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Int Econ Econ Policy (2011) 8:275–305

DOI 10.1007/s10368-010-0169-5

O R I G I N A L PA P E R

The choice of exchange rate regimes

in the MENA countries: a probit analysis

Mohamed Daly Sfia

Published online: 18 June 2010

# Springer-Verlag 2010

Abstract This paper analyses empirically the determinants of exchange rate regime

choice in 17 MENA countries. For this purpose, we use both de jure and de facto

regime classifications test and estimate a series of binomial and multinomial probit

models. Regressions results highlight the important influence of two factors: trade

openness and oil export capacity.

Keywords MENA countries . Exchange rate regimes . Probit models

JEL Classifications F33 . C25

1 Introduction

The choice of an appropriate exchange rate regime has been at the centre of the debate in

international economics for a long time. At the start of the 1960’s, the literature on

optimal exchange rate arrangements was cast as a general debate over the choice

between fixed or flexible rates. There was broad agreement that both regimes had

advantages and disadvantages.1 During this period of time and until 1973, exchange rate

policy was dominated by the Bretton Woods agreement of 1944, with its commitment to

currencies convertible for current account transactions and fixed exchange rates (beyond

a narrow band of permissible flexibility) but adjustable if necessary.

Five years after the breakdown the Bretton Woods arrangement, the International

Monetary Fund (IMF) decided that member countries can choose any exchange

arrangement that suits them, provided that it is declared to the IMF and provided that

it is consistent with the general objectives of the IMF.2 Most major industrial nations

1

See for instance Friedman (1953), Kindleberger (1969).

2

See IMF’s Article IV of agreement on obligations regarding exchange arrangements.

Acknowledgment I would like to thank Sami Mouley and two anonymous referees for their insightful

suggestions and comments.

M. D. Sfia (*)

Faculté des Sciences Economiques et de Gestion, University of Tunis, B P 477, Le Bardo,

Tunis 2000, Tunisia

e-mail: sfia_daly@yahoo.ca

276

M.D. Sfia

have abandoned their fixed exchange regimes, with the collapse of the Bretton

Woods system, and moved to a system of floating exchange rates. However, the

option of a pure floating exchange rate did not seem to be feasible for small

developing countries since it was recognized that flexible exchange rates would be

associated with inappropriate changes in household’s purchasing power, misdirected

investment and detrimental effects on trade.

Four decades after the collapse of the Bretton Woods system of fixed but

adjustable exchange rates, developing countries (and all IMF members) have the

option of adopting a potentially wide variety of exchange rate regimes. Today, in the

official classification by the IMF, exchange rate arrangements are divided into three

broad categories: pegged or fixed arrangements, flexible arrangements and an inbetween category of arrangements with “limited flexibility”. Fixed arrangements

include: Currency Unions, Currency Boards and truly fixed exchange rates. Floating

exchange rates are divided into free floats where monetary authorities do not

intervene and allow the exchange rate to be determined by market forces, and

managed floats where intervention is done to “lean against the wind”. Finally,

intermediate arrangements run the continuum from an adjustable peg under which

countries can periodically realign their pegs; to crawling pegs in which the peg is

frequently reset in a series of devaluations; to a basket peg where the exchange rate

is fixed in terms of a weighted basket of foreign currencies; to target zones (or

exchange rate regimes with bands) where the authorities intervene when the

exchange rate hits pre announced margins on either side of a central parity. Among

188 countries classified by the IMF at the end of 2008, 91 (48%) countries where

listed as having fixed exchange arrangements (Italy, Germany, Djibouti, Egypt etc.),

84 (44%) maintained flexible arrangements (United States, Mexico, Israel, etc.) and

13 (8%) were regarded as having intermediate arrangements (Iran, Slovenia, China

etc.).3 This illustrates the fact that different types of exchange rate arrangements may

be appropriate for different countries, depending on their structural characteristics,

external environments, and macroeconomic and political circumstances. Clearly,

national choices of exchange regimes reveal a lack of consensus in the world today.4

In spite of the wide range and diversity of exchange rate arrangements adopted by

the Middle East and North African (MENA) countries5, only few contributions have

been dedicated to the question of the choice of exchange rate regime by these

economies (see e.g. Jbili and Karamenko 2003). These countries are small in

economic size, have a low level of export and import diversification and exhibit

strong trade with the European Union (EU). Over the period 1990–2000, nearly 70%

of Tunisia’s and Algeria’s exports have been oriented to the “Euroland” market, 60%

of Morocco’s and 50% of Egypt’s. The same dependency exists for their imports,

since the EU is also the main source of MENA imports.6 These common structural

3

See IMF (2008) for more details.

In Jeffrey Frankel’s Words “No Single Currency Regime is Right for All Countries or At All Times”.

5

In this study the MENA countries include: Algeria, Bahrain, Djibouti, Egypt, Iran, Jordan, Kuwait,

Lebanon, Libya, Morocco, Oman, Qatar, Saudi-Arabia, Syria, Tunisia, United Arab Emirates (UAE) and

the Yemen.

6

The importance of MENA countries in “Euroland” external trade is much smaller; it does not exceed

5%.

4

The choice of exchange rate regimes in the MENA countries: a probit analysis

277

characteristics would lend support to the hypothesis that these countries should peg

to the Euro, however most of the MENA countries including the Gulf Cooperation

Council (GCC) members7, Jordan and Lebanon pegged their currencies to the US

dollar, while others like Algeria or Egypt opted for a managed float exchange rate

regime (for more details see Appendix A, Table 6).

This study attempts to uncover possible systematic relationships between the choice

of an exchange rate regime by the MENA countries and some traditional determinants

proposed in the existing literature. The inspiration for doing so is three-fold. One, the

choice of exchange rate regime is and will always be one of the most important issues for

any economy. The recent dramatic episodes of exchange rate crisis in several

developing, emerging and industrialized countries have demonstrated that the

consequences of inappropriate choices of exchange rate arrangements can be

devastating. Secondly, as stated before, in spite of the relative economic and financial

importance of the MENA economies, only a few studies have been devoted to the issue

of exchange rate regime choice by these countries. Thirdly, during the last few years, a

number of MENA countries have made considerable progress in liberalizing trade,

opening up their financial systems, and adopting market-based monetary policy

instruments. For example, the six GCC members have agreed to establish a monetary

union in the near future with a supranational central bank and a single currency pegged

to the US dollar. Moreover, several MENA countries have signed Euro-Mediterranean

Agreements with the UE in the so-called Barcelona Process. These fundamental changes

are likely to increase the number as well as the amount of both trade and financial flows

across the Mediterranean Sea. International capital flows may thereby represent a

serious threat to financial stability for MENA countries, if the exchange rate

arrangements adopted by these countries are not appropriate to control the situation.

This makes the question of the identification of the factors that influence the choice of

exchange rate in the MENA countries particularly relevant. For this purpose, we utilize

four different exchange rate classifications to estimate several binomial and multinomial

Probit models of various specifications of the traditional optimum currency area (OCA)

theory and of newer hypothesis of exchange regime choice. Regressions results for 17

MENA countries over the 1974–2004, the 1974–2000 and the 1990–2001 periods show

that among theoretical long-run determinants proposed by the OCA theory only trade

openness has exercised an influence on the choice of exchange rate regime through the

periods studied. We also find that other newer theories like the political view or the

capital account openness approach cannot adequately explain the choice of the exchange

rate regime by these countries. Finally, our results show that oil export capacity plays a

significant role in the choice of exchange regimes in the MENA economies.

The remainder of this paper is structured as follows. The next section tackles the

classical question of exchange rate regimes classification. Section 3 briefly reviews the

most important theories of exchange rate regime choice. Section 4 summarizes

previous empirical studies on factors affecting exchange rate regime choice. Section 5

presents the data used in our analysis and outlines the methodology. Section 6

discusses the empirical results. Section 7 concludes and presents some policy

implications.

7

The GCC members are Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE.

278

M.D. Sfia

2 Exchange rate regime classifications

For many years (since the early 1950’s) the IMF used de jure classifications to describe

the shares of fixed versus floating arrangements among its member countries. In this old

taxonomy, regimes were mainly classified into three categories: (1) peg (a peg to a single

currency or a composite of currencies), (2) limited flexibility, and (3) more flexible

(managed floating and independently floating). Although comprehensive in terms of

country and historical coverage, this de jure classification system had a serious

drawback, in practice, exchange rate regimes often differed from what they were

officially announced to be. Indeed, many countries that in theory have a flexible rate

intervene in exchange markets so pervasively that in practice very little difference exists

with countries that have explicit fixed exchange rate regimes. Also, several countries

that declare fixed exchange rates undertake periodic and frequent devaluations that it

becomes difficult to differentiate the peg from a managed float regime. For many years

and until the late 1990’s, most empirical studies of exchange rate regime relied on the de

jure regime classification reported in the IMF’s Annual Report on Exchange Rate

Arrangements and Exchange Restrictions. Consequently, these empirical analyses

risked reaching completely false conclusions and drawing misleading policy implication. Recognizing the merits of classifying regimes more realistically, the IMF switched

in 1999 to a mixed (hybrid) de jure-de facto classification. This new classification

combines the information obtained from specific countries about their exchange rate

regime and monetary policy framework with the analysis of the variance of exchange

rates, interest rates and official reserves. The new IMF system classifies member

countries into eight categories: (1) exchange arrangements with no separate legal tender,

including formal dollarization and currency unions, (2) currency boards, (3) other

conventional fixed pegs, (4) pegged exchange rates within horizontal bands, (5)

crawling peg, (6) exchange rates within crawling bands, (7) managed floating with no

predetermined path for the exchange rate, and (8) independently floating.

Despite the considerable progress made by the IMF’s de facto classification, it

remained relatively limited from an empirical angle since it offers a short historical

data base. With the view to addressing this shortcoming, several researchers have

tried to generate their own de facto classifications. Among these, the classifications

of Bubula and Otker-Robe (2002) BB(2002), Reinhart and Rogoff’s (RR) (2004)

and Levy-Yeyati and Sturzenegger (LYS) (2005) are considered as the most

important and are the most frequently used in the empirical research on exchange

rate regimes today. Following the new IMF classification system, BB (2002)

constructed historical data on de facto regimes for all the IMF member countries for

the period from 1990 to 2001. As in the new IMF classification, they classify the

members’ regimes on the basis of the degree of flexibility of the arrangement or a

formal or informal commitment to a given exchange rate path. This classification is

also based on information obtained through provision of technical assistance to

member countries and regular contact with IMF country economists. These views

are supplemented with other sources of information, including press reports, news

articles, and other relevant papers, and are also supported by analyses of observed

exchange rate and reserves behavior. The BB (2002) taxonomy classifies exchange

rate systems into 13 categories: (1) formal dollarization, (2) currency union, (3)

currency board, (4) conventional fixed peg to a single currency, (5) conventional

The choice of exchange rate regimes in the MENA countries: a probit analysis

279

fixed peg to basket, (6) pegged within a horizontal band, (7) forward-looking

crawling peg, (8) backward-looking crawling peg, (9) forward-looking crawling

band, (10) backward-looking crawling band, (11) tightly managed floating, (12)

other managed float with no predetermined exchange rate path, and (13)

independently floating.

The RR (2004) classification adopted a broadly similar nomenclature to that of the

IMF’s de facto classification. However, the authors relied on monthly database of

exchange regimes for 153 countries over the period 1946–2004, computed a variety of

descriptive statistics and distinguished between countries which have a unified

exchange market and those where there are multiple exchange rates. The “Natural”

classification generated by Reinhart and Rogoff (2004) divides exchange regimes into

14 categories (1) no separate legal tender, (2) preannounced peg or currency board

arrangement, (3) preannounced horizontal band that is narrower than or equal to

+/−2%, (4) de facto peg, (5) preannounced crawling peg, (6) preannounced crawling

band that is narrower than or equal to +/−2%, (7) de facto crawling peg, (8)

preannounced crawling band that is wider than or equal to +/−2%, (9) de facto

crawling band that is narrower than or equal to +/−2%, (10) de facto crawling band

that is narrower than or equal to +/−5%, (11) moving band that is narrower than or

equal to +/−2%, (12) managed floating, (13) freely floating, and (14) freely falling.8

Finally, and unlike the IMF’s de jure classification, The LYS (2005) classification

ignored completely the declarations of countries about their exchange rate and

monetary practices to focus only on describing the de facto practices by analyzing the

volatility of the exchange rate and of the official reserves. According to LYS, Floats

will be characterized by high variance in the exchange rate and low variance in official

reserves while pegged regimes will display low exchange rate variance and large

swings in reserves. The authors resorted to the cluster analysis technique to classify

exchange regimes into 5 distinct categories (1) fix, (2) crawling peg, (3) dirty float,

and (4) flexible, plus an “inconclusive” category. The cluster analysis that was applied

to all IMF reporting countries over the period 1974–2000 was made in two rounds.

After the first round, only 1,062 among 2,860 observations were classified, the

remaining 1,798 observations were submitted to the same procedure to reduce the

number of inconclusive observations. The second round procedure reclassified 1,100

observations. The remaining 698 (24%) observations were finally included in the

“inconclusive group”. This is considered as the main drawback of the LYS

classification.

To summarize, we can say that exchange rate classifications can be grouped into three

different categories. The first is a pure de jure classification based exclusively on the

country’s self description of its regime. The second type of classifications can be

described as “hybrid” since it combines the self declared regime by one country with

information obtained from analysis of statistical data and some specific time series.

Finally, the third category of exchange regime classification is a pure de facto

classification where the self-described regimes are totally ignored by the researchers and

where the assignment of countries into fixed, intermediate or floating regime is based

solely on the behaviour or variability of exchange rates, international reserves and

interest rates.

8

Countries whose twelve-month rate of inflation is above 40%.

280

M.D. Sfia

3 Theories of exchange rate regime choice

Theories of exchange rate regime choice can be grouped under three broad headings;

the optimum currency area (OCA) theory initiated in the early 1960’s, the political

economy theory and the currency crisis (capital account openness) approach.

The starts of the OCA theory are the seminal contributions by Mundell (1961),

Mckinnon (1963) and Kenen (1969).9 OCA theory suggests that the balance of

advantages and disadvantages between fixed and flexible exchange rates varies

according to the manner and extent of economic integration between countries. In

essence, it relates the choice of an exchange rate regime to some structural

characteristics, criteria or properties that are relatively stable over time. Mundell

(1961) suggested that the degree of factor (labour) mobility was a key determinant of

the optimal choice of exchange rate regime. According to Mundell (1961), a great

degree of labour mobility will make it costlier to adjust to external shocks with a

flexible regime. Mckinnon (1963) focused on the relevance of openness, as

measured by the ratio of tradable goods production to non tradable goods

production. The openness of an economy was regarded a key determinant of regime

choice. He argued that, other things being equal, the greater the openness of an

economy, the greater would be the responsiveness of domestic wages and prices to a

change in the nominal exchange rate, so the stronger was the case for fixed exchange

rate; as openness increases.10 Both Mundell (1961) and McKinnon (1963)

emphasized the fact that the size of an economy can be a key determinant of

exchange regime choice. Openness is expected to be greater, the smaller the

economy, therefore the larger the economy, the stronger is the case for flexible

regime. The third main contribution on OCA theory is due to Kenen (1969). He

suggests production diversification as a characteristic for optimum currency areas

and stressed that a well diversified economy will rarely confront changes in demand

for its export products. According to Kenen (1969), Product diversification decreases

the likelihood of asymmetric shocks and alleviates their negative effects. Thus, the

greater the diversity of an economy’s production activities the less severe would be

the costs of unpredictable disturbances, so the stronger was the case for fixed

exchange rates.11 During the 1980’s and the early 1990’s, contributions on the OCA

issue have continued to emphasize the criteria approach underlying the choice of an

optimum currency domain as well as to enumerate the benefits and drawbacks of

monetary integration. More recently, the theory of optimum currency areas has been

modified to take in consideration the new views on the long run ineffectiveness of

monetary policy and the short run Phillips curve, on the credibility issue and the time

9

An OCA is defined as the optimal geographic domain of a single currency, or of several currencies,

whose exchange rates are irrevocably pegged and might be unified. OCA theory assumes that factors of

production, such as labour and capital are mobile between regions of the currency area but immobile out

of the OCA. It also assumes that there is limited price and wage flexibility in the economy.

10

As openness increases flexible exchange rate become both less effective as a control device for external

balance and more damaging to internal price level stability McKinnon (1963, p.719)

11

Following these three main contributions, several other criteria were developed, including the degree of

financial integration (see Ingram 1969), the similarity of inflation rates (see Haberler 1970; Fleming 1971),

the degree of policy integration (see Tower and Willet 1976), the degree of financial or economic

development (see Holden et al. 1979). For a comprehensive review of these criteria see Ishiyama (1975)

and Tavlas (1993)

The choice of exchange rate regimes in the MENA countries: a probit analysis

281

inconsistency problems12 and the new hypothesis on the possible endogeneity of

OCA.13

The Political economy theory of exchange regime choice developed mainly from the

concept of “time inconsistency” first introduced by Kydland and Prescott (1977) and

popularized by Barro and Gordon (1983). The time inconsistency problem arises

because there are incentives for a policymaker to pursue discretionary policy to

achieve short-run objectives, such as higher growth and employment, even though the

result is poor long-run outcomes (high inflation).14 This stand of literature emphasizes

the role of credibility and political factors in the choice of an exchange rate regime.

The argument runs as follows. A country whose authorities have a reputation of

perusing inflationary policies will find it difficult to shed that reputation without a long

and costly process of disinflation. The time-consistency literature argues that, to gain a

reputation of credibility, authorities must pursue a policy rule that is time consistent.

One way to gain credibility is by “tying the hands” of the authorities by fixing the

exchange rate vis-à-vis the currency of a country with relatively high anti-inflation

credibility (Giavazzi and Pagano 1988).15 In other words, In an environment of high

inflation, as was the case in most countries in most countries the 1970’s and the

1980’s, pegging to the currency of a country with low inflation or joining a monetary

union is viewed as a pre-commitment mechanism to anchor inflation expectations. The

optimal choice of regime would therefore rest upon a balance between credibility (or

discipline) and flexibility. A flexible exchange rate regime allows a country to have an

independent monetary policy providing the economy with flexibility to accommodate

domestic and foreign shocks, including changes in external terms of trade and interest

rates, but this flexibility usually comes at the cost of some loss in credibility and tends

to be associated with higher inflation. Alternatively, fixed exchange rates reduce the

degree of flexibility of the system, but they are regarded as a commitment technology

that national authorities can employ, if they choose, to enhance their credibility.

According to the currency crisis or the capital account openness hypothesis countries

are (or should sooner or later be) moving to the corner solutions. They are said to be

opting either, on the one hand, for full flexibility, or, on the other hand, for rigid

institutional commitments to fixed exchange rates, in the form of currency boards or full

monetary union with the dollar or euro.16 It is said that the intermediate (or soft pegged)

exchange rate regimes are no longer feasible because of the belief that they have

12

Tavlas (1993, 1994)

Frankel and Rose (1997)

14

This argument was based on the theory developed by Barro and Gordon (1983) who discuss the case of

a central bank using discretionary monetary policy to generate surprise inflation in order to reduce

unemployment. They demonstrate that with rational expectations the outcome will be higher inflation but

unchanged employment because the inflationary consequences of the central bank’s actions will be

incorporated in workers’ wage demands.

15

As stressed by Tavlas (1993), the time inconsistency literature reverses the ordering between the

criterion of similarity of inflation rates, as identified by the OCA theory, and currency area participation;

similarity of inflation is no longer a precondition, but it becomes a desirable outcome.

16

Summers (2000) wrote that “the choice of appropriate exchange rate regime, which, for economies with

access to international capital markets, increasingly means a move away from the middle ground of

pegged but adjustable fixed exchange rates towards the two corner regimes of either flexible exchange

rates or a fixed exchange rate supported, if necessary, by a commitment to give up altogether an

independent monetary policy.”

13

282

M.D. Sfia

proven themselves to be highly susceptible to speculative crises.17 However, the so

called bipolar view or the hypothesis of vanishing intermediate regimes has absolutely

no theoretical foundation; it is only a corollary to the principle of the impossible

trinity.18

The hypothesis of vanishing intermediary regimes has been challenged by a

number of economists including John Williamson and Morris Goldstein. These

authors tried to identify a viable middle ground that would give the monetary

authorities some policy independence, while eliminating some of the excessive

volatility that might otherwise be associated with a completely free float. Williamson

(2000) has proposed a modification of the former target zone variant for emerging

market economies. The new proposal is called the BBC regime, where BBC stands

for basket, band and crawl. Goldstein (2002) has also championed a new exchange

rate system for emerging markets, called “Managed Floating Plus”. Unlike the BBC

proposal, which gives prominence to the exchange rate, the “Managed Floating

Plus” proposal uses a domestic inflation target as the nominal anchor for monetary

policy and gives greater attention to stabilizing the domestic economy than to fixing

the exchange rate. The bipolar view has also been discredited by recent empirical

studies on the concepts of “fear of floating” (Calvo and Reinhart 2002) and “fear of

pegging” (LYS 2005).

4 Empirical studies of exchange rate regime choice

Several empirical studies have tried to explain how the underlying exchange rate

regime is related to the economic, political and financial characteristics of a set of

countries. In the models used by these studies, the dependent variable can be either

discrete, taking values representing the exchange rate regime (for example zero for

fixed and one for flexible) or it could be continuous, taking the form of a measure of

exchange rate flexibility, the latter being constructed as an index obtained by the

combination of several monetary and financial series. These values can be based on

de jure, hybrid or de facto classifications and they can be more or less aggregated

(for example, fixed, intermediate and floating or even more classification categories).

The explanatory variables, on the other hand, are numerous but they can be

classified under the three categories: (1) OCA factors, (2) political economy factors

and (3) Currency crisis factors. Finally, several methods have been used to estimate

and test the models of exchange rate regime choice. These include discriminant

analysis, probit models, ordinary least squares (OLS) on a continuous dependent

variable, multinomial logit, two-stage probit and ordered/non-ordered logit.

Efforts to estimate models of exchange rate regime choice go back to the 1970’s, soon

after the availability of data following the breakdown of the Bretton Woods system.

Dreyer (1978) is among the first studies of this kind who analyzed the determinants of

exchange rate regimes with data for 88 developing countries for the year 1976. He

relies in his estimates on explanatory variables that are exclusively related to optimum

17

Obstfeld and Rogoff (1995) and Fischer (2001).

A country cannot achieve the three objectives of exchange rate stability, monetary independence and

financial market integration simultaneously.

18

The choice of exchange rate regimes in the MENA countries: a probit analysis

283

currency theory (size of the economy, openness, geographical and structural

diversification) and estimates an ordinal Probit model. His results show that the

OCA theory is strongly confirmed; larger countries would tend to opt for higher

degree of flexibility while a higher degree of geographical and structural diversification strengthens the tendency toward greater fixity of an exchange regime. Heller

(1978) applied a non parametric statistical technique (discriminant analysis) to a crosssectional sample of 85 advanced and developing countries and showed that a large

size, a small foreign trade sector, a high degree of financial integration, a large

inflation differential in comparison to the world average and a diversified foreign trade

pattern tend to be associated with floaters. While these two studies relied in their

analysis on IMF’s official de jure classification, Holden et al. (1979) chose to measure

the degree of flexibility of one country’s exchange rate regime by computing an

exchange rate flexibility index.19 They estimated a linear equation using ordinary least

squares where the flexibility index is the dependent variable and six explanatory

variables (openness, capital mobility, external sector’s diversification, geographical

concentration of trade, degree of economic development and inflation differential) on a

cross-sectional data for 75 countries. Their results also provide empirical support for

the OCA theory.

Following these three main contributions, several other empirical works have

tried to analyze the choice of exchange rate regime using many of the OCA variables

meanwhile as to assess other newer theories such as the political economy view and

the currency crisis theory. These studies, including Melvin (1985), Bosco (1987),

Savvides (1990), Cuddington and Otoo (1990, 1991), Honkapohja and Pikkarainen

(1994), Collins (1996), Edwards (1996, 1999), Rizzo (1998), Frieden et al. (2000),

Berger et al. (2000) and Poirson (2001), have been surveyed by Juhn and Mauro

(2002). According To them, the existing empirical literature is inconclusive.

Moreover, drawing on a large dataset with many potential explanatory variables

and a variety of exchange rate regime classifications, the authors tested old and new

theories and confirmed that no robust empirical regularities emerge. Other studies

followed including Méon and Rizzo (2002), Levy-Yeyati et al. (2002), Papaioannou

(2003), Bleaney and Francisco (2005), Piragic and Jameson (2005), Markiewicz

(2006), Von Hagen and Zhou (2007), Carmignani et al. (2008), Calderón and

Schmidt-Hebbel (2008), Kato and Uktum (2008) and Hossain (2009). The results of

these studies are also mixed, being highly sensitive to the country coverage, sample

period, estimation method and exchange rate regime classification, confirming the

predictions of Juhn and Mauro (2002). For example, no robust results have been

obtained for the importance of openness, the most frequently used variable. Some

studies found openness to be associated with fixed exchange rates, others found it to

be associated with flexible exchange rates, and a third group of studies found out

that it was unrelated to any of the regimes (See Appendix A, Tables 7, 8 and 9).

19

The index of exchange rate flexibility of a country is measured by the ratio of the sum of the absolute

value of monthly percentage changes in the trade-weighted exchange rate to the sum of the absolute

changes in official holdings of foreign exchange expressed in US dollars divides by the sum of imports

plus exports. See Holden et al. (1979) for more details.

284

M.D. Sfia

5 Data and methodology

For our empirical analysis, we concentrate on the determinants of exchange rate regime

choice in 17 MENA countries. We use four different de jure, hybrid and de facto

exchange rate regime classifications, namely the IMF, the BB (2002), the RR (2004)

and the LYS (2005) classifications, in order to capture more adequately the choice of

exchange regimes by these countries. The sample periods used for our estimations

depend from the classification adopted. More specifically, these periods run from 1974

to 2004 for the IMF and the RR (2004) classifications, 1990–2001 for the BB (2002)

classification and 1974–2000 for the LYS (2005) classification, the choice of these

sample periods being largely determined by data availability for the dependent

variable.

Our analysis of the potential determinants of exchange regime choice involves many

of the explanatory variables that have been suggested by theory and used in previous

empirical studies. Detailed definitions and data sources are given in Appendix A

(Table 10). The economic fundamentals include the economy size (SIZE), the degree

of openness of the economy (OPEN), the level of economic development (DEVL) and

the geographical concentration of foreign trade (GEOC). Two different measures of

government characteristics were used in the econometric tests to proxy for government

strength. The first is an additive eleven-point scale (0–10) composite indicator of

democracy (DEMOC) developed by Marshall and Jaggers (2005).20 The second

variable, (DURAB) correspond to regime durability as measured by the number of

years that the incumbent administration has been in office. Five different monetary and

financial variables are used to reflect macroeconomic stabilization policies and to

account for the risk of currency crisis. These variables are the degree of financial

sector development (DEF), the annual consumer price index inflation (INF), the level

of international reserves (RES), the current account balance (SDCC) and the volatility

of nominal exchange rate (ERVOL). Finally, we include a variable (PETROL), which

is a dummy variable that takes the value of 1 if the country is a major oil exporter and

zero otherwise. Table 1 summarizes our discussion on the determinants of exchange

rate regime choices, their proxies used in the empirical estimations, and the directions

of their influence on regime choices.

As stated before, previous empirical studies used various methods and techniques

to measure the variable “exchange rate regime choice”. These range from

discriminant analysis (e.g. Heller 1978), flexibility index and Ordinary Least

Squares (e.g. Holden et al. 1979; Poirson 2001) to ordered/non-ordered Probit and

Logit models with discrete qualitative choice variables. The latter consist of the

following categories: two regimes with fixed and flexible rates (e.g. Dreyer 1978;

Savvides 1990; Bosco 1987) three regimes with fixed, intermediate, and float (e.g.

Bosco 1987; Rizzo 1998) and four or more regimes with single-currency peg, basket

peg, crawling peg and float (e.g. Melvin 1985; Juhn and Mauro 2002). In our study,

we use both binomial (by dividing exchange regimes into fixed and floats) and

multinomial (by dividing exchange regimes into fixed, intermediary and flexible

20

The democracy variable is an index which takes values from 0 to 10, and captures the competitiveness

of political participation, the openness and competitiveness of executive recruitment, and the existence of

constraints on the power of the executive.

The choice of exchange rate regimes in the MENA countries: a probit analysis

285

Table 1 Predictions concerning the choice of exchange rate regimes

Determinants

Proxies

Preferred regime

Large economic size

SIZE

Flexible

High degree of economic openness

OPEN

Fixed

High level of economic development

DEVL

Flexible

High geographical trade concentration

GEOC

Fixed

High level of democracy

DEMOC

Flexible

High political regime durability

DURAB

Fixed

OCA

Political view

Stabilization and currency crisis

High level of financial development

DEF

Flexible

High level of inflation

INF

Fixed

High level of international reserves

RES

Fixed

High current account deficit

SDCC

Flexible

High nominal exchange rate volatility

ERVOL

Fixed

Oil exporter

PETROL

Flexible

categories) specifications.21 To control for country heterogeneity we use the random

effects probit model, which is one of the commonly adopted methodologies to

discuss categorical dependent variables. Even though, the Hausman specification test

for the random versus fixed effects was inconclusive, the choice of a random effects

specification is justified in our case for several reasons. First, the between (across

individuals) variations for the explanatory variables has been found to be greater

than their within variations. Second, in our case, the random effect specification is

more appropriate than the fixed effects one because of the presence of a time

invariant explanatory variable (PETROL). Finally, given that the dependent variable

is a qualitative variable and as demonstrated by Chamberlain (1980), the fixed

effects model encounters an incidental parameters problem that renders the

maximum likelihood estimator (MLE) inconsistent (As the sample size (N) tends

to infinity for a fixed time period (T), the number of fixed effects increases with N).

Hence, fixed effects cannot be estimated consistently for a fixed T. Although MLE is

consistent as T tends to infinity, T is usually small for panel data (T varies between

12 and 31 in our case). The biased fixed-effects estimates trigger inconsistency of

the coefficient estimates. If country-specific effects are not introduced, MLE

provides consistent estimates for the coefficients.

Three additional common problems that need to be addressed are the potential

correlation between the unobserved effect and the set of explanatory variable,

endogeneity and multicollinearity. Estimating a standard random effects probit model

implicitly assumes zero correlation between the unobserved effect and the set of

explanatory variables. However, this assumption is most likely not to hold in our context.

21

In the binomial specification, the fixed and intermediary regimes are grouped in one category, the

flexible category remains unchanged. For more details on how we aggregate exchange rate regimes into

two or three categories, see discussion of results.

286

M.D. Sfia

Hence, two random effects probit specifications are estimated. The first is a standard

random effects probit model where it is assumed that the random effects are uncorrelated

with the regressors. In the second specification, the assumption of exogeneity of all the

regressors with the random individual effects is relaxed following the Mundlak (1978)

and Chanberlain’s (1984) approach by assuming a linear relationship between the

unobserved effects and the time means of the explanatory variables.22

The second main problem related to our analysis is the potential presence of

endogeneity caused by contemporaneous interaction between economic fundamentals

and the exchange rate regimes. In cross-section studies, some of the independent

variables are frequently instrumented to deal with this problem using time-invariant

country characteristics (e.g., openness instrumented with land area or a landlocked

dummy variable). However, since our data has an important time-series dimension, we

cannot adopt the traditional instruments, which are devoid of time variance. More

recently, in panel data the methodology by Arellano and Bond (1991) has become

popular, which uses internal instruments. However, due to its discrete-time

characteristic, our model does not lend itself to this approach. To tackle the question

of endogeneity, we adopt the Hausman (1978) and Wu (1973) two-step test procedure

to select relevant internal instruments, which is a suitable methodology for the discrete

data analysis. For this, we first determine whether there is an endogeneity problem, we

then identify the affected variables and finally select the instruments.

Endogeneity can be a problem for any of the explanatory variables. To see if this is

the case, we run the Hausman-Wu test (Appendix B, Table 11). We run the regression

of the dependent variable Y on Xi and Zi, where Xi is the explanatory variable i, and

Zi is the predicted value of Xi obtained by regressing it on the instrumental variable(s).

As instruments, we chose Xj, with j≠i, and Xi, t−2. Under the null hypothesis of

endogeneity, the coefficient of Zi should be significantly different from zero. Test

results cannot reject the hypothesis that there is no reverse causality for the variables

GEOC in the multinomial specification using the IMF classification, DEF in the

binomial specification using the IMF and RR (2004) classifications and in the

multinomial specification using the RR (2004) classification, INF in the binomial

specification using the IMF and in the multinomial specification using the IMF and the

LYS (2005) classifications and finally for the variable ERVOL in the binomial model

using the IMF classification. We then repeated the test using instrumental variables,

starting with the second lag for each variable. We observe that the problem of reverse

causality for the affected variables is completely resolved. In addition, the instruments

are in general highly correlated with the independent variables and insignificantly

correlated with the error term, indicating that they are good instruments.

The third potential problem in our analysis concerns the possibility of multicollinearity among the explanatory variables of the study. Multicollinearity occurs

when variables are so highly correlated with each other that it is difficult to come up

with reliable estimates of their individual regression coefficients. One way to assess

the possibility of multicollinearity among our study variables is to perform

correlations. The results (Appendix B, Tables 12, 13 and 14) indicate that there is

no sign of high multicollinearity among the explanatory variables.

22

In terms of implementation, this simply has the effect of adding time means to the set of explanatory

variables.

The choice of exchange rate regimes in the MENA countries: a probit analysis

287

6 Empirical results

In this section, we report the empirical evidence concerning the importance of the

hypotheses emanating from the three approaches to the exchange rate regime choice

for the 17 MENA countries. Table 2 reports the results of estimations using the IMF

classification obtained from both binomial and multinomial specifications. In the

multinomial probit model, we aggregated exchange rate regimes into three

categories: (1) fixed regimes, (2) intermediate regimes and (3) flexible regimes.

Fixed exchange rate regimes include the categories: exchange arrangements with no

separate legal tender, including formal dollarization and currency unions, currency

boards and other conventional fixed pegs. Intermediate exchange rate regimes

include the categories pegged exchange rates within horizontal bands, crawling peg,

and exchange rates within crawling bands. Finally, flexible exchange arrangements

include the two remaining categories managed floating with no predetermined path

for the exchange rate and independently floating. As indicated before (footnote 21),

in the binomial models, fixed and intermediate regimes are grouped in one category

while the flexible regimes category remains unchanged.

A first point to note is that the estimations obtained from model 1 and model 2 are

relatively close suggesting the robustness of the results. Regarding the OCA theory, the

estimates reported in Table 2 suggest that the fundamentals considered by this

traditional theory of exchange rate regimes affect the choices of the MENA

economies. The variable SIZE has a negative sign in all regressions (but it is only

significant in the estimations obtained from model 2), indicating that larger economies

opted for fixed exchange rate arrangements. This result contradicts the predictions of

the OCA theory according to which smaller economies would tend to favor fixed

exchange rate regimes. However, this result validates the reality of exchange rate

regime choices in the MENA region since most of the MENA economies

characterized with high GDP’s, especially the Gulf countries, opted for fixed exchange

rates. The variable DEVL has the positive expected sign. The coefficient associated

with this variable is also significant in all regressions indicating that economic

development has a positive influence on the probability of a country choosing a

flexible exchange rate regime. As predicted, the variable OPEN has a negative sign

with a statistically significant coefficient suggesting that economies that are

characterized with a high degree of openness would tend to choose pegged exchange

rate regimes. Finally, the variable GEOC is never significant as a determinant of

exchange rate regime choice.

Turning to political variables, the coefficients of the variables DEMOC and DURAB

are never significant. Also, we did not find any regularity in the signs of these variables.

These results bring out one clear conclusion; the credibility hypothesis has played no

role in the choice of exchange rate regimes by the MENA countries.

Concerning the variables emanating from the macroeconomic stabilization and

currency crisis theories, we observe that all the included variables, exception made for

the variable RES, have kept a constant sign. The variable DEF and PETROL have

negative and statistically significant signs in all regressions. However, these results

contradict predictions of both theoretical and empirical works that addressed the linkage

between the choice of exchange rate regimes and these variables. The negative

coefficient of the variable DEF suggests that more advanced financial development

288

M.D. Sfia

Table 2 Determinants of exchange regime choice, IMF classification, 1974–2004

Model 1

Model 2

Binomial

Multinomial

Binomial

Multinomial

SIZE

−2.24e-11(−0.8)

−1.79e-11(−0.7)

−7.25e-11b(−2.87)

−6.61e-11b(−2.62)

OPEN

c

−12.5 (−8.2)

−7.48 (−4.87)

c

−21.3 (−13.8)

−12.57c(−8.15)

DEVL

c

c

c

0.0019c(12.1)

0.001 (8.7)

c

0.0005 (3.84)

0.003 (19.7)

GEOC

−0.01(−0.31)

0.029(0.83)

−0.072(−2.08)

0.034(0.98)

DEMOC

−0.06(−0.038)

−0.017(−0.009)

−8.8(−1.17)

0.011(0.001)

DURAB

0.006(0.17)

−0.012(−0.33)

−0.21(−6.13)

0.071(2.01)

DEF

−0.073 (−4.7)

−0.029 (−1.92)

−0.04(−3.15)

−0.067c(−4.32)

c

b

c

INF

0.14 (0.9)

0.016(0.104)

0.11(.75)

0.086(0.56)

RES

−0.15(−0.7)

0.05(0.25)

0.019(.08)

−0.083(−0.373)

SDCC

0.0006(0.0004)

0.0005(0.0003)

0.0008(.0005)

0.0003(0.0002)

ERVOL

1.74(0.29)

0.39(0.065)

3.31(.56)

0.29(0.049)

PETROL

−13.4c(−7.7)

−4.59c(−2.65)

−5.9(−3.4)

−11.3(6.5)

Observations

225

225

225

225

Log-Likelihood

−42.56

−56.61

−24.55

−36.19

A positive (negative) sign of a coefficient means that an increase of the associated variable raises the

probability of adopting a flexible (fixed) regime. Model 1 is the standard random effects probit model

where it is assumed that the random effects are uncorrelated with the regressors. Model 2 is the random

effects probit model where it is assumed that the random effects are correlated with the regressors.

Marginal effects (calculated at means) are displayed between parentheses

a

test-statistic is significant at the 10% level

b

signficant at the 5% level

c

signficant at the 1% level

makes pegged exchange regimes more likely. Habitually, as demonstrated by empirical

facts, we expect economies with advanced financial sectors to adopt flexible exchange

rates. This is certainly the case of several industrialized countries such as the US, Canada

or Australia or other emerging market economies with developed financial sector like

Brazil, Chile or Mexico. Our contradictory result for the MENA countries may reflect

the tendency of financial developed economies from this region (one more time the Gulf

countries) to peg their exchange rates to the US dollar. The negative sign associated with

the coefficient of the variable PETROL indicates in this case that the more an economy

is an oil exporter, the more it is predisposed to peg its exchange rate, a result that is also a

specificity of the MENA economies. Both variables INF and SDCC have positive signs

implying that countries suffering from high inflation rates and large current deficits

prefer to let their exchange rates float. The first result comparable to Rizzo (1998),

Poirson (2001) or Von Hagen and Zhou (2007), among others, corroborates the fact

that pegged exchange rate regimes are difficult to sustain in an environment of high

inflation. The second result indicates that a strong external position increases the

probability of adopting a flexible exchange rate regime. Finally, the estimated results

indicate that the highest marginal effects (the increase in the probability of adopting a

The choice of exchange rate regimes in the MENA countries: a probit analysis

289

fixed (flexible) exchange rate regime due to a unit-increase in the explanatory variable

under consideration) are associated the variable OPEN.

Table 3 presents the estimation results obtained using the BB (2002) classification

for the 1990–2001 period. As previously, in the multinomial specification, exchange

rate arrangements were grouped into fixed, intermediate and flexible categories.

Fixed exchange regimes include the categories: formal dollarization, currency union,

currency board, conventional fixed peg to a single currency and conventional fixed

peg to basket. Intermediate regimes include the categories: pegged within a

horizontal band, forward-looking crawling peg, backward-looking crawling peg,

forward-looking crawling band, backward-looking crawling band and tightly

managed floating. Finally, Flexible exchange rate regimes include the regimes other

managed float with no predetermined exchange rate path and independently floating.

Although none of the variables reported in Table 3 is significant, we observe

regularity in the sign for some of them. The variable OPEN continues to have the

same negative sign obtained previously from the IMF classification confirming the

fact that open economies are more likely to adopt fixed exchange rate regimes. By

contrast, the other three structural variables suggested by OCA theory, namely SIZE,

DEVL and GEOC seem to play no role in the determination of exchange rate regime

choice of the MENA countries. Political variables are still insignificant and do not

keep a constant sign through the different regressions. Hence, the same conclusion as

previously could be drawn; the credibility theory does not influence exchange

regimes choice in the MENA region. Finally, regarding the other variables, only the

variable PETROL continues to have the same sign. The estimates for this variable

are also comparable to those obtained from the IMF classification. In other words,

the MENA oil exporters tend to peg their exchange rates.

Table 4 gives the estimation results obtained using the de facto RR (2004)

classification for the 1974–2004 period. In this case, fixed exchange rate arrangements include the systems no separate legal tender, preannounced peg or currency

board arrangement, preannounced horizontal band that is narrower than or equal to

+/−2% and de facto peg. Intermediate regimes include the categories preannounced

crawling peg, preannounced crawling band that is narrower than or equal to +/−2%,

de facto crawling peg, preannounced crawling band that is wider than or equal to

+/−2%, de facto crawling band that is narrower than or equal to +/−2%, de facto

crawling band that is narrower than or equal to +/−5% and moving band that is

narrower than or equal to +/− percent. Finally, flexible exchange rate regimes

include the categories managed floating, freely floating and freely falling.

Regarding OCA variables, only one out of four coefficients (OPEN) is significant

in Table 4. Once again, a negative sign is associated with the coefficient of the

variable OPEN confirming the predictions of OCA theory that open economies tend

to prefer fixed exchange rate regimes. Marginal effects for this variable indicate that

an increase by 1% in OPEN raises the probability of adopting fixed exchange rates

by 3.6% (when we refer to model 1). As indicated, the other three structural

variables, SIZE, DEVL and GEOC are never significant and do not demonstrate a

regularity in the signs of theirs coefficients. Similarly, our results indicate that the

choice of exchange rate regimes in the MENA region do not depends on the political

conditions of the countries. Finally, among the six other macroeconomic and

financial variables, two appear to be particularly interesting. First, the variable DEF

290

M.D. Sfia

Table 3 Determinants of exchange regime choice, BB (2002) classification, 1990–2001

Model 1

Model 2

Binomial

Multinomial

Binomial

Multinomial

SIZE

5.35e-09(2.2)

1.07e-09(4.5)

8.28e-10(3.6)

−1.50e-10(−6.69)

OPEN

−9.8(−6.6)

−12.92(−8.7)

−15.5(−1.02)

−7.8(−5.9)

−0.006(−5.1)

DEVL

0.029(2.3)

0.072(5.7)

0.0074(5.6)

GEOC

−6.8(−1.8)

7.0(1.8)

−0.7(−1.88)

0.81(2.1)

DEMOC

7.4(0.57)

16.1(−1.2)

−35.0(−5.3)

25.1(3.8)

DURAB

−17.2(−5)

−15.6(−4.6)

−1.29(−4.0)

1.53(4.7)

−0.88(−6.1)

DEF

−9.2(−6.3)

3.8(2.6)

−0.22(−15.7)

INF

−29.3(−1.03)

8.5(3.01)

−6.9(−2.6)

−1.13(−4.3)

RES

34.5(1.4)

−10.4(−4.4)

15.2(6.5)

−9.16(−3.9)

SDCC

−0.13(−0.83)

0.01(−0.11)

0.017(−0.11)

0.02(.16)

ERVOL

3.3(5.0)

−15.4(−2.3)

7.1(11.8)

−15.1(−2.4)

−5.7(−3.0)

PETROL

−12.5(−6.7)

−7.5(−4.0)

−2.6(−1.42)

Observations

90

90

90

90

Log-Likelihood

−7.12

−7.3

−4.3

−5.9

A positive (negative) sign of a coefficient means that an increase of the associated variable raises the

probability of adopting a flexible (fixed) regime. Model 1 is the standard random effects probit model

where it is assumed that the random effects are uncorrelated with the regressors. Model 2 is the random

effects probit model where it is assumed that the random effects are correlated with the regressors.

Marginal effects (calculated at means) are displayed between parentheses

a

test-statistic is significant at the 10% level

b

significant at the 5% level

c

significant at the 1% level

reflecting the level of financial development has a negative (even though non

significant) coefficient in all regressions implying that having a more developed

financial sector increases the likelihood that a country will choose a fixed exchange rate.

Second, the variable PETROL continues to have the previously found negative sign. In

the case where this variable is significant, the computed marginal effect suggests that an

increase by 1% raises the probability of adopting fixed exchange rates by 1.05%.

Finally, estimations results obtained from the LYS (2005) de facto classification

for the period 1974–2000 are given in Table 5. Here, we define their category fixed

regimes as “fixed regimes pegs”, crawling pegs and a dirty float as “intermediate

regimes,” and float as “freely floating regimes”. The results from the LYS (2005)

classification are completely in line with our previous findings. As can be seen, even

though the coefficients of most of the explanatory variables included in the

estimations are insignificant, it is clear that a robust regularity exists for the variables

OPEN and PETROL. In all regressions, the coefficient of the variable OPEN is

negative. This coefficient is also significant in the majority of regressions suggesting

that openness favors the implementation of fixed exchange rate regimes. Also, it is

worth noting that the marginal effects associated with this variable (between 3.1%

and 10%) are relatively high. Finally, regarding the variable PETROL, we can

The choice of exchange rate regimes in the MENA countries: a probit analysis

291

Table 4 Determinants of exchange regime choice, RR (2004) classification, 1974–2004

Model 1

Binomial

Model 2

Multinomial

Binomial

Multinomial

SIZE

1.45e-07(6.2)

−1.17e-11(−0.42)

1.85e-08(8.0)

−4.18e-11(−1.81)

OPEN

−5.23(−3.1)

−5.53a(−3.6)

−2.23(−1.35)

−3.79a(−2.27)

0.0006(3.86)

DEVL

−2.48(−15.1)

0.0003(2.3)

−0.32(−2.0)

GEOC

8.7(2.6)

0.035(1.03)

5.62(1.6)

−0.012(−0.37)

DEMOC

−5.42(−5.5)

0.015(0.001)

1.42(1.46)

−2.26(−0.58)

DURAB

−7.3(−1.1)

−0.05(−1.27)

−4.33(−1.2)

−0.22(−6.42)

DEF

−18.2(−2.0)

−0.003(−0.194)

−1.31(−8.9)

−0.08(−5.46)

INF

13.9(9.7)

0.021(0.14)

2.49(1.75)

−0.33(−2.37)

RES

−7.9(−3.5)

0.072(0.29)

−9.1(−4.0)

−1.3a(−6.07)

SDCC

2.23(0.97)

0.0007(0.007)

0.13(0.06)

−0.0002(−0.0001)

ERVOL

3.68(1.07)

−0.49(−0.08)

7.4(13.5)

−0.345(−0.0628)

PETROL

−6.9(−3.6)

−1.73a(−1.05)

−4.06(−2.1)

−0.345(−4.1)

Observations

200

200

200

200

Log-Likelihood

−29.0

−36.01

−12.80

−12.69

A positive (negative) sign of a coefficient means that an increase of the associated variable raises the

probability of adopting a flexible (fixed) regime. Model 1 is the standard random effects probit model

where it is assumed that the random effects are uncorrelated with the regressors. Model 2 is the random

effects probit model where it is assumed that the random effects are correlated with the regressors.

Marginal effects (calculated at means) are displayed between parentheses

a

test-statistic is significant at the 10% level

b

significant at the 5% level

c

significant at the 1% level

conclude that a systematic relationship does exist between the choice of exchange

regimes by the MENA countries and oil export capacity.

7 Conclusion and policy implications

This paper investigated empirically the determinants of exchange rate regime choice

in 17 MENA countries. For this purpose, we utilized four different exchange rate

regimes classifications and estimated several probit models specifications. The

explanatory variables considered included three sets of criteria highlighted in recent

theoretical analyses: long-run determinants proposed by the OCA, political factors

and currency crisis variables. In contrast to some of the existing empirical literature,

our estimations show that among the variables suggested by the OCA, only openness

is an adequate and robust predictor of exchange regime choice in the MENA

countries. As predicted by the OCA theory, it is shown that open economies tend to

opt for fixed exchange rate regimes. Our analysis indicates at the same time that

neither the political economy variables nor the currency crisis measures are robust or

significant predictors of exchange rate regimes.

292

M.D. Sfia

Table 5 Determinants of exchange regime choice, LYS (2005) classification, 1974–2000

Model 1

Binomial

Model 2

Multinomial

Binomial

Multinomial

SIZE

3.98e-11(1.42)

−1.17e-11(−0.42)

6.46e-10(2.3)

−2.85e-11(−1.02)

OPEN

−10.5a(−6.8)

−5.53a(−3.6)

−4.77(−3.1)

−15.38b(−10.0)

DEVL

0.0005(3.35)

0.0003a(2.32)

0.0084(4.9)

0.0007(4.5)

GEOC

−0.013(−.37)

0.03(1.03)

0.153(4.45)

0.065(1.9)

DEMOC

0.056(0.005)

0.01(0.001)

6.7(2.33)

0.069(0.02)

DURAB

−0.10(−2.6)

−0.05(−1.27)

0.73(1.9)

−0.15a(−4.1)

DEF

0.003(0.21)

−0.003(−0.19)

−0.097(−5.9)

−0.005(−0.34)

INF

−0.093(−0.66)

0.021(0.14)

0.0003(.002)

0.023(0.16)

RES

−0.36(−1.48)

0.072(0.29)

−3.39(−13.8)

0.075(0.31)

SDCC

−0.0007(−0.008)

0.0007(0.007)

−0.002(−0.029)

0.0001(0.002)

ERVOL

1.93(0.32)

−0.49(−0.08)

2.38(0.40)

−0.87(−0.14)

PETROL

−20.58(−12.53)

−1.73a(−1.05)

−1.8(−1.1)

−3.8a(−2.31)

Observations

179

179

179

179

Log-Likelihood

−21.33

−36.68

−11.55

−32.11

A positive (negative) sign of a coefficient means that an increase of the associated variable raises the

probability of adopting a flexible (fixed) regime. Model 1 is the standard random effects probit model

where it is assumed that the random effects are uncorrelated with the regressors. Model 2 is the random

effects probit model where it is assumed that the random effects are correlated with the regressors.

Marginal effects (calculated at means) are displayed between parentheses

a

test-statistic is significant at the 10% level

b

significant at the 5% level

c

significant at the 1% level

Among the other variables included in our analysis of the determinants of

exchange regime choice in the MENA countries, oil export capacity seems to play

an important role. Contrarily to what was expected, our results show that oil

exporting economies tend to favour fixed exchange regimes. However, in our case,

this result seems to be quite reasonable given the fact that pegged exchange

arrangements represent a main feature of the MENA oil exporting economies.

Several motivations could explain the tendency of these countries to peg their

exchange rate to the US dollar. First, as advanced by the political economy view,

pegging to the dollar allows these emerging economies, especially those with weak

economic and political institutions, to import the United States’ relatively stable

monetary policy. Second, in spite of the relatively low proportion of trade between

the MENA countries and the US, their exports consisting mainly of energetic

products (especially oil), as well as a large share of their foreign assets are

denominated in dollar. Pegging to the dollar ensures them a relative stability in

foreign revenues; it also reduces the risks of financial instability that exchange rate

volatility might cause. Finally the implementation of pegged exchange rate regimes

could have been facilitated by the huge amounts of international reserves hold by the

MENA countries. In our opinion, by pegging their exchange rates, the MENA oil-

The choice of exchange rate regimes in the MENA countries: a probit analysis

293

exporting economies are making a policy mistake. First, we believe that the

disadvantages associated with importing the monetary policy of the US (an oil

importer economy) are more likely to overweight its gains. In fact, the imported

monetary policy from the US could be harmful and ill-suited to needs of the MENA

countries since external shocks, whether they are transitory or permanent, would be

automatically transmitted to these economies. An additional argument that plays

against exchange rate pegging in the MENA countries is that it implies too much

deflation or inflation during adjustments. By contrast, Countries that allow their

currencies to float (even with extensive management) would likely experience a

nominal appreciation when oil is strong and a nominal depreciation when oil is weak

(Frankel 2006). Finally, as demonstrated by the recent experiences of several

emerging market economies, pegged exchange rate regimes have proven themselves

to be highly vulnerable to currency crisis in the context of financial liberalization.

Hence, the sustainability of the fixed exchange rate regimes implemented by the

MENA countries would be questionable, especially when for a reason or another,

international reserves are dwindling.

1 Appendix A

Table 6 Exchange rate regimes in the MENA countries, 2008

Monetary policy framework

Exchange rate anchor

Dollar

Euro

Composite

Other

Monetary

aggregate

target

Inflation

Other

targeting

framework

Exchange rate arrangement

Exchange arrangement with no separate legal tender

Currency board

arrangement (1)

Djibouti

Other conventional fixed Bahrain

peg arrangement (12) Jordan

Lebanon

Oman

Qatar

Saudi-Arabia

UAE

Yemen

Kuwait

Libya

Morocco

Tunisia

Pegged exchange rate

within horizontal bands

(1)

Syria

Crawling peg (1)

Iran

Crawling band

Managed floating (2)

Algeria

Independently floating

IMF, Annual Report on Exchange Arrangements and Exchange Restriction, 2008

Egypt

294

M.D. Sfia

Table 7 Empirical studies on the choice of exchange rate regimes

Author

Juhn and Mauro (2002)

Méon and

Rizzo (2002)

Levy Yeyati et

al. (2002)

Papaioannou

(2003)

Sample

184 countries

125 countries

183 countries

6 Latin American

countries

Time frame

1990, 1995, 1999, 2000

1980–1994

1974–1999

1974–2001

Methodology

Bivariate probit and

multinomial logit

Binomial

probit (Panel)

Multinomial

logit (Panel)

Multinomial

probit/logit

Openness

−*

+*

−*

Economic development

+/−*

−*

Economic size

+*

+*

+/−

−*

Explanatory Variables

OCA

+

−

+*

+*

−*

−

Inflation differential

Capital mobility

Geographical concentration +/−

of trade

−

Financial integration

Population

Other macro/external/structural factors

Financial development

+*

Dollarization

−*

Country growth

+*

Growth in the US

−*

Recession

Inflation

+*

+

Hyperinflation

−*

World Inflation

inflation volatility

+

inflation volatility in the US

−

International reserves

+/−

Capital controls

+/−*

Production/investment

volatility

−

−

−*

Production volatility in the

US

+

Real exchange rate

volatility

−

Real exchange rate

misalignment

Gov consumption volatility

+

Budgetary balance

Current account

Current account restrictions

External debt

+

The choice of exchange rate regimes in the MENA countries: a probit analysis

295

Table 7 (continued)

Author

Juhn and Mauro (2002)

Méon and

Rizzo (2002)

Levy Yeyati et

al. (2002)

Papaioannou

(2003)

Sample

184 countries

125 countries

183 countries

6 Latin American

countries

Time frame

1990, 1995, 1999, 2000

1980–1994

1974–1999

1974–2001

Methodology

Bivariate probit and

multinomial logit

Binomial

probit (Panel)

Multinomial

logit (Panel)

Multinomial

probit/logit

Domestic credit growth

−

Monetary shocks

Real shocks

+/−*

Currency crisis

Banking crisis

IMF program

Credibility and political factors

Political instability

+*

+*

Number of parties in

coalition

Government (years in office)

−*

Share of government seats

in the legislature

+*

Index of political freedom

Socio-political risk

Electoral cycle

Gov (Left party)

Gov (Right party)

A positive (negative) sign means that the variable is positively associated with the probability of adopting

flexible (pegged) exchange rate regimes. An asterisk (*) means that the coefficient is generally significant

at 10% or higher levels. For a summary of the results of the studies produced prior to 2002, see Juhn and

Mauro (2002)

Table 8 Empirical studies on the choice of exchange rate regimes

Author

Bleaney and

Francisco (2005)

Piragic and Jameson Markiewicz

(2005)

(2006)

Von Hagen and Zhou

(2007)

Sample

102 developing

countries

26 Latin American

countries

23 transition

countries

More than 100

emerging countries

Time frame

1990–2000

1960–1994

1993–2002

1981–1999

Methodology

Binomial/ordered

Logit

Orderd multinomial

logit (panel)

Ordered logit

(panel)

Multinomial logit

(panel)

+*

−*

−/+

+*

Explanatory Variables

OCA

Openness

296

M.D. Sfia

Table 8 (continued)

Author

Bleaney and

Francisco (2005)

Piragic and Jameson Markiewicz

(2005)

(2006)

Von Hagen and Zhou

(2007)

Sample

102 developing

countries

26 Latin American

countries

23 transition

countries

More than 100

emerging countries

Time frame

1990–2000

1960–1994

1993–2002

1981–1999

Methodology

Binomial/ordered

Logit

Orderd multinomial

logit (panel)

Ordered logit

(panel)

Multinomial logit

(panel)

−*

Economic development

Economic size

−*

+*

Inflation differential

+*

+*

Capital mobility

Geographical

concentration of trade

+*

+*

+*

−*

Financial integration

Population

+*

Other macro/external/structural factors

Financial development

+*

Dollarization

−*

+*

Country growth

Growth in the US

Recession

Inflation

+*

+*

+*

−*

Hyperinflation

World Inflation

inflation volatility

inflation volatility in the US

International reserves

+

−/+

+/−

Capital controls

−*

+

−*/+

Production/investment

volatility

−*

Production volatility in the

US

−*

Real exchange rate volatility

Real exchange rate

misalignment

Gov consumption volatility

−/+

Budgetary balance

Current account

+

Current account restrictions

+*

External debt

−*

−*

−/+

+*

Domestic credit growth

Monetary shocks

Real shocks

Currency crisis

−*

The choice of exchange rate regimes in the MENA countries: a probit analysis

297

Table 8 (continued)

Author

Bleaney and

Francisco (2005)

Piragic and Jameson Markiewicz

(2005)

(2006)

Von Hagen and Zhou

(2007)

Sample

102 developing

countries

26 Latin American

countries

23 transition

countries

More than 100

emerging countries

Time frame

1990–2000

1960–1994

1993–2002

1981–1999

Methodology

Binomial/ordered

Logit

Orderd multinomial

logit (panel)

Ordered logit

(panel)

Multinomial logit

(panel)

−*

+*

+

−*

−*/+*

Banking crisis

IMF program

Credibility and political factors

Political instability

Number of parties in

coalition

Government (years in office)

Share of government seats

in the legislature

Index of political freedom

+*

Socio-political risk

Electoral cycle

Gov (Left party)

Gov (Right party)

A positive (negative) sign means that the variable is positively associated with the probability of adopting

flexible (pegged) exchange rate regimes. An asterisk (*) means that the coefficient is generally significant

at 10% or higher levels. For a summary of the results of the studies produced prior to 2002, see Juhn and

Mauro (2002)

Table 9 Empirical studies on the choice of exchange rate regimes

Author

Carmignani et

al. (2008)

Calderón and

Schmidt-Hebbel

(2008)

Kato and Uktum

(2008)

Hossain

(2009)

Sample

96 countries

110 countries

144 countries

(3 currency zones)

34 countries

Time frame

1974–2000

1975–2005

1982–1999

1973–1996

Methodology

Ordered probit/ Probit/logit (panel)

logit (panel)

Ordered probit

(panel)

Ordered logit

−*

−*

−*

Explanatory Variables

OCA

Openness

Economic size

Inflation differential

−*

−/+

Economic development

+*

+*

+*

+*

−*

+

298

M.D. Sfia

Table 9 (continued)

Author

Carmignani et

al. (2008)

Calderón and

Schmidt-Hebbel

(2008)

Kato and Uktum

(2008)

Hossain

(2009)

Sample

96 countries

110 countries

144 countries

(3 currency zones)

34 countries

Time frame

1974–2000

1975–2005

1982–1999

1973–1996

Methodology

Ordered probit/ Probit/logit (panel)

logit (panel)

Ordered probit

(panel)

Ordered logit

+/+

+*

Capital mobility

Geographical concentration of

trade

+*

+*

Financial integration

+*

Population

Other macro/external/structural factors

Financial development

+*

Dollarization

−*

+*

Country growth

Growth in the US

Recession

Inflation

+

+

−*

−

Hyperinflation

World Inflation

inflation volatility

inflation volatility in the US

International reserves

Capital controls

+

−*

Production volatility in the US

+

−*

Production/investment volatility

+*

Real exchange rate volatility

Real exchange rate misalignment

+*

−/+

Gov consumption volatility

Budgetary balance

−

Current account

+*

Current account restrictions

External debt

Domestic credit growth

Monetary shocks

+*

Real shocks

−

Currency crisis

Banking crisis

IMF program

+

Credibility and political factors

Political instability

Number of parties in coalition

+*

−/+

−

The choice of exchange rate regimes in the MENA countries: a probit analysis

299

Table 9 (continued)

Author

Carmignani et

al. (2008)

Calderón and

Schmidt-Hebbel

(2008)

Kato and Uktum

(2008)

Hossain

(2009)

Sample

96 countries

110 countries

144 countries

(3 currency zones)

34 countries

Time frame

1974–2000

1975–2005

1982–1999

1973–1996

Methodology

Ordered probit/ Probit/logit (panel)

logit (panel)

Ordered probit

(panel)

Ordered logit

Government (years in office)

+*

Share of government seats in the

legislature

Index of political freedom

Socio-political risk

+*

Electoral cycle

+*

−*

Gov (Left party)

−

Gov (Right party)

−

A positive (negative) sign means that the variable is positively associated with the probability of adopting flexible

(pegged) exchange rate regimes. An asterisk (*) means that the coefficient is generally significant at 10% or higher

levels. For a summary of the results of the studies produced prior to 2002, see Juhn and Mauro (2002)

Table 10 Explanatory variables, definitions and data sources

Variables

Definition

Source

SIZE

GDP in dollars

IFS

OPEN

Ratio of exports plus imports to GDP

IFS

DEVL

Per capita, PPP (current international $)

IFS

GEOC

Share of major total partner in total exports

DOTS

DEMOC

Index of democracy (0–10)

Polity IV

DURAB

Years the incumbent administration has been in office

WPI

OCA

Political view

Stabilization and currency crisis

DEF

Ratio of domestic credit to private sector to GDP

INF

Annual consumer price index inflation

WDI

WDI

RES

International reserves as percentage of imports

WDI

SDCC

Current account balance as a percentage of GDP

WDI

ERVOL

The standard deviation in the monthly change of the nominal bilateral

exchange rate between the domestic currency and the US dollar

IFS

PETROL

Dummy variable taken the value of 1 if the country is a major oil exporter

and zero otherwise.

WEO

IFS IMF International Financial Statistics, DOTS Direction of Trade Statistics, Polity IV Marshall and

Jaggers (2005), WPI World Bank Database of Political Institutions, WDI World Bank Development

Indicators, WEO IMF World Economic Outlook

0.846

0.405

0.291

0.000

0.068

0.157

0.885

0.009

OPEN

DEVL

GEOC

DEF

INF

RES

SDCC

ERVOL

0.591

0.349

0.503

0.256

0.114

0.173

0.423

0.333

0.543

0.520

0.230

0.102

0.002

0.142

0.004

0.311

0.207

0.433

0.823

0.809

0.189

0.019

0.189

0.187

0.192

0.299

0.474

0.592

0.871

0.500

0.451

0.718

0.811

0.016

0.611

0.122

0.452

0.910

0.370

0.222

0.461

0.781

0.111

0.455

0.441

0.543

0.100

0.199

0.481

0.155

0.931

0.190

0.520

0.100

0.207

0.299

0.616

0.314

0.111

0.930

0.450

0.823

0.230

Multinomial

0.192

0.199

0.111

0.167

0.000

0.130

0.106

0.312

0.119

Binomial

RR(2004)

0.848

0.691

0.000

0.991

0.510

0.411

0.711

0.989

0.322

0.910

0.311

0.418

0.555

0.001

0.239

0.129

0.634

0.681

0.611

0.210

0.515

0.900

0.871

0.112

0.521

0.711

0.236

Multinomial

0.155

0.861

0.100

0.711

0.910

0.777

0.444

0.109

0.600

Binomial

The second entry in each cell is the p-value for the corresponding independent variable lagged with 2 periods

0.111

0.821

0.212

0.123

0.910

0.815

0.735

0.355

0.693

LYS (2005)

The first entry in each cell (in the binomial or multinomial model) is the p-value for the corresponding contemporaneous independent variable

0.213

SIZE

Binomial

Binomial

Multinomial

BB(2002)

IMF

Table 11 Hausman-Wu tests

1 Appendix B

0.139

0.555

0.319

0.000

0.611

0.120

0.181

0.732

0.491

0.196

0.310

0.122

0.130

0.414

0.678

0.181

0.766

0.450

Multinomial

300

M.D. Sfia

−0.4

−0.2

−0.0

−0.02

0.1

0.05

0.1

0.2

DEF

INF

RES

SDCC

ERVOL

PETROL

−0.01

−0.01

0.4

−0.01

−0.06

0.2

0.09

−0.06

−0.4

−0.2

0.2

−0.1

DURAB

0.4

0.01

DEMOC

1

−0.1

0.1

−0.01

GEOC

DEVL

0.2

0.5

−0.3

0.2

DEVL

1

1

−0.3

OPEN

OPEN

SIZE

SIZE

Table 12 Correlation matrix, 1974–2004

−0.03

0.3

−0.1

−0.08

−0.1

−0.3

0.3

0.2

1

DEMOC

−0.08

0.1

0.1

0.0

0.3

0.1

−0.07

1

GEOC

0.2

−0.07

0.09

0.3

−0.2

−0.0

1

DURAB

−0.4

−0.01

0.03

−0.13

0.09

1

DEF

−0.1

0.09

0.02

−0.1

1

INF

0.2

−0.01

0.03

1

RES

0.04

−0.0

1

SDCC

0.02

1

ERVOL

1

PETROL

The choice of exchange rate regimes in the MENA countries: a probit analysis

301

−0.2

−0.01

−0.08

−0.4

−0.09

−0.02

0.06

0.09

0.02

0.21

RES

SDCC

ERVOL

PETROL

0.4

−0.4

−0.18

−0.05

INF

0.2

−0.16

DEF

−0.2

DURAB

0.01

0.53

DEMOC

1

−0.17

0.50

−0.36

0.38

0.121

GEOC

−0.2

0.03

DEVL

1

DEVL

0.19

0.2

OPEN

OPEN

0.75

1

−0.4

SIZE

SIZE

Table 13 Correlation matrix, 1990–2001

−0.2

0.3

−0.07

−0.06

−0.2

−0.4

−0.4

0.2

1

DEMOC

−0.1

0.04

0.3

−0.0

0.4

0.18

−0.18

1

GEOC

0.2

−0.1

0.09

0.3

−0.4

0.02

1

DURAB

−0.39

−0.0

−0.02

0.1

0.031

1

DEF

−0.03

0.3

0.01

−0.1

1

INF

0.06

−0.04

0.03

1

RES

0.10

−0.0

1

SDCC

−0.02

1

ERVOL

1

PETROL

302

M.D. Sfia

0.25

0.19

RES

PETROL

−0.01

INF

0.04

−0.2

−0.03

DEF

0.02

−0.4

0.4

DURAB

ERVOL

0.2

−0.15

−0.0

DEMOC

SDCC

0.13

0.2

0.0

GEOC

−0.04

−0.01

0.4

−0.02

−0.05

0.2

0.17

−0.07

−0.4

−0.3

1

−0.1

0.5

−0.3

0.16

DEVL

1

1

−0.3

DEVL

OPEN

OPEN

SIZE

SIZE

Table 14 Correlation matrix, 1974–2000

−0.1

0.2

−0.18

−0.08

−0.18

−0.3

−0.2

0.2

1

DEMOC

−0.06

0.07

0.04

0.0

0.3

0.14

−0.06

1

GEOC

0.1

−0.1

0.08

0.3

−0.2

0.0

1

DURAB

−0.4

0.02

0.08

−0.1

0.1

1

DEF

−0.1

0.2

0.00

−0.04

1

INF

0.2

−0.04

0.04

1

RES

−0.0

−0.0

1

SDCC

−0.02

1

ERVOL

1

PETROL

The choice of exchange rate regimes in the MENA countries: a probit analysis

303

304

M.D. Sfia

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