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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

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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.

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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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