Discussion Paper No. 571

A CURRENCY UNION IN EAST ASIA

Jong-Wha Lee
Yung Chul Parkand
Kwanho ShinMarch 2003

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, JapanA Currency Union in East Asia*

Jong-Wha Lee
Yung Chul Parkand
Kwanho Shin
Korea University

May 2002
Preliminary draft

Abstract
This paper investigates prospects of a currency union in East Asia, focusing on tradeand financial integration occurring in the region. We find, based on a dynamic factor
model, regional common shocks have been quantitatively important for output
variations in the Asian economies. We expect that continuing trade integration in theregion will lead to further synchronization of business cycles, thereby encouraging East
Asian countries to create a currency union in the region. In contrast to trade, however,financial liberalization in East Asia tends to lead to more global integration, rather thanregional integration, of the financial systems, and thereby is not likely to develop
favorable conditions for forming a regional currency union among East Asian countries.

*

This paper was prepared as a background paper for the Asian Development Bank’s “Study on

Monetary and Financial Cooperation in East Asia” (TA6000-REG). We are grateful for research
assistance from Woongjai Baek.

1. Introduction

This paper assesses the feasibility and desirability of a common currency arrangementin East Asia. We examine empirically whether East Asia satisfies preconditions for an
optimum currency area, and then discuss prospects of forming a successful currency
union among East Asian countries in the near future, especially focusing on trade andfinancial integration occurring in the region.
A number of studies have shown that the de facto currency pegs against the US
dollar was one of the major causes of recent financial crises in East Asia. After the
eruption of the crises, East Asian countries were forced to abandon the dollar peg
system and adopt a floating exchange rate. An important question is whether the new
exchange rate system gives enough confidence to these economies, and thus in the longrun it can serve as a permanent choice to them. Although it is too early to judge the roleof the flexible exchange rate in the post-crisis performance of the East Asian economies,much concern has raised on the undesirable aspects of increased instability in theforeign exchange market. Further, critics say that contrary to expectations, the freefloating system has not clearly enabled East Asian countries to retain their monetary
autonomy.
This post-crisis experience has led many researchers to advocate alternative
exchange rate arrangements that can enhance stability and credibility in the exchange
rate. Williamson (1999) and Dornbusch and Park (2000) advocate a common-basket
exchange rate peg for the East Asian region. They argue that East Asian countries can
expect to stabilize the overall export competitiveness by pegging their currencies to abasket of the yen, dollar and euro. This proposal seems to receive supports from many1Japanese government officials and economists. Other prominent U.S. economists suchas Barro (2001) and McKinnon (1999) also proposed a currency union adopting the US
dollar as a common currency.
This paper makes an assessment of the feasibility and desirability of a currency
union in East Asia. The theory of common currency area (OCA) lists many importantcriteria for a common currency area in a region. They include the symmetry of shocks
across countries, trade and financial integration, and labor mobility and wage flexibility.
Bayoumi and Eichengreen (1999), and Baek and Song (2001) argue that on pure OCA
grounds East Asia is a plausible candidate for a common currency area, as the Euro area.
We extend their work by improving the methodology of assessing the symmetry ofshocks. We consider a model in which output of an economy is influenced by threedifferent shocks- global, regional, and country-specific. The importance of a common
regional shock would provide a case for a regional common currency.
After decomposing shocks among the world common, the region common andthe country-specific shocks for each economy, we will evaluate the feasibility of acommon currency area in East Asia by comparing the estimates between Asian andEuropean countries. The greater the size of the region common shocks is, the moredesirable it will be for that area to form a common currency arrangement. This paper
then investigates the determinants of the relative size of the region common shocks
across countries. We investigate how variables such as intra-region trade, industrial andtrade structure, and income difference have effects on the size of common shocks
among countries.
Given the extent to which the East Asian countries have managed to liberalize
their current and capital account transactions over the last decades, an interesting2

question is whether the continuing trade and financial integration would increase market
pressures for forming a common currency arrangement in the region. We assess therecent development of trade and financial integration in East Asia and discuss theperspectives for a currency union.
The paper is organized as follows. Section 2 develops the empirical model that
we use to analyze output co-movements across countries. Section 3 presents theestimates of each economy’s output variances decomposed by the world common, theregion common and the country-specific shocks. We use these estimates to compare thesize of regional common shocks in East Asia to that in European countries. Section 4
investigates the determinants of output co-movements in the region. We discuss
perspectives for an Asian currency union in relation to trade integration among East
Asian countries in Section 5. Section 6 will focus on the implications of capital market
integration for a currency union in East Asia. Concluding remarks follow in Section 7.

2. An Empirical Specification of Output Co-Movements

As pointed by Mundell (1961), the major costs of joining a currency union are
the loss of independent monetary policy. The costs are, however, significantly lessened
if business cycles are synchronized among the member countries, because in this casethe common monetary policy can do as good as the individual monetary policy. Hence,
to judge the costs of joining a currency union, it has been crucial to measure how
asymmetric shocks are across candidate countries.

3

In the OCA literature the degree of how symmetric shocks is mainly measured
by a simple correlation between countries. Usually an anchor country is picked up, andthe correlation is calculated between a country and the anchor country. We believe that
this approach has some disadvantages in the following reasons. First, since we are
eventually interested in the net benefits of adopting a common monetary policy acrosseconomies in the region, the degree of region-wide co-movements, rather than bilateralones, seem to be a more appropriate measure. Second, the simple correlation does notoffer the sources of the shocks: it may be the third factor such as the world common
shocks that induce a high correlation between countries. Then forming a currency union
solely based on the regional context could be misleading, and more globalized
arrangement would be desirable. The third problem with using bilateral measures in
Asia is that no single country plausibly offers a regional anchor. In Europe, Germany
has been playing the pivotal role in acting as a center country in shaping EMU, however,in Asia, it would be difficult to expect Japan to play a similar role.
We set up a model in which the world economy consists of many different
regions and each region consists of many different countries. We interpret that the
movement of an aggregate output in each country j is decomposed into threecomponents: a world common component, a region common component and a countryspecific component. The world common component influences on every country in theworld, while the region common component influences only on the countries thatbelong to the same region. The influence of the country-specific component isrestricted to the specific country. For example, the output of Korea fluctuates due toshocks to the world economy, shocks to Asian region or Korea-specific shocks.

4The above interpretation of the world economy can be described by a dynamic
factor model. The dynamic factor model has been used by many studies, as it was
popularized by Stock and Watson (1991). Other studies based on the dynamic factor
model include Geweke (1977), Geweke and Singleton (1980), Sargent and Sims (1977)
and Gregory, Head and Raynauld (1997).
Let Y jtr denote a measure of aggregate output at time t for country j belonging
to region r. There are three regions considered: Asia, Europe and North America
regions. If r=1, that country belongs to Asia, if r=2, it belongs to Europe, and if r=3, it
belongs to North America. Let Wt be an unobservable component of world economic
activity common to all the countries and Rtr be an unobservable component common toeach country belonging to the same region r. We will refer to these as the worldcommon and the region common factors. Further we assume that the output series foreach country is decomposed into three separate components:

Y jtr = α wjWt + α rj Rtr + ε jt

(1)

Here ε jt represents a country specific factor to country j. The coefficients, α wj and α rj ,are impact coefficients on factors, Wt and Rtr , for country j. The fact that we allow the
impact coefficients to differ across countries implies that the world common and theregion common factors influence each country with different degrees of magnitude.

5

Following Stock and Watson (1991) we assume that the three factors follow astationary univariate autoregressive process. More specifically we further assume thatthese processes are all first-order as follows:

Wt = ρ wWt −1 + η tW

(2)

Rtr = ρ r Rtr−1 + ηrtR(3)

ε jt = ρ j ε jt −1 + η εjt(4)

We also assume that all the errors, η tW , η rtR and η εjt in equations (2), (3) and (4) are
uncorrelated both serially and contemporaneously. These are the fundamental sourcesof the shocks in the economy.
The assumption that the errors are contemporaneously uncorrelated implies that
there is no explicit co-movement among factors. This assumption is needed to identify
the factors in our model.
Before the raw data are applied to the model, they are detrended to generate
cyclical components using a standard method: the raw data are first-differenced inlogarithms and then cycles in growth rates are analyzed. We do not adopt anotherstandard method, the Hodrick-Prescott filter, that decomposes each series into trend andcyclical components because the series are annual and rather short.
There are two additional considerations to be made before the actual estimationis performed. First, for all detrended series, the sample mean is removed and thevariance is standardized to one. This is necessary for all series to receive the equal
weights in estimating the common factors. Since the estimation is obtained by6minimizing the variance of country-specific errors, if the variance of the series is notequalized across countries, those countries with high variances can overly influence theestimation procedure. By standardizing the series, we ensure that all the countries
receive equal treatment irrespective of the size.
Second, the variances of the error terms in equation (2) and (3), and thecoefficients in equation (1) cannot be separately identified. For example, by setting thevariance of η tW arbitrarily smaller, the magnitude of the world common factor gets also
smaller and this is balanced by a larger estimate of the coefficient, α wj for all countries.
We get around this problem by normalizing the variances of η tW and η rtR to unity. Since
this assumption affects the magnitude of the estimated coefficients, we rather base ourquantitative analyses on variance decompositions.
It is straightforward to transform the dynamic factor model consisting ofequation (1), (2), (3) and (4) into the usual state-space/measurement equation form.
This is a necessary step to implement the model in the empirical analyses.1
Based on this model, we can measure the quantitative influence of the different
common factors on fluctuations in aggregate output in each country. Let S jf denote theshare of the variance of aggregate output for country j accounted for by variation in thefactor f = w, r , ε . Under the assumption that the world common, the region common,and the country-specific factors are orthogonal, the variance of aggregate output for
country j can be decomposed into three terms:

1

A detailed derivation and a numerical maximization method are explained in appendix A and

B respectively.

7

σ 2j = α wj2 σ w2 + α rj2σ 2r + σ εj2(5)where σ 2f , f = w, r and σ εj 2 are variances of the world, the region common, and thecountry-specific factors respectively. Further the normalization to unity of theinnovations to the world common, the region common, and the country-specific factors
enables us to compute estimates of S jf as follows:

α 2fj
1 − ρ 2f
α 2fjσ 2ff
Sj = 2 2=, f = w, r (6)
α wj2
α rj2
σ ηj 2
α wjσ w + α rj2σ r2 + σ ε2 j++
1 − ρ w2 1 − ρ r2 1 − ρ 2jand

σ εj 2
1 − ρ 2j
ε
Sj =
α wj2
α rj2
σ ηj 2++
1 − ρ w2 1 − ρ r2 1 − ρ 2j(7)

where σ ηj 2 is the variance of η εjt .
The estimates of the shares accounted for by the world common, the regioncommon and the country-specific factors will play a crucial role in evaluating if
countries belonging to the same region are eligible to form a regional currency
arrangement. Especially a large value of the share accounted for by the region common

8

factor – capturing how symmetric shocks are within a region - constitutes a prima faciecase for a currency union. For example, if a country exhibits a large value of the shareaccounted for by the region common factor, then its business cycle movement is largely
synchronized to the region, indicating that a regional common monetary policy is moreor less capable to respond to the disturbances. On the other hand, if a country possessesa smaller value of the share accounted for by the region common factor and a larger
value of that accounted for by the country-specific factor, it needs to rely more heavily
on its own independent counter-cyclical monetary policy.

3. Decomposition of Output Variations in East Asian and European Countries

For estimation of the model, annual data on real GDP are collected for 10 East Asian
countries, 16 European countries and 2 North American countries over the period 197899. The East Asian countries include 5 ASEAN countries (Indonesia, Malaysia, thePhilippines, Singapore and Thailand) plus 5 other East Asian countries (Japan, China,
Hong Kong, Korea and Taiwan).2 The European countries are Austria, Belgium,
Denmark, France, Ireland, Italy, Netherlands, Finland, Greece, Germany, Norway,
Portugal, Spain, Sweden, Switzerland and the U.K. The North American countries are
the U.S.A and Canada. All the data are drawn from IFS CD Rom (IMF).
A number of studies in the literature following Bayoumi and Eichengreen (1994)
decompose shocks between demand and supply shocks a la Blanchard and Quah (1989).

2

Six other ASEAN countries are excluded due to lack of data.

9

They argue that, if the structural shocks are not analyzed, information on the
disturbances could be conflated with economies’ responses. Further, they argue that
supply shocks are more crucial disturbances to which independent monetary policy
wishes to respond. Our framework does not follow this convention based on thefollowing reasons. First, we see little justification that only supply shocks matter. If
supply shocks are permanent shocks, there may not be much room left for the policy
maker to react to. Further if demand shocks do not originate from policy
implementations, the monetary policy authority may wish to counteract to these
disturbances. Second, supply shocks and demand shocks derived from the BlanchardQuah methodology are innovations that are serially independent. Then we cannot
impose any realistic dynamics on the shocks: the AR(1) dynamics imposed to thefactors in equation (1) is inconsistent with this property of demand and supply shocks.
Considering that any important steps towards a currency union such as theMaastricht Treaty of 1991 may have influenced on the nature of regional co-movementof output, we have divided the entire sample period into two sub-sample periods: 19781990 and 1990-1999.3 In table 1, shares of variances accounted for by the worldcommon, the region common and the country-specific factors are illustrated for Asian(table 1A) and European (table 1B) countries for both periods. Columns from 2 to 5
correspond to the former period and those from 6 to 9, the latter period. In column 2
and 6, the volatility of growth rates of output for each country is reported for each3Frankel and Rose (1998) have shown that increased international trade induces more tightly

correlated business cycles across countries. It follows, then, measures adopted to enhance acurrency union, by endogenously increasing international trade among the relevant countries,
the share of regional co-movement can be endogenously increased.

10

period. The volatility of output reported in column 2 and 6 are decomposed into sharesof variances in the next three columns. These shares correspond to the measurescalculated based on equation (6) in section 2. The negative sign in parentheses implies
that the coefficient multiplied to the factors, α wj or α rj , is negative, indicating that theoutput is moving along the common factor, but in different directions. Note, however,that the absolute values of the shares sum to one for each country. In the last row, theaverage volatility and the average (absolute) shares of variances accounted for by thethree factors are reported for each region.
In terms of volatility, the size is much larger in the Asian region. In the former
period the average volatility for the Asian countries (3.113) is about 1.75 times morevolatile than that of the European countries (1.770). In the latter period, it increases inboth regions, the difference widening in the latter period, so that the average volatility in
the Asian region (3.888) is almost twice as large as that in the European region (1.983).
This shows that the Asian region consists of more volatile countries and, if this is due tonon-policy related shocks, there needs for more active implementation of monetary
policies.
In the Asian region, the share of variances in output accounted for byfluctuations in the country-specific factor significantly decreases in the latter period.
This decrease is compensated mostly by the increase in the share of variances accounted
for by the region common factor, while that accounted for by the world common factor
also increases. We can observe qualitatively the same phenomenon for the European
region: the shares of variances accounted for by the world common and the regioncommon factors increase at the expense of that accounted for by the country-specificfactor. However, the decrease in the share of variances accounted for by the country11

specific factor is relatively more absorbed by the increase in that accounted for by theworld common factor rather than by the region common factor.
The finding that the world common factor gets more important in the latter
period is consistent with the belief that, as time passes, since the world economy
interacts more closely across countries, the importance of the world component
becomes larger4. Besides, the finding that the share of variances accounted for by theregion common factor becomes larger in the latter period can be also understood in asimilar manner. However the fact that the importance of the region common factor
reveals more distinctively in the Asian region in the latter period is somewhat surprising,
because more efforts are believed to have been made in the European region towards
integration, which should act to increase the role of the region common factor in thelatter period.
We believe that this finding can be explained based on the following two factors. First,
the European region was already significantly integrated in 1980s and the marginalincrease in integration occurred in the second period was relatively small. Second, more
importantly, the Asian crisis that occurred in the late 1990s influenced most Asian
countries simultaneously, playing an important role of amplifying the region common
factor. Data in Table 1 shows that the most crisis-affected economies, such as Indonesia,
Korea, Malaysia, and Thailand show higher degree of co-movements, while the leastaffected China and Singapore had very only negligible regional components. .5

4Gregory, A.W., A.C. Head and J. Raynauld (1997) and Kose, Otrok and Whiteman (2001)

both find that the world common factor is an importance source of volatility in output.
5

In order to control for the influence of the Asian crisis, we have also estimated the model

based on the sample of two periods – 1978-1987 and 1988-1996- by excluding the crisis period

12While somewhat exaggerated by the Asian crisis, if focusing on the latter period,
we find that the Asian region is even better prepared for the regional currency union: theshare of the region common factor is over .5 on average, indicating that the regional comovement explains more than a half of the fluctuations in the individual country’s
output. Especially Indonesia (.888), Malaysia (.845), Korea (.792), Thailand (.604),
Philippines (.585), most of them heavily influenced by the Asian crisis, show that their
movement of output is closely linked to the region common factor.
Similar findings, if not as strong as ours, have been made by a number ofstudies in the literature. Bayoumi and Eichengreen (1994), based on a decomposition ofshocks between demand and supply shocks, find that there is little difference between
Europe and Asia. Eichengreen and Bayoumi (1999), using the OCA index developed in
Bayoumi and Eichengreen (1996), find that the economies of East Asia would seem to
be more or less as plausible candidates for a currency union as the members of theEuropean Union.6 Bayoumi and Mauro (1999) also find that, while East Asian countries
are less suited for a regional currency arrangement than Europe, the difference is notlarge.7

after 1997. We find the share of regional common shocks in output variances decline
substantially in the latter period, particularly in the crisis-hit Asian countries.
6

Eichengreen and Bayoumi (1999) argue, however, that the Asian region lacks important

institutions to implement a currency union, such as sound financial systems, political network,and a long history of integrationist tradition.
7

On the other hand, Wyplosz (2001) and Chow and Kim (2000) argue less positively for thepreconditions of East Asia to implement a currency union.

13

Among 16 European countries, the degree of how tightly a single country
belongs institutionally to the region significantly varies. For example, ten countries
have successfully joined EMU, three countries (Denmark, Sweden and the U.K) havedecided to opt out, Greece was not qualified, and two countries (Switzerland andNorway) are not even EU members. When we divide the European countries into EMU
and non-EMU countries, some interesting observations arise. In table 1.B, we canconfirm that the six non-EMU countries are those that exhibit a lower share of theregion common factor in both periods: Denmark ((-).0015, .0002), Greece(.2381, .2489), Norway (.0128, (-).0747), Sweden (.2472, .1191), Switzerland(.2148, .2718) and the U.K (.0528, (-).1486).8 Further, the 10 EMU countries generally
show that the share of the region common factor is already large in the former period
and gets even higher in the latter period: it increases in eight countries.
This pattern seems to show that the decision made by the 6 non-EUM countries,
whether voluntary or not, is closely related to the OCA criteria. Also, countries
belonging to EMU have endogenously evolved to favor joining the common currency
arrangement.

4. Determinants of Output Co-Movements

In the last section we examined output movements for a group of European and Asian
countries to distinguish common from idiosyncratic disturbances. The estimation, based

8

The value appearing first in the parentheses corresponds to the former period and thatappearing second, the latter period.

14

on the dynamic factor model, identified the existence of substantial common shocks
across countries in the same region. The region common shocks played a significant
role in the fluctuations of national outputs both in the Euro and the Asian regions.
This section investigates the determinants of the synchronization in businesscycles of the economies. The decision on whether an economy joins a currency union
involves the degree of output co-movements. A difficult question is that the degree of
output co-movements will be changed after a common currency is adopted. For
instance, as a currency union is formed in a region, intra-region trade will increase,subsequently leading to positive effects on the extent of output co-movements.9 Hence,
the assessment of net benefits from a currency union involves predicting the output comovements that would apply after an adoption of a common currency in the region.
A number of existing studies attempt to look at the determinants of common
movements of business cycles. In the literature of international business cycles, trade isemphasized as the channel of output co-fluctuations.10 The spillover of aggregate
demand shocks will tend to make business cycles correlated internationally since anincrease in domestic spending will increase demand for both domestic and foreign
goods. Greater integration in trade can also stimulate the spread of technology shocks
across economies. Kraay and Ventra (2000) present another channel of spillovers
through the change in relative prices of factors and products. A capital-augmenting

9

Rose (2000) finds that a currency union increases intra-regional trade. Further, Frankel andRose (1998) show that increased trade strengthens the extent of output co-movements acrosstrading parties.
10

Imbs (1999) provides a survey of the literature.

15

shock in one country results in relative scarcity of labor-intensive goods and thus, asmuch as they trade freely, leads to higher wage and employment throughout the world.
Empirically, Canova and Dellas (1993) and Frankel and Rose (1998) find that moretrade interdependence increases the extent of output co-movements.
It is, however, ambiguous that the degree of output synchronization will always
increase as countries trade more. If more trade leads to specialization in different
industries, industry-specific shocks will make the outputs of two economies move in
different directions, leading to less synchronization of business cycles (Eichengreen,
1992 and Frankel and Rose, 1998). In this context, not just the size of trade but also thesimilarity of trade structure is considered to be important in explaining output cofluctuations. We expect that, when bilateral trade concentrates more on intra-industry
trade than inter-industry trade, the tendency of synchronizing output fluctuations
strengthens.
On the other hand, several studies emphasize the similarity in production
structure as an important determinant of co-movements of output. Industry-specificshocks can cause more business cycle synchronization among countries with similar
production structures. Imbs (2001) provides evidence that the extent of specialization inindustry structure has strong explanatory power for output co-movements in a sample of
49 countries. Clark and van Wincoop (1999) find that for the US and EU regions moresimilarity in industry structure is associated with higher co-movement of employment.
In this section we attempt to find what are the significant factors for the extent ofsynchronized business cycles in the European and Asian economies. We consider therole of the level of intra-region trade, the similarity of trade structure and industry
structure on output co-movements.

16

We estimate the following regression:

SRjt = a0 + a1*ln(Intra-region trade)jt +a2*ln(Trade structure similarity)jt+a3*ln(Industry structure similarity)j,t + a4* ln(difference in per capita GDP)j,t+ a5* ln(per capita GDP)j,t + gj,t
(8)

The dependent variable, SRjt, is the fraction of an economy j’s output variations
attributable to regional common shocks over the period t. Hence, this study contrasts
other empirical studies that have used bilateral correlations as a measure of output comovements
Intra-region trade is measured by the share of an economy’s trade with the restof economies in the same region in total trade.11 To measure similarity in trade
structure, we use the export similarity index. The index is defined as
1−
i

1
∑
2 i

i

i

a

b

s −s ,iwhere s a and s a denote the share of export for commodity i in countries a and b. Thus,
the index measures the extent of similitude in the composition of total exports between
a pair of economies.12 In order to measure each country’s trade structure similarity

11

Clark and van Wincoop (2000) use a trade intensity measure, which is the size of bilateral

trade relative to per capita GDP. Our regression includes per capita GDP (as a log) separately
and thus allows for an interpretation of the trade variable as an intensity measure.
12

The sectoral classification is based on the three-digit SITC trade data. Total exports data does

not include exports of (raw) agricultural products. When we use an alternative measure based on
the sectoral classification at a more broad level including agriculture it has less explanatory
power in the regressions. This may indicate that more trade dependence through intra-industry
trade, rather than inter-industry trade, contributes to output comovements.

17

relative to the region’s average structure, we use an unweighted average of bilateralexport similarity indices with the rest of the countries that belong to the same region.
The index ranges between 0 (most dissimilar) and 1 (most similar).
Following the same method, we construct an index of the similarity of industry
structure, which compares the differences in the sectoral composition of GDP between
an economy and the rest of economies in the region. We use data on gross valued added
data classified at 10 broad categories (ISIC one digit).13 We have also constructed
another measure based on detailed three digit manufacturing industries, but find it does
not have any explanatory power (the results not reported).
The regression includes the difference between an economy’s per capita GDP
and the region’s average per capita GDP (as a log of the absolute value). The differencein per capita GDP can reflect the differences in economic structure including industrial
and financial market structure. The log of per capita GDP itself is also included tocontrol for differences in economic development that can affect asymmetry in businesscycles.
To minimize reverse-causality problems, all explanatory variables are measured
at the beginning of each period (that is, 1980 and 1990). The regression is estimatedallowing for random effects, which control for any unobserved country specific factors.

13

The 10 broad industries are agriculture, forestry and fishing; mining; construction;

manufacturing; transportation and communications; electricity; wholesale and retail trade;finance and real estate; government; community and personal services and others. The data
come from OECD’s National Accounts for European countries and Asian Development Bank’s
database for Asian countries.

18

Table 2 displays the result of the basic regression that is applied to a total of 30
observations for 15 European countries over the two ten-year periods from 1979 to
1999.14
Column 1 includes the index of industry structure similarity, the difference inper capita GDP, and the log of per capita GDP as explanatory variables. It also adds theperiod dummy for the 1990s in order to control for shocks that were specific to thesecond period. The initial industry structure similarity has a positive correlation with
output co-movements, which is consistent with the prediction that sharing the similar
production structure results in high cycle synchronization. But, the coefficient is notstatistically significant (1.681, s.e.=1.099), Given the estimate, an increase in the log of
the index of industry structure similarity by one standard-deviation (0.047) increases theregional co-movement by 0.079, which is about 30% of a standard deviation. The
coefficient on the difference of per capita GDP has a negative sign. Hence, the larger
the difference in per capita income the smaller is the output co-movements. But thecoefficient is not statistically significant (-0.075, s.e.=0.075). This result contrasts Imbs
(2000) in which differences in per capita income have a strong negative impact onbilateral output correlation in a sample of 49 countries. The log of per capita GDP and adummy for the 1990s enter positively, but they are statistically insignificant.
Column 2 adds the intra-region trade share and the index of trade structure
similarity along with per capita GDP and a dummy for the period of 1990s. Column 3
includes all explanatory variables by adding the index of industry structure similarity
and the per capita GDP difference. The result shows a strong and statistically significant

14

The sample does not include Switzerland because of lack of trade and industry structure data.

19

relation between initial intra-regional trade share and output co-movements. The
coefficient in column 3 implies that an increase in the log of the intra-regional trade by
0.15 (its standard deviation) raises the extent of co-fluctuations by 0.11.
The test also finds a strong positive association between trade structure
similarity and output co-movements, implying that countries with more similar trade
structure in the initial year tend to have a higher degree of output co-movements in thesubsequent period. An increase in the log of the trade structure similarity index by aone-standard deviation, 0.11, increases the extent of co-fluctuations by 0.18.
The coefficient for per capita GDP has a significantly negative sign. Hence, anincrease in per capita GDP leads to smaller output co-movement of an economy withthe rest of the economies in the region. This result can be interpreted as follows. First, in
our sample, relatively higher income countries tended to move more closely with worldwide shocks, rather than region-wide shocks. In addition, when an economy grows
faster than the rest of the economies, its business cycle becomes less synchronized thanthe others. The coefficient implies that an increase in the log of per capita GDP by 0.27
(its standard deviation) decreases co-movements by 0.29.
In Column 3, the inclusion of the industry structure similarity variable does notchange the effects of trade variables on output co-movements. The estimatedcoefficients on the level of intra-region trade and the similarity of trade structure are still
positive and statistically significant. On the contrary, the industry structure similarityand per capita income difference variables become statistically insignificant. This result
is interesting in light of the findings of Imbs (2000). He argues that industry structure
differences are more important than trade size for output co-movements. Our results
indicate that trade factors play a more important role for output co-movements. Our

20specification differs from his as we use the size of regional common components innational output variations instead of bilateral GDP correlations as a measure of output
co-movements.
Column 4 adds all explanatory variables except the dummy for the 1990s. The
results are similar to those of column 3. Again, the index of similarity in the sectoralcomposition of aggregate output is not statistically significant for output co-movements.
Table 3 reports the results of the same type of the regressions using the panel
data set of 10 Asian economies over two periods. In columns (1)-(3), we find nostatistically significant effects of intra-region trade share, and trade and industry
structure similarity on output co-movements. But, the period dummy for the 1990s is
highly statistically significant and positive. Thus, shocks that were specific to the 1990s
explain a lot of variations of output co-movements in East Asian economies. This
reflects the substantial change in the extent of cycle synchronization that occurred
between two periods. One problem is that with this small sample size, the periodspecific dummy can wash out any effects of other explanatory factors on the over-time
variations of the extent of output co-movement. For instance, the extent of intra-region
trade and trade structure similarity had increased substantially in East Asian countries
between the 1980s and 1990s (see Table 4.1), which could have some positive effects
on output co-fluctuations. But, the dummy for the period of 1990s would take away
substantial parts of these effects. Confirming this conjecture, in the column (4) where
the dummy for the 1990s is excluded, the index of trade structure similarity becomes
statistically significant at the 5% level. In column (5) we replace the dependent variableby the estimates of the shares of regional common shocks for the 10 Asian countries
that correspond to the periods 1978-1987, and 1988-1996. In this sample that the crisis

21years are excluded, trade structure similarity becomes again statistically significant at
the 5% significance level.

5. Trade Integration and Currency Union in East Asia

Countries that have more synchronized business cycles can benefit more from joining acurrency union. Therefore, whether East Asia is suitable for an optimum currency area
depends on how the national outputs would co-vary across economies when they adopt
a common currency. The empirical evidence presented in section 4 shows that the levelof intra-regional trade, and the similarity of trade structure turn out to be the mostimportant factors for output synchronization.
East Asia looks quite favorable for an OCA criterion in terms of its substantial
degree of regional trade integration. Table 4 presents intra-regional trade as a percent of
total trade for the Asian and European economies. Intra-Asia trade is substantial. The
share of intra-regional trade in total trade was about 51% on average in East Asia in
2000. Hong Kong had the highest intra-region trade share of 64%, while Japan had thelowest of 38%. On average, the intra-region trade in East Asia was somewhat lower
than the corresponding value for the Euro area, which was 66% in 2000. One reason for
the relatively lower intra-region trade for Asian countries is that they trade relatively
more with the United States than Euro countries do. The share of trade with the UnitedStates in total trade was about 19% for East Asian economies on average, contrasting toabout 8% for Euro countries. In East Asia, the extent of intra-regional trade has shown
an upward trend. It increased from 42% in 1980, to 48% in 1990 and 51% in 2000.

22

Integration of trade in East Asian region is expected to continue, and thus helps toreduce the extent of asymmetric shocks.
The trade structures of East Asian economies have also become more similar
over time. Table 5 shows trade structure similarity for East Asian and European
countries. In 1980 the average of the similarity indices was 0.45 for 10 East Asian
economies, which was far lower than the average for 15 European countries, 0.63. But,
the similarity index in East Asia increased to 0.56 in 1990 and 0.64 in 2000. Hence, interms of the extent of trade structure similarity East Asian countries are no lessfavorable than that in Euro countries. East Asian economies would benefit more from acurrency union as their business cycles become more synchronized with more similar
trade structure.
It is likely that regional trade integration taking place in East Asia will increase
market pressures for policy coordination for stable exchange rates among regional
currencies, and eventually for adopting a common currency.

6. Implications of Capital Market Integration for an Asian Currency Union

In East Asia there has been a rapid increase in capital mobility as more advanced
East Asian countries including Thailand, Indonesia, Malaysia and Korea have managed
to deregulate and open their financial markets since the early 1990’s. A World Bank
study (1997) uses three different measures to determinate the extent to which countries
are financially integrated. In constructing an overall index of integration the WorldBank study uses the access to international financial markets, ability to attract private
external financing, and the level of diversification of financing in terms of the23composition of financial flows. According to the World Bank estimation, changes inthe degree of financial integration were high in East Asian countries in early 1990s.
The continuing financial integration taking place in East Asia raises a questionof whether, like trade integration, it can bring the countries closer to monetary
integration in the region. This section will examine whether financial market
integration could contribute to formation of a successful OCA in East Asia.
Will a high degree of financial integration among a group of countries be acriterion for membership in an OCA? That is, would countries with close financial
linkages benefit from a common currency? As in trade, a common currency brings
benefits such as lower transactions costs and elimination of risks of exchange ratechanges associated with trading in financial instruments between countries withdifferent moneys. More important, however, is that financial integration among a groupof countries could facilitate formation of a common currency area for the group as it
reduces the cost of adjustment to shocks to demand and supply through facilitating
migration of capital in the long-run and cross-border financing of current accountimbalances in the short-run.
An important criterion for an OCA is the speed of adjustments to shocks. Even if
disturbances are asymmetric across economies, the faster adjustment to shocks helps an
economy to mitigate the costs of relinquishing independent monetary policy. In an
economy where factor prices and price are not completely flexible, capital mobility canmake real adjustments easier and less costly. In this regard, financial integration would
increase the benefits from joining a common currency arrangement. However, there arecounterbalancing effects of capital market integration on forming a currency union. As
suggested by Kalemli-Ozcam, Sorensen and Yosha (2001), better income insurance

24attained through greater capital market integration may induce higher specialization of
production and hence larger asymmetric shocks across countries. This lower
synchronization of business cycles due to stronger capital integration implies thatcandidate countries can be less willing to join a currency union
One important aspect is that, unlike trade integration, financial integration it isoccurring at more global level rather than at regional level. Given the extent to which
the East Asian countries have managed to liberalize their capital account transactions inrecent years, one might expect that financial markets of these economies may havebecome more closely linked with one another among East Asian countries than in thepast. However, the available empirical evidence does not provide clear evidence for thetendency that the East Asian countries have been more financially integrated among
them. Regionally integrated financial markets are yet to emerge and prospects forfurther financial integration in East Asia are not promising. Empirical studies show that
financial integration among Asian countries has not been substantial (see Park and Song,
2001). In contrast, the financial markets of the East Asian countries became
increasingly integrated with the markets of developed countries (Glick and Hutchison,
1990, Cheng and Mak, 1992, Bekaert and Harvey, 1995, and Kuen and Song, 1996).
Considering tremendous evidence of home bias in equities at country level andtighter financial integration within a country, the fact that the same phenomenon does
not occur at regional level is puzzling. As recently surveyed by Lewis (1999), there are
a number of reasons suggested in the literature to explain home bias at country level.
First, domestic equities can provide a better hedge for risks that are specific to the homecountry. For example, hedges against domestic inflation and hedges against wealth thatis not traded in capital markets, such as human capital are better provided through25

domestic assets. Further, hedges with foreign returns implicit in equities of domestic
firms that have overseas operations are also available. Second, the gains from global
diversification can exceed costs involved. If the costs of acquiring and holding foreign
equities are sufficiently large, then investors may find it better to keep their savings at
home. Third, information is much more easily communicated at a country level. This
information superiority enables portfolios solely based on domestic assets to performbetter than global portfolios.
The first two arguments do not seem to well generalize to regional level.
Further if information sharing is also not easier at regional level than at global level,
then it would be more advantageous to directly go to the global markets and form global
portfolios. Hence further liberalization of capital account transactions in the East Asian
countries is expected to strengthen their financial linkages with developed countries
more than with one another in the region. This means that financial liberalization in
East Asia may not necessarily produce market pressure or incentives to create regional
financial arrangements such as the Asian Monetary Fund for regional financial
cooperation and in the long-run a common currency area in East Asia.
There are also several institutional and structural characteristics of the East
Asian economies, particularly in the financial systems that have constrained, and arelikely to limit the extent of regional financial integration in the future. One of the mostimportant characteristics in East Asia is related to underdevelopment of financial
systems that are largely bank-oriented. Except for Japanese banks, most of the banks inother East Asian countries, which are small in size have a limited access to international
capital markets relatively limited experience in international corporate banking and a26

small branch network in East Asia. By and large, their customer bases are confined todomestic borrowers and lenders.
Bond markets still remain relatively small in size; they are narrow in terms of
maturity and issues; and are not easily accessible to foreign investors after a decade ofliberalization. And the markets for financial derivatives have only recently begun todevelop in these countries. There are few domestic investment banks, securities firms,
and mutual funds that are efficient enough to compete against their counterparts fromthe developed countries.
In the absence of these securities market institutions, underwriting securities ininternational capital markets, organizing large syndicated loans, and negotiating
multinational M&As in East Asia have been dominated by American and European
investment banks and more so since East Asian countries took steps to open theirfinancial markets. According to the database of International Financing Review, amajor fraction of financing through debt instruments in East Asian countries areundertaken by American and European Investment banks, rather than Asian banks(Table ?). Before the crisis, Japanese banks were active in lending to other East Asian
countries and accounted for the bulk of syndicated loans to these countries. Since thecrisis, however, Japanese banks have withdrawn their lending to Asian countries somuch that it was less than 6 percent of their total external lending in 2001 (See Table 6).
The dominance of Western investment banks has become more pronounced in recent
year in East Asia as corporations and financial institutions in the region haveincreasingly sought to raise funds from international capital markets than before with
deregulation of external financing.

27

Another important hurdle to integrating financial systems in East Asia issavings and investment profile that is not matched well within the region. Throughout
the 1980’s and until the mid-1990’s, the ASEAN states and Korea were net borrowers
as they were running deficits on their current accounts. China, Taiwan, and Japan were,on the other hand, accumulating huge amounts of current account surpluses, which
made East Asia as a whole a net lender financing the bulk of U.S. and European current
account deficits. External financing for the East Asia’s deficit countries ultimately
came from the three East Asia’s surpluses countries, but it was arranged and managed inpart by Japanese banks, but mostly by western financial institutions. That is, East Asiansavers and investors were intermediated by western financial institutions through New
York and London markets. Since the outbreak of the crisis in 1997, all of the East
Asian crisis countries have become net lenders and are likely to continue to do so. Not
surprisingly, they have been relying on western financial institutions in investing theircurrent account surpluses in foreign securities. Together with China, Taiwan, and Japan,
the East Asia has become a larger net saver of the global economy than before,
financing the bulk of balance of payment deficits of the rest of the world. In investing
their surpluses, East Asian countries will continue to rely on international financial
markets in New York and London, simply because western financial institutions with
global reach and network are likely to be more efficient in allocating East Asian savings.
Hong Kong and Singapore have been two important regional financial centers inEast Asia. Have they played any role in driving financial integration in East Asia withthe onset of financial liberalization in the region? It should be noted that they wereserving East Asian borrowers and lenders well before financial market opening got
underway in the region. These two centers were essentially outposts of and hence28

tightly linked with major international capital markets in advanced countries. The crisis
in 1997, which almost brought Hong Kong to the brink of collapse, has undermined
their importance as regional centers as East Asian corporations and banks haveincreasingly moved directly to New York and London markets for their financial service
needs and transactions. Hong Kong and Singapore may have gravitated more towardlinking financially East Asian economies with advanced economies than integratingthem with one another.
Most of the East Asian countries have managed to open substantially theirfinancial services industries to foreign competitions over the last decade. Foreign
financial institutions now receive a national treatment that provides a level playing field
when they enter financial markets of East Asian countries. Many western banks haveestablished a wide network of branches and subsidiaries throughout East Asia, and sohave western securities firms, investment banks, insurance companies, and other nonbank financial institutions. There are numerous emerging market funds operating out of
New York to invest in East Asian securities. There is little doubt that the hold ofwestern financial institutions in East Asian has increased since the early 1990s. This
pervasive influence of western financial institutions is likely to expand and strengthen
East Asia’s financial ties with advanced countries with the continuing financial
liberalization in the region.
Overtime, local investment banks and other financial institutions may become
more competitive and new markets for financial derivatives may emerge to the extent
that they enjoy advantage in collecting and assessing local information for theirfinancial activities compared to western institutions. Such an advantage may disappear
as a result of the on-going information and communication technology. On the other29

hand, the gap in financial technology and expertise between East Asian and Western
financial institutions is likely to persist. As a result, borrowers and lenders from East
Asia are likely to go to the New York and London markets because these markets offer
low costs of financing and more attractive savings instruments in terms of returns
adjusted for risks. Many of the branches and subsidiaries foreign financial institutions
have no doubt become indigenous institutions, but they are likely to serve as conduits
for integration of East Asian financial markets with the markets of developed countries.
Although the odds are against them, countries in East Asia have been working
together to develop regional financial markets as part of their strategy to deepen
economic integration in the region. The Chiang Mai Initiative reflects such regional
efforts for integration. In contemplating developing regional financial markets andsupporting multilateral banks specialized in regional finance in East Asia, East Asianpolicymakers will be faced with answering many questions related to benefits and costsof regional financial integration. Will regional financial markets help improve
allocation of resources in East Asia? Will regional financial markets reduce thelikelihood of recurrence of financial crisis in the future?
In recent years there have been discussions on the extent of which East Asian
countries would gain by cooperating in establishing an East Asian regional stock
exchange and an East Asian regional bond market, focusing more on the latter.
However, there is no guarantee that a regional bond market in East Asia will be large
and efficient enough to survive competition against Eurobond or Yankee bond markets.
Furthermore, a viable East Asian bond market will require establishing beforehand aregional financial infrastructure that includes regional credit agencies, clearing andsettlement systems, cross-border securities borrowing and lending mechanisms, credit

30

enhancement and guarantee agencies, and regional trading mechanisms. Tax treatments
for securities transactions will also have to be harmonized at the regional level. It willtake many years, if not many decades, for the East countries with diverse legal andregulatory systems and at different stages of financial development to resolve theirdifferences to establish the requisite financial infrastructure. Efficiency of the regionalbond market may also require its integration with global markets.
In conclusion, it is not clear, at this stage of development, whether further
financial liberalization would encourage countries in East Asia to form a common
currency area. In the long-run, financial integration through liberalization facilitates
mobility of real capital between countries as evidenced by a large increase in intraregional foreign direct investment in recent years, in particular Japanese investment in
China and ASEAN. As apposed to this development, there have been strong market
forces integrating East Asian financial markets into global financial markets. This trendof financial globalization in East Asia has become more noticeable in recent years as aresult of growing current account surpluses at the regional level and deeper penetrationof western banking and securities institution of East Asian financial markets.
Combining these two developments, financial liberalization leaves uncertain as towhether it will generate incentives for the East Asian countries to join and remain in an
Asian currency union, whereas it raises pressures for maintaining their currencies stablevis-à-vis global currencies.
As in trade, however, causality may run from currency union to financial
integration: that is, a political decision to form a common currency area will unleash
market forces for further financial integration, contributing to creation of regional
financial markets. However, the formation of currency union will not weaken East

31

Asia’s financial linkages with advanced countries. In deciding whether to form a
regional currency union, East Asian countries may have to examine closely whether thecurrency union would help develop efficient regional financial markets that could
survive competition vis-à-vis other global financial markets.

7. Concluding Remarks.

In this paper, we have tried to judge prospects of forming a currency union in East Asia,
especially focusing on trade integration and financial liberalization occurring in the area.
There are several interesting findings. First, the region common shocks in East Asia in
1990s are at least comparable to those in Europe, implying that the East Asian region iswell prepared for a regional currency union. Second, two most important determinantsof business cycle synchronization are intra-region trade share and trade structure
similarity in the region. In this regard, increasing share of intra-region trade and thepattern of trade structure getting more alike in East Asia are expected to put morepressure on forming a currency arrangement. Third, financial liberalization, which has
rapidly occurred in this area, tends to lead to more global integration, rather thanregional integration, of the financial systems. Therefore, further financial liberalization
is not likely to encourage East Asian countries to form a regional currency union, rather
increasing incentives for pegging their currencies to a global currency.
More appropriate criteria for a successful currency union, however, are involved
with how the pattern of trade and financial integration would be shaped after the set upof a currency union. There is some evidence that joining a currency union leads toincreased trade among member countries, further strengthening the formation of the32currency union. However there is more uncertainty in predicting whether endogenous
adjustments of financial systems after forming a currency union will act for or against
the currency union. It is more likely, though, that at least the currency union does nothinder further financial integration among member countries.
Other issues are related to the sequence of approaching to the currency union. It
has been pointed out that political cooperation and institutionalization are theprerequisites for a successful currency union in East Asia. How the economies in theregion can proceed smoothly to transform the current exchange rate arrangements into acommon currency peg, if desirable, will be a critical question. The experiences from theEuropean Monetary Union will be especially valuable in this regard. Further analyses
in these issues remain to be seen in future research.

33

Appendix

A. Derivation of the State-Space Model.

The state-space/measurement equation form can be represented in two steps. First, thestate-space equation represents a transition of unobserved states that, in our model,
correspond to the three factors, Wt , Rtr and ε jt . For the notational purposes, assume
that there are n1 countries belonging to region r=1 and n 2 countries belonging to region
r=2. Further assume that n = n1 + n 2 . Then the state-space equation can be represented
as follows:

Ft = T ⋅ Ft −1 + ω t

where Ft = (Wt , Rt1 , Rt2 , ε 1t ,..., ε nt ) ’ and ω t = (η tW ,η1R ,η 2R ,η1t ,...η nt ) .
Second, the measurement equations link the observed variable, output in each country,
to the states. Let Yt = (Y11t ,..., Yn11t , Y1t2 ,..., Yn22t )' be an ( n × 1 ) vector. Then themeasurement equations relate the observed series, output, to the unobserved states andcan be represented as follows:

Yt = Z ⋅ Ft

where Z is appropriately defined from equation (1) as follows:

34

1

1


1
Z =
1
1


1


1 0 1 0 ,..., 0 
1 0 0 1 ,..., 0 
1 0 0 0 ,..., 1 
0 1 1 0 ,..., 0 
0 1 0 1 ,..., 0 
0 1 0 0 ,..., 1 

The estimation of the above model is obtained by maximizing the log likelihood
function based on the assumption that the errors terms follow normal distribution.

B. Finding the Starting Point for the Numerical Maximization.

The model as specified, even under the assumption that the errors are uncorrelated andthe unobserved factors follow AR(1), includes quite a few number of parameters. There
are 4 (for the one world and the three region common factors) + 28 (for the errors)parameters for AR(1) coefficients, 28 parameters for the variances of the countryspecific shocks, 28 parameters for the sensitivity coefficients to the world common
factors and 28 parameters for the sensitivity coefficients to the region common factor,
summing up to 116 parameters. Due to the size of the model, it is to the utmost crucial
to provide good initial parameter values for the numerical maximization of the loglikelihood function.
We have calculated the initial parameter values by mimicking the original model
as closely as possible. For example, the parameter values of the impact coefficients on
factors for each country, α wj and α rj , are derived from the following equation:35

Y jtr = α wjYt w + α rjYt r + ε jt

(B1)

where Y jtr is the cyclical measure (the growth rate) of output at time t for country j
belonging to region r, Yt w , the cyclical measure (the growth rate) of output at time t for
the world economy, and Yt r , the cyclical measure (the growth rate) of output at time t
for the region. Yt w is calculated by taking the weighted average of the growth rates, withthe size of real GDP being used as weights.15 To preserve the orthogonal property
between factors, instead of using a similarly-calculated regional measure, we have used
the residuals in the regression of the regional measure on the world measure. In otherwords, we calculate the weighted average growth rate for a region and, by regressing it
to Yt w , the residual is derived to use for Yt r .
The parameter values for AR(1) coefficients are calculated by regressing Yt w ,Yt r and the residuals from equation (B1) on the lagged variables:

Yt w = ρ wYt −w1 + ηtw(B2)

Yt r = ρ rYt −r 1 + ηrtR

(B3)

15

To allow for comparison of the real GDP across countries, we have used the Penn
World Table developed by Summers and Heston (1991) data set for the available years,
and extended it using the last year’s value for the unavailable years.

36

ε jt = ρ j ε jt −1 + η jt

(B4)

Since, in the original model, the innovations to the world common, region-specificcommon, and country-specific factors are normalized to unity, Yt w and Yt r are redefinedby normalizing the variances of η tW and η rtR to unity, which essentially adjust thenumerical values of the impact coefficients by the factor of normalization. The varianceof the idiosyncratic shocks is derived from the residual in equation (B4).
The initial parameter values, while useful for the numerical maximization, aremeaningful by themselves as an alternative way of decomposing shocks. Based on theinitial parameter values, we have also derived the shares of variances accounted for by
the world common, the region common and the country-specific factors in a similar way
explained in section 2. Main results in section 4 preserve without significant
differences (the results not reported), which confirms that our results are quite robust.

37

Reference

Alesina, Alberto and Robert J. Barro (2000), “Currency Unions”, NBER Working Paper
7927.
Bekaert, G., and Harvey, C.(1995), Time-varying world market integration, J.Finance
50, 403-444
Baek, Seung-Gwan and Chi-Young Song (2001), “Is Currency Union a Feasible Optionin East Asia?”, unpublished manuscript.
Barro, Robert, (2001), “Currency Unions”, unpublished manuscript, Harvard University.
Bayoumi T and B. Eichengreen (1994), ‘One Money or Many? Analyzing the Prospectsof Monetary Unification in Various Parts of the World’, Princeton Studies inInternational Finance No 76.
Bayoumi, T and B. Eichengreen (1997), “Ever Close to Heaven? An OptimumCurrency-Area Index for European Countries, European Economic Review 41,
761-770.
Bayoumi, T. and P. Mauro (1999), “The Suitability of ASEAN for a Regional Currency
Arrangement,” IMF WP/99/162.
Bayoumi, Tamim, Barry Eichengreen and Paolo Mauro (2000), “On Regional Monetary
Arrangements for ASEAN”, Journal of the Japanese and International
Economics, 14, 121-148.
Blanchard, Olivier J., and Quah, Danny (1989), “The Dynamic Effects of Aggregate
Demand and Supply Disturbances,” American Economic Review, 655-673.
Canova, Fabio and Harris Dellas (1993), “Trade Interdependence and the International
Business Cycle,” Journal of International Economics, 34, 23-47.

38

Cheng, Y., and Mak, S. (1992), The international transmission of stock market
fluctuation between the developed markets and the Asian-Pacific markets, Appl,
Finan. Econ. 41, 43-47.
Chow, Hwee Kwan and Yoonbai Kim (2000), “A Common Currency Peg in East Asia?
Perspectives from Western Europe”, unpublished manuscript.
Clark, Todd, and Eric van Wincoop (1999),“Borders and Business Cycles,” Journal ofInternational Economics, forthcoming.
Dornbusch, R. and Y. Park (1999), “Flexibility or Nominal Anchors?,” in Exchange
Rate Policies in Emerging Asian Countries eds by S. Collignon, J. Pisani-Ferry,and Y. Park, Routledge, London.
De Brauwer, Gordon (2000), “Does a Formal Common-Basket Peg in East Asia Make
Economic Sense?” paper presented at the Financial Markets and Policies in East
Asia at ANU, Canberra, Australia.
Eichengreen, Barry and Tamim Bayoumi (1999), “Is Asia An Optimum Currency Area?
Can It Become One?: Regional, global and historical perspectives on Asianmonetary relations”, in S. Collignon, J. Pisani-Ferry, and Y. C. Park(eds.)
Exchange Rate Policies in Emerging Asian Countries, 347-366.
Frankel, Jeffrey and Andrew Rose (1998), “The Endogeneity of the Optimum Currency
Area Criteria,” Economic Journal 108, 1009-1025.
Geweke, J. (1977), “The Dynamic Factor Analysis of Economic Time Series Models,”in D.J. Aigner and A.S. Goldberg, eds., Latent Variables in Socio-economic
Models, (Amsterdam: North Holland).

39

------------ and K. Singleton (1980), “Interpreting the Likelihood Ratio Statistics inFactor Models when the sample size is small,” Journal of the American
Statistical Association 75, 133-337.
Glick, R, and Hutchinson, M.(1990), Financial liberalization in the Pacific Basin:
Implication for real interest rate linkage, J.Japan.Int.Econ. 4, 36-48.
Gregory, A.W., A.C. Head and J. Raynauld (1997), “Measuring World Business
Cycles,” International Economic Review, vol. 38, No. 3, pp.677-701.
Heston, Alan and Robert Summers (1991), "The Penn World Table (Mark 5): An
Expanded Set of International Comparisons, 1950-1988", Quarterly Journal of
Economics, pp.327-368.
Imbs, Jean (1999), “Co-Fluctuations,” CEPR Discussion Paper No. 2267.
Kalemli-Ozcam, Sebnem, Bent Sorensen and Oved Yosha (2001), “Economic
Integration, Industrial Specialization, and the Asymmetry of Macroeconomic
Fluctuations,” Journal of International Economics, Vol.33, pp.107-137.
Kose, Otrok and Whiteman (2001), “International Business Cycles: World, Region,
and Country-Specific Factors” unpublished manuscript.
Kraay, Aart and Jaume Ventura (2000), “Product Prices and the OECD Cycle,” NBER
Working Paper, No. 7788.
Kuen, T.Y., and Song, T.K.(1996), Interest rate parity and dynamic capital mobility:
The experience of Singapore, in "Financial Deregulation and Integration in East
Asia" (T. Ito and A.Krueger, Eds.), pp.335-354, NBER East Asia Seminar on
Economics Vol.5, University of Chicago Press, Chicago

40

Kwan, C. H. (1998), “The Theory of Optimum Currency Areas and the Possibility ofForming a Yen Bloc in Asia”, Journal of Asian Economics, Vol. 9, No. 4, 555580.
Lewis, Karen K. (1999), “Trying to Explain Home Bias in Equities and Consumption”,
Journal of Economic Literature, Vol. 37, pp.571-608.
McKinnon, R. (1999) ”The East Asian Dollar Standard, Life after Death?” Working
Paper, Stanford University.
Mundell, R. 1961, “A Theory of Optimum Currency Area,” American Economic Review,Vol. 60, pp.657-665.
Park, Y.C., and Song, C.Y., (2001), "Managing Capital Account Liberalization: The
Experiences of Indonesia, Malaysia, Korea, and Thailand", a paper presented tothe high-level Conference on Capital liberalization in Transition Economies
hosted by IMF, the National Bank of Ukraine, and the Netherladsche Bank in
Kiev, Ukraine on October 21-26, 2001.
Rose, Andrew K. (2000), “One Money, One Market: The Effects of Common
Currencies on Trade”, Economic Policy 30.
Sargent, T.J. and C. Sims (1977), “Business Cycle Modeling without Pretending toHave Too Much A Priori Economic Theory,” in New Methods in Business
Cycle Research: Procedures from a Conference (Minneapolis: Federal Reserve
Bank of Minneapolis, pp.45-110.
Stock, J.H. and N.W. Watson (1991), “A Probability Model of Coincident Economic
Indicators,” in K.Lahiri and G.H. Moore, eds., Leading Economic Indicators:
New Approaches and Forecasting Records, Cambridge: Cambridge University
Press, pp.63-89.

41

Williamson, J. (1999), “The Case for a Common Basket Peg for East Asian
Currencies,” in Exchange Rate Policies in Emerging Asian Countries eds by S.
Collignon, J. Pisani-Ferry, and Y. Park, Routledge, London.
World Bank (1997), Private Capital Flows to Developing Countries: The Road to
Financial Integration, World Bank
Wyplosz, Charles (2001), “A Monetary Union in Asia? Some European Lessons,”
unpublished manuscript.

42

Table 1. Shares of Variances Accounted for by World, Region and Country-Specific Factors
A. Asian Countries

Period : 1979-1990

Period : 1991-1999

Volatility

World

RegionCountry

Volatility

World

RegionCountry

China

3.61000.57580.16050.26372.2957(-)0.1390

0.01220.8488Hong Kong3.98160.0034(-)0.9966

03.5456(-)0.1782

0.73760.0841Indonesia

2.1027(-)0.0012(-)0.4467

0.5521

7.0028(-)0.1123

0.88770Japan

1.03330.04870.06850.8827

2.1536(-)0.1593

0.40290.4378

Malaysia

3.2142(-)0.0026(-)0.1846

0.81285.5176(-)0.1517

0.84490.0033

Philippines

4.7982(-)0.5088(-)0.2503

0.24092.45750.38900.58480.0262Singapore

3.5015(-)0.0297(-)0.2870

0.68333.5393(-)0.0537(-)0.0439

0.9024

Taiwan2.52830.1716(-)0.7084

0.12000.9027(-)0.3369

0.27980.3834Thailand2.9225(-)0.0017(-)0.0020

0.9963

6.3965(-)0.2804

0.60440.1152Korea

3.43330.0706(-)0.0209

0.90855.0675(-)0.1268

0.79210.0811

Average

3.11260.14140.3126

0.54603.88780.19270.51900.288243

B. European Countries
Period : 1979 – 1990

Period : 1991-1999

Volatility

World

RegionCountry

Volatility

World

RegionCountry

GERMANY

1.82560.45890.31710.22403.8391(-)0.3179

0.54830.1338AUSTRIA

1.58090.00110.40140.59750.9526(-)0.0045

0.69290.3027BELGIUM1.49220.08080.53370.38551.45220.24300.44710.3098DENMARK

1.88550.3919(-)0.0015

0.6066

1.55850.48950.00020.5103

FRANCE

1.1560(-)0.0089

0.40640.58471.23500.28570.47270.2416IRELAND

1.90910.03340.1746

0.79203.17030.80060.08690.1125ITALY

1.43220.26680.73320

1.06780.30670.40700.2863

NETHERLANDS

1.96170.60400.12630.26972.12480.4617

0.29160.2467

FINLAND

1.69460.01640.72320.26044.34330.9971(-)0.0029

0

GREECE

1.72410.21890.2381

0.54301.55140.26700.24840.4846NORWAY

2.01680.45680.01280.5304

1.42740.1276(-)0.0747

0.7977PORTUGAL

2.6879(-)0.2848

0.40400.31122.16920.23100.63990.1291SPAIN

1.8680(-)0.0198

0.02450.95581.58100.37930.62070

SWEDEN

1.14100.52920.24720.22362.42560.72060.11910.1603

SWITZERLAND

1.72430.17200.21480.6132

1.0602

0.2346

0.27180.4936

UNITED KINGDOM

2.2166

0.04320.05280.90401.76570.7558(-)0.1486

0.0956

Average

1.76980.18610.2882

0.48761.98280.41390.31710.269044

Table 2. Determinants of Output Co-Movements among European Countries(1)
Log (intra-region trade)
Log (trade structure
similarity)
Log (industry structure
similarity)
Log (per capita GDP
difference
Log (per capita GDP)
Dummy for the 1990s
Constant
Number of obs.
R2

1.6814(1.0987)-0.0753(0.0753)-0.2218(0.2326)
0.0861(0.0924)
1.1482(0.5467)
30
0.18

(2)
0.7638(0.3716)
1.9947(0.6518)-0.7803(0.3065)
0.0818(0.0904)
3.3422(1.0116)
30
0.18

(3)
0.7380(0.3817)1.5965(0.7557)
0.8569(1.0868)-0.0589(0.0819)-0.7454(0.3291)
0.0966(0.0924)
3.2534(1.0388)
30
0.24

(4)
0.8089(0.3722)1.3739(0.7350)0.8017(1.0903)-0.0484(0.0809)-0.5602(0.2819)2.7697(0.9498)30
0.20

Notes: The dependent variable is the fraction of the output variation attributable to regionalcommon shocks for 15 European countries in each of the periods 1979-1989, and 1990-1999.
Random effects specification is applied to the panel data for the two ten-year periods. Standard
errors of the estimated coefficients are reported in parentheses.

45

Table 3. Determinants of Output Co-Movements in East Asian Economies(1)
Log (intra-region
Log (trade structure
similarity)
Log (industry structure 2.2269(2.2240)
similarity)
0.0499Log (per capita GDP(0.0886)
Difference)-0.0875
Log (per capita GDP)
(0.1013)0.7937
Dummy for the 1990s(0.1747)0.4773
Constant(0.6661)
20
Number of obs.
2
0.62
R

(2)
-0.4003(0.3448)-0.2323(0.7257)-0.1314(0.1065)0.9341(0.2370)-0.6555(0.4468)
20
0.63

(3)
-0.3637(0.7043)-0.2042(0.9823)0.3125(4.3855)
0.1159(0.1157)-0.1271(0.1275)
0.9161(0.3146)-0.5181(1.7645)
20
0.63

(4)
0.1245(0.8473)
1.7747(0.8786)
3.2847(5.2828)0.2087(0.1162)-0.0033(0.1489)2.2743(1.8346)
20
0.39

(5)
0.0656(0.6563)
2.0635(0.9153)
1.9430(4.0861)0.2356(0.1078)
0.3772(0.1188)-0.0819(0.2931)
1.7663(1.6440)20
0.58

Notes In columns (1)-(4) the dependent variable is the fraction of the region-wide common
movements in national output variation for 10 Asian countries in each of the periods 1979-1989,
and 1990-1999. In column (5) the dependent variable is replaced by the estimates of the sharesof regional common shocks corresponding to the periods 1978-1987, and 1988-1996. See thenotes to Table 2.

46

Table 4. Trends of Intra-region Trade in East Asia and Europe
Intra-region Trade(percentage of total trade)
1980
1990

2000

East Asia
China
Hong Kong
Indonesia
Japan
Korea
Malaysia
Philippines
Singapore
TaiwanThailandAverage

42.4
46.7
62.6
23.8
32.7
49.2
37.4
49.4
34.9
40.1
41.9

58.9
60.4
56.8
28.0
34.2
55.1
40.0
50.7
43.1
47.5
47.5

48.7
64.1
54.4
38.1
42.2
56.1
46.5
57.5
50.9
54.2
51.3

EuropeAustria
Belgium
Denmark
FinlandFranceGermany
GreeceIreland
Italy
Netherlands
NorwayPortugal
Spain
SwedenSwitzerland
United Kingdom
Average

69.8
74.9
74.4
57.4
57.8
63.9
48.2
77.6
56.1
70.5
78.6
58.7
43.2
73.8
69.0
55.8
64.4

75.6
80.4
75.5
66.6
67.1
67.4
70.1
76.1
67.3
77.0
75.2
78.5
68.2
78.9
72.1
62.8
72.4

71.0
72.5
74.8
61.7
66.7
59.1
56.1
61.3
59.1
67.3
73.7
78.6
67.8
68.4
66.9
57.0
66.4

Source: International Monetary Fund, Direction of Trade Statistics.
Note: Intra-region trade is measured by the share of an economy’s trade with the rest of theeconomies that belong the same region, in total trade.

47

Table 5. Trends of Trade Similarity Index among East Asia and European
Economies

1980

Index
1990

2000

East Asia
China
Hong Kong
Indonesia
Japan
Korea
Malaysia
Philippines
Singapore
TaiwanThailandAverage

0.476
0.436
0.379
0.357
0.490
0.482
0.438
0.453
0.514
0.488
0.451

0.591
0.603
0.442
0.480
0.628
0.542
0.586
0.523
0.620
0.589
0.560

0.629
0.636
0.512
0.606
0.662
0.651
0.659
0.623
0.703
0.686
0.637

EuropeAustria
Belgium
Denmark
FinlandFranceGermany
GreeceIreland
Italy
Netherlands
NorwayPortugal
Spain
SwedenSwitzerland
United Kingdom
Average

0.646
0.664
0.613
0.697
0.706
0.668
0.488
0.581
0.655
0.621
0.609
0.539
0.669
0.616
--0.664
0.629

0.674
0.662
0.622
0.572
0.703
0.696
0.438
0.583
0.651
0.668
0.590
0.523
0.654
0.647
--0.691
0.625

0.703
--0.643
0.559
0.716
0.706
0.490
0.518
0.663
0.687
0.611
0.588
0.661
0.660
--0.702
0.636--- Not available.
Source: Constructed from the World Bank Trade and Production Database (which is originallyfrom United Nations’ Comtrade Database)Note: The index measures the extent of similitude in the composition of total merchandise
exports excluding agricultural products at the three-digit SITC level between an economy andthe rest of the economies in the same region. It is an unweighted average of bilateral similarity
indices. The index ranges between 0 and 1 (most similar).

48

Table 6. Distribution of International Bank Lending(million US dollars)
1996.6
1999.122001.6
European American Japanese European American Japanese European American Japanese
banks
banks
banks
banks
banks
banks
banks
banks
banks
Developed

84697

13906

26526 2885881

367998545636 3377764

497028728725Countries(0.183)(0.106)(0.159)(0.787)(0.676)(0.688)(0.824)(0.704)(0.752)

Asia

12768235594

115471 132817

26793

65050

96156

29042

51934

(0.276)(0.272)(0.693)(0.036)(0.049)(0.082)(0.023)(0.041)(0.054)

18076

3963

21622

20600

4096

12491

15762

4073

9626

(0.039)(0.030)(0.130)(0.006)(0.008)(0.016)(0.004)(0.006)(0.010)

26859

10451

22512

24449

8829

12592

16953

5988

10110

(0.058)(0.080)(0.135)(0.007)(0.016)(0.016)(0.004)(0.008)(0.010)

7872

1973

8131

8275

1419

6029

7184

2404

5843

(0.017)(0.015)(0.049)(0.002)(0.003)(0.008)(0.002)(0.003)(0.006)

4698

3774

1402

8835

3003

2921

6643

1891

3066

(0.010)(0.029)(0.008)(0.002)(0.006)(0.004)(0.002)(0.003)(0.003)

18092

5456

37552

11929

826

13075

8091

1358

7979

(0.039)(0.042)(0.225)(0.003)(0.002)(0.016)(0.002)(0.002)(0.008)462230130975(1.000)166701 3665913(1.000) (1.000)

544124(1.000)792676 4099965(1.000) (1.000)

705569(1.000)969425(1.000)Indonesia

Korea

Malaysia

Philippines

Thailand

Total(1.000)Note: American banks are the United States and Canadian banks.

49