Chapter

10 Exits From Heavily Managed Exchange Rate Regimes: How Good Has the Going Been?

Author(s):
Charalambos Tsangarides, Carlo Cottarelli, Gian Milesi-Ferretti, and Atish Ghosh
Published Date:
September 2008
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Author(s)
ENRICA DETRAGIACHE, ASHOKA MODY and EISUKE OKADA 

As more and more countries, especially those with emerging markets and developing economies, abandon tightly managed exchange rate regimes in favor of more flexibility, the question of when and how to effect the transition is widely debated. Most recently, the debate has been particularly intense in reference to China, whose authorities have voiced their intention of moving away from the U.S.-dollar peg, but have done so only very gradually so far.1

A widely accepted piece of advice has been to exit when the going is good. Exit is best undertaken when the exchange rate is not under speculative pressure to depreciate; better still, some would argue, exit should occur when the exchange rate is likely to strengthen (Eichengreen and others, 1998). In his characteristically lucid manner, Eichengreen (2004, p. 5) explains:

There is never a convenient time to abandon a currency peg following an exchange-rate-based stabilization. But the easiest time to do so is when capital is flowing in and the exchange rate is strong. If the authorities wait too long, capital flows may have turned around in response to a deceleration in growth, problems in the banking system, or another negative event. At that point, having a more flexible exchange rate will be essential. But obtaining it smoothly, in the face of adverse speculation and without further disturbing already volatile expectations, will be nigh well impossible.

The reason for avoiding exit under pressure to depreciate is that when a country does so, national authorities may lose control, confidence in the country’s prospects may weaken, and the costs may be borne in the form of a heavy output loss (typically lasting between one and two years).

In practice, judging whether conditions are right to change a country’s exchange rate regime and choosing what alternative regime to adopt are likely to be tricky, and the change in regime may bring about speculative pressures. Given the risks involved, policymakers may prefer to keep the status quo as long as the times are good (AgÉnor, 2004). In particular, moving from a peg to a band may create the expectation that the band is likely to be widened in the future, inviting speculators to test the government’s resolve to maintain the band. Equally, when the reason for introducing flexibility is to allow for currency depreciation to counter the overvaluation of the real rate, the determination of the extent of overvaluation is typically not straightforward. Frankel (1999) raises a more serious question in regard to the viability of the original strategy in cases of a decision to peg or manage the exchange rate when the peg is deployed to break persistent high inflation. If the peg in this case is to be followed by a flexible regime, with knowledge that a future depreciation is likely, he asks: “will the stabilization be credible in the present?” (p. 28). Thus, writing in September 1999, when by the standards of the second half of the 1990s conditions were calm, he concludes: “Argentina seems to have done well, all things considered, by sticking with a binding commitment” (p. 27).

Our purpose in this chapter is a simple one: to characterize the types of exits from a heavily managed to a more flexible regime that occurred between 1980 and 2002. Have countries followed the approach of exiting when economic conditions are favorable? If they have not, has the outcome always been a disorderly exit with high costs? And to the extent that we observe both orderly and disorderly exits, are there identifiable differences in the conditions under which these two forms of exit occur?

We use the Reinhart and Rogoff (2004) system to classify countries according to exchange rate regimes. This system has several advantages relative to other systems that classify countries according to the same criterion. For instance, it captures the actual behavior of monetary authorities and the foreign exchange market rather than the official exchange rate classification reported to the IMF, it provides a very detailed breakdown of observed regimes, and it takes into account the presence of active parallel markets.2 Obviously, an accurate regime definition is essential for identifying and dating transitions to more flexible arrangements. For our purposes, an additional advantage of the Reinhart-Rogoff classification is that it offers a natural definition of orderly and disorderly exits, since it identifies a “freely falling” category, one in which a country experiences a high rate of inflation and/or a speculative attack and large depreciation of its currency. We take the transition to the freely falling category to be a “disorderly” one, whereas all other transitions to more flexible regimes are considered orderly.

We have three main findings. First, a simple tabulation of the data shows that over the roughly 20-year period 1980–2002, the vast majority of exits have been followed by a depreciation of the nominal exchange rate. Second, in about half the episodes, the exit was orderly and did not lead to a currency crisis or high inflation within the following 12 months. Third, based on a multinomial analysis that distinguishes among no exit, orderly exit, and disorderly exit, we find no robust differences between the general circumstances of orderly and disorderly exits. On the other hand, the time periods in which exits (of either type) occur do differ from tranquil times, in that when exits occur, the real exchange rate is more overvalued, the country loses reserves, and the government steps up its borrowing from the central bank. In addition, we find exits to be more likely in periods of high international interest rates and when pegs are not yet well established.

These findings indicate that, in practice, countries do not heed the advice to move away from heavily managed exchange rate regimes when the going is good, but rather wait until the parity is under pressure to depreciate. Nonetheless, the outcome is not always disastrous, as in about half of the cases, a crisis is averted. Unfortunately, the data do not offer clear indications as to what circumstances most improve the chances of an orderly transition to more exchange rate flexibility.

The rest of this chapter is organized as follows. In the second section, we present data on episodes in which more exchange rate flexibility has been introduced. The third section describes the empirical methodology and the explanatory variables. The fourth section presents the results of a multinomial and binomial logit analysis. The fifth section presents a review of related research. The final section offers some concluding remarks.

The Frequency and Features of Orderly and Disorderly Exits

The Reinhart-Rogoff “natural” classification categorizes exchange rate regimes into 6 coarse and 15 fine groups (Table 10.1). We define an exit as a move to a more flexible exchange rate regime. More specifically, an exit occurs when a country moves from coarse categories 1 or 2, corresponding to pegs or heavily managed exchange rate regimes, to coarse categories 3–6, corresponding to more flexible regimes. Furthermore, a disorderly exit is one in which the transition is to the “freely falling” category, either immediately or within 12 months of the original exit. According to Reinhart and Rogoff (2004), the exchange rate is freely falling if the rate of currency depreciation is great, there is high inflation, or a speculative attack against the currency takes place.3 In the empirical work, we examine the sensitivity of the results to alternative definitions of exit.

In the period 1980–2002, there were 156 periods of contiguous observations with a heavily managed exchange rate regime (categories 1–2), often more than one per country. Of the total number of episodes, 62, or 40 percent, ended in an exit. Flexibility was introduced in an orderly manner in 31 instances, and the remaining 31 were disorderly exits (Table 10.2).4 Thus, in contrast with the earlier findings of Eichengreen and others (1998), our sample based on the Reinhart-Rogoff regime classification indicates that orderly exits are possible and just as common as disorderly ones.5

The average duration of an episode was 199 months, and episodes ending in disorderly exits were considerably shorter than those ending in orderly ones (Figure 10.1). Most of the exits occurred in nonemerging developing countries, but this is just a reflection of the larger number of such countries in the sample.6 In relative terms, exits were more frequent among emerging countries. The highest concentration was that of disorderly exits in emerging markets during the 1980s, corresponding to the international debt crisis.

Table 10.1.Reinhart-Rogoff Natural Exchange Rate Classification
CoarseFine
11No separate legal tender
12Preannounced peg
13Preannounced horizontal band
14De facto peg
25Preannounced crawling peg
26Preannounced crawling band (narrow)
27De facto crawling peg
28De facto crawling band (narrow)
39Preannounced crawling band (wide)
310De facto crawling band (wide)
311Moving band
312Managed floating
413Freely floating
514Freely falling
615Dual market/no parallel market data

Next, to get a sense for whether exits occurred when the parity was under pressure to appreciate or depreciate, we turn to the behavior of the nominal exchange rate following the change in regime. The fifth column in Table 10.2 shows the rate of change of the exchange rate vis-à-vis the reference currency (usually the U.S. dollar) in the six months following the exit relative to the previous six months. In all cases except three, exits, including orderly ones, were followed by a depreciation of the nominal exchange rate. Similar results are obtained when a 12-month window is used. This suggests that, contrary to the policy recommendation provided by the conventional wisdom, countries facing pressures to let the exchange rate appreciate rarely respond by making the exchange rate regime more flexible. It is only when there are pressures to devalue that policymakers go for regime change.

What might explain this asymmetry is clear: when a country’s exchange rate is weak, the country will run out of reserves unless action is taken, whereas when the exchange rate is strong, there is no identifiable upper bound to how much foreign exchange can be accumulated. Nonetheless, the near absence of cases in which a movement toward flexibility is followed by an appreciation is perhaps surprising. Of course, this behavior may just reflect the myopia of policymakers or other distortions and need not be optimal.7

Figure 10.1.Exits by Duration

(Average across all countries in category; indices, 2000 = 100)

Sources: Reinhart and Rogoff (2004) and authors’ calculations.

Next, we turn to an econometric model to conduct a more rigorous investigation of the circumstances that lead countries to introduce greater exchange rate flexibility and those that determine the orderly or disorderly character of the transition.

Determinants of Exits

Methodology and Data

To study the circumstances surrounding the introduction of more flexibility into exchange rate regimes, we estimate a multinomial logit econometric model that distinguishes periods in which orderly and disorderly exits occur from “tranquil” times. In this model, the probability of exiting a heavily managed regime in an orderly or disorderly fashion at a given time relative to the probability of not exiting is estimated as a function of several explanatory variables. More formally, let t, o, and d denote the three possible events (tranquil time, orderly exit, and disorderly exit), let β be a vector of coefficients to be estimated, and let X be a vector of explanatory variables. Choosing tranquil times as the base category, the multinomial logit model can be written as

Table 10.2.Exits to More Flexible Exchange Rate Regimes, 1980–2002
CountryYearMonthFromToEpisode

Length

(months)
Exchange

Rate

Change

(six months

postexit)
Anchor1
Orderly exits
Australia1982NovemberDe facto crawling band (narrow)Managed floating5156.8USD
Burundi1985SeptemberDe facto crawling band (narrow)De facto crawling band (wide)549-7.1USD
China1981MarchDe facto crawling band (narrow)Managed floating8712.6USD
Colombia1983OctoberDe facto crawling band (narrow)Managed floating11513.0USD
Czech Republic1996MarchDe facto crawling band (narrow)De facto crawling band (wide)67-1.9DM
El Salvador1982AugustDe facto crawling band (narrow)Managed floating5120.0USD
Greece1981JulyDe facto crawling band (narrow)Managed floating37510.2USD
Guinea2000MayDe facto crawling band (narrow)Managed floating1683.4USD
Haiti1989MayDe facto crawling band (narrow)De facto crawling band (wide)5930.0USD
Honduras1985AprilPreannounced pegDe facto crawling band (wide)4180.0USD
Hungary1999JanuaryDe facto crawling band (narrow)Preannounced crawling band (wide)560.6DM
Iceland2000OctoberDe facto crawling band (narrow)Managed floating1707.8DM
raq1982JanuaryPreannounced pegManaged floating5050.0USD
Israe1989JanuaryDe facto crawling band (narrow)Preannounced crawling band (wide)2513.8USD
Israe1991FebruaryDe facto crawling band (narrow)De facto crawling band (wide)1211.5USD
Jamaica1993MayDe facto pegDe facto crawling band (wide)512.6USD
Kenya1987JanuaryPreannounced pegManaged floating5656.0SDR
Madagascar1985JulyDe facto crawling band (narrow)Managed floating14410.0FRC
Mauritania1983NovemberDe facto crawling band (narrow)De facto crawling band (wide)5274.6USD
Mauritius1982JuneDe facto crawling band (narrow)De facto crawling band (wide)785.0USD
Nepal1992MarchDe facto crawling band (narrow)De facto crawling band (wide)1260.0USD
New Zealand1985MarchDe facto crawling band (narrow)Managed floating543-15.6AUS
Paraguay1999JulyDe facto crawling pegDe facto crawling band (wide)10213.1USD
Philippines1993MayDe facto crawling band (narrow)De facto crawling band (wide)999.7USD
Singapore1998DecemberDe facto crawling band (narrow)Managed floating7080.2USD
Slovak Republic1997SeptemberDe facto crawling band (narrow)De facto crawling band (wide)540.1DM
Sri Lanka2000JanuaryPreannounced crawling band (narrow)Preannounced crawling band (wide)3313.8USD
Sweden1992DecemberDe facto crawling band (narrow)Managed floating49022.8DM
United Kingdom1992SeptemberPreannounced horizontal bandManaged floating2412.0ECU
Venezuela1983MarchPreannounced pegManaged floating5190.1USD
Zimbabwe1983JulyDe facto crawling band (narrow)Managed floating418.9USD
Disorderly Exits
Argentina1981MarchPreannounced crawling pegFreely falling2794.3USD
Argentina1986AprilPreannounced pegFreely falling1113.7USD
Argentina2001DecemberPreannounced pegFreely falling129114.8USD
Brazi1986SeptemberPreannounced pegFreely falling78.7USD
Brazi1989ApriPreannounced pegFreely falling4146.2USD
Brazi1999FebruaryPreannounced crawling band (narrow)Freely falling5644.5USD
Chile1982JunePreannounced pegFreely falling5346.8USD
Costa Rica1980OctoberPreannounced pegManaged floating27838.4USD
Ecuador1982MarchPreannounced pegFreely falling10918.5USD
Ecuador1997OctoberDe facto crawling band (narrow)Freely falling811.1USD
Finland1992SeptemberDe facto crawling band (narrow)Freely falling30019.8DM
Guatemala1989JuneDe facto crawling pegFreely falling124.3USD
ndonesia1997AugustDe facto crawling pegFreely falling22588.0USD
Israel1986SeptemberPreannounced crawling band (narrow)Freely falling122.4USD
Italy1992SeptemberDe facto crawling band (narrow)Freely falling11717.1DM
lamaica1990OctoberPreannounced pegFreely falling13815.6USD
Ionian1988OctoberPreannounced pegFreely falling58635.1SDR
Korea, Republicof 1997DecemberDe facto crawling pegFreely falling28464.3USD
Lao P.D.R.1997JanuaryDe facto crawling band (narrow)Freely falling809.4USD
Malawi1997AugustPreannounced pegFreely falling3219.5USD
Malaysia1997AugustDe facto crawling band (narrow)Freely falling69236.9USD
Mexico1982FebruaryDe facto crawling pegFreely falling6073.1USD
Mexico1994FebruaryDe facto pegPreannounced crawling band (wide)2635.9USD
Moldova1998JuneDe facto pegFreely falling4014.7USD
Philippines1997JulyDe facto pegFreely falling2323.7USD
Poland1991JunePreannounced pegFreely falling1816.9USD
Tajikistan1998OctoberPreannounced pegFreely falling1222.5USD
Thailand1997JulyDe facto pegFreely falling49942.4USD
Uganda1989OctoberPreannounced pegFreely falling3873.9USD
Uruguay1982DecemberPreannounced crawling pegFreely falling50137.0USD
Uruguay1991DecemberPreannounced crawling band (narrow)Freely falling1324.4USD
Sources: Reinhart and Rogoff (2004) and authors’ calculations.Note: There are some episodes in which depreciation rates appear to be zero. These are cases of multiple exchange rates in which the market rate was depreciating while the official rate was not.

The anchor currencies are U.S. dollar (USD), deutsche mark (DM), Australian dollar (AUS), Special Drawing Rights (SDR), French franc (FRC), and European Currency Unit (ECU).

This exit episode was followed by “freely falling” within the subsequent twelve months and thus was considered disorderly.

Sources: Reinhart and Rogoff (2004) and authors’ calculations.Note: There are some episodes in which depreciation rates appear to be zero. These are cases of multiple exchange rates in which the market rate was depreciating while the official rate was not.

The anchor currencies are U.S. dollar (USD), deutsche mark (DM), Australian dollar (AUS), Special Drawing Rights (SDR), French franc (FRC), and European Currency Unit (ECU).

This exit episode was followed by “freely falling” within the subsequent twelve months and thus was considered disorderly.

for e = d, o. So for each outcome (orderly exit and disorderly exit) the coefficients to be estimated (the βs) represent the effect of a change in the explanatory variable on the logarithm of the ratio of the probability of that outcome to the probability of a tranquil observation.

The model is estimated on a panel of observations containing all the episodes during which the exchange rate was tightly managed in the Reinhart-Rogoff sample over 1980–2002. Observations are classified as exits if they refer to the month before more flexibility is introduced. In two alternative specifications, observations are classified as exits for the entire six- or twelve-month period before a transition. The wider window takes into account that the changes in the explanatory variables that trigger the change in regime may occur some time before the actual transition. Comparing the three alternative specifications also allows us to assess whether the factors associated with the exits were exercising their influence within the different time horizons represented by the specifications. Exits are considered disorderly if the new regime is coded as freely falling either right away or within the 12 months following the exit. Exits are classified as orderly otherwise.

The multinomial logit also allows us to test formally, through a Wald test, the hypothesis that orderly and disorderly exits are indistinguishable events with respect to the independent variables. Should this hypothesis not be rejected, then the data would indicate that the appropriate model is a bivariate logit that discriminates only between tranquil times and exits, not between types of exit.

Although exit episodes in the Reinhart-Rogoff data sets number over 60, data limitations constrain our econometric exercise to 40 episodes in the benchmark specification, of which 18 are orderly and 22 disorderly.8 Thus, although the total number of observations is large (about 10,000), the sample is still quite small because exits are rare events. Estimation is by maximum likelihood. The standard errors are clustered by country to allow for possible correlation of the error term within each country. We find clustered standard errors to be markedly larger than robust standard errors in these data, suggesting that failure to cluster may lead to overrejection of the null hypothesis of no significant effect.

The Explanatory Variables

A number of factors identified in the exchange rate regime literature have been included as explanatory variables. The deviation of the real exchange rate from a moving average of the previous five years captures a possible misalignment in the real exchange rate that may contribute to the imbalance of the country’s external accounts. Changes in foreign exchange reserves indicate pressures on the parity. Real economic conditions and trade performance are captured by export growth. Government borrowing from the central bank is introduced to test whether exits are triggered by potential inconsistencies between fiscal and exchange rate policy, as emphasized by first-generation models of balance of payments crises. Private credit growth may signal a credit boom ushering in financial sector vulnerabilities that destabilize the exchange rate regime, as in the Asian crises. Trade openness may also affect the size of external shocks and the ability of an economy to respond to such shocks under limited exchange rate flexibility. GDP per capita controls for the level of development of the country, and the U.S. real interest rate captures global macroeconomic conditions. Finally, the logarithm of the number of months since the peg began measures the duration of the exchange rate regime, which may affect the credibility of the regime, or it may proxy unobserved country characteristics that affect the likelihood of exit. Details on the construction of the explanatory variables, as well as sources, are provided in Appendix 10.1.

Table 10.3 shows the means and standard deviations of the explanatory variables for the three categories of observations: tranquil times and orderly and disorderly exits. Some differences across means are apparent, though standard deviations are quite large: for instance, before disorderly exits the real exchange rate is more overvalued than in tranquil times, private credit growth is faster, government borrowing from the central bank accelerates more rapidly, reserves decline (as compared to remaining almost unchanged in tranquil times), and export growth is slower. Differences between the time period immediately preceding orderly exits and tranquil times are typically less pronounced, but mostly go in the same direction, with the exception of private credit growth, which is somewhat slower before orderly exits than in tranquil times. Thus, differences in means point in the direction of exits—of either type—being preceded by deteriorating economic conditions. In addition, exit observations tend to be “younger” in terms of the age of the peg than tranquil ones, the more so for disorderly exits, indicating that less well-established regimes may be more prone to change.

Table 10.3.Summary Statistics of Explanatory Variables by Type of Observation
MeanStandard DeviationMinMax
Real exchange rate appreciation-0.0400.153-0.5840.601
-0.0300.138-0.3290.263
0.0140.125-0.3680.272
Trade openness0.4880.2420.0001.587
0.4160.1900.0861.136
0.3990.2810.0561.556
Private credit growth0.0710.153-0.9750.907
0.0480.094-0.1880.236
0.1500.239-0.2521.005
Government borrowing growth0.0520.468-1.1117.676
0.3590.812-0.5034.332
0.3361.073-0.8715.644
Changes in reserves/import0.0050.325-1.6771.693
-0.0470.354-1.6420.788
-0.0520.429-1.4921.515
Export growth0.0520.295-1.0002.011
-0.0450.270-0.7890.683
0.0170.188-0.4550.803
Real GDP per capita7.82311.1040.14546.895
4.3076.7290.19426.936
5.2705.6720.16424.355
U.S. money market rate0.0300.019-0.0420.087
0.0360.021-0.0030.087
0.0310.021-0.0420.079
Duration291.200234.8261744
270.664216.3401593
146.729187.3121692
Number of observations:
Tranquil9,699
Orderly107
Disorderly151
Sources: Reinhart and Rogoff (2004); IMF, World Development Indicators; and authors’ calculations.
Sources: Reinhart and Rogoff (2004); IMF, World Development Indicators; and authors’ calculations.

Results

Results from the Multinomial Logit

The first three columns in Table 10.4 present the determinants of the probability of orderly and disorderly exits relative to tranquil times for three different windows (1 month, 6 months, and 12 months before an exit). The fourth and fifth columns provide the same information for two variants of the benchmark model, one using a more restrictive and the other a less restrictive definition of heavily managed exchange rate regime (fine categories 1–4 and categories 1–11, respectively; the benchmark model defines categories 1–8 as heavily managed).

Table 10.4.Multinomial Logit Estimation Results (Tranquil as Base Category)
Benchmark SpecificationStricter

Peg

Definition
Looser

Peg

Definition
1 month6 month12 month6 month6 month
Orderly
Appreciation of real exchange rate0.61

(0.37)
1.31

(0.87)
1.69

(1.18)
2.96

(3.55)***
1.89

(1.43)
Trade openness–0.21

(0.21)
–0.83

(0.96)
–0.74

(0.84)
–1.78

(1.06)
–1.63

(0.89)
Annual private credit growth–0.33

(0.31)
–1.45

(1.29)
–1.88

(1.76)*
–0.01

(0.01)
–2.94

(2.83)***
Annual government borrowing growth0.51

(3.35)***
0.49

(3.01)***
0.48

(2.64)***
0.74

(0.72)
0.55

(2.22)**
Changes in scaled reserves–0.29

(0.47)
–0.23

(0.74)
–0.02

(0.07)
–0.51

(1.58)
–0.20

(0.67)
Annual real export growth–0.98

(1.75)*
–1.01

(1.70)*
–0.63

(1.47)
–0.16

(0.37)
–0.62

(0.97)
Real GDP per capita–0.06

(1.69)*
–0.04

(1.40)
–0.03

(1.23)
–0.61

(1.25)
0.00

(0.04)
U.S. real money market rate24.51

(1.85)*
20.39

(1.72)*
18.92

(1.72)*
44.80

(3.79)***
42.79

(4.04)***
Duration of episodes–0.14

(0.69)
–0.12

(0.53)
–0.10

(0.41)
–0.71

(4.85)***
–0.06

(0.35)
Constant–6.04

(4.92)***
–3.92

(3.10)***
–3.31

(2.50)**
–0.40

(0.42)
–5.11

(4.92)***
Disorderly
Appreciation of real exchange rate2.19

(1.67)*
2.69

(2.69)***
2.73

(2.75)***
2.97

(3.38)***
1.49

(1.45)
Trade openness–0.19

(0.16)
–0.61

(0.52)
–0.45

(0.39)
–0.52

(0.45)
–1.09

(1.02)
Annual private credit growth–0.06

(0.07)
0.49

(0.57)
0.87

(0.93)
0.56

(0.57)
0.63

(0.64)
Annual government borrowing growth0.32

(2.16)**
0.26

(1.30)
0.25

(1.06)
–0.94

(0.71)
0.40

(2.14)**
Changes in scaled reserves–2.48

(4.01)***
–0.55

(1.68)*
–0.23

(1.21)
–0.09

(0.41)
–0.72

(2.65)***
Annual real export growth–0.17

(0.38)
–0.51

(1.45)
–0.49

(1.34)
–0.24

(0.50)
–0.36

(0.87)
Real GDP per capita–0.02

(1.09)
–0.03

(1.41)
–0.03

(1.39)
–0.14

(3.58)***
–0.03

(1.53)
U.S. real money market rate7.01

(0.54)
3.28

(0.25)
6.75

(0.57)
16.69

(0.84)
9.36

(0.84)
Duration of episodes–0.30

(2.27)**
–0.30

(2.13)**
–0.31

(2.09)**
–0.51

(3.39)***
–0.19

(1.45)
Constant–5.00

(4.51)***
–2.58

(2.55)**
–2.04

(2.04)**
–1.54

(1.73)*
–3.07

(3.81)***
Number of observations9,7579,7579,7574,38611,437
Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

A number of factors are revealed as distinguishing exits of either type from tranquil periods: first, exits, particularly disorderly ones, are preceded by an overvalued real exchange rate. Not surprisingly, the effect is particularly in evidence in the variant using the strictest definition of managed exchange rate, when the nominal exchange rate has hardly any flexibility. Losses in reserves are significant for disorderly exits, though not in all specifications, and an acceleration in government borrowing from the central bank and higher U.S. interest rates seem to precede orderly exits. Regimes ending in a disorderly exit are more likely to be short-lived.

Although there are some differences among the determinants of the two types of exit, a Wald test rejects the null that the two types of events are indistinguishable only when the window is one month and in the variant in which a looser definition of managed exchange rate is considered.

Another way to gauge the difference between orderly and disorderly exits is to reestimate the multinomial logit using orderly exits as the base category. The coefficients of the probability of disorderly exits, then, indicate which variables increase the probability of a disorderly exit relative to that of an orderly exit (Table 10.5). Although some variables are significant in some specifications, no explanatory variable is robust. So the multinomial logit suggests that, at least with regard to the explanatory variables considered here, there is no significant robust difference between the circumstances preceding orderly and disorderly exits.

We subject this conclusion to additional sensitivity tests (not reported). For instance, we include high-inflation episodes (defined as observations with inflation exceeding 40 percent per year), and we control for U.S. GDP growth. For these alternative models the hypothesis that orderly and disorderly exits are indistinguishable also cannot be rejected.

Besides the small sample size, a possible reason for the lack of robust results is that the decision to exit may be nonmonotonic with respect to some of the explanatory variables. Specifically, countries may be more likely to introduce flexibility both when reserves grow strongly and when reserves decline rapidly, since in both cases there are pressures on the parity. Similarly, a very overvalued or undervalued real exchange rate may prompt a move to a more flexible regime. However, a visual inspection of the frequency distribution of the various explanatory variables by category (tranquil, orderly exit, disorderly exit) does not suggest nonmonotonicities of this sort. We also rerun the benchmark model, splitting the change in reserves between gains and losses, and find that gains in reserves do not develop a positive and significant coefficient, suggesting that nonmonoto-nicities are not present (Table 10.6).

Table 10.5.Multinomial Logit Estimation Results (Orderly as Base Category)
Benchmark SpecificationStricter

Peg

Definition
Looser

Peg

Definition
1 month6 month12 month6 month6 month
Tranquil
Appreciation of real exchange rate–0.61

(0.37)
–1.31

(0.87)
–1.69

(1.18)
–2.96

(3.55)***
–1.89

(1.43)
Trade openness0.21

(0.21)
0.83

(0.96)
0.74

(0.84)
1.78

(1.06)
1.63

(0.89)
Annual private credit growth0.33

(0.31)
1.45

(1.29)
1.88

(1.76)*
0.01

(0.01)
2.94

(2.83)***
Annual government borrowing growth–0.51

(3.35)***
–0.49

(3.01)***
–0.48

(2.64)***
–0.74

(0.72)
–0.55

(2.22)**
Changes in scaled reserves0.29

(0.47)
0.23

(0.74)
0.02

(0.07)
0.51

(1.58)
0.20

(0.67)
Annual real export growth0.98

(1.75)*
1.01

(1.70)*
0.63

(1.47)
0.16

(0.37)
0.62

(0.97)
Real GDP per capita0.06

(1.69)*
0.04

(1.40)
0.03

(1.23)
0.61

(1.25)
0.00

(0.04)
U.S. real money market rate–24.51

(1.85)*
–20.39

(1.72)*
–18.92

(1.72)*
–44.80

(3.79)***
–42.79

(4.04)***
Duration of episodes0.14

(0.69)
0.12

(0.53)
0.10

(0.41)
0.71

(4.85)***
0.06

(0.35)
Constant6.04

(4.92)***
3.92

(3.10)***
3.31

(2.50)**
0.40

(0.42)
5.11

(4.92)***
Disorderly
Appreciation of real exchange rate1.58

(0.73)
1.39

(0.76)
1.04

(0.61)
0.02

(0.01)
–0.41

(0.25)
Trade openness0.02

(0.01)
0.22

(0.14)
0.28

(0.19)
1.26

(0.67)
0.53

(0.26)
Annual private credit growth0.27

(0.20)
1.94

(1.46)
2.76

(2.12)**
0.56

(0.36)
3.56

(2.40)**
Annual government borrowing growth–0.19

(0.97)
–0.23

(1.05)
–0.22

(0.90)
–1.68

(1.04)
–0.15

(0.54)
Changes in scaled reserves–2.19

(2.53)**
–0.32

(0.71)
–0.21

(0.65)
0.42

(1.15)
–0.52

(1.25)
Annual real export growth0.81

(1.16)
0.51

(0.75)
0.14

(0.25)
–0.08

(0.13)
0.26

(0.39)
Real GDP per capita0.04

(1.10)
0.01

(0.38)
0.01

(0.20)
0.47

(0.94)
–0.03

(0.80)
U.S. real money market rate–17.50

(0.96)
–17.12

(1.00)
–12.17

(0.78)
–28.11

(1.20)
–33.43

(2.21)**
Duration of episodes–0.15

(0.71)
–0.18

(0.72)
–0.21

(0.74)
0.20

(1.07)
–0.13

(0.63)
Constant1.04

(0.64)
1.34

(0.82)
1.27

(0.75)
–1.14

(0.86)
2.04

(1.55)
Number of observations9,7579,7579,7574,38611,437
Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

Results from the Binomial Logit Model

Distinguishing between orderly and disorderly exits having proved inconclusive, we now turn to estimating a bivariate logit model in which observations can be only exits or tranquil times. This should give us an indication of what prompts moves to more exchange rate flexibility. The results show that changes occur when there are pressures to devalue the exchange rate (Table 10.7): the real exchange rate is overvalued, reserves are falling, and the government is increasingly relying on the central bank for deficit financing. In addition, world interest rates tend to be higher before a change in regime, suggesting that reversals in capital inflows may contribute to triggering exits. Finally, there is some evidence that less well-established regimes are more likely to be abandoned. These results are fairly robust to changing the window and the definition of exit.

Turning now to the performance of the model, it is customary to compare fitted probabilities with the sample frequency of each event. If the fitted probability exceeds the sample frequency, then the model provides useful information about the event. Based on the benchmark model with a six-month window, the fitted probability of exit exceeds the sample frequency in 64 percent of the exit cases; the same is true for tranquil observations. The percentage of observations correctly predicted is higher (reaching 80 percent) when the definition of peg is stricter. Similar results obtain in the alternative specifications.

Table 10.8 presents the results of further sensitivity tests: excluding high-inflation countries does not change the results much, and neither does replacing GDP per capita with dummies for emerging and developing countries. When the real effective exchange rate is used to measure exchange rate overvaluation, this variable is not significant, although the sign remains positive, and government borrowing also loses significance. The specification in the table’s last column contains a new explanatory variable: changes in political regime. It appears that this variable is positively correlated with changes in exchange rate regime, and when it is introduced, the coefficients of the other explanatory variables do not change much.

Table 10.6.Multinomial Logit Results: Gains and Losses in Reserves
Benchmark SpecificationStricter

Peg

Definition
Looser

Peg

Definition
1 month6 month12 month6 month6 month
Orderly
Appreciation of real exchange rate0.62

(0.38)
1.30

(0.86)
1.69

(1.17)
2.78

(3.35)***
1.97

(1.47)
Trade openness–0.15

(0.14)
–0.86

(0.96)
–0.74

(0.83)
–2.11

(1.28)
–1.42

(0.77)
Annual private credit growth–0.31

(0.30)
–1.46

(1.31)
–1.89

(1.78)*
–0.11

(0.09)
–2.90

(2.83)***
Annual government borrowing growth0.51

(3.35)***
0.50

(3.01)***
0.48

(2.65)***
0.72

(0.67)
0.54

(2.16)**
Gains in reserves/imports0.08

(0.07)
–0.41

(0.55)
–0.01

(0.02)
–2.42

(1.82)*
0.62

(0.91)
Losses in reserves/imports0.58

(0.80)
0.08

(0.12)
0.03

(0.04)
–0.61

(0.65)
0.94

(1.70)*
Annual real export growth–0.96

(1.74)*
–1.02

(1.69)*
–0.63

(1.47)
–0.15

(0.34)
–0.62

(1.00)
Real GDP per capita–0.06

(1.64)
–0.04

(1.37)
–0.03

(1.20)
–0.63

(1.20)
0.00

(0.01)
U.S. real money market rate24.79

(1.86)*
20.26

(1.73)*
18.91

(1.73)*
43.91

(3.75)***
43.67

(4.17)***
Duration of episodes–0.14

(0.68)
–0.12

(0.54)
–0.10

(0.41)
–0.72

(4.87)***
–0.05

(0.30)
Constant–6.17

(5.04)***
–3.86

(3.20)***
–3.32

(2.52)**
0.13

(0.13)
–5.48

(4.76)***
Disorderly
Appreciation of real exchange rate2.22

(1.69)*
2.71

(2.69)***
2.74

(2.75)***
2.88

(3.25)***
1.53

(1.50)
Trade openness–0.05

(0.04)
–0.42

(0.37)
–0.28

(0.25)
–0.69

(0.58)
–0.97

(0.92)
Annual private credit growth–0.04

(0.05)
0.53

(0.63)
0.91

(0.98)
0.50

(0.52)
0.64

(0.66)
Annual government borrowing growth0.32

(2.15)**
0.26

(1.30)
0.25

(1.07)
–0.93

(0.70)
0.40

(2.14)**
Gains in reserves/imports–0.52

(0.54)
0.45

(0.96)
0.63

(1.45)
–0.88

(1.45)
–0.02

(0.04)
Losses in reserves/imports2.77

(4.32)***
1.22

(2.22)**
0.98

(1.87)*
–0.68

(0.89)
1.13

(2.34)**
Annual real export growth–0.16

(0.36)
–0.48

(1.43)
–0.48

(1.35)
–0.23

(0.47)
–0.36

(0.89)
Real GDP per capita–0.02

(1.06)
–0.03

(1.34)
–0.03

(1.32)
–0.14

(3.67)***
–0.03

(1.49)
U.S. real money market rate7.53

(0.57)
3.62

(0.28)
7.09

(0.60)
16.30

(0.83)
9.79

(0.87)
Duration of episodes–0.29

(2.21)**
–0.29

(2.09)**
–0.30

(2.06)**
–0.51

(3.43)***
–0.18

(1.42)
Constant–5.29

(4.56)***
–2.92

(2.81)***
–2.37

(2.33)**
–1.28

(1.42)
–3.30

(4.10)***
Number of observations9,7579,7579,7574,38611,437
Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

Table 10.7.Binomial Logit Estimation Results
Benchmark SpecificationStricter

Peg

Definition
Looser

Peg

Definition
1 month6 month12 month6 month6 month
Exits
Appreciation of real exchange rate1.51

(1.78)*
1.72

(1.98)*
1.86

(2.10)*
1.90

(2.22)*
1.37

(1.83)*
Trade openness–0.64

(0.83)
–1.03

(1.35)
–0.78

(1.01)
–1.47

(1.44)
–1.59

(1.64)
Annual private credit growth0.51

(0.68)
0.28

(0.35)
–0.08

(0.11)
1.06

(1.53)
0.04

(0.05)
Annual government borrowing growth0.42

(4.29)***
0.42

(3.14)***
0.40

(2.32)*
0.39

(0.59)
0.46

(3.26)***
Changes in scaled reserves–1.55

(3.69)***
–0.31

(1.66)*
–0.09

(0.64)
–0.24

(1.28)
–0.52

(3.20)***
Annual real export growth–0.74

(1.96)*
–0.73

(2.22)*
–0.58

(1.91)*
0.07

(0.20)
–0.49

(1.32)
Real GDP per capita–0.02

(1.41)
–0.03

(1.64)
–0.03

(1.56)
–0.18

(4.36)***
–0.01

(0.72)
U.S. real money market rate14.94

(1.69)*
13.45

(1.57)
13.51

(1.68)*
31.10

(2.89)***
24.01

(3.08)***
Duration of episodes–0.22

(2.19)*
–0.30

(2.71)***
–0.31

(2.50)*
–0.58

(5.28)***
–0.23

(2.49)*
Constant–4.61

(7.21)***
–2.06

(3.22)***
–1.37

(2.07)*
–0.51

(0.89)
–2.61

(4.63)***
Number of observations9,9579,9579,9574,50711,678
Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

A Review of the Literature

Most of the empirical work on exchange rates has focused either on the choice of regime and its economic performance or on the circumstances preceding speculative attacks.9 There is, however, a smaller literature on exits from pegs or other heavily managed regimes, whether or not the exits are associated with speculative attacks.

Eichengreen and others (1998) identify changes in the exchange rate regime in developing countries using the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions. Accordingly, recorded episodes reflect changes in the official (or de jure) regime, rather than in the de facto one. The definition of exit includes exits from single-currency or basket pegs but excludes exits from crawling pegs, target zones, and unofficially pegged regimes. Twenty-nine cases are identified during 1977–95, of which 23 are currency crises, based on the definition of Frankel and Rose (1996). Hence, orderly exits are extremely rare in this sample, and the authors do not attempt to distinguish between orderly and disorderly episodes through an econometric analysis of the data. Based in part on the high rate of disorderly exits, the study concludes that countries should introduce exchange rate flexibility in good times, when pressures push for the exchange rate to appreciate and reserves are accumulating. This view is reiterated in Eichengreen (2004).

Table 10.8.Binomial Logit Estimation Results: Robustness
Excludes

High

Inflation

6 month
Real

Effective

Exchange

Rate

6 month
Country

Group

Dummies

6 month
Political

Change
Exits
Appreciation of real exchange rate2.07

(2.38)**
0.98

(0.78)
1.68

(1.92)*
1.82

(2.09)**
Trade openness–0.69

(0.96)
–0.36

(0.54)
–0.91

(1.25)
–0.99

(1.28)
Annual private credit growth–0.36

(0.49)
1.21

(1.48)
0.09

(0.11)
0.30

(0.39)
Annual government borrowing growth0.38

(2.61)***
0.10

(1.03)
0.42

(3.19)***
0.41

(2.99)***
Changes in scaled reserves–0.40

(1.80)*
–0.41

(1.97)**
–0.30

(1.67)*
–0.30

(1.58)
Annual real export growth–0.76

(2.20)**
–0.39

(1.06)
–0.77

(2.30)**
–0.69

(2.21)**
Real GDP per capita–0.03

(2.09)**
–0.02

(1.36)
–0.02

(1.35)
U.S. real money market rate11.51

(1.27)
4.29

(0.41)
13.68

(1.59)
14.41

(1.67)*
Duration of episodes–0.22

(1.72)*
–0.38

(3.42)***
–0.29

(2.61)***
–0.31

(2.84)***
Constant–2.44

(3.13)***
–1.88

(3.04)***
–2.72

(3.20)***
–2.18

(3.35)***
Political change0.78

(2.31)**
Emerging markets dummy0.73

(1.43)
Developing country dummy0.48

(1.04)
Number of observations9,7579,3899,9699,957
Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

Note: Robust z–statistics are given in parentheses.

statistically significant at 10 percent level;

at 5 percent level;

at 1 percent level.

Klein and Marion (1997) examine the duration of exchange rate pegs in Latin America using a binomial logit econometric model. In contrast with our study, in Klein and Marion’s article, an exit need not imply a change in the exchange rate regime, but can be (and often is) simply a change in the parity. As in Eichengreen and others (1998)), the regime is identified based on the de jure IMF classification. In addition, there is no attempt to distinguish among types of exits (orderly or disorderly, new peg or more flexible regime). The main findings are that exits tend to occur when the real exchange rate is overvalued and reserves are low; that trade openness and political stability are associated with more exchange regime stability; and that exit becomes less likely the longer is a regime’s duration.

Using the IMF de facto classification of Bubula and Ötker-Robe (2002), Duttagupta and Ötker-Robe (2003) take a comprehensive look at changes in exchange regimes. In this study, the definition of exit includes one-off changes in the parity (including revaluations), as well as shifts to less flexible regimes.10 Duttagupta and Ötker-Robe further distinguish between orderly and disorderly exits, with the latter being defined as an exit accompanied by a large depreciation of the exchange rate. They find that orderly exits tend to be associated with more government borrowing and trade openness than tranquil times, whereas disorderly exits are associated with declining reserves, lower export revenues, and an overvalued real exchange rate.11 In contrast to Klein and Marion (1997), Duttagupta and Ötker-Robe find that a longer duration for a peg is more likely to trigger a crisis. Finally, at conventional significance levels their empirical model rejects the hypothesis that observations involving regime changes differ from tranquil observations in all cases, except for those involving exits to more flexible regimes (both orderly and disorderly).

In a recent paper, Asici and Wyplosz (2003) study what sets apart orderly and disorderly exits, identified based on the Reinhart-Rogoff classification, but do not study how times surrounding exits differ from tranquil times. Their conclusions support the conventional wisdom that countries that exit when macroeconomic performance is good avoid crises. Corruption and financial depth are found to make an exit more likely to be disorderly.

The IMF (2004) undertakes a descriptive review of emerging market transitions toward more exchange rate flexibility using the IMF de facto classification. The focus is mostly on the evolution of monetary and financial institutions during the transition. Among the findings is that countries moving to more flexibility tend to introduce more central bank independence, move toward an inflation-targeting framework, and have better bank supervision and more developed securities markets than other countries.

Conclusions

In this chapter, we find that exits from managed exchange rate regimes toward more flexibility occur when the parity is under pressure to devalue, whereas exits when the exchange rate is under pressure to appreciate are exceedingly rare. Exits over the period studied were about evenly divided between orderly and disorderly events, but differences in economic conditions preceding orderly and disorderly exits are not sharp and cannot be picked up by our econometric tests.

Thus, over the period considered here, countries did not heed the conventional policy advice to exit tight pegs when the going is good. To the contrary, they seem to have relied more on the popular wisdom summarized by the principle of “if it isn’t broken, why fix it?” Waiting until conditions deteriorate did not always prove disastrous, as exits remained orderly in about half the cases examined. Nonetheless, a policymaker might wish to know what might improve chances of a smooth exit. In this respect, our findings are disappointing, because none of the variables we studied, which capture many of the factors highlighted in the literature, helps to discriminate between orderly and disorderly exits.

Our failure to uncover clear patterns may be due to the small size of our sample: although we have data for many years and countries, exits remain relatively rare events. But our results may also point to a more fundamental indeterminacy of the effects of changes in the exchange rate regime, which in turn may explain why country authorities wait until they are left with little choice: if an exit at any time can go wrong, then postponing change is always an attractive option.

Appendix 10.1 Explanatory Variables in the Study
Table 10.A.1.Data Description
LabelVariableSource
AReinhart and Rogoff regime classificationReinhart and Rogoff (2004)
BNominal exchange rate appreciation (vis-à-vis U.S. dollar)International Financial Statistics, line RF.ZF
CConsumer price indexInternational Financial Statistics, line 64.ZF
DAnnual exportsInternational Financial Statistics, line 99C.ZF/99C.CZF
EAnnual importsInternational Financial Statistics, line 98C.ZF/98C.CZF
FBank claims on private sectorInternational Financial Statistics, line 22d.ZF
GCentral bank claims on governmentInternational Financial Statistics, line 12A.ZF
HTotal reserves minus goldInternational Financial Statistics, line 1L.DZF
IMoney market interest rateInternational Financial Statistics, line 60B.ZF
JGross domestic productInternational Financial Statistics, line 99B.ZF/99B.CZF
KPopulationInternational Financial Statistics, line 99Z.ZF
LReal effective exchange rateINS/IFS/GDF
MMonthly exportsDirection of Trade Statistics, line 70.DZD001
NMonthly importsDirection of Trade Statistics, line 71.DZD002
OReal GDP per capitaWorld Development Indicators, line NYGDPPCAPKD
PPolity variableshttp://www.cidcm.umd.edu/inscr/polity
QReal exchange rate appreciation (vis-à-vis U.S. dollar)Rate of change relative to past-five-year average
RTrade openness(D + E)/J; interpolated to allow it to vary monthly
SAnnual private credit growthYear-on-year growth rate of F/C
TAnnual government borrowing growthYear-on-year growth rate of G/C
UChanges in scaled reservesH divided by the past-one-year average of imports (N)
VAnnual real export growthYear-on-year growth rate of M/C
WReal GDP per capitaO interpolated to allow it to vary on a monthly basis
XU.S. real money market rateI adjusted by yearly U.S. inflation rate
YPolitically unstable periodsDummy for six months before and after changes or transitional periods (P)
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The authors are grateful for valuable comments from Barry Eichengreen, Simon Johnson, Kalpana Kochhar, Laura Kodres, Sandy Mackenzie, Jonathan Ostry, Eswar Prasad, Raghuram Rajan, and David Robinson.

1

For a comprehensive review of the policy debate on China, see Prasad, Rumbaugh, and Wang (2005).

2

For a detailed description of the methodology used and comparison with other existing classifications, see Reinhart and Rogoff (2004)).

3

Exits to coarse category 6 (Dual market/no parallel market data) have been excluded from the “freely falling” category, since it is not possible to determine whether the exit was orderly or disorderly. The sample includes only a handful of such episodes.

4

If an orderly exit is followed by “freely falling” within the subsequent 12 months, it is reclassified as a disorderly exit.

5

This is true even if we use a tighter definition of managed exchange rate regime, closer to that used by Eichengreen and others (1998). Using the IMF de facto classification on a sample covering 1985–2002, Duttagupta and Ötker-Robe (2003) find that orderly exits were even more frequent. Specifically, they identify 41 episodes in which more flexibility was introduced in an orderly fashion and 30 episodes in which a sharp depreciation of the currency followed the exit (see “A Review of the Literature” later in the chapter).

6

As in Husain, Mody, and Rogoff (2004), emerging market countries are defined using the Morgan Stanley Capital International (MSCI) classification, implying that international investors have a real interest in these economies.

7

For a model explaining policymakers’ status quo bias, see, for instance, Fernandez and Rodrik (1991).

8

As is customary in cross-country studies, we exclude from the sample very small countries, defined as those with population less than one million. Also, to eliminate outliers, we exclude observations in which measurements of explanatory variables are beyond four standard deviations from the mean. Results do not change much if extreme observations are included, however.

9

For a recent review of the first group of studies, see Husain, Mody, and Rogoff (2004). For the latter, see among others, Eichengreen, Rose, and Wyplosz (1995) and Frankel and Rose (1996).

10

The IMF de facto classification is based in part on qualitative judgment of IMF desk economists. Unlike in the Reinhart-Rogoff classification, parallel foreign exchange markets are not taken into account.

11

As in other studies, real overvaluation is measured as deviation from a linear trend estimated over the sample period. Because large nominal devaluations typically also entail a large real devaluation, which likely pulls the entire trend down, finding the real exchange rate above trend before a disorderly exit is almost tautological. Also, failure to cluster standard errors by country may lead to underestimation of standard errors in this study.

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