Chapter

11 Long-Run Determinants of Exchange Rate Regimes: A Simple Sensitivity Analysis

Author(s):
Charalambos Tsangarides, Carlo Cottarelli, Gian Milesi-Ferretti, and Atish Ghosh
Published Date:
September 2008
Share
  • ShareShare
Show Summary Details

Do countries deliberately choose the exchange rate regime that best suits their economic and institutional characteristics? Or do countries attempt to live with the exchange rate regimes they inherit from the accidents of history or from the complex interactions that took place among the economic and political conditions that were prevailing when a major shock struck in the distant past? Both theoretical and empirical studies have extensively analyzed the determinants of exchange rate regime choice, and some studies have found systematic empirical relationships. However, our review of the existing evidence shows that different empirical studies have often obtained different results, suggesting that it is very difficult to make simple generalizations about how countries choose their exchange rate regimes. Nevertheless, in the present study, we attempt to give a fair hearing to old and new theories by testing them on the basis of both old and new exchange rate regime classifications. Existing theories (for example, optimum currency area theory) primarily relate to the long-run determinants of exchange rate regime choice, focusing on variables (for example, size and openness) that do not change much over time. In order to maintain that focus on long-run determinants, we rely on crosscountry regressions in our empirical analysis. Our results confirm that very little is known about how countries choose their exchange rate regimes.

Empirical studies on the determinants of exchange rate regimes in a cross section of countries have been abundant ever since many countries moved to floating rates in the aftermath of the breakup of the Bretton Woods system.1 We review these studies in the chapter’s second section, showing that different studies have produced vastly different results for each given potential determinant of exchange rate regime choice, depending on their sample of countries, period analyzed, methodology, and other determinants included in the estimation. In our own empirical analysis, we consider a large number of such potential determinants.

A set of ever-present difficulties in empirical studies of exchange rate regime choice relates to regime classification. Most previous studies have analyzed an official classification of exchange rate regimes entirely based upon countries’ self-reporting and published by the International Monetary Fund in its Annual Report on Exchange Arrangements and Exchange Restrictions. However, many countries’ actual behavior diverges considerably from the descriptions reported to the IMF: in particular, the exchange rates of many “floaters” are de facto maintained within very narrow bands vis-á-vis a major currency, such as the U.S. dollar, through active management of reserves and interest rates (see, for example, Calvo and Reinhart, 2002). Beginning with the 1999 issue (which presented end-1998 data), the IMF classification began incorporating the IMF staff’s views, “correcting” some cases in which the de facto system for a particular country was clearly different from what the country’s authorities had reported. We present results based on the IMF classifications for the end of 2000 and the end of 1990 (revised), which make adjustments based upon the IMF staff’s views.2 In addition, we use the “natural” classification produced by Reinhart and Rogoff (2004), which includes information on the actual behavior of the exchange rate and on multiple exchange rate practices/capital controls, and the classification produced by Levy Yeyati and Sturzenegger (2005), which is entirely based upon the observed behavior of exchange rates and reserves. To facilitate comparison with earlier studies, we also use the original IMF classification for end-1990 data.

This is not the first “skeptical” study on this topic. Its closest precursor in spirit is a study by Honkapohja and Pikkarainen (1994), who use a similar empirical strategy and report that country characteristics such as openness, development, geographical diversification of trade, and fluctuations in the terms of trade have hardly any power in explaining exchange rate regime choice. They find only tentative support for the view that small countries with low commodity diversification of foreign trade tend to peg their exchange rates. Our study benefits not only from a decade of additional data but also from new exchange rate classifications. In particular, we are able to use measures of de facto exchange rate flexibility constructed by other researchers. Moreover, the last decade has seen the emergence of new testable hypotheses, such as the view that only extreme regimes are sustainable. Such hypotheses provide a more solid theoretical rationale for using nonordered estimation techniques, though these techniques had already been used by Honkapohja and Pikkarainen (1994). At a broader methodological level, our approach is similar to that of Levine and Renelt (1992), who conducted a sensitivity analysis of cross-country growth regressions.

Finally, we introduce two small technical innovations into this literature: in some specifications, (1) we address the potential endogeneity of openness to exchange rate regime choice by using country characteristics such as land area and a landlocked dummy as instrumental variables,3 and (2) we restrict our cross-country regressions to those countries that have not changed their exchange rate regime for a number of years, to focus on countries that appear to be in a “long-run equilibrium” with respect to their exchange rate regime.

The remainder of this chapter is structured as follows. The second section provides a very selective overview of old and new theories of exchange rate regime choice. The third section reviews previous empirical findings. The fourth section presents the data and methodology. The fifth section reports the results. The sixth section concludes.

A Word on Theories of Exchange Rate Regime Choice

The theoretical literature on the long-run determinants of exchange rate regime choice is vast and often controversial.4 Some authors argue that variables such as large size and low openness are likely to be associated with floating exchange rates, as predicted by the literature on optimum currency areas that originated in the 1960s.5 Other authors, beginning in the 1970s, have emphasized the size and nature of economic shocks as potential determinants of exchange rate regime choice.6 Some have argued that, for example, higher volatility of terms of trade might be associated with floating regimes, which help cushion terms of trade changes or other real external shocks. There is a debate, however, even regarding the potential impact of some of these variables with a longer tradition in the literature, or at least regarding the interpretation of any empirical findings related to them. For example, whereas some authors argue that openness may provide an incentive to maintain fixed rates, others point out that foreign shocks are more important in countries that are more open, increasing the appeal of floating rates as a shock absorber, or that greater openness provides more scope for a deep market for foreign exchange, making it easier to have a floating regime. In addition, it has been argued that openness itself might be endogenous to the exchange rate regime, casting doubt on whether an association between openness and—say—fixed exchange rate regimes could be given an unambigious causal interpretation.

With the reemergence of financial globalization beginning in the mid-1990s, several authors have placed renewed attention on the consequences of heightened capital mobility, pointing out that policy requirements to maintain exchange rate pegs have become more stringent. According to this “hollowing of the middle” hypothesis, greater capital mobility prompts countries to move toward either extreme end of the spectrum between hard pegs, such as currency unions or currency boards, and pure floats (see, for example, Obstfeld and Rogoff, 1995, and Eichengreen, 1994,1998). Taken to the data, this hypothesis implies that countries with an open capital account should tend to have either hard pegs or pure floats. As always, the interpretation of the empirical findings will be clear only to the extent that the relevant measure of capital account openness is used. For example, capital controls are likely to be subject to reverse causality: controls might make it easier to sustain a fixed exchange rate regime (though they might not be needed by countries with hard pegs such as currency unions and currency boards). De facto capital openness, measured by the ratio of inflows plus outflows to GDP, might be a better candidate as an exogenous regressor for testing this recent hypothesis.

A final set of potential long-run determinants of exchange rate regime choice relates to the institutional and historical characteristics of a country. Even in this case neither theory nor casual observation provides unambiguous answers. Lack of institutional strength or political instability may make it more difficult for a country to sustain a peg, but may also increase the attractiveness of tying one’s hands through a currency board, as has been done by many transition or postchaos countries or countries with a history of high inflation.7 In a similar vein, some currency boards and currency unions that were established by the colonial powers have survived to this day, whereas others were jettisoned by the new authorities soon after the country gained independence.

Review of Previous Empirical Findings

A number of empirical studies have analyzed the determinants of exchange rate regime choice in a cross section of countries. Among the first studies of this kind were Heller (1978), which analyzed the determinants of exchange rate regimes using data from the mid-1970s, soon after the generalized floating that followed the breakup of the Bretton Woods system, Dreyer (1978), Holden, Holden, and Suss (1979), Melvin (1985), Bosco (1987), Savvides (1990), and Cuddington and Otoo (1990,1991). Further efforts in this direction include Rizzo (1998) and Poirson (2001).

Some studies, such as those by Collins (1996), Edwards (1996,1999) and Frieden, Ghezzi, and Stein (2000), have used random-effects panel estimation to analyze also the determinants of changes in exchange rate regime. As such, they can be seen as somewhat related to the literature on predicting exchange rate crises. We include these studies in our review because they report findings on the role of country characteristics that are relatively stable over time (such as openness) in determining exchange rate regime choice.8 Another study, by Berger, Sturm, and de Haan (2000), uses panel data in an attempt to identify the long-run determinants of exchange rate regime choice.

A detailed review of existing studies including an explanation of their methodological differences is beyond the scope of this chapter. Suffice it to say that these are original and useful contributions to the literature and that they have used sensible methodologies. This merely reinforces our key point that it is genuinely very difficult to explain how countries choose their exchange rate regimes.

The vast majority of previous studies have attempted to explain choice of exchange rate regime as self-described by countries in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions. A few studies have constructed and used measures of the degree of de facto floating on the basis of the actual observed volatility of exchange rates and reserves: these include Holden, Holden, and Suss (1979) and, more recently, Poirson (2001). As mentioned in the introduction to the chapter, in some of our estimates we rely on the de facto exchange rate regime classification drawn from Levy Yeyati and Sturzenegger (2005).9Table 11.1 summarizes the approaches and findings of previous studies with regard to the impact of several variables on observed exchange rate regime choice. Most studies consider some of the optimum currency area variables, such as trade openness (typically measured as imports plus exports, divided by GDP), the size of the economy (gross domestic product in common currency), the degree of economic development (GDP per capita), and geographical concentration of trade (the share of trade with the country’s main partner). Among macroeconomic variables, several studies include inflation (either the country’s own inflation or inflation in excess of partner countries) and foreign exchange reserves. Many studies include an indicator of either capital controls (typically also drawn or constructed from the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions) or de facto capital openness (for example, the ratio of foreign assets of the banking system to the money supply). Some studies include measures of volatility of domestic output, exports, domestic credit, or the real exchange rate, although no two studies seem to have looked at the same measure of volatility. A few studies consider variables related to political economy or institutional strength. Most studies analyze some variables that are not included in any preceding (or subsequent) studies. Collectively, the studies consider more than 30 potential determinants of exchange rate regime choice. (We include in our table only the variables considered by more than one study.)

No result appears to be reasonably robust to changes in country coverage, sample period, estimation method, and exchange rate regime classification. For example, openness—the most frequently analyzed variable—is found to be significantly associated with floating regimes by four studies, significantly associated with fixed exchange rates by four studies, and not significantly associated with any particular exchange rate regime by another four studies. Per capita GDP is found to be significantly associated with floating regimes by three studies, significantly associated with fixed exchange rates by two studies, and not significantly associated with any particular exchange rate regime by another three studies. And so on.

There are a few possible exceptions, notably size of the economy and inflation. Size of the economy turns out to be positively associated with floating in all of the studies that consider it, though not always significantly. Inflation is always, among studies that consider it, positively associated with floating, and often significantly. However, in the case of inflation there are serious questions regarding the functional form of the relationship. In a number of studies, the authors use the inflation rate or the inflation differential (rather than their logarithms or similar transformations), leaving open the possibility that the results might be driven by a few influential observations. Morever, Collins (1996) finds that high inflation affects exchange rate regime choice in the opposite direction than low/moderate inflation does, and significantly so.

Abstracting from the issue of functional form, there are serious questions about possible causal interpretations of the association between inflation and exchange rate regimes. First, causality might run in both directions: high inflation may make it difficult to sustain exchange rate pegs, but exchange rate pegs (especially hard pegs) may also help curb inflation. Second, both inflation and exchange rate regime are intimately connected as part of the overall monetary policy package chosen by a country. And it does not seem appropriate to think of a country’s monetary policy as a long-run, structural feature of the country. Rather, inflation and the exchange rate regime are likely determined by other long-run country characteristics. In our preferred specifications we therefore omit the inflation rate. Nevertheless, to permit comparability with other studies, we show that the association between inflation and floating can be reproduced at least in some specifications using our sample. Similarly, we are concerned that causality does not run only from reserves to exchange rate regimes: countries that decide to float will not need large reserves. We include reserves in our list of regressors, but again only for comparability with other studies.

Table 11.1Studies on Determinants of Exchange Rate Regimes(Likelihood to Float)
Author

Sample

Time frame

Methodology
Heller (1978)

86 countries

1976

Discriminant analysis
Dreyer (1978)

88 developing countries

1976

Probit
Holden, Holden, and Suss (1979)

76 countries

1974-75 (monthly)

OLS on a continuous measure
Melvin (1985)

64 countries

1976-78

Multinomial logit
Savvides (1990)

39 developing countries

1976-84

Two-stage probit
Cuddington and Otoo (1990,1991)

66 countries

1980, 1983, 1986

Ordered/nonordered multinominal/binomial logit
Honkapohja and Pikkarainen (1994)

125 countries

1991

Logit and probit
Collins (1996)

24 Latin American and Caribbean countries

1978-92

Probit (panel)
Edwards (1996)

63 countries

1980-92

Probit (panel)
Edwards (1999)

49 developing and middle-income countries

1980-92

Probit (panel)
Rizzo (1998)

123 countries

1977-95

Probit
Frieden, Ghezzi, and Stein (2000)

26 Latin American countries

1960-94

Ordered logit (panel)
Berger, Sturm, and de Haan (2000)

65 developing countries

1980-94

Probit (panel)
Poirson (2001)

93 countries

1990-98

Ordered probit
Explanatory variables
Optimum currency area factors
Openness--^^-^^-+/–^-^+^+^-^^+^^-
Economic development+^^+^^-^+^-^^+-^+/
Size of economy+++^^+++^^+^^+^
Inflation differential++^^++^
Capital mobility--^^
Geographical trade concentration--^^--+-^^+
International financial integration++/-
Other macro/external/structural factors
Growth+^^+^^+
Negative growth-^-^^
Inflation+^^+^^+^^+^
Moderate to high inflation+^^-^^
Reserves-^^-^+/–^^+^^+^-^
Capital controls+/–^+^-^
Terms of trade volatility++^^-^^+^
Variability in export growth+^^+
External variability x Openness-^^-^^
Real exchange rate volatility+^^+^+^^
Product diversification-^^-^^+^
Current account-^^+/–^^
External debt+^+^^
Growth of domestic credit
Money shocks-^^-
Foreign price shocks+^+^
Political/historical factors
Political instability+^^+^^-^^+^+^^
Central bank independence++^
Party in office has majority-^-^
Number of parties in coalition++
Coalition government--
Note: + indicates that the coefficient of the explanatory variable is positive and - that it is negative; +/– indicates that the coefficient is either positive or negative depending on the specification or method used; ^^ indicates that the coefficient is statistically significant in most cases; ^ indicates that the coefficient is statistically significant in some specifications; indicates that the coefficient is not significant but that the coefficient’s sign is not reported by the author.
Note: + indicates that the coefficient of the explanatory variable is positive and - that it is negative; +/– indicates that the coefficient is either positive or negative depending on the specification or method used; ^^ indicates that the coefficient is statistically significant in most cases; ^ indicates that the coefficient is statistically significant in some specifications; indicates that the coefficient is not significant but that the coefficient’s sign is not reported by the author.

A number of the variables related to volatility also turn out to be significant in the individual studies that consider them. Leaving aside the issue of robustness across more than one study, it is not clear whether these volatilities can be assumed to be exogenous to the exchange rate system, when theory suggests that a floating exchange rate operates as a shock absorber.10 In our own estimation, we analyze only terms-of-trade volatility, for which concerns about possible endogeneity may be somewhat mitigated.

The lack of robust findings across studies for practically all variables does not bode well for attempts to find systematic determinants of exchange rate regime choice. Nevertheless, we strive to search for the potential determinants in a thorough manner, using many of the variables that have been employed by previous researchers, as well as some that have not. When we exclude variables that have been used by previous researchers, we explain our rationale for omitting them. We apply standard estimation methods to a variety of country samples and exchange rate regime classifications.

Exchange Rate Regime Classifications, Data, and Estimation Strategy

Exchange Rate Regime Classification

Like the majority of previous studies, ours begins with the IMF classification drawn from the Annual Report on Exchange Arrangements and Exchange Restrictions. As mentioned in the introduction to the chapter, while still beginning from the countries’ self-reporting, the IMF about a decade ago began “correcting” this classification on the basis of the IMF staff’s views on de facto exchange rate regimes for cases in which deeds are clearly different from words (see Johnston and others, 1999). Most of the corrections relate to countries that state they are floating, despite de facto stability of their exchange rate vis-á-vis a major currency such as the U.S. dollar.

For the IMF classification at end-2000, we group exchange rate regimes as follows: (1) “hard pegs,” including currency unions, currency boards, and countries with no separate legal tender; (2) “floats,” including managed floats and independent floats; and (3) “intermediates,” including all others, that is, conventional pegs, crawling pegs, crawling bands, and basket pegs. As a variant on this approach, we define a group of “pure floats” consisting of independent floats only, and shift managed floats into the residual group of “intermediates.” For end-1990 (whether using the original IMF classification, or a revised classification incorporating the IMF’s staff’s views on de facto regimes at the time), we group exchange rate regimes as (1) “pegs,” including not only hard pegs, but also conventional pegs; (2) “floats,” including managed floats and independent floats; and (3) “intermediates,” including all others, that is, crawling pegs, crawling bands, and basket pegs. We define group (1) more broadly for end-1990 to ensure that we have enough countries with available data in this group and also to make our approach somewhat more similar to that of studies using data from 1990. Again, we consider a variant in which we define a group “pure floats” consisting of independent floats only.

An alternative to the IMF’s classification is the “natural” classification of exchange rate regimes (Reinhart and Rogoff, 2004). Specifically, we use the “coarse” version of the classification, which consists of the following groups: (1) pegs (including no separate legal tender, preannounced peg or currency board arrangement, preannounced horizontal band that is narrower than or equal to +/–2 percent, de facto peg); (2) limited flexibility (preannounced crawling peg, preannounced crawling peg that is narrower than or equal to +/–2 percent, de facto crawling peg, de facto crawling band that is narrower than or equal to +/–2 percent, preannounced crawling band that is wider than or equal to +/–2 percent); (3) managed floating (preannounced crawling band that is narrower than or equal to +/–5 percent, moving band that is narrower than or equal to +/–2 percent, managed floating); (4) freely floating; and (5) freely falling (countries with very rapid depreciation and high inflation). In some estimates, we omit the “freely falling” countries from the sample, for two reasons. First, our analysis focuses on the post-1990 period and, with the worldwide decline in inflation, during this period, the freely falling countries are a relatively small group. Second, freely falling countries tend to be undergoing severe economic distress, and we could hardly be assuming that these countries are in a steady-state situation with respect to exchange rate regime choice. In our baseline multinomial logit estimates, we merge groups (2) and (3) into a category that we label “intermediate regimes.”

A further alternative classification, entirely based upon deeds rather than words, has been produced by Levy Yeyati and Sturzenegger (LYS, 2005), who apply cluster analysis to countries’ observed volatility of exchange rates and international reserves. The cluster with high volatility of reserves and low volatility of exchange rates identifies the group of fixers, and the cluster with low volatility of reserves and high volatility of exchange rates identifies the group of floaters. We use LYS’s three-way classification (fix, float, and intermediate) yielded by their “second-round” estimates and leave as “not available” those countries for which their second-round results are inconclusive. We consider both the 1999 and the 1990 classifications produced by LYS (www.utdt.edu/~ely/papers.html).

To summarize, we consider the following six classifications/years: (1) IMF for end-2000; (2) IMF for end-1990, revised on the basis of the IMF staff’s views on de facto regimes; (3) IMF for end-1990, original classification based upon self-reporting only; (4) Reinhart and Rogoff; (5) LYS for 1999; and (6) LYS for 1990. (The results using the third classification are not reported in tables, to conserve space; the results are similar to those obtained for the first two classifications.)

Sample of Countries

Our sample includes all countries for which data are available. For some variables we have data for up to 184 countries, but the sample size declines when we include several explanatory variables in our estimates. Depending on the set of explanatory variables, our main regressions include between 75 and 130 countries for the IMF classification and the Reinhart–Rogoff classification and between 45 and 75 countries for the LYS classification. We also estimate regressions (available upon request) for subsets of countries such as all nonadvanced countries, including both developing and transition economies. One advantage of restricting some of the estimates to nonadvanced countries is that we can abstract from the case of Economic and Monetary union (EMu), for which it has often been argued that the choice to move to monetary union was based upon political as much as economic considerations.

Many countries seem to change their exchange rate regime often, and many adopted their end-2000 exchange rate regime not long before the time the data refer to. In these cases it might be particularly difficult to argue that the exchange rate regime observed at end-2000 is necessarily a long-run solution. We therefore present a number of regressions in which we include only those countries with the same exchange rate regime in 1990, 1995, and 2000.

List of Potential Determinants of Exchange Rate Regimes

The potential determinants of exchange rate regimes we include in our analysis are the following. (Unless indicated otherwise, these are averages of available data over 1990-99 in regressions with the end-2000 classification as a dependent variable and over 1980-89 in regressions with the end-1990 classification as a dependent variable.) The data are drawn from the IMF’s International Financial Statistics (IFS) unless indicated otherwise.

Optimum Currency Area Variables

  • Trade openness: ratio of imports plus exports to GDP.
  • Share of trade with the largest trading partner: exports to the largest trading partner as a share of total exports, from the IMF’s Direction of Trade Statistics.
  • Economic size: the logarithm of total GNP in U.S. dollars at purchasing power parity, from the World Bank’s World Development Indicators (WDI).
  • Per capita GNP: Total GNP in U.S. dollars at purchasing power parity in 1999 from the WDI, divided by population.
  • Standard deviation of terms of trade: from the United Nations Conference on Trade and Development (UNCTAD) for nonadvanced countries and IFS for advanced countries.
  • Fuel exporters: dummy variable taking the value of one if the country is a major fuel exporter according to the IMF’s World Economic Outlook database (findings not reported for the sake of brevity).

The instrumental variables intended to control for potential endogeneity of openness to the exchange rate regime are land area (from the WDI) and a dummy variable for whether the country is landlocked.

Capital Openness Variables

  • Capital controls (0–4 scale): the sum of four dummy variables that take the value of one if the country has (a) multiple exchange rates, (b) current account restrictions, (c) capital account restrictions, and (d) export proceeds surrender requirements, respectively, all from the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions.
  • De facto openness to capital flows: absolute value of inward and outward flows of financial assets and liabilities (the sum of the absolute values, if available, of IFS lines 78bdd, 78bed, 78bfd, 78bgd, 78bhd, and 78bid),11 as a share of GDP.
  • Emerging markets: dummy that takes a value of one if a country is included in the JPMorgan bond index, that is, if the country can issue bonds on international markets (findings not reported for the sake of brevity).

Macroeconomic Variables

  • Inflation: the logarithm of one plus the inflation rate (in percent).
  • Reserves: reserves as a share of imports.

Historical and Institutional Variables

  • Post-1945 independence: dummy variable taking a value of one if the country became independent after 1945, zero otherwise.
  • Years since independence: date of independence minus 1945, when the country became independent after 1945, zero otherwise.
  • Political instability (0–12 scale): the average of (a) government stability, (b) external conflicts, and (c) political violence and internal conflicts, from the Political Risk Services Group’s International Country Risk Guide.
  • Transition countries: dummy variable taking a value of one if a country is defined as a transition economy in the IMF’s World Economic Outlook database, zero otherwise.

Estimation Strategy

We adopt a variety of approaches, in an effort to establish that our failure to find convincing relationships does not result from overlooking a particular approach. The upshot of this is a very large number of regression results, which we report in summary form in the main tables. The full details are provided in Juhn and Mauro (2002). We estimate crosscountry regressions for a variety of years (either end-2000 or end-1990), estimation techniques (bivariate probits or multinomial logits), regime classifications (see “Exchange Rate Regime Classification”), and sets of right-hand-side variables (see “List of Potential Determinants of Exchange Rate Regimes”).

Our interest in long-run determinants of exchange rate regime choice leads us to confine our estimation to cross-country regressions. Using panel data to analyze the long-run determinants of exchange rate regime choice would be problematic, as many potential determinants do not change much over time for many countries. Unobserved factors for a given country in a given year are highly likely to be correlated with the same unobserved factors for the same country in previous years, casting doubt on the assumptions needed to use random effects. Few observed factors change sufficiently over time to make fixed-effects panels attractive; in any case, those variables that do change over time, such as inflation or perceptions of political instability, might even be more likely to be endogenous in a time-series context than they are in a cross-country context, and the use of lags to correct for this problem would not be fully satisfactory as these variables are highly autocorrelated.

We begin by reporting summary statistics for the potential determinants of exchange rate regime choice, by regime group, and test whether the means of such potential determinants are significantly different across regime groups. We then turn to regression analysis. In cases in which we collapse the exchange rate regimes into two groups (hard pegs versus all others; floats versus all others; or pure floats versus all others), we use probits. In cases in which we collapse the exchange rate regimes into three groups (hard pegs, floats, and all others; or hard pegs, pure floats, and all others), we use multinomial logits.12

We initially estimate regressions of exchange rate regimes on a large set of potential determinants, including trade openness, share of trade with the main trading partner, size of the economy, degree of economic development, capital controls, de facto capital openness, postwar independence, number of years since independence, political stability, inflation, and reserves. This is not the full list of variables for which we have data, but adding variables would force us to restrict the sample. In our judgement, this is as large a set of variables as is compatible with having a full data set for a reasonably large number of countries.

As shown in the next section, we find that, with a few exceptions in some specifications but not others, basically no variable turns out to be individually significant in these initial regressions. It might be argued that, with all these variables in the regression, one would be unlikely to get significant results. Moving to a more parsimonious baseline specification by applying a formal “general to the specific” procedure would be difficult on the basis of the generally insignificant results we obtain. Instead, we select three variables for our baseline regression on the basis of the following criteria: (1) they have been used by many previous studies; (2) they have a solid theoretical underpinning, though as noted previously, there is no consensus on the direction of their effects; and (3) data for them are available for a large number of countries. These variables are trade openness, the logarithm of gross national product at purchasing power parity, and the share of trade with the country’s largest trading partner.13 We then estimate regressions including four explanatory variables, by adding to the baseline variables, in turn, each of the variables for which we have data. Our exercise is similar to that undertaken by Levine and Renelt (1992), but substantially simplified, as in our case it is relatively easy to drive the point home.

Results

We begin by reporting the means of a number of potential determinants of exchange rate regimes for hard pegs, intermediate regimes, and floats, based upon the IMF end-2000 classification of regimes (left panel of Table 11.2). Using one-way analysis of variance, we find several instances in which the means of possible determinants of exchange rate regimes are significantly different across groups of countries: at the 5 percent level of significance, large countries, countries with a low share of trade in GDP, countries with high inflation, politically stable countries, and transition countries are all more likely to float than to have hard pegs or intermediate regimes, and countries with low capital controls are more likely to have hard pegs than intermediate or floating regimes. Broadly similar results are obtained using the Reinhart–Rogoff classification (right panel of Table 11.2): economic size and trade openness are significant at the 1 percent level in this exercise; recent independence and capital controls are significant at the 10 percent level; other variables are not significant. As shown later in this section, most of these bivariate relationships are no longer significant when other variables are controlled for in the context of regression analysis.

Table 11.2.Summary Statistics of Explanatory Variables under Different Exchange Rate Regimes
IMF End-2000 ClassificationReinhart–Rogoff Classification (averaged over 1991-1999)
FixedIntermediateFloatp-value for comparison of meansPeggedLimited flexibilityManaged floatingFreely floatingFreely fallingp-value for comparison of means
Share of trade with largest trading partner (in percent)29.88 (39)28.74 (51)29.47 (74)0.9526.3 (52)32.0 (32)23.6 (29)33.4 (9)27.4 (6)0.14
Economic size (log of total GDP)23.45 (37)23.46 (53)24.53 (71)0.0123.8 (53)24.0 (33)25.0 (30)26.0 (9)23.6 (5)0.01
Per capita GNP (in U.S. dollars)9,018.38 (37)6,926.90 (52)5,917.81 (70)0.118,384.5 (53)6,293.3 (33)7,524.0 (30)11,020.4 (9)3,504.8 (5)0.35
Standard deviation of terms of trade17.48 (23)24.32 (31)22.45 (47)0.1917.9 (32)23.2 (21)20.4 (22)15.9 (7)30.5 (3)0.41
Trade openness (in percent)93.05 (36)87.20 (49)67.27 (57)0.0192.4 (49)82.5 (27)67.5 (26)41.7 (8)83.9 (5)0.01
Capital control (0–4 scale)1.37 (43)1.95 (61)1.93 (76)0.031.4 (57)1.6 (33)2.0 (30)1.1 (9)2.5 (6)0.10
De facto openness to capital flows (in percent)19.81 (39)143.99 (52)10.86 (71)0.2718.2 (52)14.6 (33)13.6 (30)7.4 (9)11.4 (5)0.45
Inflation (log of 1 + inflation)0.10 (37)0.12 (55)0.35 (71)0.000.1 (53)0.3 (33)0.2 (29)0.1 (9)0.8 (5)0.11
Reserves (as a percentage of imports)0.22 (36)0.35 (47)0.27 (59)0.060.3 (49)0.3 (29)0.3 (25)0.2 (8)0.1 (5)0.41
Years since independence16.55 (42)16.02 (59)16.56 (75)0.9815.3 (56)16.0 (34)11.4 (30)1.7 (9)25.5 (6)0.09
Political instability (0–12 scale)8.74 (25)8.69 (36)7.94 (50)0.038.7 (36)8.3 (21)8.5 (23)8.7 (8)8.3 (3)0.75
Fuel exporters (dummy)0.07 (46)0.16 (61)0.05 (77)0.060.1 (58)0.1 (34)0.1 (30)0.0 (9)0.0 (6)0.78
Emerging markets (dummy)0.11 (46)0.13 (61)0.18 (77)0.500.2 (58)0.1 (34)0.3 (30)0.2 (9)0.0 (6)0.25
Transition countries (dummy)0.09 (46)0.07 (61)0.25 (77)0.000.1 (58)0.3 (34)0.2 (30)0.1 (9)0.5 (6)0.34
Post–1945 independence (dummy)0.70 (46)0.72 (61)0.68 (77)0.850.6 (58)0.6 (34)0.6 (30)0.4 (9)0.8 (6)0.74
Note: The table reports the mean for each explanatory variable for the countries in each of the exchange rate regime categories; the number of observations is given in parentheses. For each explanatory variable, the null hypothesis is that the mean of the variable is the same in all exchange rate regime categories. The p-value (from the one-way analysis of variance framework) is the probability of falsely rejecting the null hypothesis and is computed omitting the “freely falling” category. The explanatory variables are defined as follows: Share of trade with largest trading partner is exports to the largest trading partner as a share of total exports; Economic size is the logarithm of total GNP in U.S. dollars at purchasing power parity; Per capita GNP is in U.S. dollars at purchasing power parity; Trade openness is imports plus exports as a percentage of GDP; Capital controls are defined as the sum of four dummy variables: (a) multiple exchange rates, (b) current account restrictions, (c) capital account restrictions, and (d) export proceeds surrender requirements; each dummy variable takes the value of one for a particular country if the corresponding characteristic is present in that country. De facto openness to capital flows is the absolute value of inward and outward flows of financial assets and liabilities (the sum of the absolute values, if available, of International Financial Statistics lines 78bdd, 78bed, 78bfd, 78bgd, 78bhd, and 78bid), as a percentage of GDP; Inflation is the logarithm of one plus the inflation rate; Reserves are as a share of imports; Political instability is on a 0-12 scale—the average of: (a) government stability, (b) external conflicts, and (c) political violence and internal conflicts—from the International Country Risk Guide issued by Political Risk Services; Fuel exporters is a dummy variable taking the value of one if the country is a major fuel exporter, according to the IMF’s World Economic Outlook database; Emerging markets is a dummy variable taking the value of one if the country is included in the JPMorgan bond index; Transition countries is a dummy variable taking a value of one if the country is defined as a transition economy in the World Economic Outlook database; Post-1945 independence is a dummy variable taking the value of one if the country became independent after 1945; Years since independence is the year of independence minus 1945 when the country became independent after 1945, otherwise zero.
Note: The table reports the mean for each explanatory variable for the countries in each of the exchange rate regime categories; the number of observations is given in parentheses. For each explanatory variable, the null hypothesis is that the mean of the variable is the same in all exchange rate regime categories. The p-value (from the one-way analysis of variance framework) is the probability of falsely rejecting the null hypothesis and is computed omitting the “freely falling” category. The explanatory variables are defined as follows: Share of trade with largest trading partner is exports to the largest trading partner as a share of total exports; Economic size is the logarithm of total GNP in U.S. dollars at purchasing power parity; Per capita GNP is in U.S. dollars at purchasing power parity; Trade openness is imports plus exports as a percentage of GDP; Capital controls are defined as the sum of four dummy variables: (a) multiple exchange rates, (b) current account restrictions, (c) capital account restrictions, and (d) export proceeds surrender requirements; each dummy variable takes the value of one for a particular country if the corresponding characteristic is present in that country. De facto openness to capital flows is the absolute value of inward and outward flows of financial assets and liabilities (the sum of the absolute values, if available, of International Financial Statistics lines 78bdd, 78bed, 78bfd, 78bgd, 78bhd, and 78bid), as a percentage of GDP; Inflation is the logarithm of one plus the inflation rate; Reserves are as a share of imports; Political instability is on a 0-12 scale—the average of: (a) government stability, (b) external conflicts, and (c) political violence and internal conflicts—from the International Country Risk Guide issued by Political Risk Services; Fuel exporters is a dummy variable taking the value of one if the country is a major fuel exporter, according to the IMF’s World Economic Outlook database; Emerging markets is a dummy variable taking the value of one if the country is included in the JPMorgan bond index; Transition countries is a dummy variable taking a value of one if the country is defined as a transition economy in the World Economic Outlook database; Post-1945 independence is a dummy variable taking the value of one if the country became independent after 1945; Years since independence is the year of independence minus 1945 when the country became independent after 1945, otherwise zero.

Before moving to regression analysis, we report the correlation matrix for the potential determinants of exchange rate regimes we consider (Table 11.3). Even though many of these potential determinants are correlated with one another, there do not seem to be obvious signs that multicollinearity underlies the absence of significant and robust results that we find in our regressions.

Turning to regression analysis, we begin with probit regressions of exchange rate regimes on the largest number of possible determinants that is consistent with having a reasonably large sample of countries (Table 11.4). For the six classifications/years listed in “Exchange Rate Regime Classification” earlier in the chapter, we consider, in turn, hard pegs (or pegs, or fixed rates, depending on the exact classification) versus all other types of exchange rate regime, floating regimes versus all others, and pure floats versus all others. Although a few variables turn out to be significant, none is significant with the same sign in a reasonable number of specifications.

As noted previously, the correlation matrix for the potential determinants of exchange rate regimes does not lead us to suspect that mul-ticollinearity underlies the absence of significant and robust results. Nevertheless, it seems desirable to narrow the set of potential determinants to a baseline consisting of trade openness, the share of trade with the largest trading partner, and economic size, adding various alternative fourth potential variables, one at a time. Panels (a)–(c) of Table 11.5 report the results obtained starting from our more parsimonious, baseline specification. For each exchange rate classification (and point in time), we estimate 11 regressions: one with just the baseline variables, and 10 with the baseline variables adding a fourth variable. For each baseline variable we report the minimum and maximum z-statistic obtained in the 11 regressions and the number of times that the variable is significant. For the additional variables, we report the z-statistic and highlight whether it is significant. The coefficient on size is significant in several specifications: the larger the country, the more likely it is to float—or to have a pure float—and the less likely it is to have a hard peg. There are also indications that trade openness is negatively associated with floats and pure floats. The share of trade with the country’s main trading partner turns out not to be significant in more than a few specifications. Among the additional variables, higher inflation is often positively and significantly associated with floats (and negatively and significantly with pegs or hard pegs), though in the case of this final result, causality is especially dubious. Other potential determinants of exchange rate regimes enter significantly in very few specifications.

Turning to multinomial logit regressions, again we do not find robust regularities in the data, despite signs that larger economies are more likely to have floating rates than intermediate regimes and less likely to have hard pegs than intermediate regimes (panels (a)-(c) of Table 11.6). Of particular interest with this technique, neither de facto capital openness nor capital controls is a robust predictor of whether countries tend to have floats (or pure floats) or hard pegs, rather than intermediate regimes. This suggests that newly popular (and eminently sensible) theories of the determination of exchange rate regimes do not have much predictive power when applied to the data using available indicators.

Table 11.3.Potential Determinants of Exchange Rate Regimes: Correlation Matrix
shmajtrdloggnpgnpppctotsddfuelopenkcontroldemkaopenloginfresimpdtransdind1945depyricrg3
Share of trading with largest trading partner (shmajtrd)1.00
Economic size (loggnp)–0.28*1.00
Per capita GNP (gnpppc)–0.20*0.47*1.00
Standard deviation of terms of trade (totsd)0.03–0.30*–0.39*1.00
Fuel exporters (dfuel)0.070.000.010.45*1.00
Trade openness topeiy0.00–0.40*0.150.020.031.00
Capital control (kcontrol)0.05–0.23*–0.56*0.30*0.08–0.161.00
Emerging markets (dem)–0.090.38*–0.06–0.080.03–0.160.081.00
De facto openness to capital flows (kaopen)0.09–0.100.22*–0.11–0.010.41*0.03–0.041.00
Inflation (loginf)0.130.08–0.23*0.040.04–0.100.21*0.14–0.051.00
Reserves (resimp)–0.100.03–0.060.090.01–0.130.120.15–0.04–0.061.00
Transition countries (dtrans)0.000.06–0.08–0.15–0.130.18*0.050.05–0.040.37*–0.111.00
Post-1945 independence (dind1945)0.00–0.42*–0.40*0.27*0.050.25*0.37*–0.29*0.060.01–0.070.071.00
Years since independence (depyr)0.06–0.42*–0.25*0.33*–0.020.48*0.22*–0.26*0.050.18*–0.160.50*0.67*1.00
Political instability (icrg3)–0.21*0.38*0.70*–0.27*–0.020.33*–0.43*0.000.19–0.39*0.07–0.25*–0.14 1.00

statistically significant at the 5 percent level.

statistically significant at the 5 percent level.

Table 11.4.Probit Regressions
Pegs versus All OthersFloats versus All OthersPure Floats versus All Others
(1)(2)(3)(4)(5)(1)(2)(3)(4)(5)(1)(2)
Hard pegsPegsFixFixHard pegsFloatsFloatsFloatsFloatsFloatsPure floatsPure floats
IMF2000IMFREV90LYS1999LYS1990IMFREP90IMF2000IMFREV90LYS1999LYS1990IMFREP90IMF2000IMFREV90
Trade openness–0.013–0.002–0.063–0.015–0.0550.004–0.0100.024–0.022–0.012–0.002–0.060
(1.38)(0.29)(2.28)*(0.81)(1.74)(0.49)(1.28)(1.54)(1.49)(0.84)(0.29)(2.89)**
Share of trade with largest0.003–0.006–0.016–0.017–0.257–0.0080.0020.0110.0020.0400.0010.011
trading partner(0.29)(0.52)(0.69)(0.84)(2.15)*(0.80)(0.24)(0.66)(0.17)(2.16)*(0.13)(0.82)
Economic size–0.199–0.231–1.1060.062–3.6280.2870.0370.722–0.5650.4670.267–0.395
(1.12)(1.37)(2.37)*(0.19)(2.15)*(1.88)(0.26)(2.43)*(2.09)*(1.66)(1.67)(1.70)
Per capita GNP0.000–0.000–0.000–0.0000.0000.000–0.0000.0000.000–0.0000.0000.000
(0.17)(1.19)(0.15)(1.51)(0.45)(0.09)(0.42)(0.32)(1.85)(1.88)(0.35)(1.01)
Standard deviation of terms–0.004–0.0060.0240.0280.005–0.034–0.0460.016–0.017
of trade(0.28)(0.21)(0.74)(0.48)(0.38)(1.34)(1.78)(0.65)(1.06)
De facto openness to0.0270.0060.0600.1590.026–0.026–0.001–0.107–0.020–0.017–0.0040.020
capital flows(1.48)(0.54)(0.76)(1.66)(1.00)(1.10)(0.11)(1.66)(0.25)(0.55)(0.22)(1.30)
Capital control0.0190.107–0.083–0.6591.3380.083–0.2190.0090.568–0.658–0.047–0.774
(0.08)(0.57)(0.20)(1.66)(1.53)(0.41)(1.22)(0.04)(1.79)(1.94)(0.22)(2.16)*
Post-1945 independence–0.250–0.8012.905–0.962–3.207–0.0800.669–2.9551.042–0.1090.5181.086
(0.41)(1.40)(1.80)(0.92)(1.41)(0.15)(1.39)(2.87)**(1.41)(0.17)(0.93)(1.34)
Years since independence–0.0070.067–0.1080.1770.2250.033–0.0420.181–0.152–0.0930.018–0.052
(0.17)(2.22)*(1.16)(1.97)*(2.18)*(0.93)(1.41)(2.45)*(2.09)*(1.82)(0.51)(0.92)
Political instability–0.0251.0090.1430.530–0.081–0.2710.074–0.044–0.214
(0.11)(1.86)(0.82)(1.41)(0.42)(0.92)(0.48)(0.23)(1.09)
Inflation–2.945–5.735–3.525–3.556–49.4872.1330.202–2.053–2.1386.1121.655–0.021
(1.76)(3.55)**(1.20)(0.97)(2.54)*(1.85)(0.38)(1.65)(1.66)(2.18)*(1.48)(0.03)
Reserves–1.368–0.264–0.458–2.449–0.464–0.037–0.3991.9284.470–4.0380.9660.097
(1.07)(0.25)(0.18)(1.11)(0.13)(0.03)(0.34)(1.07)(2.43)*(1.98)*(0.88)(0.06)
Number of observations751064755747510647557475106
Note: Absolute value of z-statistics in parentheses. IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff views. IMFREP90 is the original IMF classification for 1990, based purely on countries’ self-reporting. IMFREV90 is a revision of the original IMF classification for 1990, incorporating the IMF staff’s views. LYS1999 and LYS1990 are the Levy Yeyati–Sturzenegger classifications for 1999 and 1990, respectively.

statistically significant at the 5 percent level;

statistically significant at the 1 percent level.

Note: Absolute value of z-statistics in parentheses. IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff views. IMFREP90 is the original IMF classification for 1990, based purely on countries’ self-reporting. IMFREV90 is a revision of the original IMF classification for 1990, incorporating the IMF staff’s views. LYS1999 and LYS1990 are the Levy Yeyati–Sturzenegger classifications for 1999 and 1990, respectively.

statistically significant at the 5 percent level;

statistically significant at the 1 percent level.

Table 11.5.Probit Estimates
a. IMF classification
Data sourceIMF2000IMFREV90IMF2000IMFREV90IMF2000IMFREV90
Dependent variable(1 if hard peg, 0 otherwise)(1 if peg, 0 otherwise)(1 if float, 0 otherwise)(1 if float, 0 otherwise)(1 if pure float, 0 otherwise)(1 if pure float, 0 otherwise)
Baseline variablesmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sig
Trade openness–0.291.010–1.951.870–2.50*–0.875–2.28*–1.39(.–2.39*–1.197–3.47**–2.31*11
Share of trade with largest trading partner–0.280.530–1.280.490–0.480.8800.521.1800.310.7900.531.370
Economic size–2.51*–0.842–4.99**–1.25100.843.06**60.291.4601.482.13*2–1.800.380
Additional variablesz-valuez-valuez-valuez-valuez-valuez-value
Per capita GDP2.44*–1.65–2.69**–0.15–0.882.71**
Standard deviation of terms of trade–1.493.26**0.73–0.33–0.98–1.19
De facto openness to capital flows–0.690.24–0.79–0.05–0.210.72
Capital controls–1.762.94**1.56–1.36–0.79–3.25**
Post-1945 independence–1.651.932.09*0.501.06–0.57
Years since independence–1.343.01**2.63**–1.080.54–1.09
Political instability1.33–2.45*–1.300.10–1.241.79
Inflation–1.80–4.26**2.58**0.451.310.57
Reserves–1.960.41–0.18–0.550.38–0.34
Transition countries–0.552.13*–0.70
b. Reinhart–Rogoff classification
2000199020001990
Dependent variable(1 if peg, 0 otherwise)(1 if peg, 0 otherwise)(1 if intermediate, 0 otherwise)(1 if intermediate, 0 otherwise)
Baseline variablesmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sig
Trade openness0.571.4600.772.332–0.450.700–1.431.180
Share of trade with largest trading partner–1.89–1.3700.391.0000.711.390–0.92–0.090
Economic size–1.50–0.520–1.94–0.2200.692.6850.212.817
Additional variablesz-valuez-valuez-valuez-value
Per capita GNP1.230.76–1.87–0.92
Standard deviation of terms of trade–1.35–0.240.51–0.66
De facto openness to capital flows–1.210.61–0.88–0.80
Capital controls–1.65–0.402.05*1.11
Post-1945 independence–1.92–0.72–0.45–1.24
Years since independence–0.90–1.69–0.60–2.17*
Political instability1.290.55–0.25–0.03
Inflation–0.45–2.34*0.382.01*
Reserves–0.56–2.71**2.07*1.78
Transition countries–0.041.29
c. Levi Yeyati–Sturzenegger classification
Data SourceLYS1999LYS1 990LYS1 999LYS1 990
Dependent variable(1 if fix, 0 otherwise)(1 if fix, 0 otherwise)(1 if float, 0 otherwise)(1 if float, 0 otherwise)
Baseline variablesmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sig
Trade openness–1.210.0701.252.30*6–0.620.550–1.69–0.530
Share of trade with largest trading partner–2.56*–1.688–0.340.0400.982.11*6–0.86–0.480
Economic size–3.61**–2.19*10–2.73**0.4153.03**2.03*10–0.321.180
Additional variablesz-valuez-valuez-valuez-value
Per capita GDP1.86–0.120.761.77
Standard deviation of terms of trade–0.280.14–1.68–1.61
De facto openness to capital flows0.580.49–0.990.53
Capital controls–0.480.380.07–0.81
Post-1945 independence1.131.08–0.63–0.48
Years since independence0.962.34*0.09–1.36
Political instability1.851.88–0.451.07
Inflation–0.72–0.86–1.31–1.89
Reserves–0.68–0.560.321.52
Transition countries0.39–0.77
Note: Each set of regressions includes the baseline regression (the three baseline regressors only) and 10 additional regressions (the three baseline regressors plus one of the additional variables as a fourth regressor). For the baseline variables, the table reports the minimum (min. z) and maximum (max. z) z-statistic obtained in any of the 11 regressions, as well as the number of cases in which the variable is significant (#sig).In panel (a), IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff views. IMFREV90 is a revision of the original IMF classification for 1990, incorporating the IMF staff’s views.In panel (b), the first two columns estimate the determinants of the probability that a country will adopt a peg (category 1 in the Reinhart-Rogoff coarse classification), using data for 2000 and 1990, respectively. The last two columns estimate the determinants of the probability that a country will adopt an intermediate regime (the union of categories 2 and 3 in the Reinhart-Rogoff coarse classification), using data for 2000 and 1990, respectively. Freely falling regimes are omitted from the estimations.In panel (c), LYS1999 is the Levy Yeyati-Sturzenegger classification for 1999, and LYS1 990 is the classification by those same authors for 1990.

statistically significant at the 5 percent level;

statistically significant at the 1 percent level.

Note: Each set of regressions includes the baseline regression (the three baseline regressors only) and 10 additional regressions (the three baseline regressors plus one of the additional variables as a fourth regressor). For the baseline variables, the table reports the minimum (min. z) and maximum (max. z) z-statistic obtained in any of the 11 regressions, as well as the number of cases in which the variable is significant (#sig).In panel (a), IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff views. IMFREV90 is a revision of the original IMF classification for 1990, incorporating the IMF staff’s views.In panel (b), the first two columns estimate the determinants of the probability that a country will adopt a peg (category 1 in the Reinhart-Rogoff coarse classification), using data for 2000 and 1990, respectively. The last two columns estimate the determinants of the probability that a country will adopt an intermediate regime (the union of categories 2 and 3 in the Reinhart-Rogoff coarse classification), using data for 2000 and 1990, respectively. Freely falling regimes are omitted from the estimations.In panel (c), LYS1999 is the Levy Yeyati-Sturzenegger classification for 1999, and LYS1 990 is the classification by those same authors for 1990.

statistically significant at the 5 percent level;

statistically significant at the 1 percent level.

Table 11.6Multinomial Logit Estimates
a. IMF classification
Data sourceIMF2000IMFREV90
Baseline variablesFixFloatFixFloat
min. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sig
Trade openness–1.1–0.180–2.39*–0.124–2.84**0.81–2.05*–0.961
Share of trade with largest trading partner–0.370.620–0.860.960–1.880.860–0.011.140
Economic size–1.080.2600.442.21*2–4.58**–1.0910–0.910.210
Additional variablesz-valuez-valuez-valuez-value
Per capita GDP1.22–1.77–1.67–0.45
Standard deviation of terms of trade–1.31–0.073.30**1.67
De facto openness to capital flows–0.91–0.970.240.06
Capital controls–1.010.842.56*–0.12
Post-1945 independence–0.691.432.40*1.54
Years since independence0.022.23*2.85**0.78
Political instability0.71–0.70–2.52*–0.76
Inflation–0.771.80–3.91**–1.27
Reserves–2.40*–1.490.09–0.55
Transition countries0.692.00*0.000.00
b. Reinhart-Rogoff classification
Year20001990
PegFloatPegFloat
Baseline variablesmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sig
Trade openness0.201.050–2.190.5720.282.245–2.17–1.422
Share of trade with largest trading partner–1.80–1.220–0.451.0100.570.950–1.62–0.660
Economic size–2.18–0.6320.341.830–2.26–0.831–1.75
Additional variablesz-valuez-valuez-valuez-value
Per capita GNP1.660.93–0.192.11*
Standard deviation of terms of trade–1.07–0.330.32–1.11
De facto openness to capital flows0.17–1.630.600.50
Capital controls–2.11 *–1.17–0.70–1.93
Post-1945 independence–0.980.770.04–0.49
Years since independence–0.21–0.50–0.02–0.49
Political instability0.870.540.231.87
Inflation–0.69–1.27–2.32*–1.29
Reserves–1.20–2.08*–2.10*–1.03
Transition countries–0.700.000.000.00
c. Levi Yeyati-Sturzenegger classification
Year19991990
FixedFloatFixedFloat
Baseline variablesmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sig
Trade openness–1.810.030–0.840.650–2.00*2.15*7–1.880.670
Share of trade with largest trading partner–1.90–0.3100.091.100–0.20–0.930–0.96–0.060
Economic size–2.98**–1.2380.231.540–2.65**0.503–2.19*0.371
Additional variablesz-valuez-valuez-valuez-value
Per capita GDP2.33*1.820.961.96*
Standard deviation of terms of trade–1.25–1.99*–0.62–1.65
De facto openness to capital flows0.44–0.941.261.27
Capital controls–0.49–0.160.01–0.53
Post-1945 independence0.94–0.101.000.25
Years since independence1.150.722.18*0.46
Political instability1.790.462.58**2.08*
Inflation–1.18–1.62–1.25–1.86
Reserves–0.72–0.130.221.37
Transition countries0.08–0.61
Note: Each set of regressions includes the baseline regression (the three baseline regressors only) and 1 0 additional regressions (the three baseline regressors plus one of the additional variables as a fourth regressor). For the baseline variables, the table reports the minimum (min. z) and maximum (max. z) z-statistic obtained in any of the 11 regressions, as well as the number of cases in which the variable is significant (#sig).In panel (a), IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff views. IMFREV90 is a revision of the original IMF classification for 1990, incorporating the IMF staff’s views. Intermediate regimes are the third regime in the multinominal logit estimation.In panel (b), the estimates refer to the determinants of the likelihood that a country will adopt a peg (category 1 in the Reinhart-Rogoff coarse classification), a free float (category 4), or an intermediate regime (the union of categories 2 and 3). Intermediate regimes are the base category in the multinomial logit estimation. Freely falling regimes are omitted from the estimations.In panel (c), intermediate regimes are the third regime in the multinomial logit estimation.

statistically significant at the 5 percent level;

statistically significant at the 1 percent level.

Note: Each set of regressions includes the baseline regression (the three baseline regressors only) and 1 0 additional regressions (the three baseline regressors plus one of the additional variables as a fourth regressor). For the baseline variables, the table reports the minimum (min. z) and maximum (max. z) z-statistic obtained in any of the 11 regressions, as well as the number of cases in which the variable is significant (#sig).In panel (a), IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff views. IMFREV90 is a revision of the original IMF classification for 1990, incorporating the IMF staff’s views. Intermediate regimes are the third regime in the multinominal logit estimation.In panel (b), the estimates refer to the determinants of the likelihood that a country will adopt a peg (category 1 in the Reinhart-Rogoff coarse classification), a free float (category 4), or an intermediate regime (the union of categories 2 and 3). Intermediate regimes are the base category in the multinomial logit estimation. Freely falling regimes are omitted from the estimations.In panel (c), intermediate regimes are the third regime in the multinomial logit estimation.

statistically significant at the 5 percent level;

statistically significant at the 1 percent level.

The absence of robust and significant relationships does not seem to be due to endogeneity of the potential determinants of exchange rate regimes, to the extent that we are able to control for this problem through instrumental variables. We include land area and a landlocked country dummy as instrumental variables, in an attempt to control for the possibility of reverse causality between openness and exchange rate regimes. Table 11.7. reports the results for the IMF end-2000 classification. Using instrumental variables, openness becomes always positively associated with hard pegs (and negatively associated with floats and pure floats), with a larger coefficient than in the regressions without instruments. At the same time, the relationship between economic size and exchange rate regime becomes much less clear.

Table 11.7Probit with Instrumental Variables: IMF2000 Classification
Dependent variableHard PegsFloatsPure Floats
Baseline variablesmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sig
Trade openness0.171.030–1.47–1.100–1.44–0.780
Share of trade with largest trading partner0.170.580–0.740.040–0.520.500
Economic size–0.270.750–1.13–0.370–0.950.290
Additional variablesz-valuez-valuez-value
Per capita GDP–0.480.851.00
Standard deviation of terms of trade–1.410.57–0.83
De facto openness to capital flows–1.120.690.68
Capital controls0.36–0.86–1.08
Post-1945 independence–1.230.960.77
Years since independence–1.261.751.15
Political instability0.410.580.76
Inflation–1.250.900.34
Reserves0.15–1.260.87
Transition countries–0.961.740.31
Note: Each set of regressions includes the baseline regression (the three baseline regressors only) and 10 additional regressions (the three baseline regressors plus one of the additional variables as a fourth regressor). For the baseline variables, the table reports the minimum (min. z) and maximum (max. z) z-statistic obtained in any of the 11 regressions, as well as the number of cases in which the variable is significant (#sig). IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff views. None of the individual coefficients are statistically significant at the 5 percent level in this table.
Note: Each set of regressions includes the baseline regression (the three baseline regressors only) and 10 additional regressions (the three baseline regressors plus one of the additional variables as a fourth regressor). For the baseline variables, the table reports the minimum (min. z) and maximum (max. z) z-statistic obtained in any of the 11 regressions, as well as the number of cases in which the variable is significant (#sig). IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff views. None of the individual coefficients are statistically significant at the 5 percent level in this table.
Table 11.8Multinominal Logit: IMF Classification, Cross-Section of Countries with Same Exchange Rate System in 2000, 1990, and 1995
Data sourceIMF2000IMFREV90
Hard PegFloatHard PegFloat
Baseline variablesmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sigmin. zmax. z#sig
Trade openness–1.9*–0.251–2.10*–1.523–0.720.900–1.71–0.460
Share of trade with largest trading partner–0.061.7300.711.380–0.481.1100.210.710
Economic size–3.16**–2.00*110.801.310–2.36*0.005–0.271.550
Additional variablesz-valuez-valuez-valuez-value
Per capita GDP–1.68–0.67–1.34–1.22
Standard deviation of terms of trade0.470.561.341.24
De facto openness to capital flows–0.62–0.090.72–0.20
Capital controls1.40–0.262.22*0.91
Post-1945 independence1.651.012.21*1.62
Years since independence0.180.332.45*2.06*
Political instability–1.04–0.420.910.65
Inflation–1.820.88–1.79
Reserves–2.00*–1.67–1.140.03
Transition countries0.000.00
Note: Each set of regressions includes the baseline regression (the three baseline regressors only) and 10 additional regressions (the three baseline regressors plus one of the additional variables as a fourth regressor). For the baseline variables, the table reports the minimum (min. z) and maximum (max. z) z-statistic obtained in any of the 11 regressions, as well as the number of cases in which the variable is significant (#sig). IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff’s views. IMFREV90 is a revision of the original IMF classification for 1990, incorporating the IMF staff views.

statistically significant at the 5 percent level;

statistically significant at the 1 percent level.

Note: Each set of regressions includes the baseline regression (the three baseline regressors only) and 10 additional regressions (the three baseline regressors plus one of the additional variables as a fourth regressor). For the baseline variables, the table reports the minimum (min. z) and maximum (max. z) z-statistic obtained in any of the 11 regressions, as well as the number of cases in which the variable is significant (#sig). IMF2000 is the IMF classification for 2000, which begins with countries’ self-reporting but incorporates the IMF staff’s views. IMFREV90 is a revision of the original IMF classification for 1990, incorporating the IMF staff views.

statistically significant at the 5 percent level;

statistically significant at the 1 percent level.

Finally, in an attempt to consider only those countries that seem to have reached their “long-run” exchange rate regime, we restrict the sample to those countries that belong to the same exchange rate regime category in 2000, 1995, and 1990. The results are broadly similar to those obtained for the full sample of countries, with large countries more likely to have floats and less likely to have hard pegs (Table 11.8).

Concluding Remarks

This chapter shows that very little is known about the positive determinants of exchange rate regime choice, despite a vast literature on the normative determinants. We show that, viewed as a whole, the existing empirical literature is inconclusive. Our own estimates confirm that neither the variables identified by old theories, including optimum currency area theory, nor other economic or political variables identified by newer theories, including the “hollowing of the middle” hypothesis, are robust predictors of exchange rate regimes in cross sections of countries. If we had to pick one variable that seems to bear some relationship to exchange rate regime choice, the size of a country’s economy (total GNP) may be positively associated with floats and pure floats and negatively associated with pegs and hard pegs. But even in that case the relationship is not fully robust.

Our generally negative findings do not necessarily show that it would be impossible to trace how a given country arrived at its current exchange rate regime, nor do they imply that the choices made along the way were unwise. Rather, particular circumstances may have led the authorities to choose a new regime for good reasons; subsequently they may have maintained it, regardless of whether it could be viewed as the optimal choice in a long-run framework, because within a limited time horizon the costs of changing an exchange rate regime may outweigh the benefits of doing so.

References

    AlesinaAlbertoand AlexanderWagner2006“Choosing (and Reneging on) Exchange Rate Regimes,”Journal of the European Economic AssociationVol. 4No. 477099.

    • Search Google Scholar
    • Export Citation

    BergerHelgeJan-EgbertSturmand Jakobde Haan2000“An Empirical Investigation into Exchange Rate Regime Choice and Exchange Rate Volatility,”CESifo Working Paper No. 263 (Munich: Center for Economic Studies, Ifo Institute for Economic Research). Available via the Internet: http://www.cesifo-group.de/DocCIDL/cesifo_wp263.pdf

    • Search Google Scholar
    • Export Citation

    BoscoLuigi1987“Determinants of the Exchange Rate Regimes in LDCs: Some Empirical Evidence,”Economic NotesNo. 1 pp. 11943.

    CalvoGuillermoand CarmenReinhart2002“Fear of Floating,”Quarterly Journal of EconomicsVol. 117 (May) pp. 379436.

    CollinsSusanM.1996“On Becoming More Flexible: Exchange Rate Regimes in Latin America and the Caribbean,”Journal of Development EconomicsVol. 51 (October) pp. 11738.

    • Search Google Scholar
    • Export Citation

    CuddingtonJohnT. and SamuelK. Otoo1990“Choice of Exchange Rate Regime: A Multinomial Logit Model,”Department of Economics Working Paper No. 9018 (Washington: Georgetown University).

    • Search Google Scholar
    • Export Citation

    CuddingtonJohnT. and SamuelK. Otoo1991“An Analysis of the Choice of Exchange Rate Regimes in the 1980s,”Department of Economics Working Paper No. 91–02 (Washington: Georgetown University).

    • Search Google Scholar
    • Export Citation

    DreyerJacobS.1978“Determinants of Exchange-Rate Regimes for Currencies of Developing Countries: Some Preliminary Results,”World DevelopmentVol. 6 (April) pp. 43745.

    • Search Google Scholar
    • Export Citation

    EdisonHaliJ. and MichaelMelvin1990“The Determinants and Implications of the Choice of an Exchange Rate Regime,” in Monetary Policy for a Volatile Global Economyed. by WilliamS. Haraf and ThomasD. Willett (Washington: AEI Press).

    • Search Google Scholar
    • Export Citation

    EdwardsSebastian1996“The Determinants of the Choice Between Fixed and Flexible Exchange-Rate Regimes,”NBER Working Paper No. 5756 (Cambridge, Massachusetts: National Bureau of Economic Research).

    • Search Google Scholar
    • Export Citation

    EdwardsSebastian1999“The Choice of Exchange Rate Regime in Developing and Middle Income Countries,” in Changes in Exchange Rates in Rapidly Developing Countries: Theory Practice and Policy Issuesed. by TakatoshiIto and AnneO. KruegerNBER-East Asia Seminars on Economics,Vol. 7 (Chicago: University of Chicago Press).

    • Search Google Scholar
    • Export Citation

    EichengreenBarry1994International Monetary Arrangements for the 21st Century (Washington: Brookings Institution).

    EichengreenBarry1998“The Only Game in Town,”The World TodayVol. 54 (December).

    FischerStanley1977“Stability and Exchange Rate Systems in a Monetarist Model of the Balance of Payments,” in The Political Economy of Monetary Reformed. by RobertZ. Aliber (London: Macmillan).

    • Search Google Scholar
    • Export Citation

    FischerStanley2001“Exchange Rate Regimes: Is the Bipolar View Correct?”Journal of Economic PerspectivesVol. 15 (Spring) pp. 324.

    • Search Google Scholar
    • Export Citation

    FrankelJeffreyA. and DavidRomer1999“Does Trade Cause Growth?”American Economic ReviewVol. 89 (June) pp. 37999.

    FriedenJeffryPieroGhezzi and ErnestoStein2000“Politics and Exchange Rates: A Cross-Country Approach to Latin America,”Research Network Working Paper No. R-421 (Washington: Inter-American Development Bank). Available via the Internet:http://www.iadb.org/res/publications/pubfiles/pubR-421.pdf

    • Search Google Scholar
    • Export Citation

    HellerH.Robert1978“Determinants of Exchange Rate Practices,”Journal of Money Credit and BankingVol. 10 (August) 308–21.

    HoldenPaulMerleHolden and EstherSuss1979“The Determinants of Exchange Rate Flexibility: An Empirical Investigation,”Review of Economics and StatisticsVol. 61 (August) pp. 32733.

    • Search Google Scholar
    • Export Citation

    HonkapohjaSeppo and PenttiPikkarainen1994“Country Characteristics and the Choice of the Exchange Rate Regime: Are Mini-Skirts Followed by Maxis?” in Exchange Rate Policies in the Nordic Countriesed. by JohnnyAkerholm and AlbertoGiovannini (London: Centre for Economic Policy Research).

    • Search Google Scholar
    • Export Citation

    JohnstonR.Barry and others1999Exchange Rate Arrangements and Currency Convertibility: Developments and IssuesWorld Economic and Financial Surveys (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    JuhnGrace and PaoloMauro2002“Long-Run Determinants of Exchange Rate Regimes: A Simple Sensitivity Analysis,”IMF Working Paper 02/104 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    LevineRoss and DavidRenelt1992“Sensitivity Analysis of Cross-Country Growth Regressions,”American Economic ReviewVol. 82 (September) pp. 94263.

    • Search Google Scholar
    • Export Citation

    LevyYeyatiEduardo and FedericoSturzenegger2001“Exchange Rate Regimes and Economic Performance,”IMF Staff PapersVol. 47 (September) pp. 6298.

    • Search Google Scholar
    • Export Citation

    LevyYeyatiEduardo and FedericoSturzenegger2005“Classifying Exchange Rate Regimes: Deeds vs. Words,”European Economic ReviewVol. 49 (August) pp. 160335.

    • Search Google Scholar
    • Export Citation

    MassonPaul2001“Exchange Rate Regime Transitions,”Journal of Development EconomicsVol. 64 (April) pp. 57186.

    MelvinMichael1985“The Choice of an Exchange Rate System and Macroeconomic Stability,”Journal of Money Credit and BankingVol. 17 (November) pp. 46778.

    • Search Google Scholar
    • Export Citation

    MundellRobert1961“A Theory of Optimum Currency Areas,”American Economic ReviewVol. 51 (September) pp. 65765.

    ObstfeldMaurice and KennethRogoff1995“The Mirage of Fixed Exchange Rates,”Journal of Economic PerspectivesVol. 9 (Autumn) pp. 7396.

    • Search Google Scholar
    • Export Citation

    PoirsonHelene2001“How Do Countries Choose Their Exchange Rate Regime?”IMF Working Paper 01/46 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    ReinhartCarmen and KennethRogoff2004“The Modern History of Exchange Rate Arrangements: A Reinterpretation,”Quarterly Journal of EconomicsVol. 119 (February) pp. 148.

    • Search Google Scholar
    • Export Citation

    RicciLucaAntonio1997“A Model of an Optimum Currency Area,”IMF Working Paper 97/76 (Washington: International Monetary Fund).

    RizzoJean-Marc1998“The Economic Determinants of the Choice of an Exchange Rate Regime: A Probit Analysis,”Economics LettersVol. 59 (June) pp. 28387.

    • Search Google Scholar
    • Export Citation

    SavvidesAndreas1990“Real Exchange Rate Variability and the Choice of Exchange Rate Regime by Developing Countries,”Journal of International Money and FinanceVol. 9 (December) pp. 44054.

    • Search Google Scholar
    • Export Citation

    TavlasGeorgeS.1994“The Theory of Monetary Integration,”Open Economies ReviewVol. 5 (March) pp. 21130.

Helpful comments and suggestions from Eduardo Borensztein, Susan Collins, Hali Edison, Helene Poirson, and especially Paul Masson are gratefully acknowledged. Saji Thomas, Jim Berry, and Priyadarshani Joshi made a useful contribution to the data-gathering and data management effort.

1

An excellent survey of the pre-1990 literature on the causes and consequences of exchange rate regime choice is Edison and Melvin (1990).

2

The same type of classification is now published in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions and has been used by Fischer (2001).

3

This follows Frankel and Romer (1999) in a different context.

4

For a survey see, for example, Tavlas (1994).

5

The optimum currency area literature began with Mundell (1961). A review of this literature is provided by, for example, Ricci (1997).

6

An early contribution is Fischer (1977).

7

Alesina and Wagner (2006) suggest that countries with poor institutional quality have difficulty in maintaining exchange rate pegs and abandon them more often; in contrast, emerging market countries with relatively good institutions de facto manage their exchange rates more than announced, perhaps to signal their differences from those countries incapable of maintaining promises of monetary stability.

8

We nevertheless recognize that this was a by-product of estimations whose main objective was different than the key question examined in the present chapter.

9

The Levy Yeyati–Sturzenegger classification has already been used by Levy Yeyati and Sturzenegger (2001) and Masson (2001). Our goal is not to assess the relative merits of various classifications, but simply to show that our main conclusions hold regardless of which classification is used.

10

Savvides (1990) makes an admirable attempt to take into account the joint determination of real exchange rate volatility and the exchange rate regime.

11

If the data are missing for more than four of these lines, then the observation for the country in question is recorded as not available.

12

We do not estimate multinominal logits for classifications into more than three regime groups. As is well known, the independence-of-irrelevant-alternatives property renders multinomial logits inappropriate when two or more of the alternatives are close substitutes, because the relative probabilities of choosing two existing alternatives are unaffected by the addition of an irrelevant category. We are comfortable that, say, hard pegs, intermediate regimes, and floats are not close substitutes, but worry that with further subdivisions (for example, of intermediate regimes into crawling pegs, crawling bands, etc.) some categories would become excessively close substitutes.

13

We also experimented with a baseline including gross national product per capita at purchasing power parity in addition to the three variables previously mentioned, and obtained essentially the same results.

    Other Resources Citing This Publication