Discussion Paper No. 574

TRADE INTEGRATION AND
BUSINESS CYCLE SYNCHRONIZATIONIN EAST ASIA

Kwanho ShinandYunjong Wang

March 2003

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Trade Integration and Business Cycle Synchronizationin East Asia
Kwanho Shin* and Yunjong Wang**
January 2003
Abstract
As trade integration deepens in East Asia, it is expected that there will
be closer links in business cycles among East Asian countries.
Theoretically, however, increased trade can lead business cycles acrosstrading partners to shift in either direction: while inter-industry trade
resulting in higher specialization would induce less synchronization,
intra-industry trade could overturn this tendency. By using the data for
twelve Asian economies, this paper finds that intra-industry trade is the
major channel through which business cycles become synchronizedamong Asian economies, although increased trade itself does notnecessarily lead to close business cycle coherence. This result has
important implications for the prospects of a currency union in theregion.
JEL Classification: E32, F36, F41
This paper was presented at the Asian Economic Panel meeting on October 8-9, 2002,
at Columbia University, New York. We are grateful for Soyoung Kim, Warwick
Mckibbin, Alan Siu, and Wing Thye Woo for helpful comments and suggestions.
Byung Wook Ko provided excellent research assistance. Part of this paper was written
while Kwanho Shin was affiliated as a visiting foreign scholar with Institute of Socialand Economic Research, Osaka University.
*

Department of Economics, Korea University, 1-5 Anam-Dong, Sungbuk-Gu, Seoul,
Korea, khshin@korea.ac.kr**
Senior Research Fellow, KIEP, 300-4 Yomgok-Dong, Seocho-Gu, Seoul, Korea,
yjwang@kiep.go.kr

1. Introduction

The volume of trade in East Asian countries has continuously increased. The loweringof tariffs that started in the 1980s accelerated this trend of increasing trade worldwide.
However, the growth in trade has been far speedier in East Asia than in any other region.
A number of studies have observed that this rapid increase in trade is one of the key
factors behind the region’s rapid growth in income.1
Another important implication of increased trade is that countries involved withheavier trade integration are becoming more closely linked in macroeconomic
performance. Thus, the influence of important trading partners is now essential tounderstanding the business cycle fluctuations of domestic economies.

From atheoretical point of view, however, increased trade can lead business cycles acrosstrading partners to be patterned in either direction – towards convergence or divergence.
For example, if trade occurs mainly à la Heckscher-Ohlin or is of the Ricardian type,higher specialization would induce the industrial structures of the trading countries todiverge, resulting in less synchronized movements of business cycles. In contrast, if

1

For the effects of trade on growth, see, among others, Frankel and Romer (1999).

1

trade occurs mainly through intra-industry trade, this conclusion could be overturned.2
One of the main objectives of this paper is to empirically analyze how thebusiness cycles of the Asian economies are influenced by increased trade within theregion. As the regional trade began accelerating in the 1980s, trade linkages seemed tohave an increasing influence on business cycle co-movements. As well, the possibilityof free trade agreements in this region implies that this trend may persist even further.
Assessing business cycle co-movements of Asian countries has another very
important implication in terms of evaluating the benefits and costs of adopting acommon currency within the region. As pointed out in the seminal paper written by
Robert Mundell (1961), the major cost of adopting a common currency is the loss ofmonetary independence. In adopting a common currency, all member countries mustyield their independent monetary policies to a supranational authority. When
asymmetric macroeconomic shocks occur across the member countries, monetary policy
cannot be tailored to an individual economy’s particular disturbances. Hence it is lesscostly for the economies to form a common currency if their business cycles aresynchronized. In order to find potential candidates in the region for a currency union, it
is necessary to be aware of the changing patterns of business cycle co-movements
2

In section 2, we will explain the detailed mechanisms by which trade integration affects business cycle
synchronization.

2

among the East Asian countries.3 In this sense, knowing how trade integration will
shape business cycles patterns among East Asian countries is of tremendous importancein gauging the prospects for a currency union in the region.
This paper is organized as follows. Section 2 discusses the mechanisms bywhich increased trade affects the pattern of business cycle co-movements. Section 3
describes data and statistical findings. Section 4 presents empirical analyses. In Section
5, we discuss the implication of our results on the prospects of a currency union in East
Asia. The last section concludes.

2. Trade Integration and Its Implications for Business Cycle
Synchronization

Since trade is an important linkage between countries, many researchers haveconjectured that it must play a crucial role in transmitting disturbances, influencing
business cycle co-movements across countries. However, on the question of whether
3

Mundell (1973) presented a new view of currency union as a means of smoothing shocks by better
reserve pooling and portfolio diversification. The possibility of international risk-sharing implies thatsimilarity of shocks is not a strict condition for sharing a common currency if all members of the currency
union are financially integrated and hold claims on each others’ outputs. This point has important
implications for a debate about the size of a common currency area (Karlinger, 2002).

3

increased trade leads to more or less correlation of business cycles across countries,
there is no consensus. For example, Eichengreen (1992), Kenen (1969) and Krugman(1993) argued that as trade linkages increased, greater specialization of production
would occur, resulting in less synchronization of business cycles. In particular, this is
more so if business cycles are dominated by industry-specific technological shocks.
Frankel and Rose (1998) countered the above argument, insisting that if intraindustry trade was more pronounced than inter-industry trade, business cycles would
become more positively correlated as trade became more integrated. As well, there areat least two additional important linkages that lead to a positive relation between
business cycle co-movements and increased trade. First, if demand shocks drive aboom in one country, the effects can spill over to trading partners through an increased
volume of imports.

Second, increased trade may create a greater need for morecoordinated fiscal as well as monetary policies, which synchronize policy shocks. Bothof these linkages imply that increased trade leads to tighter business cycle comovements.
In sum, the theoretical implications of trade integration on business cycle comovements are not unambiguous; to test the validity of the theories, an empiricalinvestigation is in order. Canova and Dellas (1993) investigated this issue and found4

that there was some evidence of trade affecting the transmission of disturbances acrosscountries but it was not robust to the choices of the detrending method. Recently,
Frankel and Rose (1998) found more positive results. Based on 21 industrializedcountries, they found that the more countries traded with each other, the more correlated
their business cycles were. That is, there is a strong positive relationship between the
degree of bilateral trade intensity and the cross-country bilateral correlation of outputs.
Following a similar method, Choe (2001) also finds that, based on ten East Asian
countries, economic fluctuations are more synchronized as trade interdependence
deepens in the region.
These results suggest that, as countries trade more, it becomes moreadvantageous to form a currency union not only in terms of benefits but also in terms ofcosts. This is what Frankel and Rose (1998) emphasized as the endogenous nature of
the decision to join a currency union. In other words, a naïve examination of historical
data may give a misleading picture of a country’s eligibility for entry into a currency
union since the economic structure is likely to change dramatically as a result of joining
one. The results of Frankel and Rose can be interpreted to support an early introductionof a currency union; it will also decrease the costs of adopting a currency union bylowering asymmetric shocks through increased trade. Even a country that is not suited5ex ante to joining a currency union can be justified ex post facto in joining one due tolowered asymmetrical shocks.
However, an important step is missing in this analysis. Frankel and Rose(1998) conjectured that their results are due to intra-industry trade, but did not further
try to identify the channel through which increased trade affects business cycle comovements.4 In this paper, by extending Frankel and Rose’s important contribution, we
try to investigate how increased trade affects business cycle co-movements.
We call the four different channels affecting business cycle co-movements (1)inter-industry trade, (2) intra-industry trade, (3) demand spillovers, and (4) policy
coordination channels. Only the first channel implies that increased trade leads to lesssynchronization of business cycles. The last three channels have in common that
increased trade induces more synchronization of business cycles.
While it is apparent in Frankel and Rose’s study that the first channel is not theone that facilitates convergence of business cycles, it is not clearly specified exactly
which channel out of the last three channels drives their findings. To find the answer, it

4

In their empirical work, Frankel and Rose (1998) do not control for variables other than bilateral tradeintensity. Other researchers contend that controlling for sectoral similarity in the regressions lead tosmaller coefficients on trade. However, Clark and van Wincoop (2001) and Otto, Voss, and Willard(2001) also control for industrial or sectoral similarity and the coefficients on trade are still statisticallysignificant (Kose and Yi, 2002). Following Heathcote and Perri (2002), Kose and Yi (2002) find that
under international financial autarky the model can generate stronger correlations for pairs of countries
that trade more, but the increased correlation falls far short of the empirical findings.

6

is necessary to consider these three channels separately and analyze how each one
influences business cycle co-movements.
Recently, Fidrmuc (2001) has shown that, based on a cross-section analysis ofOECD countries between 1990 and 1999, convergence of business cycles relates tointra-industry trade, but there is no direct relation between business cycles and bilateral
trade intensity. Loayza, Lopez and Ubide (2001) analyze East Asia as a region and findsignificant short-run and long-run co-movement of business cycles. They find that thisco-movement is based on the countries’ highly similar trade structures. Based on apanel regression, Shin and Wang (2002) also find that intra-industry trade is the major
channel through which Korea’s business cycle becomes synchronized with that ofeleven other Asian economies, although increased trade itself does not necessarily leadto close business cycle coherence.

These recent empirical analyses suggest thatbusiness cycle co-movements are strengthened only when increased trade isaccompanied by more intra-industry trade (Imbs, 2002).
In the next sections, we will investigate whether the findings from the case of
Korea can be extended to other East Asian countries by identifying the most importantchannel leading to positive links between trade and business cycle co-movements inEast Asia.

7

3. Data and Statistical Findings

To measure output co-movements, annual data on real GDP are collected for twelve
Asian countries over the period 1976-1997. The countries considered are five East
Asian countries (China, Hong Kong, Japan, Korea and Taiwan), five ASEAN countries(Indonesia, Malaysia, the Philippines, Singapore and Thailand) and two other Asian
countries (Bangladesh and India). 5 The period after 1997 is excluded because webelieve that the data is distorted by the Asian crisis and, by including it, theconventional measure of business cycle co-movements could be exaggerated.6 All thedata are drawn from the IMF International Financial Statistics CD-Rom.
The trade volume data are collected from the IMF Direction of Trade Statistics
CD-Rom. Industry-level trade data are available in Nicita and Olarreaga (2001), which
reorganize the United Nations Statistics Department’s Comtrade database through theWorld Bank’s World Integrated Trade Solution (WITS) software. The industry
disaggregation in the database follows the International Standard Industrial
5

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

8

Classification (ISIC) and is provided at the 2 digit level (9 industries), the 3 digit level(28 industries) and at the 4 digit level (81 industries) manufacturing industries only. The
trade data includes both imports and exports, and mirror exports (reported by othertrading partners) are obtained using WITS.
Table 1 shows changes in real GDP and the volume of trade in East Asian
countries for the period between 1977 and 1997. The first column is the average growthrate in real GDP. The figures show that this region was in the midst of rapid growth
before the Asian crisis, with the average growth rate over 7% in China, Korea, Malaysia,
Singapore and Taiwan and over 6% in Hong Kong and Indonesia.

Japan, thePhilippines and Bangladesh are exceptional cases with average growth rates lower than
4 %.
The second column is the average ratio of total trade (exports + imports) to
GDP during the same period. This ratio is quite high for this region and it is generally
higher for faster growing countries. We can see this by simply calculating the simple
correlation coefficient between the first and the second columns, which turns out to be
0.31. This figure is consistent with a more formal analysis of the impacts of trade on
growth done by, among others, Frankel and Romer (1999).

6

Recently Lee, Park and Shin (2002) have found, for the latter period, that co-movements of outputs inEast Asian countries are much more pronounced when the Asian crisis period is included.
9

The third column is the average increase in percentage points for the ratio of
total trade to GDP.7 This figure is positive in all countries except for Japan, which
implies that total trade increased at a faster rate than GDP. Therefore, trade becomes
more important as the volume of GDP increases. The fourth column is the ratio ofregional trade to total trade. The figure is on average 44.9%, which shows that regional
trade is quite important. Finally, the fifth column is the average increase in percentage
points for the ratio of regional trade to total trade. This figure is positive in all countries,
which implies that, as time passes, regional trade becomes increasingly important forevery country in the region.

Insert <Table 1>

To investigate how the bilateral trade intensity in each pair among the twelve
Asian countries influences business cycle co-movements, we use three different proxies
for bilateral trade intensity, following Frankel and Rose (1998): wxt , wmt and wtt . The
first uses export data only, the second, import data, and the third, both export and import
data. The variables are defined as follows:7

For example, if the ratio increases from 10% to 30% for 20 years, then the average increase is 1
percentage point.
10

wxt (i, j ) =wmt (i, j ) =wt t (i, j ) =

xijt

(1)

X it + X jt
mijt

(2)

M it + M jt
xijt + mijt

(3)

X it + M it + X jt + M jt

where xijt denotes total nominal exports from country i to country j during period t;
mijt denotes total nominal imports from country j to country i during period t; and X i and
M i denote total global exports and imports for country i. A higher value of any of these
indices indicates greater trade intensity between country i and country j.
A measure of intra-industry trade intensity is derived à la Grubel and Lloyd(1975). In constructing the measure, an important consideration is how detailed aclassification of industries should be used.

If we want to measure trade in morehomogeneous sectors, we need to further disaggregate industries. However, as theindustries are further disaggregated, the portion of intra-industry trade will shrink andeventually go to nil. Rather than a priori determining a proper industry classification,
we construct three measures based on two-, three- and four-digit industry classifications
following the International Standard Industrial Classification (ISIC). The constructed11

measure is∑ x −m
IIT = 1 −∑ (x + m )
i
kjt

i
kjt

i
kjt

i
kjt

i

(4)

i

where xijtI is total nominal exports from country i to country j and mijtI is total nominal
imports from country j to country i. Depending on how an industry is classified, we canhave three measures: IIT2 for two-digit; IIT3 for three-digit; and IIT3 for four-digitclassifications. Note that, since the second term on the right hand side in (4) decreases
as more intra-industry trade occurs, we subtract it from 1 so that the index will
monotonically increase as intra-industry trade increases.
Considering that any time-series change in trade patterns may influence thenature of business cycle co-movements, we divide the whole sample period into threesub-sample periods: 1976-83 (period 1), 1984-90 (period 2) and 1991-97 (period 3).
We use two methods to derive cyclical parts of total real outputs by (1) first-differencesin logarithms and (2) Hodrick-Prescott filter and then a simple correlation of thecyclical parts across countries is used as a proxy for business cycle co-movements.
In <Table 2>, for each country, we report the average measure of output

12

correlation, trade intensity and intra-industry trade with the other Asian countries ineach period. The average correlation is based on a simple arithmetic mean for thecorrelation measures of each country with the other East Asian countries. For example,
we calculate the correlation measures for Korea with each of the other eleven countries
and use the mean as the measure for Korea. The first column is based on firstdifferences in logarithms and the second column, the Hodrick-Prescott filter. The thirdand fourth columns are the mean of trade intensity for each country against the other
East Asian countries. The third column is a simple mean and the fourth column, aweighted mean using trade intensity measures as weights. The fifth and the sixth
columns are the means of the intra-industry trade measure for each country against theother East Asian countries. The fifth column is a simple mean and the sixth column, aweighted mean using trade intensity measures as weights.

Insert <Table 2>

Interestingly, there is no clear evidence that there has been an increase in comovements of business cycles recently. 8 For both correlation measures, even for
8This fact is closely related to the fact that we have eliminated the period during the financial crisis andthereafter. If we include the period, the co-movement of business cycles substantially increases.

13

countries where the correlation measure increases from period 1 to period 2, it decreases
back in period 3. This indicates that, while there is some weak evidence of increasing
co-movements of business cycles among East Asian countries in the early period, thispattern does not seem sustained. On the other hand, trade intensity, whether based onexports, imports or total trade, has continuously increased. This shows that, as time
passes, Asian countries are increasingly becoming important trading partners to eachother. Another interesting phenomenon is that the intra-industry measure, whether basedon 2-digit, 3-digit or 4-digit industries, has also continuously increased.

4. Methodology and Results

To more formally analyze this relationship, we set up the following equationand estimate the coefficients in the regression:corr (i, j )τ = α + α 1 * trade intensity (i, j )τ + α 2 * intra-industry trade (i, j )τ+ α 3 * fiscal policy coordination (i, j )τ + α 4 * monetary policy coordination (i, j )τ + ε ijτ(5)14where corr (i, j )τ refers to the correlation of output between country i and country j
during period τ . For trade intensity, we used three measures, wxt , wmt and wtt , andfor intra-industry trade, another three measures IIT2 , IIT3 and IIT4 . For fiscal policy
coordination, we calculate the correlation of the ratio of budget deficit to GDP between
country i and country j. For monetary policy coordination, we consider the correlation
coefficient of the M2 growth rates across each pair of countries.
Each term on the right hand side of equation (5) represents the channel throughwhich increased trade influences co-movements of output across countries. The firstterm, trade intensity, indicates how demand spillovers influence business cycle
correlation.

Since demand spillovers increase as trade intensity increases, tradeintensity can be used as a proxy of demand spillovers. The second term, intra-industry
trade, indicates how co-movements of output are influenced by intra-industry trade.
The third and the fourth terms indicate how policy coordination, either fiscal ormonetary policy, affect co-movements.

Finally, if increased trade is made mainly

through inter-industry trade and if this channel dominates the other channels, then thecoefficient of the first term should be negative. In this way we can identify the most15

important channel of trade influencing co-movement of outputs across countries. 9
Frankel and Rose (1998) also estimated an equation similar to (5), but they
included only the first term in their regression. They pointed out that a simple OLS
regression would generate a biased estimation due to an endogeneity problem. This
problem arises because countries are likely to link their currencies deliberately to thoseof their most important trading partners in order to capture gains associated with greater
exchange rate stability.

That is, trading partners lose the ability to set policies

independently of their neighbors and this tied policy coordination could result in a
spurious association between trade intensity and business cycle co-movements. Theysuggested using 1) log of distance; 2) a dummy variable for common border; and 3) adummy variable for common language for instrumental variables.
There are two problems in adopting their methodology to our case. First, these
instrumental variables are expected to be highly correlated to trade intensity, but not to
intra-industry trade. It is, however, difficult to find appropriate instrumental variables
for intra-industry trade. Second, our regression results are based on a panel regressionthat includes times series variations as well as cross-section variations. Since these

9This approach cannot identify different ways by which various shocks influence the co-movements. For
example, some shocks originating from one country can have asymmetric effects on the other. To fully
incorporate the various aspects of the shocks, a structural model should be developed, which is beyond
the analysis covered in the paper.

16

instrumental variables do not change over time, we cannot estimate all the coefficientsof time-varying variables.
Imbs (1998) suggests that the above problem cannot be solved through an
instrumental variable estimation because the real problem is not an endogeneity
problem but one of omitted variables. He argued that, in a panel regression, unobserved
components of time-invariable characteristics for each country can be handled byintroducing fixed effects. However, we believe that a panel regression with fixed
effects cannot entirely solve the problem pointed out by Frankel and Rose. To tackle
the problem directly, we need to introduce the omitted variables that cause the spurious
relationship between trade intensity and co-movements.
In equation (5), we try to control the effects of policy shocks by explicitly
considering two types of policy coordination. In measuring the degree of fiscal policy
coordination, we first calculate the ratio of budget deficit to GDP for each country andthen calculate the correlation coefficient of this ratio across each pair of countries. In
measuring the degree of monetary policy coordination we follow similar steps: we first
calculate the M2 growth rate for each country and calculate the correlation coefficientof the M2 growth rates across each pair of countries.10

10

Clark and van Wincoop (2001) used a different proxy for policy coordination. They measured thedifference in the monetary policy of two countries by the standard deviation of the interest rate
17

The regression results with cyclical parts generated by first-differences inlogarithms are summarized in <Table 3>.11 In <Table 3-A>, the regression results frompooling the three periods are reported. In columns 1 through 6, either trade intensity or
intra-industry trade is used as a regressor. In columns 7 through 9, both trade intensity
and intra-industry trade are included as regressors. Since the results for intra-industry
trade in columns 4 through 6 are very similar for the different measures of intra-industry
trade, only IIT4 is used for the measure of intra-industry trade in the estimation incolumns 7 through 9. Indeed regression results using IIT2 or IIT3 instead of IIT4 arevery similar and hence not reported.
The regression results in <Table 3-A> indicate that, as expected, the estimatedcoefficient for trade intensity is positive and statistically significant either at 1% or at
5%. Furthermore the coefficient for intra-industry trade is also positive and statisticallysignificant indicating a positive relationship between output correlation and intraindustry trade. However, when we include both trade intensity and intra-industry trade,the coefficient of intra-industry trade, though still positive, becomes statisticallydifferential and the difference in fiscal policy by the budget deficit differential. In our case, the interest
rate is not available for many countries and hence we used the monetary growth rate instead. Further
using the standard deviation instead of the correlation coefficient does not change our major results in
Section 4.
11
We have also performed the regression analyses with cyclical parts generated by the Hodrick-Prescottfilter. In this case the fitting of the model is generally poorer than in the case with first differences inlogarithms and more coefficients are not statistically significant. However the overall conclusions are
similar. These results can be obtained from the authors upon request.

18

insignificant. The coefficients for policy coordination are of the right sign, but only the
one for monetary policy coordination is statistically significant.
In <Table 3-B>, the results for a panel regression with fixed effects arereported. Compared to the OLS estimation with pooling, the estimation with fixed
effects eliminates unobservable country-specific components and is more appropriate
for analyzing the effects of time series patterns of trade. In this case, the coefficientestimates for intra-industry trade are, in most cases, statistically significant at 10% andquite robust across different specifications. Furthermore, even after including the tradeintensity measure, the estimates change very little, with the same level of significancemaintained. On the other hand, the coefficient estimates for trade intensity are notstatistically significant at the 10% level. Even when only trade intensity is used as aregressor, the coefficient estimates are in the right sign, but they are not statisticallysignificant. When both trade intensity and intra-industry trade variables are used as
regressors, the coefficient estimates for trade intensity are still insignificant at 10%.
Given that panel regression results generate a better specification of the model,
our results suggest that intra-industry trade might be a major source of business cycle
co-movements. Unlike Frankel and Rose (1998), our results indicate that increasing
trade itself does not induce synchronization of business cycles. Especially, if increasing

19

trade occurs mainly across different industries, it does not foster co-movements of
outputs with trading partners.

Insert <Table 3>

We have also performed various robust checks against our specification andgenerally arrived at the same conclusion.12 First, one might argue that Japan is the mostheavily involved in trade integration in the region and the exclusion of Japan would
substantially weaken our results. In <Table 4> the same regression results are reportedwith Japan excluded from the sample. While <Table 4.A> leads to generally the sameconclusion, contrary to our expectations, <Table 4.B> significantly enhances ourarguments. In <Table 4.B>, whose specification is more favored than that of <Table
4.A>, we find that the coefficients of intra-industry trade are significant even at 5% incolumns 7 through 9 where trade intensity measures are included as well. In contrast,
the coefficients of trade intensity are not significant and t-ratios are generally lower thanthose in <Table 3>.

12

As explained earlier, the general conclusion from the Hodrick-Prescott filter results is similar.

20Insert <Table 4>

Second, we further exclude Hong Kong and Singapore in our sample, because aconsiderable portion of trade for these countries is attributable simply to theirintermediatiation of trade between other countries. The regression results for this caseare reported in <Table 5>. While the significance is weakened as the sample size
shrinks, generally the same conclusion is preserved: in <Table 5.B>, the coefficients for
intra-industry trade are statistically more significant than those for trade intensity. In
this sense we can conclude that co-movements of business cycles are influenced moreby intra-industry trade than the volume of trade itself.

Insert <Table 5>

5. Implications for a Currency Union

The recent Asian currency crisis has induced Asian countries to seriously consider
forming a common currency area as an alternative to the past soft-peg as well as the21current floating exchange rate regimes. The successful launch of the euro in 1999 andits claimed benefits are also encouraging this movement.

For many developing

countries in the region with common export markets, the fluctuation of their bilateralexchange rates critically affects the competitiveness of their exports. Further, as tradeintegration among Asian economies accelerates, there will be more pressure, frominside, to enhance exchange rate stability among East Asian currencies. By establishinga common currency area, these countries can stabilize their overall export
competitiveness and enhance the benefits from further trade integration.
According to Rose (2000), membership in a currency union, ceteris paribus,
more than triples bilateral trade among member countries. 13 This is true even after
controlling for a number of other factors that might affect trade through the gravity
model, which shows that trade between a pair of countries is proportional to their
combined incomes, and inversely proportional to the distance between them. Thesegains from trade are made even without decreasing trade with non-member countries.
Since increasing trade by a factor of three is no easy feat, many people have questioned
Rose’s estimate. Despite the critics, Rose and his coauthors’ subsequent works suggest

13

Rose (2000) emphasizes a phenomenon known as “home bias” in international trade – much more
intense trade inside countries than between countries. McCallum (1995) quantifies the size of the intraregional bias at more than twenty to one, finding that trade between two Canadian provinces is more than
20 times larger than trade between a comparable Canadian province/American state pair. Rose seems toregard this home bias effect as one of the main driving forces for increasing trade through currency union.
22

that the increase in trade due to a currency union would be substantial.
Frankel and Rose (1998) emphasized that the fact that trade significantly
increases after the formation of a currency union has important implications: thedecision to join a monetary union has an endogenous nature. In other words, a cursory
review of historical data may give a misleading picture of a country’s eligibility forentry into a monetary union since the economic structure is likely to change
dramatically as a result of joining the monetary union. Especially, increased tradeaffects the nature of co-movements among member countries, which is the mostimportant element in gauging the costs of joining a currency union. These results canbe interpreted to encourage the early introduction of a monetary union; it will decrease
the costs of adopting a monetary union by lowering asymmetric shocks throughincreased trade. Even a country that is not suited ex ante to joining a monetary union
can be justified ex post facto in joining one due to lowered asymmetrical shocks.
However, our findings suggest that the above argument is not generally true:
business cycle co-movements are strengthened only when increased trade isaccompanied by more intra-industry trade. Therefore, caution should be exercised insearching for appropriate partners for a currency union. While trade may increase
following a currency union, if the increased trade is mainly inter-industry trade,23

business cycle co-movements can be weakened, making the currency union undesirable
ex post facto.
Most empirical studies widely confirm the hypothesis that measures of intraindustry trade relative to inter-industry trade decline steeply as the distance between the
trading partners increases. 14

In light of this finding, intra-industry trade among

geographically neighboring East Asian countries should be high. However, as shown in<Table 6>, the intra-industry trade index in East Asia was much lower than that ofEurope in 1980: 31.3 vs. 60.8 for IIT-2, 22.6 vs. 52.0 for IIT-3 and 20.0 vs. 46.6 for IIT4. This reflects the fact that the European countries had more homogenous industry
structures in 1980 compared to East Asian countries. 15 The index is monotonicallyincreasing in both regions, but the speed is much faster in East Asia, so that the gap
becomes much smaller in 1999: 56.1 vs. 67.9 for IIT-2, 51.1 vs. 58.9 for IIT-3 and 45.0
vs. 52.3 for IIT-4. This is good news for a currency union because trade in East Asia
not only increases overall but it also occurs more and more within the same industries.
If this tendency continues, then business cycle co-movements can be strengthened.

14

See for example, Balassa (1986a, 1986b), Balassa and Bauwens (1987, 1988), Bergstrand (1983),
Culem and Lundberg (1986), Hummels and Levinsohn (1995), and Stone and Lee (1995).

15

Rice, Stewart, and Venables (2002), by using the data of 22 OECD countries, find that closer countries
tend to have more similar structures of underlying export supply and import demand, and as aconsequence, intra-industry trade tends to be relatively high between close (and hence similar) countries.

24

When we examine individual countries, the intra-industry trade index is low in
China, Indonesia, Japan, Korea and Thailand – below the average in the region.
Furthermore, the index is increasing in most East Asian countries except for Hong Kong,
China and Korea. If this trend continues for these countries, then they are likely to havemore asymmetric shocks over the business cycle, which may prevent them from joining
a monetary union. However, it is very difficult to predict how this trend will change inindividual countries without analyzing what factors contribute to fostering inter- or
intra-industry trade. This will be a good subject for future research.

Insert <Table 6>

6. Conclusion

Increasing trade among Asian countries induces a higher degree of economic
integration within the region. In this sense the business cycle of a country is expected tobe continuously influenced by other economies in Asia, especially as trade within theregion grows relatively more important. In particular, we have found in this paper that25

intra-industry trade is the major channel though which the business cycles of East Asian
economies become synchronized.
Unlike other studies in the literature, however, we find that increasing trade itself
does not necessarily lead to more synchronization of business cycles. This has important
implications in considering the adoption of a currency union. We expect that the costsof joining a currency union will severely diminish only when intra-industry trade
dominates afterwards.

26

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31

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32

Table 1. GDP Growth, Trade-GDP Ratio and Regional-World Trade Ratio
GDP growth T/GDP mean T/GDP growth
R/W
R/W
(%)
(%)
(% point)
mean (%)
Growth (%)
BGD
3.9
24.6
0.9
30.3
1.2
CHN
9.3
27
6.2
51.4
0.9
HKG
6.6
234.1
4.1
53.6
1.4
IDN
6.2
16.7
0.4
56.8
0.1
IND
5
49.4
0.6
19.7
0.4
JPN
3.3
21.2
-0.2
27.4
1.1
KOR
7.2
64.6
0.3
36.4
0.4
MYS
7.1
132
4.6
55.5
0.5
PHL
2.8
59.3
3.3
40.2
0.7
SGP
7.2
291.3
-1
48.1
0.7
THA
7.1
64.3
2.4
46.1
0.4
TWN
7.6
93
-0.05
73.8
1.7
Average
6.1
89.8
1.8
44.9
0.8
Note: 1. The values less than 1/10 are all dropped.
2. In most cases, the sample period is 1977 to 1997; For China, it is 1979 to 1997.

33

Table 2. On Sub-Sample Averages of the Variables
corr_ycorr_y

wt
wt
IIT-4
IIT-4
(simple(weigted(simple(weigted(LD)(HP)
average) average) average) average)
BGD
Period 1
0.11770.16970.00160.00040.029
0.0105Period 2-0.1-0.25
0.00160.0006
0.02650.0172
Period 3-0.2315-0.18270.00180.00110.044
0.0207
CHN
Period 1-0.2440.05540.01720.0193n.a.
n.a.
Period 2-0.2727-0.3737
0.02330.03890.21020.1973
Period 3
0.15910.08050.02120.03140.29590.2273
HKG
Period 1
0.35620.17450.01820.01610.57140.1961
Period 2
0.18720.22110.02540.04610.59040.3192
Period 3-0.0589-0.0695
0.03240.07720.57330.3701
IDN
Period 1
0.30490.09840.01260.02280.05540.033
Period 2
0.20840.05090.00930.014
0.14750.0571
Period 3
0.26630.17550.01070.01110.24440.1047
IND
Period 1-0.1248-0.15890.004
0.0011
0.06180.0083
Period 2
0.16290.29120.00440.00140.13390.0236
Period 3
0.10240.06730.00440.001
0.24280.0504JPN
Period 1
0.35060.17380.01990.00690.12810.0349Period 2
0.05640.26370.02140.008
0.17290.0565Period 3-0.0954-0.1932
0.02660.01280.26320.1111
KOR
Period 1-0.0144-0.04710.00090.00090.174
0.0899Period 20.22510.25790.00110.00110.32180.1268Period 3
0.0876-0.02410.00160.00120.31920.1654
MYS
Period 1
0.2025-0.16850.01340.01440.18220.1163
Period 20.32040.06630.01420.01590.27940.1957
Period 3
0.26370.14870.01710.02020.38750.2681
PHL
Period 1
0.2537-0.24620.00480.00320.12630.0548Period 20.268
0.39070.00460.00250.22220.1061
Period 3-0.1281-0.1220.00570.00390.28210.1577
SGP
Period 1
0.1273-0.20220.02350.01860.23210.1152
Period 2
0.36410.18760.02280.0196
0.34440.1813
Period 3
0.28880.19370.02590.0256
0.41950.2721
THA
Period 1
0.25980.101
0.00730.005
0.12280.0486
Period 2
0.29950.34450.00880.00660.165
0.0883
Period 3
0.28490.07930.01230.01170.30870.1748TWN
Period 1
0.39350.163
0.01520.01580.177
0.1226
Period 2
0.08720.22140.016
0.01980.24550.183
Period 3-0.0797-0.12150.02010.0347
0.33870.293
Note: The fractions are rounded off to four decimal places, before being transformed into apercent unit.

34

Table 3. The Effects of Trade Integration on Business Cycle Co-movements.
3.A Pooled Regression
1
wx

2

3

4

5

6

4.183***(2.87)

wm

7

8

9

3.306**(2.17)
3.071**(2.57)

wt

2.244*(1.78)
4.022***(2.91)

IIT_2

3.116**(2.13)
0.374**(2.24)

IIT_3

0.384**(2.25)

IIT_4

0.399**
0.291
0.312*
0.288(2.27)(1.61)(1.72)(1.59)
bd_corr
0.077
0.064
0.073
0.061
0.064
0.07
0.08
0.07
0.08
(1.15)(0.96)(0.90)(0.90)(0.94)(1.00)(1.18)(1.05)(1.15)
m_corr 0.255*** 0.279*** 0.262*** 0.272*** 0.268*** 0.266*** 0.241*** 0.261*** 0.248***(3.24)(3.58)(3.37)(3.45)(3.39)(3.37)(3.06)(3.32)(3.16)# of obs.
166
167
167
163
163
163
163
163
163
R^2
0.13
0.12
0.13
0.11
0.11
0.11
0.14
0.13
0.14
Root MSE
0.42
0.42
0.42
0.43
0.43
0.43
0.42
0.42
0.42
Note: (i) The dependent variable is output correlation between two countries for the three sub-sample periods, 197683 (period 1), 1984-90 (period 2) and 1991-97 (period 3). The trade intensity measures, wx, wm and wt are
defined as in equation (1), (2) and (3), based on exports, imports and total trade respectively. The intraindustry trade measure, IIT_2, IIT_3, and IIT_4, are defined as in equation (4), based on ISIC 2, 3 and 4
digit classifications. Two added regressors, bd_corr and m_corr are measures of the fiscal and the monetary
policy coordination respectively.
(ii) The values in parentheses are t-ratios. The significance of the estimated coefficients are denoted by *indicating significance at 10 %, **, at 5 % and ***, at 1 %.
3. B

Panel Regression: Fixed Effects
1

wx

2

3

4

5

wm

5.133
(1.27)
9.100(1.32)

9

7.665(1.00)
0.427(1.22)

intrm_3

0.632*(1.72)

intrm_4# of obs.
overallR^2sigma_e8

5.384(1.26)

intrm_2

m_corr7
6.247(0.84)

wt

bd_corr6

8.640(1.25)

0.08(0.92)
0.093(0.99)
166

0.073(0.84)
0.102(1.09)
167

0.076(0.88)
0.097(1.05)
167

0.063(0.71)
0.090(0.93)
163

0.063(0.72)
0.090(0.94)
163

0.800*(1.94)
0.07(0.79)
0.084(0.88)
163

0.09

0.08

0.08

0.08

0.07

0.07

0.10

0.08

0.09

0.40

0.40

0.40

0.41

0.41

0.41

0.41

0.40

0.41

Note: See the note to Table 3.A.

35

0.707
(1.65)
0.08(0.89)
0.087(0.91)
163

0.787*(1.91)
0.08(0.88)
0.093(0.98)
163

0.704*(1.66)
0.08(0.90)
0.088(0.93)
163

Table 4. The Effects of Trade Integration on Business Cycle Co-movements (Japan
excluded)
4.A Pooled Regression
1
wx

2

3

4

5

6

4.754***(3.21)

wm

7

8

9

3.785**(2.43)
3.029**(2.41)

wt

1.976(1.47)
4.25***(2.90)

intrm_2

3.193**(2.10)
0.374**(2.17)

intrm_3

0.448**(2.51)

intrm_4

0.44**(2.43)
bd_corr
0.116
0.092
0.106
0.096
0.104
0.107
(1.62)(1.28)(1.48)(1.31)(1.42)(1.45)
m_corr 0.238*** 0.267*** 0.247*** 0.276*** 0.273*** 0.268***(2.81)(3.16)(2.93)(3.26)(3.23)(3.17)# of obs.
144
145
145
141
141
141
R^2
0.15
0.13
0.14
0.12
0.13
0.12
root MSE
0.42
0.42
0.42
0.42
0.42
0.42

0.302
(1.62)
0.121*(1.67)
0.23***(2.72)
141
0.16
0.42

0.35*
0.309
(1.84)(1.64)
0.105
0.114(1.43)(1.56)
0.255*** 0.239***(3.01)(2.82)
141
141
0.14
0.15
0.42
0.42

4. B Panel Regression: Fixed Effects
1
wx

2

3

4

5

wm

6.193
(1.41)
9.915(1.32)

97.779(0.93)
0.611*(1.68)

intrm_3

0.877**(2.27)

intrm_4# of obs.
overallR^2sigma_e8

6.956(1.51)

intrm_2

m_corr7
4.294(0.55)

wt

bd_corr

6

7.962(1.09)

0.104(1.08)
0.055(0.53)
144

0.097(1.02)
0.061(0.59)
145

0.098(1.32)
0.057(0.56)
145

0.086(0.89)
0.050(0.47)
141

0.081(0.84)
0.048(0.46)
141

1.077**(2.50)
0.09(0.94)
0.036(0.35)
141

0.12

0.08

0.10

0.06

0.07

0.06

0.10

0.08

0.10

0.41

0.41

0.41

0.41

0.41

0.40

0.41

0.41

0.40

Note: See the note to Table 3.A

36

1.015**(2.27)
0.09(0.98)
0.040(0.38)
141

1.086**(2.54)
0.09(0.99)
0.043(0.42)
141

0.991**(2.25)
0.09(0.99)
0.039(0.38)
141

Table 5. The Effects of Trade Integration on Business Cycle Co-movements (Japan,
Hong Kong and Singapore excluded)
5.A Pooled Regression
1
wx

2

3

4

5

5.335
(1.64)

wm

0.870(0.30)
5.566(1.39)

93.754(0.90)
0.341(1.61)

intrm_3

0.469*(2.03)

intrm_4# of obs.
R^2
root MSE

8-0.644(-0.22)

intrm_2

m_corr7
4.391(1.34)

wt

bd_corr

6

0.067(0.79)
0.27***(2.73)
111
0.10
0.43

0.060
0.058
0.054
0.053
(0.69)(0.69)(0.63)(0.62)
0.289*** 0.275*** 0.272*** 0.263***(2.91)(2.78)(2.72)(2.64)
112
112
110
110
0.08
0.09
0.10
0.11
0.43
0.43
0.43
0.43

0.506*(1.99)
0.05(0.59)
0.25**(2.48)
110
0.11
0.43

0.453*(1.77)
0.06(0.64)
0.239**(2.37)
110
0.13
0.43

0.52*
(1.97)
0.05(0.61)
0.248**(2.44)
110
0.11
0.43

0.445*(1.69)
0.05(0.58)
0.245**(2.43)
110
0.12
0.43

7

8

9

5. B Panel Regression: Fixed Effects
1
wx

2

3

4

5

wm

1.279
(0.12)
6.739(1.25)

wt

6.707(1.23)
16.779(1.22)

intrm_2

9.051(0.60)
0.430(1.09)

intrm_3

0.703*(1.67)

intrm_4
bd_corrm_corr# of obs.
overallR^2sigma_e66.795(0.65)

0.039(0.34)
0.086(0.70)
111

0.036(0.32)
0.092(0.78)
112

0.046(0.41)
0.096(0.81)
112

0.022(0.20)
0.074(0.61)
110

0.017(0.16)
0.075(0.62)
110

0.817*(1.70)
0.02(0.22)
0.063(0.52)
110

0.07

0.02

0.07

0.07

0.08

0.08

0.09

0.05

0.08

0.41

0.41

0.41

0.42

0.41

0.41

0.41

0.41

0.41

Note: See the note to Table 3.A

37

0.798
(1.56)
0.03(0.24)
0.065(0.53)
110

0.829*(1.73)
0.03(0.31)
0.075(0.62)
110

0.708(1.37)
0.04(0.32)
0.073(0.60)
110

Table 6. Trends of Intra-Industry Trade in East Asia and Europe (percentage)

IIT-2

1980
IIT-3

IIT-4

IIT-2

1990
IIT-3

IIT-4

IIT-2

1999
IIT-3

IIT-4

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

17.7
45.1
33.6
29.8
35.8
39.6
23.4
34.2
23.1
30.3
31.3

14.2
42.2
6.7
18.4
30.2
27.3
19.3
25.5
17.8
24.6
22.6

13.5
41.1
5.8
16.4
28.2
23.1
14.9
22.3
12.2
22.0
20.0

60.7
58.4
34.1
37.5
44.4
48.6
41.8
57.9
32.0
41.3
45.7

55.8
57.1
17.2
30.3
40.1
44.1
30.4
47.2
30.0
37.3
38.9

51.7
55.8
13.6
28.2
37.9
40.1
25.8
42.4
26.0
34.3
35.6

45.8
56.8
44.9
56.4
43.4
68.9
59.9
74.1
60.4
49.9
56.1

42.7
56.6
36.1
49.3
36.0
64.9
54.7
68.2
56.4
46.6
51.1

39.0
56.5
26.0
41.0
32.8
56.5
45.2
61.9
48.1
42.7
45.0

EuropeAustria
Denmark
FinlandFranceGBR
Germany
GreeceIreland
Italy
Netherlands
NorwayPortugal
SwedenAverage

60.4
58.7
49.7
78.2
74.9
67.1
42.6
60.4
71.4
70.7
58.4
32.4
65.9
60.8

52.7
50.8
43.4
65.9
66.8
62.7
22.0
55.0
58.4
63.0
50.6
26.4
58.4
52.0

47.1
44.4
40.5
60.0
61.1
59.4
16.5
45.7
52.9
55.9
46.3
21.2
54.7
46.6

70.5
66.9
59.0
79.3
79.1
71.6
39.1
71.6
69.7
73.3
65.1
45.8
71.9
66.4

60.2
56.5
54.0
71.2
72.3
68.0
30.5
62.9
60.6
65.8
52.5
40.3
64.1
58.4

53.5
49.5
48.9
64.3
65.5
64.5
23.2
51.9
53.9
58.9
47.6
32.7
59.3
51.8

71.2
72.0
63.6
79.4
83.2
71.1
32.4
69.5
72.4
70.8
64.5
54.9
77.3
67.9

63.5
63.0
51.1
71.7
74.3
68.2
26.1
58.3
63.9
62.3
48.4
49.0
66.2
58.9

56.9
53.8
45.4
66.1
65.9
64.1
21.0
49.3
56.2
56.7
43.2
40.7
61.1
52.3

38