MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Independent Currency Unions,
Growth, and Inf lation
Sebastian Edwards and I. Igal Magendzo

During the last few years, there has been a renewed interest in
currency unions. This is the result both of the recent wave of
currency crises as well as the implementation of the euro. In thispaper, the authors use panel data for 1970–98 to investigate
economic performance under historical independent currency
unions (ICUs) along three dimensions: GDP per capital growth,
growth volatility, and inflation. They use a treatment effects modelthat estimates jointly the probability of having a common currency
and its effect on performance. The authors find that ICU countries
have had a significantly lower rate of inflation, but macroeconomic
volatility has been higher. Also, ICU countries have grown fasterthan with-currency nations, but the East Caribbean Currency
Area countries are found to be the driving force behind this result.
Key words: Currency unions; Dollarization; Inflation; GDPgrowth; GDP volatility; Treatment effects

Sebastian Edwards: University of California, Los Angeles, and National Bureau of
Economic Research (E-mail: sebastian.edwards@anderson.ucla.edu)
I. Igal Magendzo: Central Bank of Chile (E-mail: imagendzo@bcentral.cl)
We have benefited from discussions with John Cochrane, Barry Eichengreen, Eduardo
Engel, Ed Leamer, Robert Barro, Andy Rose, Lars Svensson, and Ricardo Hausmann.
Marcela Aurelio provided wonderful assistance. We also appreciate the comments byRobert W. Rankin and Gabriele Galati.

215

I. Introduction
During the last few years, interest in currency unions has truly exploded. Consider
the following fact: according to the Social Science Citation Index, Mundell’s classical1961 article on optimal currency areas was cited 88 times in the period 1997–2002(up to May); in contrast, during the five years from 1982 to 1987 it was only cited
17 times. This renewed interest in currency unions has been largely the result of two
factors. First, the currency crises of the 1990s prompted a number of authors to argue
that the emerging markets should give up their domestic currencies and join currency
unions. Second, after the implementation of the euro zone in 1999, many analysts
have argued that other nations should follow suit and join a monetary union.
Others, however, have claimed that it is much too soon to evaluate the euroexperiment, and that countries in other regions should wait for hard evidence on
economic performance under the euro before joining a currency union. Still otherexperts have argued that the calibration of theoretical models can shed light on
the question of whether specific countries should join a currency union (Alesina
and Barro [2001]).
Currency unions, however, have been around for quite some time, and it ispossible to use historical data to analyze economic performance in member countries.
The purpose of this paper is to use panel data for 1970–98 to investigate economic
performance under historical independent currency unions. In this analysis, weconcentrate on countries that use a currency common to the union and issued
by the union’s central bank. We refer to this type of monetary arrangement as an“independent currency union,” or ICU. In our analysis, thus, we do not focus on
the case of “dollarized” countries, or countries that adopt an advanced nation’s
convertible currency as legal tender. There are important political and economic
differences between that type of arrangement and dollarization: under an ICU,
monetary policy is run by a common central bank, the members of the currency
union share seigniorage, and the common currency’s exchange rate may float relative
to other currencies. Under “dollarization,” on the other hand, the country inquestion completely gives up monetary independence, and monetary policy is run by
the advanced nation’s central bank. Countries can “dollarize” in a unilateral fashion—in which case they will lose the revenue from seigniorage—or they can sign amonetary treaty with the advanced country and share seigniorage.1 Also, there areimportant political economy differences between dollarized and ICU countries. As
Frieden (2001) has argued, adopting an advanced country’s currency is usuallyperceived as giving up sovereignty, and has serious political costs. These politicalcosts may be reduced, however, if the country becomes a partner in an ICU and,
consequently, has a say in the running of monetary policy. It is even possible that, by
joining an ICU, the country reaps most of the benefits of a common currency,

1. In early 2000, Florida’s senior Senator at the time, Connie Mack, introduced legislation into the U.S. Senate
aimed at sharing seigniorage with countries that decided to adopt the dollar as legal tender. The bill, however,
did not move in the legislative process.

216 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Independent Currency Unions, Growth, and Inflation

without incurring the costs associated with this measure.2 Although as mentioned,
our analysis deals mostly with ICUs, we do discuss briefly, in Section IV, some results
pertaining to strictly dollarized countries.
To be more specific, in this paper we ask the following important question: howhave the ICU countries performed relative to countries that have their own currency?
That is, we are interested in evaluating economic performance along three dimensions: inflation, GDP per capita growth, and growth volatility. Performing this typeof international comparison, however, is not easy. The problem is how to define
an appropriate “control” group with which to compare the ICU nations. Since
membership in an ICU is not a “natural experiment,” using a broad control group of
all countries with a domestic currency is likely to result in biased estimates. In thispaper, we tackle this issue by using a treatment effects model that estimates jointly theprobability of having a common currency and its effect on performance (Maddala[1983], Heckman et al. [1997], and Green [2000]).
Before proceeding, it is useful to point out the ways in which our analysis differsfrom other related work in this general area. First, we have sought to include dataon as many ICU countries as possible. We were able to obtain data on GDP per
capita growth and inflation for 34 ICU countries. Second, we focus directly on themost important macroeconomic variables—real GDP per capita growth, inflation,and growth volatility. Other studies, in contrast, have analyzed performance in an
indirect fashion, and have focused on ancillary variables such as the level of international trade and/or interest rates. For instance, Frankel and Rose (2002) haveanalyzed the way in which currency unions affect bilateral trade and, through thischannel, economic growth.3 Edwards (1998), and Powell and Sturzenegger (2000)
have investigated the way in which the exchange rate/monetary regime affects interest
rate behavior, and the cost of capital. Third, we are particularly interested in estimating as precisely as possible the actual magnitude of the “ICU effect.” That is, we wantto know, in the most accurate possible way, by how many percentage points countries
under a certain regime have outperformed (or underperformed) countries with analternative regime. Obtaining precise estimates of the “ICU effect” is important
for any cost-benefit analysis of a common currency regime. And fourth, we use a“treatment effects model” to estimate the way in which dollarization affects themacroeconomic variables of interest.
The rest of the paper is organized as follows. In Section II, we provide a preliminary analysis of historical experience with ICUs. In Section III, we use treatment
regressions to analyze the effects of “common currencies” on a group of macroeconomic variables. In Section IV, we undertake a robustness analysis and, finally, in
Section V we provide some concluding remarks.

2. In our analysis, we consider countries that adopt a nonconvertible currency as their own as an ICU. If these few
countries are excluded from the analysis, the results reported in this paper do not change, however.
3. See Klein (2002) for a discussion on dollarization and trade, including a comprehensive bibliography on thesubject.

217

II. Independent Currency Unions during 1970–98:
A Preliminary Analysis
In Table 1, we present a list of 33 ICU countries with available data for the period
1970–98.4 In addition, we present a list of 21 strictly dollarized countries, orcountries that have used an advanced country’s currency as legal tender. Two ICUs
dominate the list in Table 1: the Communauté Financière Africaine (CFA) franczone, and the East Caribbean Currency Area (ECCA), with 15 and seven members,
respectively. Both of these ICUs have a central bank of their own, and in an effort toboost credibility, both of these areas have pegged their exchange rates to an advanced
nation. The CFA franc zone is pegged to the French franc, and has an agreementwith France to finance balance of payments disequilibria. In 1994, and after yearsof overvaluation and external imbalances, the CFA was devalued and repegged to
the French franc. Until 1975, the ECCA’s East Caribbean dollar was pegged to the
British pound; since that year, it has been pegged to the U.S. dollar.
Table 1 Monetary Unions with Available Data
CFA Franc Zone
Benin
Burkina Faso
Cameroon
Central African Republic
Chad
Comoros
Congo
Côte d’Ivoire
Equatorial Guinea
Gabon
Guinea-Bissau
Mali
Niger
SenegalTogo
United States
Liberia (D)Marshall Islands (D)Micronesia, Fed. States of (D)Palau (D)Panama (D)Puerto Rico (D)FranceAndorra (also Spanish peseta) (D)French Guiana (D)French Polynesia
Guadeloupe (D)Martinique (D)Monaco (D)New Caledonia
Reunion (D)ECCA
Antigua and Barbuda
Dominica
GrenadaMontserrat
St. Kitts and Nevis
St. Lucia
St. Vincent and the Grenadines
South Africa
LesothoNamibia
Swaziland
New Zealand
Cook Islands (D)Italy
San Marino (D)Australia
Kiribati (D)Nauru (D)Tonga (D)Tuvalu (D)West Africa
Kenya
Tanzania
UgandaIndia
BhutanSingapore
Brunei
Denmark
Greenland (D)Switzerland
Liechtenstein (D)Belgium
Luxembourg (D)Note: (D) corresponds to a dollarized country.

4. These countries have data for a long enough period for at least one of two variables: GDP per capita or inflation.
In the rest of the paper, we will use the term “countries” to refer both to independent countries and to territories.

218 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Independent Currency Unions, Growth, and Inflation

In Table 2, we present comparative data on inflation, per capita GDP growth, andthe standard deviation of growth for our ICU countries. To put things in perspective,
we also present data on these three variables for an “unadjusted” control group that
includes all countries with a currency of their own. In this table, we include dataon the mean and median for the three macroeconomic variables. In column (C), we
present data on mean and median differences between the common currency
countries and the “with currency” control group. The numbers in parentheses aret -statistics for the significance of these differences. The test for the mean differences
is a standard t -statistic, while the median differences test is a t-test obtained using abootstrapping procedure. In making the computations for inflation differentials, wehave followed Engel and Rose (2002) and have excluded countries with hyperinflation.5 However, excluding these observations only affects the calculation of the meandifferences; it has no discernible effect on the computation of median differences.
The results reported in this table indicate that the difference in inflation means isquite sizable and statistically significant; on average, inflation in ICU countries as agroup (Panel A) has been 7.7 percentage points lower than in countries with theirown currency.6 The difference in inflation medians is still negative, much smaller(–2 percentage points), and still statistically significant. In terms of real per capita
GDP growth, the results in Table 2 show that there are no significant differences inTable 2 Inflation, Growth, and Volatility
ICUs versus control group
Independent
currency unions1(A)

Other countries2(B)

Difference3(C) = (A) – (B)A. Inflation
Mean

8.90

16.59

Median

7.22

9.46

–7.69
(–7.07)–2.24(–4.86)

B. Per capita GDP growth
Mean

1.36

1.16

Median1.30

1.88

C. Volatility of growth
Mean

6.23

5.31

Median

5.31

4.58

0.20
(0.76)–0.58(–3.88)0.92(7.80)
0.73(7.20)

Notes: 1. Number of observations with data for inflation is 533. There are 804 observations with datafor per capita growth.
2. Number of observations with data on inflation is 2,831. There are 3,933 observations withdata for per capita GDP growth.
3. Numbers in parentheses are t-statistics.
5. More specifically, we excluded from the control group those observations with a rate of inflation in excess of
200 percent per year. This resulted in 80 observations being dropped from the control group of countries with acurrency of their own. See Section IV for results under alternative definitions of “very rapid inflation.”
6. When hyperinflation countries are not excluded, the means difference in inflation is a staggering 62 percent.

219

the means across the ICU countries and the control group. The results also indicate,however, that the median differences are significantly negative: the median rate of
growth in the ICUs has been significantly lower—in a statistical sense—than in thecontrol group of countries with a currency of their own. Finally, our results show that
the ICUs have experienced greater growth volatility than the control group.
Although the comparisons reported in Table 2 are informative, they are subject
to two potential limitations. First, these are unconditional comparisons, as no effort
has been made to control for other factors potentially affecting macroeconomic
performance. Second, the control group may not be the appropriate one. If this is thecase, the results presented in Table 2 may be subject to a “treatment bias.”7

III. A Treatment Effects Model of Economic Performancein the ICUs
Much of the recent enthusiasm for “dollarization” and common currencies has been
based on the idea that emerging countries with a currency of their own lack credibility, and are subject to a Barro-Gordon type of “inflationary bias.” It has been argued
that by adopting an advanced country’s currency, credibility will be established andinflation will be lower than otherwise. It is difficult, however, to make the point thatan ICU central bank will be more credible than a national one. It is precisely for thisreason that many of the ICUs in Table 1 have attempted to deal with this credibility
problem by pegging the union’s currency to that of an advanced nation. Whether
they, indeed, succeed in doing this and reaching a low-inflation equilibrium is anempirical question, and one that we address in detail in the rest of this paper.
In principle, the decision to have a “common currency” could affect the growth
process through at least two potential channels. First, if exchange rate risk is very low,
as ICU supporters have argued, the union members will face a low(er) cost of capital.
This, in turn, will result in a higher rate of physical capital accumulation and ahigher rate of growth of potential output. Second, a higher level of international
trade—which, as Rose (2000) has argued, is associated with common currencies—islikely to have a positive effect on total factor productivity (TFP) growth, and onreal GDP growth. This effect has been emphasized in a number of endogenous
growth models, and operates through the effect of openness on the accumulation ofknowledge. In the final analysis, this is also an empirical issue; we deal with it in therest of this paper.
Membership in an ICU is also likely to affect the volatility of growth. Indeed,
hard-peg exchange rate regimes will tend to limit a country’s ability to accommodate
external terms of trade shocks. Thus, ICU countries are likely to have a higher degreeof growth volatility than countries with a currency of their own and, in particular,
than countries with exchange rate flexibility.8 In this section, we use a treatment
effects model to address this issue.
7. See Maddala (1983).
8. On external shocks and exchange rate regimes, see Meade (1951) and Corden (2000).

220 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Independent Currency Unions, Growth, and Inflation

A. The Model
Our objective is to undertake a comparative analysis of the effect of an ICU exchange
rate regime on macroeconomic performance. We are particularly interested on thecomparative performance of GDP per capita growth, inflation, and growth volatility.
We use an unbalanced data set for 199 countries during 1970–98. Our empiricaltreatment effects model is given by
yjt = xjtβ + γδj + µjt .

(1)

1, if δ *jt > 0
0, otherwise

(2)

δjt =

{

δ *jt = wjt α + εjt .

(3)Equation (1) is the macroeconomic performance equation, where yjt stands for eachof the macroeconomic variables of interest in country j and period t ; x jt is a vector ofcovariates that captures the role of traditional determinants of economic performance; and δjt is a dummy variable (i.e., the treatment variable) that takes a value of
one if country j in period t is an ICU country, and zero if the country has a currencyof its own. µjt is an error term, whose properties are discussed below. β and γ areparameters to be estimated. The decision to be an ICU country is assumed to be theresult of an unobserved latent variable δ *jt , as described in equation (2). δ *jt , in turn,is assumed to depend linearly on vector wjt . Some (or all) of the variables in wjt may
be included in x jt (Maddala [1983, p. 120]).9 α is a parameter vector to be estimated,and εjt is an error term. Error terms µjt and εjt are assumed to be bivariate normal,
with a zero mean and a covariance matrix given byσ ς .
ς 1 

(4)

If the performance and common currency equations are independent, the covarianceterm ς in equation (4) will be zero. Under most plausible conditions, however, it islikely that this covariance term will differ from zero.
Green (2000, p. 934) has shown that if equation (1) is estimated by least squares,
the treatment effect will be overestimated. Traditionally, this problem has beentackled by estimating the model using a two-step procedure (Maddala [1983]). In
the first step, the treatment equation (2) is estimated using probit regressions. From
this estimation, a hazard is obtained for each j,t observation. In the second step, theoutcome equation (1) is estimated with the hazard added as an additional covariate.
From the residuals of this augmented outcome regression, it is possible to computeconsistent estimates of the variance-covariance matrix (4). An alternative and inprinciple more efficient way of dealing with the model in equations (1) through (4)9. It is assumed, however, that δ *jt does not depend on yjt . Otherwise, as discussed below, the model cannot
be identified.

221

is to estimate them jointly using a maximum likelihood procedure. The results
reported in this paper have been obtained using this maximum likelihood procedure.
As shown by Green (2000), the log likelihood for observation k is given by equations(5) and (5′):σ
wkα + (yk – xkβ – δ )ς /—Lk = log Φ ————————————2√1 – ς

{}—1 ————
yk – xkβ –—δ 2– log √—
–—
2π σ,
σ
2

{

}

–wkα – (yk – xkβ )ς /—σ
Lk = log Φ ———————————2√1 – ς

{

}

2
—
—
1 ———
yk – x—kβ–—– log √ 2π σ,
σ
2

{

}

if δk = 1.

if δk = 0.

(5)

(5′)

The model in equations (1) through (4) satisfies the consistency and identifying
conditions of mixed models with latent variables if the outcome variable y jt is nota determinant (directly or indirectly) of the treatment equation—that is, if y is
not one of the variables in w in equation (3).10 This seems to us to be a reasonableassumption. See Maddala (1983) and Angrist (2000) for further discussion ofthese issues.
B. Basic Results
In this subsection, we report the results obtained from the estimation of the treatment
effects model given by equations (1) through (4). The “treatment group” is definedas all countries that belong to an ICU. That is, the dummy variable δjt takes a valueof one if in period t country j is an ICU member. The data set is an unbalanced
panel that covers 1970 through 1998, and includes 199 countries and territories. The
number of observations varies, depending on the performance variable considered;see Table 2 for details.
1. The probability of being an ICU country
The following exogenous covariates were included in the estimation of the treatment
equation (3) on the probability of being an ICU country:(a) The log of population measured in millions of people, as an index of thecountry’s size.
(b) The log of initial (1970) GDP, taken as a measure of the country’s economic
size.

10. Details on identification and consistency of models with mixed structures can be found in Maddala (1983).
See also Angrist (2000).

222 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Independent Currency Unions, Growth, and Inflation(c) An indicator of the degree of openness of the economy. For the majority ofcountries and years, we used the Sachs and Werner’s (1995) openness index,
which takes a value of one if the country in question is open to international
trade, and zero otherwise. We used data from a variety of sources to supplement the Sachs-Werner index for those countries and years not covered intheir sample.11(d) A dummy variable that takes the value of one if the country in question is
an island.
(e) A dummy variable that takes the value of one if the country has a common
border with a nation whose currency is defined by the International Monetary
Fund (IMF) as a “convertible currency.”(f ) A variable that measures the country’s geographical location, or proximity to
global markets. We call this variable “latitude.”(g) Latitude square.
(h) A dummy variable that takes the value of one if the economy in question is anindependent nation.
2. The outcome equations
Some of the traditional covariates in the outcome equations (1) for GDP growth,
inflation, and volatility are unavailable for the smaller countries. For instance, few of
the ICUs have data on education quality or on some other variables traditionally
included in growth empirical analyses (Barro [1996]). Indeed, popular data sets, suchas the World Bank’s World Development Indicators (WDI), the IMF’s International
Financial Statistics (IFS), or the Barro and Lee (1996) data set, do not include dataon all the ICU countries. Nevertheless, we have been able to include a number ofcovariates. In the estimation of the GDP growth equation, we included, as customary, initial GDP, a measure of openness, a dummy for independence, a measure for
geographical proximity (latitude), and the ICU dummy. In some specifications, we
also introduced lagged (and lagged square) terms of the dependent variable, as well as
regional dummies. In the inflation-common currencies model, we included openness,the independence dummy, lagged inflation (as a measure of persistence), regionaldummies, and the common currency dummy as covariates in the outcome equation.
Finally, in the volatility-common currency model, we included the following covariates in the outcome equation: openness; geographical location, measured by latitude;
regional dummies; and log of population and log of GDP in 1970, as a measure of
the initial level of development of the country.
3. Results
In Table 3, we summarize the results obtained from the estimation of the treatment
effects model for GDP per capita growth. Table 4 contains the results for inflation,
and Table 5 those for growth volatility. Each of these tables contains two panels. The
upper panel includes the results from the outcome equation; the lower panel contains
the estimates for the “treatment equation.”
We first discuss the results from the treatment equations on the probability of
being an ICU country: as may be seen from these tables, the results are quite similar
11. See the original Sachs and Werner (1995) article for a specific list of requirements for a country to qualify as “open.”223

Table 3 Growth and Independent Currency Unions: A Treatment Effects Model

Log(GDP0)OPEN
LATITUDE
LATITUDE2
DUMMY_ICU
Constant
EUROPA
LAC
MENA
NORAM
SASIA
AFRICA

Log(POP)
Log(GDP0)INDEP
BORDEROPEN
ISLANDLATITUDE
LATITUDE2
Constant
Number of observations
LR χ 2
Prob > χ 2

Model 1Model 2
Outcome equation
–0.45
–0.54
(–5.94)(–6.20)
3.33
3.22(11.02)(10.55)
7.94
4.84(4.15)(2.36)–10.32–4.12(–3.71)(–1.33)1.33
1.60(2.70)(3.20)
2.48
4.18(4.64)(6.06)–1.35—(–3.01)–0.02—(–0.07)–0.84—(–2.12)–0.62—(–0.78)0.45—(0.89)–2.00—(–6.06)
Treatment equation
–0.31
–0.31
(–15.25)(–15.22)
–0.05
–0.05
(–2.55)(–2.55)–1.02–1.03(–8.99)(–9.00)–0.93–0.93(–6.03)(–6.04)–3.97–3.98(–8.62)(–8.63)–0.69–0.70(–7.90)(–8.01)
2.21
2.20(2.50)(2.49)–14.63–14.66(–6.30)(–6.29)5.79
5.78(15.17)(15.16)
4,888
4,888
0.65
0.11
0.420
0.744

Note: Numbers in parentheses are t-statistics.

224 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Model 3–0.51(–6.01)
3.15(10.43)——1.19(2.43)
4.88(7.85)–0.84(–2.45)–0.08(–0.26)–0.59(–1.53)–0.18(–0.24)0.54(1.06)–2.21(–6.87)–0.31(–15.13)–0.05(–2.54)–1.03(–9.02)–0.93(–6.01)–4.01(–8.66)–0.70(–8.06)
2.21(2.49)–14.75(–6.29)5.77(15.10)
4,888
0.33
0.564

Independent Currency Unions, Growth, and Inflation

Table 4 Inflation and Independent Currency Unions: A Treatment Effects Model

OPEN
LATITUDE
INFL1L
INFL1L2
DUMMY_ICU
Constant
EUROPA
LAC
MENA
NORAM
SASIA
AFRICA

Log(POP)
Log(GDP0)INDEP
BORDEROPEN
ISLANDLATITUDE
LATITUDE2
Constant
Number of observations
LR χ 2
Prob > χ 2

Model 4Model 5
Outcome equation–12.60–4.76(–10.87)(–4.64)
–4.53
–3.20
–1.75
(–1.01)
0.34—(38.11)–0.00—(–31.89)–17.42–14.08(–9.95)(–9.33)23.36
9.16(26.37)(7.66)
3.24—(2.07)
7.58—(6.51)
0.99—(0.70)
0.93—(0.33)
1.17—(0.70)
8.86—(7.25)
Treatment equation
–0.45
–0.45
(–15.72)(–15.63)
–0.15
–0.16
(–4.32)(–4.57)–0.44–0.46(–2.11)(–2.23)
—
—
–5.26
–5.09
(–7.57)(–7.36)–1.41–1.44(–11.18)(–11.53)
0.81
0.59(0.69)(0.49)–10.45–10.08(–3.31)(–3.15)8.22
8.32(14.11)(14.18)
2,956
2,956
10.95
9.31
0.000
0.002

Model 6
–5.90(–6.19)–7.63(–3.64)0.35(39.17)–0.00(–32.65)–10.45(–6.77)
14.83(19.39)
—
—
—
—
—
—

–0.45
(–15.61)–0.16(–4.59)–0.47(–2.28)—–5.16(–7.47)–1.43(–11.35)0.47(0.40)–9.60(–3.08)8.33(14.19)
2,956
5.35
0.020

Note: Numbers in parentheses are t-statistics.

225

Table 5 Volatility and Independent Currency Unions: A Treatment Effects Model

OPEN
LATITUDE
LGINI
DUMMY_ICU
Constant

Model 7
Outcome equation–3.21(–4.40)2.32(1.42)
0.37(1.94)
3.46(4.06)
2.29(1.84)EUROPA

—

LAC

—

MENA

—

NORAM

—

SASIA

—

AFRICA—

Log(POP)
Log(GDP0)INDEP
BORDEROPEN
ISLANDLATITUDE
LATITUDE2
Constant
Number of observations
LR χ 2
Prob > χ 2

Treatment equation–0.33(–3.43)–0.06(–0.58)–1.53(–1.43)–0.11(–0.13)–4.44(–1.73)–1.31(–3.32)–0.96(–0.32)–6.21(–0.87)
7.06(3.15)
174
5.37
0.020

Note: Numbers in parentheses are t-statistics.

226 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Model 8–3.02(–4.38)–0.22(–0.10)0.26(1.29)
3.15(3.48)
3.53(2.34)
1.20(1.19)–0.84(–1.14)2.38(2.62)–1.60(–0.87)–1.30(–1.14)–0.007(–0.01)–0.38(–3.58)–0.08(–0.87)–1.96(–1.46)–0.38(–0.37)–4.06(–1.57)–1.23(–2.82)–0.49(–0.14)–7.29(–0.87)8.28(3.32)
174
3.44
0.063

Independent Currency Unions, Growth, and Inflation

across models and are quite satisfactory. They clearly indicate that the probability of
being an ICU country is higher for small countries, in terms both of population(POP) and initial GDP per capita (GDP0). Being an independent nation (INDEP)
reduces the probability of being an ICU. Proximity to international markets(LATITUDE) reduces the probability of belonging to an ICU; the coefficients of theisland (ISLAND) and border (BORDER) variables are significantly negative. Perhaps
the most interesting aspect of these results is that the open variable (OPEN) has asignificantly negative coefficient, suggesting that with other things given countries
that are more open to international trade are less likely to belong to an ICU.
This result contrasts with the case of strictly dollarized nations, where the openness
coefficient is significantly positive (Edwards and Magendzo [2001]).
4. GDP per capita growth
In Table 3, we present the results obtained from the estimation of the growth model
when maximum likelihood estimation techniques were used. Results obtained using
two-step procedures yielded very similar outcomes, and are available to interested
readers on request. These results show that for all specifications the coefficient of
the ICU dummy is positive, with a point estimate ranging from 1.19 to 1.60. Thissuggests that, with other things given, ICU countries have grown at a faster rate
than countries with a currency of their own. Notice that these results are quite
different from the simple mean differences reported in Table 2: while according
to those results there have been no differences in rates of growth across the twogroups of countries, the estimates in Table 3 indicate ICU members have grown at asignificantly faster rate than countries with a currency of their own.
The coefficient of most of the other covariates in the growth equations have theexpected signs, are statistically significant, and have signs that conform with what was
expected. The geographical dummies are quite interesting and establish that the
Middle East and Africa are particularly slow-growing regions. An additional aspectof the results in Table 3 is worth mentioning: the χ 2 test for the independence of thetreatment and outcome equations indicates that in all but one of the specifications thenull hypothesis of independence across the equations is rejected at conventional levels.
5. Inflation
The results for the inflation model are reported in Table 4. As may be seen from theoutcome equation in the upper panel, the ICU dummy is negative and significant inevery one of the specifications. The point estimates range from –17 to –10, not only
confirming that inflation has historically been lower in the ICU countries, but also
indicating that the ICU advantage is still detected after a treatment effects model isused. It is interesting to note that relative to the benchmark (Asia), Latin Americaand Africa have had a significantly higher rate of inflation. The null hypothesis ofindependent equations is rejected at conventional levels.
6. Volatility
Table 5 contains the results for the volatility models, estimated using a cross-sectionof countries. The null hypothesis of independent equations is rejected at conventional levels—the χ 2 statistics range from 3.4 to 5.4—and the dummy variables for
ICU are significantly positive, indicating that countries that belong to a currency
union have experienced a higher degree of growth volatility than countries with a227currency of their own. Openness reduces volatility—a result that is in line with anumber of theoretical results in international economics. Our estimates indicate thatcountries with a higher level of development (measured by initial GDP) exhibit
higher volatility. Also, with other factors given, the countries of the Middle East andNorth Africa (MENA) have exhibited a higher degree of volatility than countries inother regions. In what appears to be a counterintuitive result, the coefficient for theEurope dummy is positive, although not significant. The reason for this apparent
anomaly is that the Eastern European nations and some of the former Soviet Union
republics are part of the World Bank European region. Finally, we note that theestimated coefficients for the ICU dummy are significantly larger than the meandifferences in volatility presented in Table 2.

IV. Further Results and Robustness Analysis
In this section, we deal with some extensions, investigate the robustness of the results,
and inquire as to what is behind the results reported above.
A. Redefining “Very Rapid Inflation”
In our inflationary analysis, we excluded countries with extremely rapid inflation, or“hyperinflation countries.” In the estimates reported in Tables 3 through 5, thesample excluded countries with a rate of inflation in excess of 200 percent per year.12
It is possible, however, that by still allowing highly inflationary countries in thesample, the estimates obtained are being driven by extreme or outlier observations.
To investigate this issue, we re-estimated the inflationary equation under alternative
definitions of “very rapid inflation.” More specifically, in the alternative estimates wefirst excluded observations with an annual rate of inflation in excess of 100 percent;we then repeated the exercise, excluding observations with inflation in excess of
50 percent per year.
The results obtained when these new samples were used confirmed those reported
above, in the sense that inflation is significantly lower in ICU countries. Interestingly,
however, under these new definitions of very rapid inflation the ICU advantage
appears to be greater. When inflation above 100 percent was excluded, the estimatedtreatment coefficient was –14.7 for ICUs; when inflation above 50 percent was
excluded, the estimated treatment coefficient was –10.0.
B. Non-Parametric Methods
It is possible that the specification forms chosen for the outcome equations affect theresults reported above. In particular, the linearity of most of the equations may affectthe estimates of the “treatment coefficient.” To investigate whether this is an important factor, we undertook a non-parametric analysis based on “matching estimators”(see Blundell and Costa Dias [2000]). This approach consists of using the available
data to reestablish the conditions of a natural experiment. A general advantage of this
12. Engel and Rose (2002) also excluded hyperinflation observations.

228 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Independent Currency Unions, Growth, and Inflation

nonparametric method is that no particular specification of the underlying model has
to be assumed.13 Matching estimators use the existing data to construct an appropriate sample counterpart for the missing information. This is done by pairing each
ICU country with countries from the with-domestic-currency group. If the sample islarge enough, for each treated (ICU) observation we can find, in principle, at least
one untreated observation with exactly the same characteristics. Each ofthese properly selected untreated observations provides the required counterfactualfor our comparative analysis.14 The problem is that under most general conditions it
is not possible to find an exact match between a treated and untreated observation.
The matching estimator method focuses on estimating an average version of theparameter of interest.15 That is, the matching estimator consists of obtaining thedifference in outcome as an average of the differences with respect to “similar”—
rather than identical—untreated outcomes. Rosenbaum and Rubin (1983) haveshown that an efficient and simple way to perform this comparison is to rely on apropensity score, defined as the probability of participation or treatment: P(x) =Prob(D = 1/x). In our case, this is the probability of a country being a common
currency country. This reduces a multi-dimensional problem to a one-dimensional
problem, provided that we can estimate P(x). Instead of matching countries directlyon all of their characteristics, we can compare countries with similar probability of
being a common currency country.
We use two alternative methods for computing matching estimators. First, we
use a simple-average nearest neighbor estimator. According to this method, for eachtreated observation, we select a predetermined number of untreated nearest
neighbor(s). The nearest neighbors of a particular treated observation i are defined as
those untreated observations that have the smallest difference in propensity scorewith respect to i. We applied the above method to both one nearest neighbor and fivenearest neighbors. The second method consists of using local linear regressions to
identify each matching observation (Fan [1993]). The results obtained from these
two matching estimators, not reported in detail here due to space considerations,provided ample support for the findings presented in Tables 3 through 5 of thispaper. The matching results are available to interested readers; see also Edwards andMagendzo (2001).

13. If we estimate the equation above using all non-treated observations, the selection bias is given by
B(x) = E(u 0/x, D = 1) – E(u 0/x, D = 0).
14. To guarantee that all treated agents have such a counterpart in the population (not necessarily in the sample), we
also need to assume that 0 < Prob(D = 1/x ) < 1.
15. This averaged version is given by∫E (y 1 – y 0/x, D = 1)dF (x/D = 1)
S
M(S ) = —————————————,∫dF(x/D = 1)
S

where S is a subset of the support of x given D = 1.

229

C. A Comparison With “Strictly Dollarized” Countries
An interesting question is how the performance of ICU countries compares withother types of hard-peg regimes. More specifically, how does it compare with theperformance of countries that have a strictly dollarized monetary system? We havedealt with the case of “strict dollarization” in some of our previous work (see, forexample, Edwards and Magendzo [2001]). The results from a formal comparison—
which also relies on a treatment effects model—between ICU and “strictly dollarized”
countries may be summarized as follows:• In the GDP growth models, the dummy for “strict dollarization” is not significant. In one of the specifications, the point estimate is even negative. This
contrasts with the results for ICUs reported in this paper, where the treatment
dummy is significantly positive.
• In the inflation models, the dummies for both the “strictly dollarized” andthe “ICU” countries are significantly negative, confirming that both types ofcommon currency countries have been able to have a significantly lower rate of
inflation than the with-domestic-currency countries.
• In the inflation models, the point estimates for the dummies are quite different
for the two groups of countries. The two specifications that do not include theregional dummies suggest that the “low-inflation” advantage is greater for the“strictly dollarized” countries, indicating that both super hard-peg regimes haveresulted in higher real volatility than with-domestic-currency regimes.
• The results for the volatility model show that the dummies’ coefficients are
significantly positive for both groups of countries. Although the point estimates
are larger for the ICU countries, they are not significantly so.
D. What Is Really Behind These Results? Comparing the CFA and the ECCA
From a policy perspective—and in particular, from a “lessons” point of view—aninteresting question is whether a specific group of countries is behind the results
reported in the preceding sections. We are particularly interested in understanding
whether there is any pattern behind the results suggesting that ICU countries growsignificantly faster than countries with a currency of their own. To investigate thisissue, we analyzed the residuals from our regression analysis and inquired as to thecharacteristics of our ICU data set.
A detailed inspection of the residuals as well as the raw data suggests that there arevery substantial differences in economic growth between the ECCA nations,on the one hand, and the rest of the ICU countries. Indeed, for the period underconsideration average yearly GDP per capita growth in the ECCA countries has
been 3.16 percent. In the other ICU countries, on the other hand, it has only been
0.79 percent. The comparison of medians yields a similar result, with the median
growth for ECCA countries at 3.60 percent, and that for the rest of the ICUs at
0.72 percent.16 This unconditional comparison suggests that the ECCA nations’performance is behind our findings that, with other things given, ICU nations
grow at a faster rate than countries with a domestic currency. To investigate this, we
16. During this period, growth in the CFA nations was particularly low.

230 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Independent Currency Unions, Growth, and Inflation

estimated separate treatment GDP growth model effects for ECCA and other-ICU
nations.17 The estimated coefficient for the ECCA common currency dummy
variable was 2.3, with a t-statistic of 4.46, confirming that ECCA nations have outperformed by a wide margin countries with a currency of their own. The results forthe non-ECCA ICU countries were quite different, with a statistically insignificant
estimated coefficient for the treatment dummy of 0.5.18 These results, then, confirm
the notion that the driving force behind the apparent superior growth performanceof common currency countries, reported in Table 3, is fully driven by the group of
ECCA nations.

VI. Concluding Remarks
The purpose of this paper is to analyze, from a comparative perspective, economic
performance in economies that belong to an ICU and countries that have a currencyof their own. We have argued that the main difficulty in performing this type ofcomparison refers to defining the correct “control group” with which to compare theperformance of the ICU members. In this paper, we have tackled this issue by using the“treatment effects model” developed in the labor economics literature. Estimation
using this technique yields results that are rather different from those obtained fromraw comparisons using a large control group of all with-domestic-currency countries.
More specifically, we have found that both ICU countries have had a significantly
lower rate of inflation than with-currency ones. We have found that macroeconomic
volatility has been higher in ICU economies than in with-currency countries.
We believe that our results are particularly interesting with respect to GDP growth.
The estimations reported in Table 3 suggest that ICU countries have grown fasterthan with-currency nations. In fact, results from an extended analysis indicate thatICU countries also grew at a faster pace than “strictly dollarized” nations. When we
investigated these findings further, we found that the ECCA countries were the driving
force behind this estimated superior growth performance of ICUs. Indeed, once theseseven countries were excluded from the sample, we found no statistical difference in
GDP per capita growth in the rest of the ICU countries and countries with a currencyof their own. The ECCA countries constitute, indeed, a very special group: they arevery small, with an average population of less than 100,000 inhabitants. They are allislands, geographically close to major markets. Their main industry is tourism, andthey have very close economic and cultural ties with the United Kingdom. However,
we believe that their experience with an ICU may not be entirely useful for larger
countries planning to reform their exchange rate and monetary regime.

17. In the estimation, we had to respecify the treatment equation. The reason is that some of the regressors (islands,
for example) fully predicted the probability of being a currency union country.
18. These results are from a specification that includes regional dummies in the GDP growth outcome equation.
If these dummies are excluded, the ICU dummy becomes negative with a t-statistic of –1.46.

231

References

Alesina, A., and R. J. Barro, “Dollarization,” American Economic Review, 91 (2), 2001, pp. 381–385.
Angrist, J., “Estimation of Limited Dependent Variable Models with Dummy Endogenous Regressors:
Simple Strategies for Empirical Practice,” NBER Technical Paper No. 248, National Bureau of
Economic Research, 2000.
Barro, R. J., “Determinants of Economic Growth: A Cross-Country Empirical Study,” NBER Working
Paper No. 5698, National Bureau of Economic Research, 1996.
———, and J. Lee, “International Data on Educational Attainment: Updates and Implications,” CID
Working Paper, 42, Center for International Development, 1996.
Blundell, R. A., and M. Costa Dias, “Evaluation Methods for Non-Experimental Data,” Fiscal Studies,21, 2000, pp. 427–468.
Corden, M., “Exchange Rate Regimes and Policies: An Overview,” in C. Wise and R. Roett, eds.
Exchange Rate Politics in Latin America, Washington, D.C.: The Brookings Institution, 2000.
Edwards, S., “Interest Rate Volatility, Contagion, and Convergence,” Journal of Applied Economics, 1(1), 1998, pp. 55–86.
———, and I. I. Magendzo, “Dollarization, Inflation and Growth,” NBER Working Paper No. 8671,
National Bureau of Economic Research, 2001.
Engel, C., and A. K. Rose, “Currency Unions and International Integration,” Journal of Money, Credit
and Banking, 34 (4), 2002 (forthcoming).
Fan, J., “Local Linear Regression Smoothers and Their Minimax Efficiencies (in Curve Estimation),”Annals of Statistics, 21 (1), 1993, pp. 196–216.
Frankel, J. A., and A. K. Rose, “An Estimate of the Effect of Currency Union on Trade and Growth,”
Quarterly Journal of Economics, 117 (2), 2002, pp. 437–466.
Frieden, J., “The Political Economy of Dollarization: Domestic and International Factors,” in E. LevyYeyati and F. Sturzenegger, eds. Dollarization, Cambridge, Massachusetts: The MIT Press,
2001.
Green, W. H., Econometric Analysis, Fourth Edition, New Jersey: Prentice Hall, 2000.
Heckman, J. J., H. Ichimura, and P. E. Todd, “Matching as an Econometric Evaluation Estimator:
Evidence from Evaluating a Job Training Programme,” Review of Economic Studies, 64 (4),1997, pp. 605–654.
Klein, M. K., “Dollarization and Trade,” Fletcher School of Law and Diplomacy, Tufts University,
working paper, 2002.
Maddala, G. S., Limited Dependent and Qualitative Variables in Econometrics, Cambridge University
Press, 1983.
Meade, J. E., The Balance of Payments, London: Oxford University Press, 1951.
Powell, A., and F. Sturzenegger, “Dollarization: The Link between Devaluation and Default Risk,”working paper, Universidad Torcuato di Tella, 2000.
Rose, A. K., “One Money, One Market: Estimating the Effect of Common Currencies on Trade,”
Economic Policy, 15 (30), 2000, pp. 7–46.
Rosenbaum, P. R., and D. B. Rubin, “The Central Role of the Propensity Score in Observational
Studies for Causal Effects,” Biometrika, 70, 1983, pp. 41–55.
Sachs, J., and A. Werner, “Economic Reform and the Process of Global Integration,” Brookings Papers
on Economic Activity, 1, 1995, pp. 1–188.

232 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Comment

Comment
GABRIELE GALATI19
Bank for International Settlements
This paper is an interesting empirical study of exchange rate regimes. It provides
some insights on the advantages of forming a currency union and on which countries
are able to benefit from this regime. I will first summarize the paper’s main lessons
about exchange rate regimes. I will then discuss some issues related to the empiricalmethodology and comment on the interpretation of the results.

I. Main Lessons about Exchange Rate Regimes
Three main lessons emerge from this paper. First, the empirical analysis suggests that
only a limited number of economies are likely to opt for membership of a currency
union. Based on past experience, the likely candidates are small countries that are
geographically close to international financial centers. This is consistent with thebroadly held view that small countries are more likely to benefit from a corner
solution that implies irrevocably fixing the exchange rate. The authors also findthat countries which ceteris paribus are independent and open to trade are less likely
to be members of a currency union.
Second, there is evidence that countries which form a currency union tend tohave better macroeconomic performance compared to other countries. Between 1970
and 1998, members of currency unions experienced both faster output growthand lower inflation. They also outperformed countries that had dollarized. The
magnitude of these differences is both economically and statistically significant,even after controlling for a set of other factors that might have driven growth andinflation. The authors also find that currency union members exhibited higher
growth volatility compared to other countries.
Third, the analysis of the paper implies that geography may play a key role in
the performance of exchange rate regimes. The superiority of currency unions ismainly driven by one currency union in the sample, the East Caribbean Currency
Area (ECCA).

II. Methodological Issues
In terms of the empirical methodology, Edwards and Magendzo improve on theexisting literature in three respects. First, they argue that separately estimating theprobability of a country joining a currency union and the influence of this decision
on macroeconomic performance would generate a selection bias. This bias would
19. The opinions expressed in these comments are those of the author and do not necessarily reflect the views of the
Bank for International Settlements.

233

make standard econometric tests unreliable. To avoid this problem, the authors
jointly estimate both relationships using a treatment effects model borrowed from thelabor economics literature. Second, the authors checked that their comparison
between alternative exchange rate regimes is not affected by their definition of “veryrapid inflation.” Third, they carefully select a control group using two variants of anon-parametric method based on matching estimators. This method, which is again
borrowed from empirical labor economics, should prevent the results from being
contaminated by use of the wrong benchmarks for comparison. The authors reportthat using these techniques does not affect their main findings.
Edwards and Magendzo also carefully analyze their empirical results. They
identify one currency union, the ECCA, as the driving force of the superior results
for currency unions. Moreover, they mention that for some countries island effects
were dominant.
While the authors should be commended for their careful treatment of a numberof problems that have affected the empirical literature on the relative performance ofdifferent exchange rate regimes, some methodological issues still remain. First, thetiming of the decision to adopt a currency union could affect the regression results.
For example, if countries adopt a currency union following a crisis, per capita GDP islikely to grow faster after the decision to join. The paper may therefore benefit froman analysis of the conditions under which countries opted to join a currency union.
Second, exchange rate policies that were followed before and after the decision toform a currency union may matter. This is illustrated by the experience of countries
that are part of the Gulf Cooperation Council (GCC) and have decided to form a Gulf
Monetary Union in the near future. These countries have since the mid-1980s de facto(with the exception of Kuwait), and since December 2000 officially, pegged theircurrency to the dollar. This policy is likely to continue after the monetary union isformed. Given the modest volume of intra-regional trade, would the impact of fixing
intra-GCC exchange rates dominate that of keeping the external value of the currency
constant? Edwards and Magendzo’s analysis puts more emphasis on the former.
Third, the authors may wish to investigate the timing of the impact of a decision
to join a currency union on a country’s macroeconomic performance. Their empiricalmodel implies that this impact is immediate. The experience of the European Monetary
Union (EMU) and Frankel and Rose’s (1998) argument about the endogeneity ofthe optimum currency area criteria seem to suggest that the full impact may bereached only after some time.
I also have two comments on the specification of the determinants of the choice
to adopt a currency union. First, while the authors use geographical proximity tofinancial centers as an explanatory factor, a priori financial depth appears to be themore relevant variable. Second, it would be interesting if the authors could address
the role of the political will to seek closer economic and financial integration in thedecision to form a monetary union. The importance of this factor for the EMU has
recently been emphasized by Wyplosz (2001).

234 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Comment

III. Interpretation of the Results
An important issue on interpretation relates to the applicability of the results to the
EMU and Asia. Edwards and Magendzo start their paper by arguing that the adventof the EMU to a large extent explains the strong interest that currency unions havereceived in recent years. It is therefore natural to ask what the paper has to say about
the EMU. Judging from the estimates of the treatment equation, the factors that
explain the decision to enter a currency union do not apply to most of the countries
that joined the EMU. Nor do they appear to predict countries that are candidates for
the EMU’s enlargement. These considerations raise the question of whether Edwards
and Magendzo’s paper tackled the selection bias, but their analysis may be affected bywhat is sometimes referred to as an “extrapolation bias.”
Apart from the EMU, it is also interesting to look at what the paper predicts for
Asian countries. In their paper presented at this conference, Fujiki and Otani (2002)
discuss the desirability of different exchange rate regimes for Asia, including a common
currency area. Edwards and Magendzo’s empirical analysis seems to suggest that acurrency union is unlikely to be formed in Asia. It would be interesting to havesome explicit discussion of this point in the paper.
Another issue on interpretation that I would like to raise concerns the main
finding that currency union members tended to outperform other countries in termsof inflation and output. Although in principle I can think of several explanations
for this result, I have difficulty in understanding how they can matter. First, thedecision to join a currency union could improve macroeconomic performance byboosting credibility, as the authors argue. However, given the results of the treatment
equation, it is not entirely clear why countries would gain more credibility by joining
a currency union rather than choosing dollarization.
Second, Rose (2000) and Glick and Rose (2002) have shown that the decision tojoin a currency union might improve output growth by increasing trade in goods andservices. However, this channel is unlikely to play an important role for the countries
that are likely to join a currency union according to the paper, given their small size.
The recent experience of the EMU suggests that a third explanation could involvethe impact of forming a currency union on financial markets, which in turn may havesignificant benefits for the macroeconomic performance of member countries.20 Again,
the description of currency unions included in the data set of the paper seems toindicate that this factor does not explain the superior performance of currency unions.

References

Danthine, Jean-Pierre, Francesco Giavazzi, and Ernst-Ludwig von Thadden, “European Financial
Markets after EMU: A First Assessment,” CEPR Discussion Paper No. 2413, Centre for
Economic Policy Research, 2000.
Detken, Carsten, and Philipp Hartmann, “The Euro and International Capital Markets,” Working
Paper No. 19, European Central Bank, 2000.
20. See, e.g., Danthine et al. (2000), Detken and Hartmann (2000), and Galati and Tsatsaronis (2001).

235

Frankel, Jeffrey, and Andrew K. Rose, “The Endogeneity of the Optimum Currency Criteria,” The
Economic Journal, 108, 1998.
Fujiki, Hiroshi, and Akira Otani, “Do Currency Regimes Matter in the 21st Century? An Overview,”
Monetary and Economic Studies, 20 (S-1), Institute for Monetary and Economic Studies, Bankof Japan, 2002, pp. 47–79 (this issue).
Galati, Gabriele, and Kostas Tsatsaronis, “The Impact of the Euro on Europe’s Financial Markets,” BIS
Working Paper No. 100, Bank for International Settlements, 2001.
Glick, Reuven, and Andrew K. Rose, “Does a Currency Union Affect Trade? The Time Series
Evidence,” NBER Working Paper No. 8396, National Bureau of Economic Research, 2002.
Rose, Andrew K., “One Money, One Market: Estimating the Effect of Common Currencies on Trade,”
Economic Policy, 30, 2000.
Wyplosz, Charles, “Regional Arrangements: Some Lessons from Post-War Europe,” paper presented at
FONDAD, Prague, on July 21–22, 2001.

Comment
ROBERT W. RANKIN21
Reserve Bank of Australia
This is an interesting paper, both for its subject matter and its empirical techniques.
The subject matter—part of the general strand of analysis of the impact of different
exchange rate regimes—is one of contemporary relevance, not least in the Asian region,
where there is a vigorous debate on questions of exchange rate policy, including thepossible future development of a common regional currency.
The main result presented in the paper is that independent currency union (ICU)
countries have lower inflation, higher GDP growth, and higher growth volatility thancountries with their own currencies.
This result comes from an analysis using a treatment effects model that allows forthe likelihood that ICU countries are not randomly selected, but are in fact ICU
countries because of their particular characteristics. By comparing the ICU countries
with the non-ICU countries with similar characteristics, the non-random selection
problem is avoided.
There remains the problem—which the authors recognize—that there could
be other variables omitted from the model altogether which account for the ICU
outcomes. There are reasons to believe this is a real problem with the paper—we will
return to this later.
But even if we take the econometrics as sound, there remain some questions ininterpreting the results.
First, the results are in principle obtained by comparing ICU countries withcountries equally likely to be ICU countries (as is perhaps clearest in the matching
estimators tests, but the treatment effects work is essentially doing the same thing).
But why aren’t they ICU countries as well? Are they countries with pegged exchange
rates? If not, to what extent are the results dependent on the fixed exchange rate
rather than the ICU arrangement itself?
21. The content of these comments reflects the views of the author alone, which are not necessarily shared by the
Reserve Bank of Australia.

236 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

Comment

The authors address this question in a limited way by comparing the results
obtained in this paper for ICU countries with those obtained for dollarized countries,
which had also been the subject of an earlier paper (Edwards and Magendzo [2001]).
The issue needs to be addressed more broadly, at least for other hard-peg countries.
Second, why do the results differ between ICU and dollarized countries? For thelatter, in their earlier study, the authors found that dollarized countries have lower
inflation, lower GDP growth, and higher growth volatility (though the authors’
earlier paper found no significant difference in volatility).
Third, to what extent might the results depend on the performance of the countries
to which the ICUs (or the dollarized countries) have pegged their exchange rates?
Fourth, the results are clearly driven by the ECCA countries. These countries arequite distinctive—very small, with strong historical/political linkages, and witheconomies heavily geared to tourism and rural production. Their distinctivenessraises the issue noted above of omitted variables—are there some variables, notappearing in the model, which account for the performance of the ECCA countries?
If the empirical results in the paper—that being in an ICU lowers inflation andincreases growth—are valid, do we have a theoretical explanation for them?
First, if the ICU is seen merely as a monetary policy choice, the inflation results
seem plausible but the real growth results do not, at least insofar as they are directly
related to the monetary policy choice itself. Of course, some would argue that a
reduction in inflation is positive for growth (this is, after all, a common underpinning of inflation targeting). But if this is the channel for ICU to higher growth,
why do the authors find that dollarized countries achieve lower inflation but withlower growth?
Second, the ICU might be seen as eliminating exchange rate uncertainty and hence
increasing trade and/or investment. It could also be seen as part of a general reduction
in trade costs, because of the ancillary arrangements that characterize ICUs. Does
increased trade follow? If so, does it lead to increased growth? And if the answer to bothquestions is yes, why shouldn’t we again find the same result for dollarized (or evenhard-peg) countries? Moreover, would all these effects—if they exist—lead to increased
growth, or just to a one-off increase in the level of GDP?
Finally, some questions arise as to how we could apply these results to guidepolicy choices for other countries—and especially, since we are in Asia, to countries
in this region.
First, countries in this region tend to be open (and some extremely so). Second, theytend to be large (at least in comparison with ECCA countries). The authors find thesecharacteristics to be pointers toward a low probability of being an ICU country. Andof the other factors that point to a higher probability—common borders, distance frominternational markets, and being islands—none seem particularly strong. In the Asian
region, at least, the paper’s results seem to have little relevance for policy choices.
To the extent that currency union is debated in Asia, it is within a context ofincreasing integration of trade and capital markets in the future. It is part of a politicaldebate, rather than a narrowly economic one. And that leaves me wondering again
about the likelihood of there being some variables, omitted in the present paper, thatmight lead to answers to at least some of the questions I have posed.

237

Reference

Edwards, Sebastian, and I. Igal Magendzo, “Dollarization, Inflation and Growth,” NBER Working
Paper No. 8671, National Bureau of Economic Research, 2001.

General Discussion
I. Igal Magendzo responded to the comments of the discussants as follows. First, regarding the difference between independent currency unions (ICUs) and dollarization, he
explained that the former arrangement did not necessitate complete abandonment ofseigniorage and monetary policy. Concerning the channel through which ICU affectedeconomic performance, Magendzo stated that possible channels included reductions in
the cost of capital and growth in trade volume. He also reported that the results
remained robust when changes were made in econometric methods and choice of
explanatory variables. Magendzo went on to state that the paper represents no morethan a starting point, and the application of its estimation results to other regions
should be undertaken with due caution and in reference to the experiences of theEuropean Monetary Union (EMU) and others.
Some participants questioned the statistical treatment of ICU endogeneity. Jürgen
von Hagen argued that Magendzo’s paper was subject to selection bias because theimpact of economic management undertaken to satisfy accession requirements has
been treated as the economic effect of ICUs.
Stefan Gerlach, Werner Hermann (Schweizerische Nationalbank), and Vittorio
Corbo commented that the model did not include various explanatory variables
necessary in estimating economic performance. Among those cited were relationsof ICU member countries with their former colonial rulers, degree of developmentof legal infrastructure, and economic size. Shigenori Shiratsuka (Bank of Japan)
suggested that the effects of the ICU on growth and inflation rates should be estimatedsimultaneously because of their interdependence. Robert H. Rasche suggested that thesame currency equation should be used in the estimation of growth and inflation rates.
Magendzo responded that statistical tests have indicated that there was no selection
bias, and that the legal infrastructure was not a significant explanatory variable.
The following methodological suggestions were presented on how to makeeffective use of panel data. Linda S. Goldberg suggested using a fixed-effects estimator, Corbo suggested the possibility of using the time effect, and Reuven Glicksuggested the use of cross-sectional analysis. Masahiro Kawai suggested that thelong-term cumulative effect of exchange rate regimes on economic performance
should be analyzed.
The following comments were made concerning background factors contributing
to the high performance of ICU countries. Shiratsuka asked what were the specificinstitutional factors that explain it, while Roberto Rinaldi and Jorge A. Braga de
Macedo pointed to the positive impact of monetary policy coordination by ICU
countries on their fiscal and structural policies. Goldberg commented that theestimation results show that ICUs have a very high cumulative effect on growth

238 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002

General Discussion(1 percent), and asked whether this could be attributed completely to the choice of
exchange rate regimes.
Commenting on the very high growth rates achieved by the East Caribbean
Currency Area (ECCA), Angel Palerm questioned whether these were due tocurrency arrangements or, more likely, to other structural factors. Robert W.
Rankin pointed out that the high growth rates could not be attributed to trade,because intra-regional trade accounted for less than 5 percent of the trade of ECCA
countries. Kawai also was skeptical of the ICU effect, and emphasized the potential
importance of development strategies other than exchange rate regimes adopted
by the ECCA. Commenting on the fact that the ECCA had switched its peg fromthe pound to the dollar, Roger W. Ferguson, Jr. asked to what degree economic
performance had been affected by this change.
Jerry L. Jordan and Rankin argued that the estimation results of the paper implied
that, rather than moving toward dollarization, it was more desirable for Canada and
Mexico to form a currency union and adopt a dollar peg. Jordan went further to statethat the background to this was unclear.

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240 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2002