with a loss in the currency’s value. But in the estimation output not only the inflation variable takes an unexpected sigh but it is also highly insignificant. A possible explanation for this could be the observation that was made earlier, namely, the fact that inflation rates in the sample are highly variable, ranging from one to two hundred percent.
This is why in the next specification we attempt once more to include the inflation variable but, with a slight transformation.
Table 5 presents the results for specification 2, which includes the variables for growth, the interest rate, domestic credit, the duration of the initial regime, the incidence of exits over the years preceding and following the year during which the exit episode under consideration takes place, inflation and a constant. Here, the inflation variable has been transformed into a dummy variable (DUM) taking the value of 1 if the inflation rate does not exceed 5% and 0 otherwise. This means that our dummy variable would have to enter the regression with a positive coefficient this time, since an inflation rate that is less than 5% would logically increase the probability of an orderly exit. The choice of the threshold of 5% might seem to be arbitrary and indeed it is, to some extent. However, given the large number of countries in the sample, and the fact that in no case can they be considered as a homogeneous group of
countries, this choice might be justified. Indeed, the majority of countries in the sample are developing or emerging market economies, for which an inflation rate of 5% can hardly be considered as excessive. A threshold of, say, 2%, on the other hand, could arguably be seen as too restrictive.
Table 5 : Results for Specification 2
Here, all of the variables carry the expected signs and are significant, the growth, interest rate, duration and exit variables being significant at the 5% level, while the domestic credit and the dummy variable accounting for inflation are significant at the 10% level.
Now let’s take a closer look at each of these variables and their economic implications.
As far as the growth variable is concerned, not only does it enter the regression with the expected positive sign, but it is also highly significant. Indeed, the hypothesis that the coefficient of the growth rate is equal to 0 would be rejected at the 1% significance level. This result implies that a healthy growth rate can increase the probability of an orderly exit and
Dependent Variable: COND
Method: ML - Binary Probit (Quadratic hill climbing) Sample: 1 64
Included observations: 64
Variable Coefficient Std. Error z-Statistic Prob.
C 1.629011 1.221264 1.333873 0.1822
GRW 0.051074 0.014606 3.496740 0.0005 INT -0.130087 0.043799 -2.970114 0.0030 CRED -0.034588 0.019762 -1.750227 0.0801 DUR 0.009277 0.003817 2.430068 0.0151 EXIT -0.206058 0.091968 -2.240550 0.0251 DUM 0.921530 0.536354 1.718137 0.0858
Mean dependent var 0.312500 S.D. dependent var 0.467177 S.E. of regression 0.264660 Akaike info criterion 0.621140 Sum squared resid 3.992575 Schwarz criterion 0.857267 Log likelihood -12.87646 Hannan-Quinn criter. 0.714162 Restr. log likelihood -39.74953 Avg. log likelihood -0.201195 LR statistic (6 df) 53.74613 McFadden R-squared 0.676060 Probability(LR stat) 8.30E-10
Obs with Dep=0 44 Total obs 64
Obs with Dep=1 20
preclude the occurrence of a crisis. So economic fundamentals such as growth are not to be ignored, their deterioration possibly leading to crises, currency or otherwise. This would imply that first generation crisis models are still alive and kicking.
The interest rate variable also carries the expected sign and is highly significant. Its negative coefficient indicates that an increase in the interest rate decreases the probability of an orderly exit, which is consistent with economic theory. Since interest rates can also be used as a defensive instrument on the part of the authorities to defend the currency in the face of speculative pressure, this result confirms that exits undertaken in the context of market pressure resisted through en interest rate defence are rarely orderly.
The domestic credit variable is included in an attempt to determine whether the health of the banking sector plays any significant role in the conditions of.exit. It can be seen from table 5 that it carries the expected negative sign, indicating that an excessive increase in domestic credit increases the probability of a disorderly exit. It is also significant. This is in line with the mainstream view, which, on one hand, holds that deep and liquid financial markets strengthen the economy in the face of adverse shocks. On the other hand, they may also contribute to the volatility of capital flows, complicating matters for the monetary authorities when they are trying to exit into a more flexible exchange rate regime. An increase in domestic credit might point to exactly such a volatility. The results therefore seem to confirm that currency, banking and financial crises are closely related. It would be interesting to see what happens if a variable on capital flows (level and volatility) were included in the regression.
The results for the variables capturing the longevity of the fixed or intermediate exchange rate regime, and possible contagion effects are also promising : the variables on duration and the incidence of exits both carry the expected signs, indicating that longer-lived arrangements tend to be associated with peaceful exits, whereas the incidence of other exits over the years preceding and following the year during which the exit episode under consideration takes place, accounting for possible contagion effects, decreases the probability of a peaceful exit. Moreover, the null hypotheses that each of these coefficients is equal to 0 would both be rejected at the 5% level, suggesting that these two variables have significant explanatory power. The view that a duration-dependence effect is present and that contagion plays a significant role is therefore supported by the evidence, indicating that these effects should be studied in more detail, possibly with the aid of a more appropriate model.
Finally, a word on the dummy variable capturing the effects of the inflation rate. Not surprisingly, the transformation which eliminated the excessive variability of the inflation rate
in the data also improved the indicator’s performance. Now it is possible to affirm that a moderate inflation rate increases the probability of an orderly exit, a result which is consistent with economic theory. Indeed, not only does the coefficient carry the expected positive sign but it is also significant at the 10% level.
On the whole, the model gives satisfactory results : six out of the eight initial variables seem to have significant explanatory power as far as the occurrence of orderly / disorderly exits is concerned. This is particularly encouraging when one takes into account the fact that the countries in the sample are numerous and form a very heterogeneous group. Despite this, we nevertheless manage to pinpoint the macroeconomic variables that play an important role in the occurrence of orderly exits. These variables stem from first (growth, inflation, interest rate), second (credit) and third (contagion) generation currency crises models. The role of the duration variable seems to be particularly important, and merits further investigation.
All of the coefficients reported in the tables presenting the estimation outputs give the direction of the effects of each variable on the conditions of exit, but they should not be interpreted as the marginal effects on the probability of the exit being orderly. These effects, evaluated at the mean of each significant variable, are reported in the following table.
Table 6 : Marginal Effects of the significant variables
Variable Marginal effect at mean
Growth 0,003677
Interest Rate -0,009367
Domestic Credit -0,002490
Duration 0,000668
Incidence of Exits -0,014837
Source : Author’s Calculations
The figures in the above table should be read as follows : a one-point increase in the growth rate from its mean increases the probability of an orderly exit by 0.37%. Similarly, a one-point increase in the interest rate from its mean decreases the probability of an orderly exit by 0.94%, and a one-point increase in the domestic credit variation, from its mean, decreases the probability of an orderly exit by 0.25%. On the other hand, a one-month increase in the duration of the fixed or intermediate exchange rate regime increases the
probability of the exit being peaceful by 0.07%. This might seem to be rather low, but it should be kept in mind that at the mean duration, the probability of an orderly exit is already quite high, therefore the marginal effect of a one-month increase is probably negligible. The same observation applies for the rest of the variables4. Finally, the incidence of one additional exit during the year that the exit episode under consideration takes place seems to decrease the probability of a calm exit by 1.48%. These results seem to conform quite well with what economic theory would lead us to expect.
Testing the model’s explanatory power
Finally, apart from the information provided by the statistical significance of the coefficients of each explanatory variable taken individually, it is possible to test the explanatory power for the conditions of exit of each specification as a whole.
One way of doing this is via the Likelihood Ratio (LR) statistic, which is distributed as a χ2 statistic with k-1 degrees of freedom, where k-1 is the number of explanatory variables used in each specification, under the null hypothesis that the coefficients of the variables are all jointly equal to 0. The LR statistic, accompanied by its degrees of freedom, is given in the output for each specification and the way to test for its significance is provided by the
‘Probability (LR stat)” values. Not surprisingly, it is specification 2 that seems to have the most explanatory power (though specification 1 also does very well). Indeed, the ‘Probability (LR stat)” values (8.30E-10 and 1.40E-08 respectively) indicate that the null hypothesis that the coefficients are all jointly equal to 0 would be rejected at the 1% level, suggesting that these variables succeed in explaining the conditions of exit.
Another way of testing for the model’s explanatory power as a whole is the following : denoting the actual outcome in observation i as CONDi, with CONDi = 1 if the event occurs and 0 if it does not, and denoting the predicted probability of the event occurring pi, it is possible to calculate the percentage of outcomes correctly predicted, taking the prediction in observation i as 1 if pi is greater than 0.5 and 0 if it is less. Then the actual and predicted values are compared. Here, we will do this for specification 2. The results are presented in the following table.
4 For this reason, it is possible to calculate the marginal effects of each of the variables at the first quartile, for example. The effect might be more important, but the direction remains the same. This is why these calculations are not included here.
Table 7 : Actual and Predicted Events for Specification 2
COUNTRY pi CONDi PREDICTED CONDi ACTUAL
Algeria 0,988128 1 1
Argentina 0,006057 0 0
Australia 0,832241 1 1
Brazil 0,000014 0 0
Burundi 0,830459 1 1
Chile 0,000000 0 0
Chile 0,876400 1 1
Colombia 0,968496 1 1
Costa Rica 0,500816 1 0
Czech Republic 0,612257 1 0
Dominican Rep. 0,994649 1 1
Dominican Rep. 0,013398 0 0
Dominican Rep. 0,000002 0 0
Ecuador 0,784967 1 0
Ecuador 0,000015 0 0
El Salvador 0,999937 1 1
Finland 0,693184 1 0
Greece 0,000429 0 0
Guatemala 0,954327 1 0
Guatemala 0,159202 0 0
Guinea 0,841100 1 1
Guinea-Bissau 0,316640 0 0
Haiti 0,000232 0 0
Honduras 0,044708 0 0
Iceland 0,646837 1 1
Indonesia 0,951237 1 0
Israel 0,001609 0 0
Italy 0,741876 1 0
Jamaica 0,009164 0 0
Jordan 0,031375 0 0
Kenya 0,883700 1 1
Korea 0,016278 0 0
Lao 0,008427 0 0
Madagascar 0,997132 1 1
Malawi 0,926507 1 0
Malaysia 0,796607 1 0
Mexico 0,000004 0 0
Mexico 0,093386 0 0
Moldova 0,004304 0 0
Myanmar 0,424023 0 1
Myanmar 0,027114 0 0
Myanmar 0,293635 0 0
Myanmar 0,043605 0 0
New Zealand 0,996523 1 1
COUNTRY pi CONDi PREDICTED CONDi ACTUAL
Paraguay 0,98754512 1 1
Paraguay 0,00046689 0 0
Philippines 0,36319392 0 0
Philippines 0,38221052 0 0
Poland 3,5088E-31 0 0
Poland 0,8788369 1 1
Singapore 0,99660137 1 1
Suriname 0,97856356 1 1
Suriname 3,0889E-07 0 0
Sweden 0,05641899 0 1
Syria 0,86165059 1 1
Thailand 0,01565361 0 0
Turkey 0,00104413 0 0
UK 0,52391608 1 1
Uganda 2,7305E-12 0 0
Uruguay 4,6814E-13 0 0
Uruguay 3,1302E-05 0 0
Uruguay 7,0522E-05 0 0
Venezuela 0,37029716 0 0
Venezuela 0,00880062 0 0
Source : Author’s Calculations
As can be seen from Table 7, the results of this test are quite good. In fact, 53 out of the 64 outcomes are correctly predicted by specification 2, which gives a percentage of 83%
correct predictions.
Overall, the results for the estimations seem to be quite encouraging. However, the role of some variables seems to be not as clear as we would have liked. Certainly, the growth rate, the interest rate, the domestic credit variation, the dummy variable representing the inflation rate and the variables on duration and contagion have good explanatory power as far the conditions of exit are concerned, but the other variables that economic theory would seem to consider informative (the current account and foreign reserves), do not emerge with significant coefficients, even though these coefficients carry the expected signs.
There could be several explanations for this. First and foremost being the dataset. As mentioned earlier, the countries included in the sample form a large and heterogeneous group, and some variables that would seem to be important in one subset could be less so in another.
This is not necessarily a shortcoming, but it would be interesting to carry out more targeted investigations, considering only certain groups of countries at a time.
Second, some methodological issues. It is possible that the data might be suffering from self-selection bias, meaning that the variables that affect the probability of an orderly exit might also affect the probability of the exit being observed in the first place. To deal with
this problem, some other estimation techniques might be more appropriate, such as the use of a multinomial logit model or Heckman’s two-step estimation procedure. Moreover, there is the problem of truncation to deal with when incorporating a variable aimed at capturing any possible duration-dependence effects. More specifically, the data might be left-truncated, meaning that a fixed or intermediate exchange rate regime might have started before the beginning of our sample period, or right-truncated, meaning that the regime will still be in place at the end of the sampling period, and therefore the exit episode is not observed.
Dealing with this problem calls for the use of a more sophisticated estimation procedure.
Duration models have the advantage of dealing with the problem of self-selection and data truncation at the same time, and therefore seem a promising tool in the literature concerned with exits from fixed and intermediate exchange rate regimes. That being said, the dataset used here doesn’t seem to be suffering excessively from either bias, as the results are relativelt good.
5. CONCLUSION
The aim of this paper has been to determine the economic variables that might affect the conditions of exit from a fixed or intermediate exchange rate regime to a more flexible arrangement. More specifically, we have sought to identify the variables that significantly influence the probability of an orderly exit, as defined by our exchange rate criterion. In order to do this, we have identified 64 exit episodes in developed, emerging, and developing countries between 1980 and 2007, and attempted to identify the variables exercising a significant influence on exit conditions using a binary probit model.
On the whole, the model gives satisfactory results : six out of the eight initial variables seem to have significant explanatory power as far as the occurrence of orderly / disorderly exits is concerned. This is particularly encouraging when one takes into account the fact that the countries in the sample are numerous and form a very heterogeneous group. Despite this, we nevertheless manage to pinpoint the macroeconomic variables that play an important role in the occurrence of orderly exits. These variables stem from first (growth, inflation, interest rate), second (credit) and third (contagion) generation currency crises models. Additional robustness tests confirmed the good explanatory power of the model. The role of the duration variable seems to be particularly important, and merits further investigation.
Any limited shortcomings in the results might be due to the problems of self-selection bias and truncation, which a proportional hazard model might be more apt to deal with.
Indeed, this is a promising venue for future research.
REFERENCES
[1] Asiçi A., Ivanova N., Wyplosz C., “How to Exit from Fixed Exchange Rate Regimes?”, HEI Working Paper no. 03/2005, June 2005.
[2] Asiçi A., Wyplosz C., “The Art of Gracefully Exiting a Peg”, The Economic and Social Review, Vol. 34, No. 3, pp. 211-228, Winter 2003.
[3] Bubula A., Otker-Robe I., “Are Pegged and Intermediate Exchange Rate Regimes More Crisis-Prone”, IMF Working Paper WP/03/223, November 2003.
[4] Collins S., “On Becoming More Flexible : Exchange Rate Regimes in Latin America and the Caribbean”, Journal of Development Economics, Vol. 51, pp. 117-138, 1996.
[5] Detragiache E., Mody A., Okada E., “Exits from Heavily Managed Exchange Rate Regimes”, IMF Working Paper WP/05/39, February 2005.
[6] Duttagupta R., Otker-Robe I., “Exits from Pegged Regimes : An Empirical Analysis”, IMF Working Paper WP/03/147, July 2003.
[7] Eichengreen B., Masson P., Savastano M., Sharma S., “Transition Strategies and Nominal Anchors on the Road to Greater Exchange-Rate Flexibility”, Essays in International Finance, No. 213, April 1999.
[8] Klein M., Marion N., “Explaining the Duration of Exchange-Rate Pegs”, Journal of Development Economics, Vol. 54, pp. 387-404, April 1996.
[9] Masson P., Ruge-Murcia F., “Explaining the Transition Between Exchange Rate Regimes”, Université de Montréal, Département de Sciences Economiques, Cahier 2003-21, June 2003.
[10] Rebelo S., Végh C., “When is it Optimal to Abandon a Fixed Exchange Rate?”, Draft, April 2002.
[11] Tudela M., “Explaining Currency Crises : A Duration Model Approach”, London School of Economics and Political Science, Centre for Economic Performance, January 2001.
[12] Wälti S., “The Duration of Fixed Exchange Rate Regimes”, IIIS Discussion Paper no. 96, August 2005.
