THE IMPACT OF A COMMON CURRENCY ON EAST ASIAN PRODUCTIONNETWORKS AND CHINA’S EXPORTS BEHAVIOR ∗By Mizanur Rahman‡First draft: March 06, 2007
This version: January 27, 2008
Abstract
Vertical fragmentation of product value chain across borders is the driving force ofgrowing economic interdependency in East Asia. A common currency, not flexible
exchange rates between national currencies, would reduce flexibility in relative prices
within East Asia. Its impact would be far greater for exports that have stronger
production network linkage. In order to test the hypothesis, the paper estimates the effectof a common currency on China’s processing and ordinary exports separately. The
distinction is necessary because the processing exports, unlike the ordinary exports, areproduced along the regional production networks, with final stages of assembly andexporting being increasingly concentrated in China. The short-run dynamics indicate thatthe effect on China’s processing exports is more than double the corresponding effect on
China’s ordinary exports. The long-run effect on the processing exports of intra-regional
RER flexibility, which is otherwise the lack of a regional currency, is almost nine times
as large as the long-run effect of a unilateral RMB appreciation. By contrast, thecorresponding long-run effect is statistically insignificant for the case of ordinary exportsthat are produced primarily by using local inputs. The long-run coefficient of this intraregional RER flexibility implies that the actual volume of processing exports is 20
percent below the potential. The magnitudes of these effects are consistent with the
hypothesis that a common currency would further integrate East Asian production
networks and promote regional economic integration.
Keywords: Production networks, fragmentation of value chain, optimum currency area,
common currency, exchange rate flexibility, China.
JEL classification: F14, F33, F36, F42∗

The author specially thanks Kaliappa Kalirajan and Koichi Hamada for their detailed comments on an
earlier draft of this paper. The author is also very grateful to the following individuals for their helpfulcomments and suggestions: David Roodman, James Rhodes, Masaru Yoshitomi, Partha Dasgupta, Ronald
McKinnon, Takashi Fukushima, Yihui Lan, Yoichi Okita, Yujiro Hayami, Wade Pfau, Willem Thorbecke,and seminar participants at FASID/GRIPS in Japan and at Hong Kong University of Science andTechnology (HKUST) and City University of Hong Kong in Hong Kong SAR. The research was conductedin part at the Research Institute of Economy, Trade and Industry (RIETI). The views expressed herein arethose of the author and do not represent the views of RIETI.
‡Assistant Professor, University of Dhaka, Bangladesh; PhD Scholar, National Graduate Institute for
Policy Studies (GRIPS), 7-22-1 Roppongi, Minato-ku, Tokyo 106-8677, JAPAN; Tel: +81-03-6439-6048;
Fax: +81-03-6439-6070; E-mail: D0305@grips.ac.jp.

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1. Introduction

In a seminal paper, Robert Mundell (1961) argued that an optimum currency area (OCA)
would be a region, not the domain of national currencies. His argument is that if factors
are sufficiently mobile across national boundaries in the region, then a flexible exchange
rate system based on national currencies becomes unnecessary, and may even be
positively harmful. McKinnon (1963) further advanced the concept in terms of the ratioof tradable to non-tradable goods. His argument is that if a number of countries tradeextensively with each other and if each pegs its currency to a representative bundle ofimports, then each currency will be pegged to the others. McKinnon argued that tomaintain the liquidity value of individual currencies, a fixed exchange rate system, or acommon currency, would be necessary. A common currency would greatly facilitatecontractual arrangements. It would thereby stimulate factor mobility among the countries
and promote economic specialization and growth in the region. In effect, both the OCA
criteria are conceptually interrelated and endogenous to the intra-regional tradeintegration. If countries in a particular region are increasingly integrated in theirproduction and trading relationships, a new demand will arise for a regional currency andagainst national currencies within the region.
The recent literature generally suggests that capital and technology have become highly
mobile across East Asian countries since the early 1990s1. The literature further indicatesthat East Asian economies have synchronized business cycles, particularly after the Asianfinancial crisis and that there is a greater degree of interdependency among the countries
in the region. In the context of East Asian production networks, it is rational to argue thatthe condition of greater labor mobility is largely attained by vertical fragmentation of
production processes across borders in East Asia2. Note that the notion of labor mobility
is meant not to be in terms of geographical and/or inter-industry dimensions as indicatedin the OCA literature, but by the way of intra-industry fragmentation of product value
chain across national borders. The present study therefore conjectures that East Asia is an
optimum currency area. However, East Asian countries have their independent national
currencies and pursue heterogeneous exchange rate policies. This leads to the researchquestion: what would happen to East Asian production networks and regional tradeintegration, had there been a common currency? To put it differently, what is theopportunity cost for East Asian exports for not having a regional currency? Particularly,
this paper’s focus is on measuring the costs to China’s processing and ordinary exports
respectively.
1See, for example, Kwan (2001), Hatch, (2003), McKinnon and Schnabl (2003), Yusuf et al. (2004),
Thorbecke and Yoshitomi (2006), Fujita (2007), Kawai (2007), and Plummer and Wignaraja (2007). The
author thanks Ronald McKinnon for referring to recent empirical evidence on increasing synchronization ofbusiness cycles in East Asia.
2
The phenomenon of international fragmentation of production processes can be construed much in linewith Raymond Vernon’s (1966) product cycle hypothesis. Vernon’s fundamental conjecture that the locus
of production will be shifted to the less-developed South as the production techniques become standardized
implicitly recognizes that parts of production process, not the entire product value chain, can also be shiftedto the less-developed South. The resultant trade pattern would be vertical intra-industry trade, not thehorizontal inter-industry trade.

4

In the empirical trade literature, the conventional approach has been to estimate a gravity
model by using a cross-country dataset, where the effect of currency union on trade,
income and other macroeconomic variables is captured by a dummy variable3. Anderson
and van Wincoop (2003), assuming complete production specialization and homothetic
preferences, obtained a theoretical gravity equation. The model predicts that bilateral
trade, after controlling for size, depends on the bilateral trade barrier between two
regions, relative to the product of their multilateral resistance indices4. They estimated themodel both in the context of a two-country setting consisting of the U.S. states andCanadian provinces, and a multi-country setting that also included 20 other industrializedcountries. Based on the estimate of elasticity of substitution (i.e., 5.0) from Hummels(1999), the study found that a tariff equivalent estimate of the U.S.-Canada border barrier
would be 48 percent. The study further found that border barrier reduced trade between
the United Sates and Canada by 44 percent of that of border-less trade. Rose and van
Wincoop (2001), using 1980 and 1990 data for a set of 143 countries, estimated theAnderson-van Wincoop gravity model to estimate the effect on trade of monetary unions.
The study replaced the multilateral resistance terms with country-specific fixed effects.
They found that the tariff equivalent estimate of the monetary barrier to trade would be
26 percent. For the case of European Monetary Union (EMU), the results showed a 58
percent trade-creating effect of currency union for the euroland countries. This is perhaps
the most conservative estimate of the effect of a currency union on trade. Klein andShambaugh (2006) developed a comprehensive database for 181 countries over the 19731999 period and used a de facto exchange rate regime classifications. The de facto
classification scheme included currency union, direct peg and indirect peg, all being
mutually exclusive meaning that any one observation can only be coded as one type of
exchange rate regime. They also estimated a standard gravity model including dummies
for three regime classifications and other usual controls. The study found that a fixedexchange rate system (direct peg) would ‘increase international trade with one another 36
percent relative to intranational trade (p. 370).’
Surprisingly, all the empirical results are, to the best of our knowledge, ex post estimatesof the effect of either a common currency or a fixed exchange rate system5. The applied
methodologies, in general, do not suggest a way to obtain ex ante estimates of the effectof a common currency, when a region is an optimum currency area but with independent
national currencies linked by flexible exchange rates. Nor does the literature offer a3

Rose and Engel (2002) and Frankel and Rose (2002) showed that a currency union would increase trade
between union members by a factor of over three. Rose and Stanley (2005, p23) reported 34 empiricalestimates of the effect on trade of currency union. The median coefficient of the currency union dummy
was shown to be 1.2, implying that membership in a currency union would, cateris paribus, triple bilateral
trade—e1.20>3.0.
4
See Kalirajan (2007) for a stochastic frontier formulation of standard gravity model to estimate the effectof country-specific resistance on bilateral trade flows.
5
Klein and Shambaugh (2006) showed that the number of observations with de facto fixed exchange rate
systems including currency union in a typical database of bilateral trade and exchange rates would be about
2 percent of total observations. They represent only 11.5 percent of world trade. A dummy variable
representation for those limited observations in a large dataset is non-random and likely to draw misleading
information into the variance-covariance matrix. Estimation inefficiency can be substantial depending on
de facto regime classifications and empirical specifications.

5

framework to estimate the effect for a particular country, which belongs to a currency
area. The present study develops a conceptual framework to that end and obtains
empirical estimates of the effect of a common currency in East Asia.
The conceptual framework essentially incorporates the features of cross-borderfragmentation of production processes and intra-regional exchange rate flexibility into themodeling. Since export production has evolved regionally with the value chain being
fragmented vertically across national boundaries, the “gross value” of exports from any
East Asian country represents the sum of incremental value-added that occurs along thecross-border production networks. Bilateral trade flows, which are recorded at “gross,”within the region are thus mostly vertical intra-industry trade (VIIT). This causes theeffect of intra-regional exchange rate flexibility on final exports to be multiplicative by
the degree of fragmentation of value chain of those exports. The study uses alternative
proxies to represent production network linkage of China’s final exports with other East
Asian countries. An inner product of the degree of production network linkage and thelog of bilateral real exchange rate between China and China’s regional trading partners
thus constitute the key variable. The variable is called the intra-regional real exchange
rate (RER) flexibility, which represents the unintended misalignment in relative prices
between China and the rest of East Asia. The variable along with other relevant
covariates enters into a multivariate modeling to explain bilateral real exports in animperfect substitutes framework. The other covariates include bilateral RER variable,
GDPs of importer and exporter, proxy for supply shift effect, and general gravity
variables. This model is termed as fully specified model and the parameter estimate of the
key variable of this model denotes the impact of intra-regional RER flexibility on
exports. Another hypothetical model is then estimated, assuming that East Asia is an
optimum currency area and hence that the intra-regional RER flexibility variable isirrelevant. That is, the hypothetical model includes all the controls but the RER flexibility
variable. The estimate of bilateral RER variable in the hypothetical model and that of theintra-regional RER flexibility variable in the fully specified model are then statisticallyreconciled in order to estimate the impact of a common currency on exports. A testable
hypothesis is that a common currency would have relatively larger effect on those exportsthat have deeper production network linkage across borders in East Asia.
The study applies the above framework to estimate the effect of a common currency on
China’s exports behavior. In doing that, the study distinguishes between China’s
processing exports and the ordinary exports. The distinction is imperative because theprocessing exports, unlike the ordinary exports, are produced along the regional
production networks, with final stages of assembly and exporting being increasingly
concentrated in China. As an outcome, China experiences increasing vertical intraindustry trade (VIIT) with the rest of East Asia and a surge in final exports to the UnitedStates, Europe and elsewhere. A common currency in East Asia would significantly
eliminate flexibility in relative prices between China and the rest of East Asia. Its impact
would therefore be far greater for the processing exports than it would be for the ordinary
exports.
The study formulates China’s export demand equation in an imperfect substitutes
framework. An autoregressive and distributed lag (ADL) specification of the model is6estimated for both the panels of China’s processing and ordinary exports. The paper does
not make the arbitrary assumptions that the variables are unit root processes and that
there exist cointegration relations. Instead, exact time series properties of the data are
obtained. The ADL specification is consistently estimated so that spurious estimates ofthe long-run parameters are not obtained. This is very likely when there are nocointegration relations in the observed data, but an arbitrary long-run model is estimated.
Since the dynamic model includes fixed effects and one or more of the right-hand side
variables can be predetermined and/or endogenous, both the pooled OLS and covarianceestimators are inconsistent. Thus, the model is estimated by using Generalized Method ofMoments (GMM) approach as suggested by Holtz-Eakin et al. (1988), Arellano and Bond
(1991) and Blundell and Bond (1998). The consistent GMM estimators are based on aset of moment conditions that are related to both the differenced equations and the levels
equations of the model.
The reminder of the paper is organized as follows. Section 2 describes background of
China’s surging exports to the rest of the world, particularly the U.S. and Europe, and it’sincreasing linkage with East Asian production networks. Section 3 presents theconceptual framework setting out a model for the empirical estimations. Section 4 details
on data and econometric methodologies. Section 4.1 discusses time-series properties ofthe observed data. Section 4.2 introduces the dynamic panel data model. Section 4.3
draws on estimation methods and specification tests. Section 4.4 describes data sources
and main variables. Section 5 contains results and interpretation. Section 6 discusses
robustness of the results. A final section brings the overall conclusions of this paper.
2. Production Networks in East Asia and China’s Exports
Three economic regions of the world, i.e., NAFTA (North American Free Trade Area),
EU-15 (European Union-15), and East Asia-15, registered rapid growth relative to theworld average over the 1985-2005 period. Figure 1 shows the pattern of growth andeconomic interdependency of these three economic regions. The question is whether the
economic interdependency within each region has been growing stronger in relation to
the growth. The figure shows that it has been so in the case of East Asia6. In fact, theshare of intra-regional trade of East Asia is approaching to that of EU. The mostdistinctive feature of East Asian growth and trade integration is that production isorganized internationally in the region. The phenomenon is widely known as East Asian
production networks.
[Insert Figure 1 here]
The production network underlies fragmentation of product value chain across borders inEast Asia7. Here Japan and NIEs-2 (South Korea and Taiwan), in general, organize those
6

Unlike NAFTA and EU-15 that shares stronger political and/or monetary union, East Asian economic
integration has been mainly through market mechanisms, with little support from region-wide politicalinstitutions (Fujita, 2007).
7
IBM’s CEO, Sam Palmisano (2006), called it ‘globally integrated business strategy’ of multinational
corporations. He argued that the global integration of production was not just to cut costs, but more to tap

7

production processes that use relatively higher skilled workers and produce sophisticatedproduct prototypes, high-tech intermediate goods and capital equipments. These
intermediate goods are transformed into finished products at assembly plants mainly in
China. The finished products are then exported throughout the world. Palmisano (2006)
noted that an estimated 60,000 manufacturing plants were built by foreign firms in Chinaalone between 2000 and 2003 and that most of these factories target the global market,
not the local Chinese market. Greenspan (2005) thus argued, “...production within Asia
has evolved, with the final stages of assembly and exporting becoming increasingly
concentrated in China.” As an outcome, trade along these production networks, which iscalled vertical intra-industry trade (VIIT), has increased substantially over time. Figure 2
shows the surging pattern of intra-industry trade between China and the rest of East Asia.
Its implication is that the dollar cost of intermediate goods imported into China from therest of East Asia represents a significant share of the ‘gross value’ of Chinese finishedexports to the Americas, Europe and elsewhere.
[Insert Figure 2 here]
Table-1 reflects on China’s foreign trade in terms of product types and the country’s
major trading partners. The table summarizes China’s trade statistics that are
disseminated by Statistics Department of Customs General Administration of thePeople’s Republic of China8. The export statistics are compiled into three categories: (a)the ordinary exports by local firms; (b) the processing exports by the foreign-owned
firms (labeled FDI-processing) and (c) the other processing exports by Chinese owned
firms. Similar nomenclature is followed for the compilation of import statistics. For theordinary exports, local value addition constitutes the substantial portion of the ‘grossvalue’ of those exports, whereas for the processing exports (both [b] and [c]), a larger
share of the ‘gross value’ originates in the upstream production blocks that are mainly
located in Japan, NIEs and ASEAN. Feenstra and Spencer (2005, p. 1) remarked thatprocessing exports were produced under contractual arrangements with foreign
multinationals, whereas the ordinary exports did not have these arrangements.
Panel A of Table-1 shows that in 2005, the processing imports that are made undercontractual arrangements with foreign multinationals accounted for 58 percent of China’s
overall imports. Of this 58 percent, about 65 percent came from other East Asian
countries. By contrast, only 13 percent came from the U.S. and EU-15. Panel B of Table1 shows that in 2005, China’s processing exports accounted for 55 percent of its overall
exports. Of this 55 percent, about 76 percent went to the Americas and Europe, if Hongnew sources of skills and knowledge. The strategic decisions are not simply a matter of off-loading noncore
activities, nor are they mere labor arbitrage. They are about actively managing different operations,expertise, and capabilities across national boundaries. Fujita (2007, p. 18) argued that it had been thestrategy of multinational firms (MNFs) to take advantage of difference in technologies, factor endowments
or factor prices, and market sizes across countries.
8
These data were made available to the author by RIETI, which purchased the data from China’s Customs
Statistics Information Center, Economic Information Agency, Hong Kong.

8

Kong is arguably treated as an entrepôt of trade and transshipment to the west9. Because
processing imports are brought-in duty-free primarily for using in the production offinished exports, the dollar cost of these imports represents a substantial share of thegross value of China’s processing exports. But, since exports by country are recorded ona gross basis rather than as value added, the widening bilateral deficits of both the U.S.
and Europe against China, measured gross, have largely been in lieu of their wider
deficits with other East Asian economies. Greenspan (2005) also emphasized on thispoint in his testimony before the U.S. Senate Finance Committee.
Panel C of Table-1 shows China’s trade account balance in 1993 and 2005. In 2005,
China incurred a deficit of $140 billions against the rest of East Asia, but a surplus of
about $290 billions against the U.S. and EU-15. In terms of product types, almost 90
percent of China’s bilateral trade deficit against the rest of East Asia occurred on accountof processing trade (both [b] & [c]). By contrast, about 83 percent of China’s bilateral
trade surplus against the U.S. and EU-15 occurred on account of the processing trade.
However, it is trade in intermediate inputs that define China’s trade deficit with the restof Asia, whereas it is trade in final goods that define China’s trade surplus with the rest of
the world. Therefore, it is the production and exporting of processed goods that are
defining parameters of China’s integration backward with East Asian production
networks and forward into the world trading system.
The present study thus distinguishes between the processing exports and ordinary exports
for analyzing the impact of a common currency on China’s exports. It is believed that theexisting heterogeneous exchange rate policies in East Asia will largely affect China’s
processing exports by affecting the growing pattern of VIIT. In order to do it, the study
first aims to provide consistent estimates on the impact of RER flexibility between China
and other East Asian countries that supply intermediate goods to China. The study thenestimates potential costs respectively for the processing and the ordinary exports for nothaving a common currency in East Asia. Accordingly, a conceptual framework isdeveloped in the following section.
3. Conceptual Framework of the Study
As evident in the previous section, the ‘gross value’ of China’s exports essentially arises
from the interlinked production networks in East Asia. In particular, the value chain of
China’s processing exports is vertically fragmented along the networks. Figure 3 depicts
a schematic view of the fragmented value chain and real exchange rate relationships
within East Asia and between East Asia and the rest of the world. Japan, NIEs-2,
ASEAN-4 and China are shown in four rectangular-shaped cells. x j represents
incremental real value added by production blocks in country j to the value chain of a
product. We assume that the production value chain is ended in China. The solid line
indicates the general pattern of intra-regional VIIT trade in East Asia. Since bilateral
9
Hong Kong has yet remained as an entrepôt to facilitate transshipment of China’s final exports to the restof world, largely to circumvent both trade-related and non-trade barriers in the U.S. and EU-15 (Kwan,
2002; and Fung and Lau, 2001).

9

exports are recorded ‘gross’ at every point of cross-border transfer, Σx j ' s rather than thex j ' s are observed. Therefore the real value of processing exports from China to country
i is the gross Σx j instead of the incremental x C , which is the Chinese value added. The
dashed line indicates the price-adjusted real exchange rates within East Asian countries
and also between East Asian countries and country i . Note the locus of the RMB(renminbi) exchange rate in the network. Though the vertical fragmentation of production
processes along the networks has blurred independence of borders in East Asia, theirexchange rate and monetary policies are rather independent and asymmetric from one
another. The issue is how changes in country i' s nominal exchange rate, say the quasiglobal currency like the U.S. dollar or its new rival the Euro, would enter into the
production networks and affect VIIT as well as final exports from China. The related
hypothetical question is what would happen, had there been a common currency in East
Asia.
[Insert Figure 3 here]
There are two possible scenarios in the actual policy environment. First, the nominal
exchange rate of country i may experience a discrete depreciation only against the
Chinese RMB. Second, it may depreciate against all East Asian exchange rates including
the RMB. In either of the above two cases, it should be recognized that nominal exchange
rates and national monetary policies are mutually determined in financially open
economies (see, Lahiri and Vegh, 2001; and Calvo and Reinhart, 2002). But East Asian
countries do significantly differ in the application of these policy instruments (see, e.g.,
Ogawa and Ito, 2002; and Ogawa and Yang, 2006). Therefore, to what extent a nominal
appreciation of either the Chinese RMB or all the East Asian currencies, particularly
against the world invoice currency, the U.S. dollar, will translate into a real appreciationof the respective currencies is unknown. However, if policy reaction by individual East
Asian countries tends to be heterogeneous and uncoordinated, there will be significant
flexibility in the real exchange rates within East Asia and between East Asia and the restof the world. Its impact on the production networks and related VIIT will be highlyreflected in the case of processing exports from China, but not so in the case of ordinary
exports.
Three sets of real exchange rate relationships, as indicated by the dashed lines in Fig. 3,
are defined for the analytical purpose: (a) the real exchange rate between country j that
supplies intermediate goods to China and country i that imports final exports from
China, RER ji ; (2) the real exchange rate between China and country i , RER ci , and (3)the real exchange rate between country j and China, RER jc 10. Ogawa and Yang (2006;
p. 17) find that East Asian countries do not have any effective coordination mechanism in
10

For the home country i and foreign country j with price levels p i and p j and e ij being the nominal

exchange rate (in terms of home currency), we say that home country experiences a real appreciation, andthe foreign country a real depreciation, when RER ij = p i / e ij p j rises. The time subscript t issuppressed for notational convenience.

10

their international macroeconomic policies and that their exchange rate policies arelargely asymmetric to movements in the world invoice currencies. They even resort tocompetitive devaluation. It is argued that difference in the reactions of the East Asianexchange rates to the depreciation of the rest of the world currencies, will create
substantial variability in the intra-regional real exchange rates. In the context of China,
the asymmetric reactions imply substantial variability in RER jc . In other words, pricestability along the production networks will be distorted, thereby affecting growing
supply-chain linkages between China and the rest of East Asia. Its impact on China’s
final exports would be compounded by the degree of supply chain linkage of those final
exports with the rest of East Asia. How do we capture the impact of intra-regional
exchange rate flexibility on exports from China? The present study thus creates a newvariable, called the intra-regional RER flexibility variable, which is defined below.
Let ω j be the weight of country j in the gross value of Chinese final exports. Then, theωterm RER jcj , which is [ω j ln( RER jc )] after log transformation, captures both the
dynamic integration of China with country j and the asymmetry in exchange rate andmonetary policies between them. For the present analysis, the new variable is thus
defined as RERw = Σ j ω j RER jc . The term ω j is proxied either by the share of country j in
China’s imports for processing or by their intra-industry trade intensity. Here, thesubscript j represents the following East Asian countries: Japan, South Korea, Taiwan,
Hong Kong, Indonesia, Malaysia, the Philippines, Singapore, and Thailand. These
countries together supplied about 70 percent of China’s processing imports in 2005. Notethe variable RER w is thus a time series variable in the panel of China’s bilateral exports.
The variable is defined as the RER flexibility between East Asian countries that organize
larger part of the fragmented (cross-border) value chain and China where final stages ofassembly are done to produce “processing exports.” Its flexibility is the unintended
misalignment in relative prices between China and the rest of East Asia11. A movement
towards establishing a currency area in the region will first minimize and then eliminate
this flexibility.
Having defined the variable of real exchange rate flexibility between China and the restof East Asia, the study then considers two cases. One is that East Asian countries
11

Since RER jc ≡ ( RER ji − RERci ) , all being in natural logs, we find that

RERw = [Σ j ω j RER jc ] ≡ [(Σ j ω j RER ji ) − {(Σ j ω j ) RERci }] . Let s t* = RER w , s1,i = (Σ j ω j RER ji ) ands 2,i = {(Σ j ω j ) RERci } , the null of real exchange rate parity between China and the rest of East Asia holds if
the model st* = b1 s1,it + b2 s 2,it + u it is stationary and b ′ = (b1 , b2 ) = (1, -1) . Here, s1, it represents the weighted
real exchange rate between all the East Asian countries other than China and country i and s 2, it thebilateral real exchange rate between China and country i . The subscript i indexes the panel of China’s
bilateral trading partners. The rejection of the null (b1 + b2 ) = 0 in favor of the alternative (b1 + b2 ) ≠ 0
would imply significant flexibility in RER w . Based on a dynamic panel estimation, we find the evidence ofsignificant real misalignment that bˆ = 0.75 , bˆ = −0.98 , and the linear combination of (bˆ + bˆ ) = −0.225 ,1

2

1

all being statistically significant at 1%.

112

continue with their heterogeneous exchange rate and monetary polices, as their exchange
rates appreciate against the rest of the world currencies, particularly the U.S. dollar. The
other is a hypothetical case that there exists perfect coordination in East Asian exchange
rate management, such as a common currency case. These two cases will jointly provide
a framework to measure the potential costs to China’s exports for not having a common
currency in East Asia. Both of these cases are illustrated below.
Let China’s export demand equation which approximates the true demand function be12y i = β1 RER w + β 2 RER ci + u i .

(1)Here β1 measures the impact of the RER flexibility between China and other East Asian
countries that supply intermediate goods to China, and β 2 measures the impact of relative
price changes between China and country i that imports China’s final exports. The
coefficient of the RER w variable is the point estimate of the lack of correspondence ofactual policy making from the desired symmetric case of having a common currency.
Let the hypothetical model, which ignores the influence of VIIT and the existence ofasymmetry in exchange rate management in East Asia be
y i = β*2 RER ci + v i .

( 2)

In this case, the RER w variable is excluded. However, the omission of RER w variable
will cause upward bias in the coefficient of RER ci , because with the existing VIIT andasymmetric policies across East Asia, model (1) is the ‘fully specified model’ from
econometric estimation point of view. The bias is p lim(βˆ *2 − β 2 ) = β1b12 > 0 . Here β̂ *2 isthe estimated upward-biased coefficient of RER w , and b12 is the regression coefficientin the “auxiliary” regression of the excluded variable RER w on the included variable
RER ci (Maddala, 1977; p.156). A greater misalignment of real exchange rates of East
Asian countries that organize upstream production processes of China’s processing
exports will tend to inflate β1 and hence β1b12 . Now consider that there exists perfectcoordination. In other words, there is dollar parity in East Asian countries. It implies that
RER ci is the relevant variable but RER w is the irrelevant variable in explaining China’s
exports behavior. Hence, the hypothetical modely i = β 2 RER ci + v i( 2a )
would now become the true model. The least square estimator βˆ → β . In other words,
2

2

p lim(βˆ 2 − β 2 ) = β1 b 12 → 0 . This is because the irrelevance of RER w would cause

β1 → 0 .

12

The set of other controls is excluded for clarity of the discussion.

12

Now the impacts of asymmetric exchange rate policies of East Asian countries and theexact measure of cost of pursuing such asymmetric policies can be estimated for China’s
exports. The coefficient β1 in the fully specified model (1) would measure the impact of
the RER flexibility between China and the rest of East Asia. But it has indirect effect too.
Note that the RMB coefficient in model (1) is β 2 = ( βˆ 2* − β 1b12 ) , where βˆ *2 < 0 andβ 1 b 12 > 0 . In other words, the RMB coefficient β 2 is inflated by an absolute β1b12 term.
Thus, the cost to China’s exports for not having a common currency would be measuredby ( β 1 + β 1b12 ) 13. In other words, had there been a common currency in East Asia andhence stable relative price relationships within East Asian countries, the coefficient
β1 → 0 and hence “the enhanced effect” β1b12 → 0 . This means that (β1 + β1 b 12 ) → 0 .
The hypothesis is that β 1 + β 1b12 PX f β 1 + β 1 b12 OX , where the subscripts PX and OX
denote the processing exports and the ordinary exports respectively.
It is of relevance to know the extent of loss in potential trade due to the presence of intraregional RER flexibility. In the empirical gravity literature, trade effect of a currency
union is estimated by exp( φˆ ) , with φ̂ being the coefficient of the indicator variable thatis unity if two countries share a common currency, and zero otherwise. This is, however,
inapplicable in the present case because our estimate of the effect of common currency is
not based on the use of a dichotomous variable. It is rather the non-linear combination
φ * = ( β1 + β1b12 ) measuring the effect of RER w , which is otherwise a time-varying
common forcing variable in the model. Though RER w captures the effect of intraregional RER flexibility, the variable itself is not a measure of risk factor. Following
Hooper and Kohlhagen (1978, p.500), the present study takes the absolute difference
between RER w and its fitted values based on a log linear trend equation to be theindicator of real exchange risk. Note that this exchange risk arises only from thevariability of Chinese real exchange rates against other East Asian countries, not all thebilateral trading partners. It is defined as d t = RERwt − RÊRwt , with RÊRwt being thelinear prediction of RERwt obtained from a log-linear trend equation14.
The study then creates an adjustment term defined as exp( d t )φ . The adjustment term has
ˆ*

temporal variation but uniform across cross-sections. If either d t → 0 or φ̂ * → 0 , the13

If the auxiliary coefficient b12 tends to be zero, the effect of a common currency would be measured by

the coefficient β1 per se.
14

The definition of d t assumes implicitly that a common currency arrangement would establish a stable

but trending relative price relationship between East Asian countries. It thereby precludes the assumptionthat the relative prices be fixed. Hooper and Kohlhagen (1978) argued that the major advantage of thismeasure of risk, compared with the standard deviation measures obtained from either a log-linear trend
equation or a first-order autoregressive equation, was that under pegged but adjustable exchange rates it
might better indicate the market’s assessment of exchange risk. Kenen and Rodrik (1986) used alternative
standard deviation measures for estimating trade effect of short-term volatility in real exchange rates. Thisis however not the purpose of the present study.

13term exp( d t )φ → 1 , otherwise exp( d t )φ < 1 because the coefficient φ̂ * ≤ 0 byˆ*ˆ*

assumption. We can now define the potential trade yit* = ŷ it / exp( d t )φ , where ŷ it is thepredicted value of Chinese exports by estimating the fully specified model. If the termˆ*exp( d t )φ is unity, ŷ it itself is the potential exports. On the other hand, if the adjustment
ˆ*term exp( d t )φ is less than unity, the potential exports yit* = ŷ it /[exp( d t )φ ] shallˆ*ˆ*

exceed the actual exports. Actual trade relative to the potential trade ( y it / yit* ) is theintended measure of trade effect due to intra-regional RER flexibility between China andthe rest of East Asia. For each cross-section i , the study reports both avg ( y *it − y it ) and( y i / y i* ) where the over-bar indicates the average taken over the time period.
This now clarifies how greater flexibility in East Asian real exchange rates that arise fromindependent national currencies and heterogeneous exchange rate policies would affect
East Asian production networks and, therefore, those exports that have stronger
production network linkage. The above framework is applied in the empirical estimation
for both the panels of ‘processing exports’ and the ‘ordinary exports’ from China.

4. Econometric Methodologies
4.1 Time Series Properties of the Data
Conventional practice has been to assume that the observed data ( y it , x it ) are unit root
processes and that there exist cointegration relations. The assumption conveniently
provides researchers a framework for modeling both the long-run equilibrium and theshort-run dynamics. However, Hylleberg and Mizon (1989, p.116) argued that an
important criterion for econometric model adequacy is congruence of the model withtime series properties of the observed data, embracing stochastic and/or deterministic
trends for the non-stationary components, and appropriate representation of the temporaldependence of the stationary components. They argued, instead of assuming that there
were cointegration relations, applied econometricians should obtain exact time series
properties of the data. This is more important in the case of panel data, since their nonstationary characteristics are difficult to assess. Even the presence of non-stationarity inthe data does not mean that the cross-sectional units are cointegrated and that theconditional distribution of the regression model would be stationary. We therefore assesstime series properties of the data before embarking on any econometric estimation.
In order to know whether non-stationarity in the data is due to a deterministic time trend
or unit root, the study conducts panel unit root tests for the main data generation
processes (DGPs). They include three cross-section and time series, i.e., bilateral realexports, real gross domestic product of importers and bilateral RMB real exchange rate,
and the only times series of intra-regional RER flexibility between China and the rest of
East Asia. First, we conduct Levin et al. (2002) panel unit root tests. The assumption of
their model is that the cross-sectional time series are independently distributed and that14

the autoregressive parameter is identical for all cross-sections. The test allows for crosssection specific intercepts and/or time trend. Moreover, the error variance is also
permitted to vary across the cross-sectional units. Their Monte Carlo simulation results
showed that the tests had smallest size distortions and performed best against thehomogenous alternative for panels of moderate size.
Panel A of Table 2 provides the results of the Levin-Lin-Chu panel unit root tests. The
results indicate that real exports and real GDP are trend stationery series with first-order
autoregressive error processes while the RMB real exchange rate is an I(0) stationary
process with higher order autoregressive error processes. By contrast, for the intraregional RER flexibility variable RER wt , which is a time series variable, we obtain boththe augmented Dickey-Fuller and the Philips-Perron unit root test statistics. The test
statistics indicate that RER wt is a unit root process, regardless the number of higherorder autoregressive terms and/or a drift term included in the estimated regression. The
general finding is that the dependent variable (real exports) is trend stationary, and the setof regressors includes at least one trend stationary series, i.e., real GDP. There is no one
series that contains both deterministic and stochastic trend components. These results
suggest that we cannot model the variables ( y it , x it ) as a cointegrated system. The study
also considers Pesaran (2007) cross-sectionally augmented Dickey-Fuller (CADF)
regression that allows for panel heterogeneity and obtains CADF panel unit root test
statistics. Panel B of Table 2 shows that y it and GDPit are trend stationary series with
serially correlated errors, whereas the RMB bilateral real exchange rate and RER wt areunit root processes. Again, since y it and GDPit are evidently trend stationary series, it isunlikely that the variables ( y it , x it ) are cointegrated. Standard estimation methods, suchas dynamic OLS or fully-modified OLS, are thus quite likely to produce spurious results
for the long-run parameters. So is the irrelevance of the empirical gravity model, which
has been the workhorse to estimate the effect of border and/or currency union on tradeintegration. These specifications are restrictive because they presuppose a long-run
equilibrium relationship in the observed data. The present study rather chooses a moregeneral dynamic panel specification as outlined below.

4.2 The Dynamic Panel Data ModelIn this study, China’s export demand function is modeled in a dynamic framework, which
is formulated as an autoregressive and distributed lag (ADL) model of order (2, 2)15:
y it = ∑k =1 α k y it-k + β ′(L)x it + γ ′z i + ηi + δ ′i d it + u it ,p

t = p + 1,L ,T; i = 1,L ,N.

(3)

15

The selection of the order of autoregressive and distributed lag terms is based on Akaike’s and Schwarz
Bayesian Information Criteria.

15

Here yit represents China’s bilateral real exports (either processing or ordinary) tocountry i , the vector x it = [ RERcit RER wt GDPit ] ′ , is the set of right-hand side variables
that can be either endogenous, predetermined, and/or truly exogenous, β ′(L) is thecoefficient vector of polynomials in the lag operator, RERcit the bilateral real exchange
rate between China and country i which imports final exports from China (an increase
denotes a real appreciation of the Chinese RMB), RER wt the intra-regional RER
flexibility between China and the rest of East Asia, and GDPit represents the real incomeof the importing country i . The vector z i is a set of gravity variables such as, thedistance between China and country i and dummy variables indicating whether the twocountries are contiguous, share a common language, and have a colonial link16. The
variables ( y it , x it ) are measured in natural logs and vary both over time and acrosscountries; while z i only vary across countries. The model also includes fixed effect ηi ,capturing unobserved factors that are not explicitly included as explanatory variables but
affect the cross-sectional units of the sample and the values of the dependent variable
observed for them. The vector d it indicates the deterministic variables (intercept and/or
trend terms) and δ i indicates the corresponding vector of coefficients. The error terms
u it are assumed to be serially uncorrelated and distributed independently across crosssectional units.
The dynamic specification (3) is intended to approximate China’s export demand
function in the imperfect substitutes framework (e.g., Chang, 1948; and Goldstein and
Khan, 1985). According to the imperfect substitutes model, the observed demand
function is the equilibrating behavior of both the supply-side and the demand-side of themodel and, therefore, price-quantity relationship is, at least in theory, simultaneous. The
empirical literature has taken the supply-side by assumption that the price elasticity ofsupply is infinite. This is restrictive given that exports production is increasingly
fragmented across national borders in East Asia. Moreover, East Asian countries not only
compete at each other’s market, but more so in the Americas and Europe. This implies
that demand schedules for exports from any East Asian country must be widelyfluctuating for various factors. E. J. Working (1927) argued that if the demand curve did
not shift much, but the supply curve did, then the intersection points would come totracing a demand curve. He added that, by “correcting” for the influence of determinants,
which cause demand curves to shift, one would obtain a better approximation of the true
demand curve, even though the original demand schedules fluctuated widely. In thepresent context, it is rational to argue that RER flexibility among East Asian countries
would have direct bearing on the demand schedules of China’s exports. Therefore, ourempirical specification (3) essentially includes the intra-regional RER flexibility variable,
which is also the variable of interest, together with other covariates as suggested by theimperfect substitutes model.

16See, e.g., Anderson, 1979; Deardorff, 1995; and Kalirajan, 1999, 2007 for theoretical development ofgravity equation and on the relevance of gravity variables in estimating bilateral trade equations.

16

The dynamic feature of the model is related to the assumption that there are types ofadjustment costs, such as transactions costs and/or that agents react only slowly tochanges in their environment due to habit or inertia. Finite distributed lags are assumed tocapture unobservable expectations about future outcomes (Hendry et al., 1984). The
model can also be considered as a serial correlation model of Anderson and Hsiao (1982).
However, we do not need to impose the implied common factor restrictions, andalternatively, the dynamics may be thought of as an empirical approximation to somemore general adjustment process, as suggested by Blundell and Bond (1998).

4.3 Estimation Methods and Specification Tests
The empirical specification (3) is a dynamic error-component model. As Hausman (1978)
argued, the unobserved fixed effects η i in dynamic panel model are highly likely to becorrelated with the observed exogenous variables and hence the model would be ‘thefixed effects model,’ rather than the uncorrelated random effects model. In a dynamic
panel model that includes unobserved fixed effects, the pooled OLS estimators are
upward biased, because they are based on the restrictive assumptions that E (x ′it u it ) = 0
and E (x ′it η i ) = 0 , for t = 1,L, T . The dynamic model with lagged dependent variablemust violate the assumptions because y it −1 and η i are correlated. Nickell (1981) showed
that, for an autoregressive model that included a vector of truly exogenous variables, thewithin estimation of the autoregressive parameter would be downward biased, while thebias in the coefficient vector of the included exogenous variables would depend on therelationship between the exogenous variables and the lagged dependent variable yit −1 .
Wooldridge (2002) showed that if u it were correlated with future values of the
explanatory variables in the sense that E (x ′it u is ) ≠ 0 for s < t , the strict exogeneity
assumption would fail in a dynamic panel model. And this will cause unknown bias in the
fixed effect estimator. In addition, if the process {x it } has very persistent elements, thewithin estimator can also have substantial bias.
The present study, therefore, follows the Generalized Methods of Moments (GMM)
approach for dynamic models of panel data as suggested by Holtz-Eakin et al. (1988),
Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998). In
fact, the approach is a generalization of the IV estimation originally proposed byAnderson and Hsiao (1981 and 1982). For example, Anderson-Hsiao IV estimators of a
dynamic panel model in first differences use either yit − 2 or ∆yit − 2 as instruments for the
lagged dependent variable ∆y it −1 . By contrast, the GMM approach exploits further
population moment conditions that can be related to both the differenced equations andthe levels equations of the dynamic model.

17

Arellano and Bond (1991) considered a dynamic model y it = αy it −1 + β ′x it + η i + u it ,where x it is a (K × 1) vector of time-varying explanatory variables17. The basic
assumption of their approach is that uit have finite moments and, in particular,E( uit ) = E( uit uis ) = 0 ∀t ≠ s . That is, uit are assumed to be serially uncorrelated. The
model does not require any other knowledge concerning initial conditions or the
distributions of the uit and the ηi . In the first-differenced equations of the dynamic
specification, the above assumptions lead to a set of linear moment conditions. However
when x it are assumed to be correlated with the unobserved fixed effects η i , the optimal
matrix of instruments crucially depends on whether the x it are endogenous,
predetermined or strictly exogenous. For example, if the x it are endogenous in the sensethat E (x′it u is ) ≠ 0 for s ≤ t but zero otherwise, then x it are treated symmetrically with thedependent variable y it . In this case, the complete set of moment conditions available has
the form of E( Z′i ∆u i ) = 0 for i = 1, L , N , where ∆u i = (∆u i 3 , L , ∆u iT ) ′ and the optimal
matrix of the instruments Z i = diag( y i1 L y is x ′i1 L x ′is ) (s = 1, L , T − 2) are the valid
instruments in the differenced equations. On the other hand, if the x it are predeterminedin the sense that E (x ′it u is ) ≠ 0 for s < t but zero otherwise, the optimal matrix of the
instruments Z i = diag( y i1 L y is x i1 L x is +1 ) are the valid instruments in the differenced
equations. If we make much stronger assumption that the x it are strictly exogenous, i.e.,
E (x ′it u is ) = 0 ∀s, t , then the complete time series x ′i = (x ′i1 ,L, x ′iT ) will be the valid
instruments in each of the differenced equations. The optimal matrix
is Z i = diag( y i1 L y is M x ′i ) for the period (s = 1, L , T − 2) .
Arellano and Bond (1991) thus suggested that lagged values of thedependent/endogenous variable itself and past, present and future values of the strictly
exogenous variables would be valid instruments for the lagged dependent variable andother non-exogenous variables in the differenced equations of later period. GMM
estimators that are based on moment conditions related to the differenced equations arereferred to as the first-differenced GMM estimators. Let the expression E( Z ′i ∆u i ) = 0 be
the appropriate orthogonality conditions to be used to construct an estimator of theunknown parameter vector B 0 . Following Hansen (1982), the random functiong N (B) = N −1 ∑i =1 Z′i ∆u i = N −1 Z ′∆u is the method of moments estimator of E( Z′i ∆u i ) ,N

where A N is a random weighting matrix . The GMM estimator B̂ is the set of elements
2in the parameter space that minimizes the sample criterion function h N (B) , whereh N (B) = A N g N (B) is the sample objective function . The first-order conditions of theminimization problem have the interpretation of setting k linear combinations of the17

The extension of the autoregressive specification to the case where a limited amount of serial correlationis allowed in uit is straightforward.

18

r sample orthogonality conditions to zero where k is the dimensionality of the parameter
space.

In a dynamic panel model r sample orthogonality conditions g N (B) = 0 often exceeds kparameters to be estimated. The weighting matrix A N in fact reduces the number of
equations to k by using linear combinations of r equations. Arellano and Bond (1991)
showed that in the first-differenced equations of the dynamic panel model, the GMMˆ′′ −1 X ′ZA N Z ′y ,estimator of the coefficient vector B ′ = (α ′, β ′) is B
diff = ( X ZA N Z X )where X is a stacked (T − 2) N × K matrix of observations on ( y it −1 , x it ) , y and Z are
accordingly defined for the appropriate choice of Z i . The alternative choice of theweighting matrix A N will give rise to GMM estimators with different asymptotic
covariance matrices. For instance, one-step GMM estimators can be obtained by setting
the weighting matrix A N = ( N −1 ∑i Z′i HZi ) −1 , where H is a (T − 2) square matrix with
twos in the main diagonal, minus ones in the first sub-diagonals and zeros otherwise. On
the other hand, one could obtain an “optimal” estimator of the weighting matrix from afamily of random weighting matrices. An optimal weighting matrix is the one that has an
asymptotic covariance matrix at least as small as any other element in the class. GMM
estimator based on the optimal weighting matrix is called the two-step estimator. White(1982) suggested another choice of A N , which would be V̂N−1 = ( N −1 ∑i Z′i v̂ i v̂′i Z i ) −1 , v̂ ibeing the residuals from a preliminary consistent estimator of B ′ = (α ′, β ′) .

Arellano-Bond first-difference GMM estimators can, however, be further biased than thewithin estimators under certain conditions. Alonso-Borrego and Arellano (1996) and
Blundell and Bond (1998) show that the first-differenced GMM estimators are weakly
identified when the instruments are weak in the sense that they have a low correlation
with the included endogenous variables. The estimators can be seriously downward
biased in two important cases. First, as the value of the autoregressive parameter ( α )approaches to unity, and second, as the relative variance of the fixed effects η i , i.e.,(σ η2 / σ 2v ) increases to infinity. In fact, when variables are persistent over time, lagged
levels of these variables are weak instruments for the regression equation in differences.
Weak instrument problem may not only cause the first-differenced GMM estimators to befurther downward biased than the within estimators, it also influences the asymptotic andsmall-sample performance of the estimators.
To solve the problem, Arellano and Bover (1995) and Blundell and Bond (1998)
proposed a new GMM estimator that would combine in a stacked system the regression
in differences with the regression in levels. The instruments for the regression indifferences are the same as suggested by Arellano and Bond (1991). But the instruments
for the regression in levels are the lagged differences of the corresponding variables18. As
18
It is based on an additional assumption that although there might be correlation between the levels of theright-hand side variables and the fixed effects, there would be no correlation between the differences ofthese variables and the fixed effects in dynamic panel model (see, e.g., Blundell Bond, 1998; and Levin et19the system of equations combines both the differenced equations and the levels equations,the instrument matrix is also an extended instrument matrix. The estimator based on thisextended instrument matrix is called the system GMM estimator19. However, the choice
between the differenced GMM estimator and the system GMM estimator is statistical anddepends on whether there is strong persistency in the observed data.
We assess persistency characteristics of each individual time varying data series included
in model (3). We estimate the univariate autoregressive model20∆y it = α i′d it + β i y it −1 + ∑ Li=1θ iL ∆y it − L + eit
P(4)Here d it is a vector of deterministic variables (e.g., intercept and/or time trend) and α i isthe corresponding vector of coefficients. For example, for the model with both intercepts
and individual specific time trends, d it = {1, t}. Here, α 1i represents cross-section
specific intercepts capturing the unobserved fixed effect parameter ηi and e it is assumed
to have finite moments and in particular E (e it ) = E (e is e it ) = 0 , for i = 1,L, N and ∀s ≠ t .
For the real export and real GDP series, we estimate the model including both thedeterministic variables (i.e., both the cross-section specific intercepts and trend term). For
the RMB RER and RER wt variables, we estimate the same autoregressive specification
but without the trend element. The choice of appropriate autoregressive order anddeterministic terms for all the cross-sectionally and time-varying series is based on the
Levin-Lin-Chu unit root results that are presented in Table 2. The consistent GMM
estimates of persistency are obtained by GMM system estimation.
Table 3 presents our results on persistency characteristics. GMM system estimators
provide better estimates of the true parameter. The evidence indicates that, though thedependent variable (the ordinary exports or the processing exports) are both trend
stationary series, they are highly persistent. GMM system estimate of the RMB RER
coefficient also indicates to a high degree of persistency. Similarly, the RER wt variable isalso found to be highly persistent. These results imply that GMM first-difference
estimators in our multivariate dynamic panel model are likely to be weakly identified andhence inconsistent. Moreover, if the model allows for endogeneity in the real exchange
rate variables, the weak instrument problem appears to be more plaguing in bothAnderson-Hsiao IV estimators and Arrelano-Bond GMM first-difference estimators. The
findings suggest that the multivariate dynamic panel data model in the present case must
resolve on the potential weak instrument problem. The study therefore uses the extended
instrument matrix as proposed by Blundell and Bond (1998) and obtains consistent theal., 2000). The assumption results from the stationary property that E ( y it + pη i ) = E ( y it + qη i ) andE ( xit + pηi ) = E ( xit + qηi ) for all p and q.
19

Appendix 1 provides further details on the moment conditions and the resultant extended instrument
matrices that are used to obtain GMM estimators under varying exogeneity assumptions.
20
The form is the Sims et al. (1990) canonical form for higher order autoregressive processes, originallyproposed by Fuller (1976).

20system GMM estimators by estimating a system of differenced and levels equations of
the dynamic specification.
As proposed in the conceptual framework, the paper estimates two benchmark
specifications of model (3) in order to measure the impact on China’s exports of acommon currency in the region. The benchmark specifications are:yit = ∑ k =1 α k yit - k + β 0GDPit + β1GDPit −1 + β 2GDPit − 2 + ξ0 RERcit + ξ1RERcit −1 +2

ξ 2 RERcit − 2 + ψ 0 RERwt + ψ 1RERwt −1 + ψ 2 RERwt − 2 + γ′z i + ηi + δ′id it + uit(3.1)

yit = ∑ k =1 α k* yit - k + β 0*GDPit + β1*GDPit −1 + β 2*GDPit − 2 + ξ0* RERcit + ξ1* RERcit −1 +2′′

ξ 2* RERcit − 2 + γ * z i + ηi + δ*i d it + vit(3.2)

The specification (3.1) is ‘the fully specified model,’ while the specification (3.2) is ‘thehypothetical model’ based on the counterfactual assumption of a common currency inEast Asia. The present study does not impose any arbitrary restrictions as to exogeneityof the included variables. In particular, we allow both RER cit and RER wt variables to bestrictly exogenous, predetermined or endogenous and accordingly define thecorresponding instrument matrices (see Appendix 1 for details). We thus obtain three setsof GMM estimators for both the specifications. In addition to the GMM-systemestimators, we also report the pooled OLS and fixed-effect estimators in each case inorder to show relative performance of the consistent system GMM estimators.
Several studies suggest that empirical export demand equation should include a supply
shift variable. Hooper (1978) first argued that the observed high estimated income
elasticity of demand for U.S. imports reflected the positive correlation between U.S.
income growth and a relevant omitted variable, namely, supply capacity in the exporting
countries, particularly the newly industrialized developing countries in East Asia. Since
imports from these countries contained many new products with zero or unduly low
weights in the standard price indices, the increased supply effect would not be reflected
in recorded movements of U.S. import prices. Hooper thus suggested for including a
supply proxy along with the normal arguments. In a similar vein, Chinn (2005) and Mannand Plück (2005) argued that exporters’ ability to produce more variety with increasing
returns to scale would cause shifts in export supply curve for the exports of fast-growing
countries such as China. The present study thus augments the benchmark specifications
by including alternative proxies to control for increased capacity of exporters to supply
more variety. The purpose is to check robustness of the parameter estimates of thebenchmark model.
Another potential debate is on the use of an appropriate deflator for the case of China’s
exports, since the country does not have consistent price index of exports. We follow the
recent empirical literature and apply three alternative deflators, i.e., Hong Kong export
price index, the U.S. consumer price index, and the U.S. import price index of
manufactured imports from non-industrial countries. Liang and Fung (2005) found that21

Hong Kong price index traced the price movement of China’s exports better than others.
It is highly plausible because of Hong Kong’s traditional role as an entrepôt to transship
China’s exports to the rest of the world. Eichengreen et al. (2004) and Thorbecke (2006)
applied the U.S. consumer price index to deflate U.S. dollar imports, arguing that themeasure would be appropriate if the bundle of goods and services exported from China
corresponds to the bundle purchased by U.S. consumers. Cheung et al. (2006) used the
U.S. Bureau of Labor Statistics (BLS) import price index of manufactured imports from
non-industrial countries to deflate dollar value of China’s exports. They found that theseries closely matched the BLS price deflator for imports from China, which had been
compiled since 2003. Again, the motivation is to check robustness of the parameters ofinterest to the use of alternative deflators.
Since our sample has N = 33 and T = 14 , we use less than the available valid moment
restrictions in order to avoid the problem of overfitting the instrumented variables andthereby causing the results biased towards those of OLS Sargan (1958) and Amemiya(1977) suggest that from the standpoint of obtaining desirable small sample properties,
one should try to conserve the number of orthogonality conditions used in the GMM
estimation (Hansen, 1982; p. 1035). Following Roodman (2006), the present study alsocollapse the “GMM-style” moment conditions into groups and sums the conditions ineach group to form a smaller set of moment conditions. Since standard errors of two-step
GMM system estimator tend to be severely downward biased, we apply a finite sample
correction to the two-step covariance matrix as suggested by Windmeijer (2005) andthereby obtain corrected standard errors estimates.
Finally, the study provides the standard specification tests. Let ∆u it be the firstdifferences of serially uncorrelated errors u it . Then E (∆u it ∆u i (t −1) ) need not be zero, but
the consistency of the GMM estimators fundamentally depends upon the assumption thatE (∆u it ∆u i ( t −1) ) = 0 . We thus report both m1 and m 2 tests for first-order and secondorder serial correlation in the first-differenced residuals, asymptotically distributed as
N(0,1) under the null of no serial correlation. They both are reported in order todiscriminate the situation if the errors in levels follow a random-walk process from thesituation if the errors in levels are not serially correlated. Next, we provide
Sargan/Hansen test of over-identifying restrictions. When the number of orthogonality
conditions (r ) , exceeds the number of parameters to be estimated (k ) , estimation of themodel parameters sets k linear combinations of the r sample orthogonality conditions
equal to zero, at least asymptotically. Thus when the model is true, there are (r − k )linearly independent combinations of the orthogonality conditions that ought to be close
to zero but are not actually set to zero (Hansen, 1982). These linear combinations ofsample orthgonality conditions are used to obtain Hansen J statistic. Hansen J statistic is
thus a test of the over-identifying restrictions, asymptotically distributed as χ 2 under thenull of instrument validity. For both one-step robust estimation (and also for two-stepestimation), the Hansen J statistic is the minimized value of the two-step GMM criterion
function and is asymptotically valid test statistic of the model restrictions.

224.4 Data21

China’s Disaggregated Trade Flows: The study uses annual data on China’s bilateralexports and imports statistics, disaggregated into ordinary and processing categories, visà-vis a panel of 33 countries over the 1992-2005 period. The data are compiled by theStatistics Department of Customs General Administration of the People’s Republic ofChina and published by the Economic Information Agency, Hong Kong.
Deflators: Hong Kong export price index is line 74d of IMF International Financial
Statistics. Both the U.S. consumer price index and the import price index of
manufactured imports from non-industrial countries are taken from the online database of
the U.S. Bureau of Labor Statistics (BLS).
Real Exchange Rates: Bilateral real exchange rates between China and country i , i.e.,
RERci , and bilateral real exchange rates between China and country j that supplies
intermediate goods to China, i.e., RER jc , are all taken from the CHELEM database(CHEPII, 2007). In the calculation of RER wt = Σω jt RER jct , both RER wt and ω jt are
annually updated over the sample period for each cross-section j . Note that RERwt is atime series variable and hence uniform across cross-sections in the panel of China’s
bilateral exports.

Real Output and Gravity Variables: Real income in the importing country i and a set of
the gravity variables are also taken from CEPII. The gravity variables include distanceand dummy variables indicating whether the two countries are contiguous, share acommon language, and have a colonial link.
Proxies for the supply-side effect: To control for exporters’ increased capacity to supply
new varieties, this study uses several alternative proxies of the variable. They include real
GDP of China (IFS series 99B_P), cumulative inward FDI to China (IFS series 78BED;
the data is taken from McKinnon and Schnabl, 2006; p. 7), and China’s fixed capitalformation (IFS series 93_E).

5. Results and Interpretation
Table 4 presents estimation results of the benchmark models for China’s processing
exports. These are the exports whose value chain is sharply fragmented across national
borders, with final stages of assembly and exporting being performed in China. The first
four columns report alternative estimation results, including the consistent system GMM
estimates, of the fully specified model. Whereas, the fifth column reports only the system
GMM estimates of the hypothetical model, which excludes the intra-regional RER
flexibility as an explanatory variable. The parameters of interest are coefficients of both
21

The author is grateful to the Research Institute of Economy, Trade and Industry (RIETI) for providing
the datasets and other research supports.

23the intra-regional RER flexibility and the bilateral RMB real exchange rate. We do notreport estimates of the coefficients of the gravity variables. Their point estimates areoften statistically insignificant, but the variables are jointly significant. Real exports data
are obtained by using Hong Kong export price index as the deflator22.
The pooled ordinary least squares (OLS) and fixed-effect (FE) estimates are reported in
the first two columns, which are followed by two GMM estimates. GMM1 assumes thatboth the RER variables are exogenous in the model, while GMM2 assumes that they arepredetermined23. Since the OLS estimates are upward biased and the FE estimates are
downward biased, they provide a range within which the true autoregressive parametershould exist. A better approximation of the true autoregressive parameter would also
correct potential bias in the estimates of other parameters of the model. This therefore
provides the first criteria to judge relative consistency of our GMM estimates. Both
GMM1 and GMM2 estimates are one-step system GMM estimators, for which we
believe inference based on the asymptotic variance matrix to be more reliable. Two-stepsystem GMM estimators are largely comparable to the one-step estimates and, therefore,
not reported. In general, the GMM estimates are relatively consistent and more efficientthan the OLS and FE estimates. We find that both GMM1 and GMM2 specifications
provide better approximation of the autoregressive parameter compared to the range
implied by the OLS and FE estimates. The tests of serial correlation in the firstdifferenced residuals are in both cases consistent with the maintained assumption of noserial correlation. However, the Sargan/Hansen test of over-identifying restrictions showsthat the null of instrument validity is rejected for the GMM1 estimates, but not for theGMM2 estimates. Therefore, GMM2 specification of column 4 provides the consistentsystem GMM estimates of the fully specified model. Finally, column 5 of Table 4 reports
GMM2 estimation results of the hypothetical model.
Here we focus on the contemporaneous effects because the study assumes that flexibilityof exchange rates in East Asia is caused by asymmetry between exchange rates ofnational currencies, not by any real disequilibrium. The assumption does not preclude
that deviations from long-run equilibrium would not have a long-run trade effect. The
long-run estimates are discussed later. According to the preferred GMM2 estimates, theimpact elasticity of RER w is −1.31 and that of RER ci is −0.75. The findings indicatethat a 10 percent unilateral RMB appreciation against the rest of the world currencies willcause China’s processing exports to decline by about 7.5 percent. By contrast, a 10
percent flexibility in relative prices between China and the other East Asian countries that
supply intermediate goods to China will cause China’s processing exports to decline by
22

In the robustness analysis presented in the following section, we discuss further estimation results thatare obtained by using alternative deflators as suggested by the contemporary literature and by including
alternative proxies to control for exporters’ increased capacity to supply new varieties.
23
The GMM estimates that are based on moment condition that arise from the assumption of endogenous
RER variables are found to be poorly identified and further downward biased than the FE estimates. We
therefore do not report them. Note that endogeneity of a variable x it requires that its lags dated ( t − 2 ) or
earlier and lagged differences dated ( t − 1 ) are the valid instruments in the differenced equations and thelevels equations respectively. If x it is, in fact, not endogenous, the assumption is restrictive and willproduce biased estimates.

24

13 percent. The term flexibility connotes misalignment in relative prices along the
production networks in East Asia, rather than the traditional concept of exchange rate
volatility. The finding shows that the impact of China’s real exchange rate flexibility withthe rest of East Asia on its processing exports is almost double the corresponding impactof a unilateral RMB appreciation.
Recall that our ultimate objective has been to quantify the costs of not having a common
currency in East Asia for China’s exports, particularly the processing exports that havestronger production network-linkage with the rest of East Asia. The above estimates are
an intermediate step to that end.
To focus on the issue of not having a common currency, we refer to column 5 of Table 4.
Column 5 reports estimates of the hypothetical model, which is based on the
counterfactual assumption that there exists a common currency and that the RER w
variable is irrelevant as an explanatory variable. The finding is that the coefficient of
RER ci is now upward biased by the magnitude of 0.20 from the consistent estimate (i.e.,−0.75) of the fully specified model. Now, we can identify two effects of the intra-regional
RER flexibility on China’s processing exports. One is its direct impact, i.e., β 1 = −1.30 ,while the other is its indirect impact on the coefficient of RER ci . The indirect impact isestimated to be ( β 1b12 ) = −0.20 24. Had there been a common currency in East Asia,
β1 → 0 and hence ( β 1b12 ) → 0 . This indicates that the cost of not having a common
currency in East Asia for China’s processing exports is (β1 + β1b12 ) = −1.50 25. The firstcomponent is the RER w coefficient, which measures the impact of real exchange rate
misalignment between China and the rest of East Asia, whereas the second component is
its indirect effect by increasing variability in the bilateral RMB real exchange rates.
Table 5 shows corresponding estimation results for China’s ordinary exports. As we
mentioned earlier, these ordinary exports are produced primarily by using local inputs. In
other words, the role of East Asian supply chain is much less important in this case.
Again, we find GMM2 to be the preferred estimators as shown in column 4. The reason isthat there is no evidence of second-order serial correlation in the first-differenced
residuals, that the null of instrument validity is not rejected and that the estimate of theautoregressive parameter is well within the range of the OLS and FE estimates. The
results show that a 10 percent unilateral RMB appreciation against the rest of the world
currencies will cause China’s ordinary exports to decline by 8.9 percent. On the other
hand, a 10 percent flexibility in relative prices between China and the other East Asian
countries will cause the ordinary exports to decline by 6.5 percent. This indicates that a
unilateral RMB appreciation would have larger impact on the ordinary exports than theprocessing exports. A relatively larger coefficient of the RMB RER is consistent with thefact that the extent of local value addition is substantial in the gross value of China’s
The notations β1 , β 2 and b12 used in this section are consistent with the notations introduced in theconceptual framework of the paper, not the coefficients of the estimated model.
25
We estimate standard error of β1 + β1 b12 by using delta method and find they are significant at 1 % level.
24

25

ordinary exports. By contrast, the impact of intra-regional RER flexibility on the ordinary
exports is just half of the corresponding impact on the processing exports. A relatively
lower magnitude of RER w coefficient signifies the weak linkage of the ordinary exports
with East Asian production networks.
By the same analogy, we find that the cost of not having a common currency area in East
Asia is modest in the case of China’s ordinary exports. Column 5 of Table 5 shows thatthe elasticity of RER ci in the hypothetical model is upward biased by a magnitude of
0.10. Therefore, the combined cost of not having a common currency for the ordinary
exports is (β1 + β1b12 ) = −0.75 . This is just half of the cost we have estimated in the caseof China’s processing exports.
The findings carry important implications for East Asian production networks andChina’s exports. Earlier we observed that ‘processing trade’ is at the heart of China’s
integration with the rest of East Asia and to the world trading system. The results inTable 4 indicate that flexibility in East Asian exchange rates, due to either anappreciation or a depreciation of world invoice currencies, does greatly affect China’s
processing exports. It does it by misaligning the relative price relationships between
China and the rest of East Asia, not between East Asia and the rest of the world. This iswhat the coefficient of RERw variable implies. The finding shows that the impact of
RER w on the processing exports is twice as much as that of a unilateral RMB
appreciation. The reason is that a unilateral RMB appreciation affects only the Chinese
value added, whereas a relative price misalignment between China and the rest of East
Asia affects the dollar costs of intermediate goods imported into China from the rest of
East Asia26. Independent national currencies linked by flexible exchange rates not only
cause exchange rate uncertainty but also impede myriad contractual arrangements related
to international trade. The resultant effect of rising trade costs would significantly affectthe processing exports, not the ordinary exports. A comparison of the opportunity costs of
not having a regional currency in terms of processing-and-ordinary decomposition isreflective of this point. The cost to China’s processing exports is just double thecorresponding cost to China’s ordinary exports. The results also imply that
contemporaneous effect of a discrete exchange rate shock can be prohibitively high forthe processing exports, and since they are produced regionally, the effect would becontiguous in nature.
We now turn to the estimates of long-run parameters and their test statistics that are basedon preferred GMM2 estimates of the fully specified model (3.1). With respect to RERci
and RERw, the respective long-run elasticities are ξˆ = ( ξˆ0 + ξˆ1 + ξˆ2 ) /( 1 − αˆ 1 − αˆ 2 )ψˆ = (ψˆ 0 + ψˆ 1 + ψˆ 2 ) /( 1 − αˆ 1 − αˆ 2 ) . For the panel of processing exports, Table 6 showsthat the long-run effect of a unilateral RMB appreciation is merely −0.93 and that of the26

Lau and Stiglitz (2005) argued that the dollar costs of imported intermediate goods would be more than
two-thirds of the gross value of China’s processing exports.

26

intra-regional RER flexibility variable is −8.527. In the long-run, we do not find any
significant evidence of indirect effect as earlier measured by the term ( β 1 b12 ) in theanalysis of short-run dynamics. The study also finds that the long-run parameter
estimates for the panel of ordinary exports are statistically insignificant, when they arebased on the consistent dynamic estimates obtained by using GMM system estimators.
The implication is that flexible exchange rates between national currencies andfragmentation of production process across borders in East Asia are incompatible in thelong-run.
Finally, the study conducts comparative static implications of intra-regional RER
flexibility, which is otherwise the lack of a common currency arrangement in East Asia,
for China’s bilateral processing exports. It is because the long-run effect of RERw is
statistically significant only for the processing exports. Table 7 reports the findings.
Column 3 shows mean differences between potential and actual volume of processing
exports for each bilateral trading partner. The averages are taken over the 1994-2005
period28. Column 4 shows t-ratios testing if these mean differences are significantly
greater than zero. The last column shows ratios between actual and potential volume ofexports at their respective averages.
Major finding is that the production and exporting of processing goods is, on an averageover the sample period, 20 percent below the potential. At a disaggregated importer level,
the extent of average trade loss is the highest for Japan ($15 billion), followed by the US($5.5 billion), European Union ($5 billion), Taiwan ($2.5 billion) and South Korea ($2.2
billion). The trade loss in the case of Hong Kong, being an entrepôt to the west, should
better be regarded as trade loss against the Americas and Europe. The finding that theactual exports volume is far below the potential level for Japan and NIEs probably
indicates that the intra-regional exchange rate asymmetry has constrained thedevelopment of East Asian production networks to reach its optimal degree. Given a
fixed exchange rates system, the production networks would further evolve to fragment
production processes for a larger class of goods. It would lead to an increasing level ofback-and-forth trade in intermediate goods between countries but along the production
networks. The networking would be both vertical and horizontal in order to takeadvantage of differences in technologies, factor endowments and market sizes acrosscountries.
The deepening integration of East Asian countries with one another, which is mostly
driven by market forces, has produced new demand for a regional currency and against
national currencies. However, East Asian countries have their independent national
27

Standard error for the estimator of the long-run parameter is obtained by using delta method. Pesaran
and Shin (1998) showed that variance estimator obtained by the delta method is asymptotically valid
irrespective of whether xt is I(1) or I(0). The test statistics indicate that the estimates of the long-run
parameters are statistically significant at 5%.
To estimate average trade effect over T is somewhat less meaningful for policy analysis because any real
misalignment would be discrete, should there be an external shock. An average understates the effect of acrisis period. Though it is done for analytical ease, the estimates would probably capture the effect of
persistent real misalignment due to RER heterogeneity within East Asia.

28

27

currencies and pursue heterogeneous exchange rate and monetary policies. The evidence
suggests that the opportunity cost of not having a regional currency can be prohibitive forthose exports that are produced along the regional production networks. The growing
pattern of East Asian integration is, therefore, very susceptible to external shocks, e.g., adiscrete depreciation of the world invoice currencies like the U.S. dollar or the Euro.

6. Robustness Analysis
Edward Leamer (1983) has argued persuasively that because any econometric analysis
involves numerous debatable decisions, findings cannot be convincing unless they are
shown to be robust. In the previous section, we already discussed robustness of ourestimates to changes in several of the modeling decisions. Here we provide further
robustness of parameter estimates to alternative variable definitions and to the inclusionof additional control variables in the estimated fully specified model29.
The first major controversy centers on the use of an appropriate export price index todeflate China’s nominal dollar exports to the real values. In the previous section, we
consistently use the Hong Kong export price index as the deflator to define the dependent
variable, China’s real exports. Now we use two other alternative deflators, the U.S. CPI
and the U.S. import price index of manufactured imports from non-industrial countries.
Columns 1 and 2 of Table 8 show the key parameter estimates for the case of China’s
processing exports. The coefficient of the RMB real exchange rate is −0.75, while that of
the intra-regional RER flexibility is in the range of −1.08 to −1.33. Columns 3 and 4
show the corresponding results for China’s ordinary exports. The results show that thecoefficient of RER ci is about −0.90, while that of RER wt is very low in magnitudes andstatistically insignificant when the deflator is the U.S. CPI. Otherwise, all these estimates
are statistically significant at any reasonable level. The estimates are within one standard
error of the corresponding estimates for the benchmark results that are reported in Tables
4 and 5 respectively. In other words, the estimates of the impact of real exchange rate
misalignment and of the costs of not having a common currency on China’s exports,
particularly the processing exports, do remain robust, regardless of how we deflatenominal dollar value of the exports. The long-run behavior of the model is alsounchanged form the benchmark case.
Next, we augment the benchmark models by including a supply shift variable to control
for structural break in the estimated relationships and to examine if the estimates of the
key parameters are robust to the inclusion of the variable. Since it is difficult to find agood proxy for the supply shift effect, we follow the recent trade literature and use
alternative proxies such as real GDP of China, cumulative foreign direct investment(FDI) into China and China’s gross fixed capital formation. It should be noted that all
29

All the estimates that are reported in this section are one-step system GMM estimators that are obtained
under the assumption that RER and RERw are predetermined (weakly exogenous) in the model. Thoughci
the pooled OLS, fixed-effect and differenced GMM estimators are relatively biased for the reasons
explained in the methodologies, the key parameter estimates under these alternative estimators arenonetheless comparable with those of the consistent system GMM estimates.

28

proxies are too endogenous. The study thus uses the lag dated (t − 2) of the proxy as anargument in the model. The study does not use China’s capital stock as a proxy, because
the variable is both endogenous and measured with much error. Instead, the gross fixed
capital formation will be a better proxy, since it is correlated with the capital stock but
uncorrelated with the measurement error.
Columns 5 through 10 of Table 8 report estimates of the key parameters of theaugmented model. The results show that the inclusion of proxy for the supply side effectsignificantly affect the short-run dynamics of the intra-regional RER flexibility between
China and the rest of East Asia. But the direction of the effect is invariably downward to
be further negative. This is true for both the processing exports and the ordinary exports,
though their magnitudes are different as expected. The findings clearly imply that thebenchmark estimates that are reported respectively in Tables 4 and 5 (column 4 in eachcase) provide a conservative lower-bound limit for the estimates of the key parameters.
The long-run behavior of the model is also unchanged that the long-run effect of intraregional RER flexibility would be prohibitively high for exports that are produced along
the production networks.
In the view of a small sample involving 34 cross-sections and 14 periods, we consistently
estimated the autoregressive and distributed lag model of the second order. As we
indicated earlier, the selection of the order of autoregressive and distributed lags is basedon the minimization of Akaike’s and Schwarz Bayesian Information Criteria. An
arbitrary first order ADL specification with (or without) the proxy for the supply shifteffect causes no change in the short-run dynamics, but only causes the long-run effect of
RERw to be −2.87. The key parameter estimates are statistically significant at 1%.
Furthermore, we found that in most cases the more distant lags of the dependent variable,
real exchange rates, and real GDP did not cause any significant changes in the estimatesof the key parameters, nor in their asymptotic efficiency. Following Grossman andLevinsohn (1989), we also tested separately for the joint significance of different lag
lengths based on nested-hypothesis testing. The results also provide general support for
the ADL(2,2) model used in the present study.

7. Concluding Remarks and Directions for Future Research
In this paper, we have developed a framework for assessing the impact of a common
currency on East Asian production networks and China’s exports behavior. The
framework produces a new variable in order to capture the asymmetry in exchange rate
and monetary policies between China and other East Asian countries that supply
intermediate goods to China. The variable is constructed by using the degree of
production network linkage of China’s final exports as the weight on the level of realexchange rate misalignment between China and another East Asian country. The overall
framework has the utility to be applied to estimate ex ante effect of a common currency
on trade integration.
We apply this framework to the observed data on China’s bilateral exports, divided into
processing and ordinary categories, to a panel of 33 countries over the 1992-2005 period.

29

The results show that the cost to China’s processing exports for not having a common
currency is more than double the corresponding cost to China’s ordinary exports. The
long-run effect of the intra-regional RER flexibility on the processing exports is almost
9.0 times the corresponding estimate of a unilateral appreciation of China’s RMB
exchange rate. The magnitudes of these estimates are consistent with the hypothesis that acommon currency in East Asia would further integrate East Asian production networks
and promote those exports whose value chains are increasingly fragmented acrossborders in East Asia.
The major limitation is that the study focuses on a very narrow domain. It investigates
only trade effect of a common currency proposition on East Asian production networks,
particularly focusing on China’s processing exports. To the extent export production ofother East Asian countries is too integrated with the production networks, it is expectedthat a fixed exchange rate system would raise their export potential in general. However,
it is imperative to investigate spatial distribution of welfare implications of the policy.
Secondly, in the literature there has been a disconnect between trade effect of exchange
rate uncertainty (as measured by shot-run volatility) and that of a common currency
arrangement. Though the present study offers a viable solution of this disconnect, it does
not offer a coherent theoretical framework. Future research can be directed to examine
how exchange rate flexibility enters into firm’s decision-making process, when
production of a value chain is organized across national borders. Thirdly, the econometricmodeling applied in the present study is though unconstrained and not forced to obey
some economic theory, it requires further refinements for drawing valid inference on
theory-consistent long-run relations. Since the observed data characterize temporal
aggregation across countries, inference on the long-run relations based on a finite-order
ADL model may not be valid. Further investigation is necessary as to panel unit roots andcointegration relations. However, it is hoped that both the conceptual framework andestimation methodologies will motivate further empirical research on trade effect of acommon currency arrangement in East Asia.

30

Appendix-1

Moment Conditions and the Instrument Matrix

This appendix provides details on the moment conditions and related GMM-style
instrument matrices for obtaining system GMM estimators. For convenience, we omit the
time-invarying set of variables, though they are relevant instruments in the levels
equations and have been used in the system GMM estimation. Let us recast our ADL(2,2) model (3) without the time-invarying variables:
yit = ∑ k =1 αk yit-k + ∑ k = 0 β′k x it − k + ηi + δ′id it + uit , t = 3,L ,T; i = 1,L ,N.
2

2

(3* )

Below we define how both the moment conditions and the resultant extended instrument
matrix depend on varying exogeneity assumptions regarding the vector x it , provided thaty t −1 is always endogenous in the first differenced equations.
1. GMM1 Estimator [Assumption: The vector x it is strictly exogenous in the model, i.e.,
E (x it u is ) = 0 ∀s, t ]i. Moment conditions for the differenced equations: E ( y it −s ∆u it ) = 0 and E (x i ∆uit ) = 0
where x ′i = (x′i1 L x′iT ) for t = 4, L , T and s ≥ 2 . This implies that lags of y dated (t − 2)and earlier and past, present and future values of the exogenous variables are valid
instruments for the lagged dependent variable in the differenced equations fort = 4, L , T . The resultant optimal instrument matrix is Z i = diag ( y i1 y i2 L yis x i1 L x iT )for t = 4, L , T and s ≥ 2 . Arellano and Bond (1991), however, did not use all the overidentifying restrictions arising from the strict exogeneity assumption of x it , but only thepresent values in their Monte Carlo experiments. Thus their instrument matrix looked likeZ i = diag ( y i1 y i2 L y is M (x ′i 4 L x ′iT ) ′ for t = 4, L , T and s ≥ 2 . This instrument matrix is
usually used to obtain the differenced GMM estimator, which will be inconsistent andinefficient in the presence the weak instrument problem. Arellano and Bover (1995) and
Blundell and Bond (1998) therefore suggested a further set of moment conditions forequations in levels.
ii. The moment conditions for the levels equations: E (∆y it −1u it ) = 0 for t = 4, L , T .
Given that lagged levels are used as instruments in the difference equations, only mostrecent lagged difference is the valid instrument in the levels equations. Using the otherlagged differences would results in redundant moment conditions.
Calculation of the system GMM estimators is essentially based on a stacked systemcomprising both the differenced equations and the levels equations of the model fort = 4, L , T . The instrument matrix for this system is called the extended instrument
matrix, which can be written as,

31

Z i
0

Z i+ =  0

M
0


0
∆y i 30M00∆y i 4
M

0

0




 , where Z i = diag ( y i1 y i2 L y is M (x ′i 4 L x ′iT ) ′ .
L ∆y i ,T −1 

L
L
L
O

0
0
0
M

2. GMM2 Estimator [Assumption: The vector x it is predetermined in the model, i.e.,

E (x it u is ) ≠ 0 for s < t but zero otherwise.]
The moment conditions for the differenced equations are E ( y it −s ∆u it ) = 0 andE (x it −s +1 ∆uit ) = 0 for t = 4, L , T and s ≥ 2 . Whereas, for the levels equations, theadditional moment conditions are E (∆y it −1u it ) = 0 and E (∆x it uit ) = 0 for t = 4,L, T . The
only difference from the case 1 will be in the context of underlying extended GMM
instrument matrix. While Z i is [diag ( y i1 y i2 L y is x ′i1 L x ′is +1 )] , Z i+ will incorporate
additional instruments [diag (∆yiT −1∆x′iT )] for levels equations, where t = 4,L, T ands ≥ 2.
3. GMM3 Estimator [Assumption: The vector x it is endogenous in the model, i.e.,

E (x it u is ) ≠ 0 for s ≤ t but zero otherwise.]In this case, the moment conditions for the differenced equations are E ( yit − s ∆uit ) = 0 andE (x it −s ∆u it ) = 0 for t = 4, L , T and s ≥ 2 . Whereas, for the levels equations, theadditional moment conditions are E (∆y it −1u it ) = 0 and E (∆x it −1u it ) = 0 for t = 4,L, T .
Again, calculation of the system GMM estimators is essentially based on a stacked
system comprising both the differenced equations and the levels equations of the model.
The extended instrument matrix Z i+ will now represent Z i = diag ( yi1 yi2 L y is x ′i1 L x ′is )being appended by the instrument set [diag (∆yiT −1∆x′iT −1 )] for t = 4, L , T and s ≥ 2 .
Note that potential endogeneity of a variable x it requires that its lags dated ( t − 2) or
earlier can only be the instruments for the differenced equations, while its lagged
differences dated ( t − 1 ) will be the valid instruments for the levels equations. If x it is, infact, not endogenous, the assumption is restrictive and causes biasness in the estimates.
The GMM3 estimates, which are based on the above moment conditions, are found to be
weakly identified and further downward biased than the FE estimates. We therefore do
not report them.

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36

The Intra-regional Trade Share in %

0

30

40

5000

50

10000

60

70

15000

GDP in billion US dollars

1985

1990
NAFTA

1995
Year

2000

2005

1985

EU15

1990
NAFTA

EA15

1995
Year

2000

2005

EU15

EA15

Source: CHEPII CHELEM Database.
Note: The figures are updated from Fujita (2007, pp. 4-5).

Figure 1. Globalization, regional growth and economic interdependency, 1985-2005

37

1992

1995

1998
Year

2001

2004

1995

1998
Year

1992

1995

2001

2004

1998
Year

2001

2004

ASEAN-10

Share of intra-industry trade
.1
.15
.2
.25

Share of intra-industry trade
0
.1
.2
.3
.4

Taiwan

1992

South Korea

Share of intra-industry trade.05 .1 .15 .2 .25

Share of intra-industry trade.05 .1 .15 .2 .25

Japan

1992

1995

1998
Year

2001

2004

Source: CHEPII CHELEM Database.

Figure 2. Intra-industry trade intensity between China and the rest of East Asia

38

ASEAN( x ASEAN )Japan( x JAPAN )China( x CHINA )Country i(e.g., the U.S.)

NIEs-2( x NIES−2 )Figure 3. Schematic view of production networks and real exchange rate relationships

39

Table-1
Panel A: China’s imports – 1993 and 2005 (in %)Partner
Import categories
1993
Total imports
a. Ordinary imports
b. Processing imports
c. Other processing imports
2005
Total imports
a. Ordinary imports
b. Processing imports
c. Other processing imports

World

Japan
(1)

S. Korea &Taiwan (2)ASEAN-5(3)

Hong Kong(4)

East Asia(5=1+2+3+4)

UnitedStates

EU-15

Rest of the
World

100.0
36.6
35.0
28.4

22.4
7.9
7.7
6.8

17.6
2.1
10.8
4.7

5.8
3.3
1.9
0.7

10.0
1.1
7.0
2.0

55.8
14.3
27.3
14.1

10.3
5.1
1.9
3.3

15.1
7.9
1.7
5.5

18.8
9.3
4.0
5.5

100.0
42.4
41.5
16.1

15.2
5.4
6.9
2.9

23.0
5.7
14.4
2.9

10.9
3.1
5.7
2.1

1.9
0.5
1.2
0.1

50.9
14.8
28.1
8.1

7.4
3.9
1.9
1.5

10.7
6.4
1.8
2.4

31.1
17.3
9.7
4.1

Panel B: China’s exports – 1993 and 2005 (in %)Partner
Export categories
1993
Total exports
a. Ordinary exports
b. Processing exports
c. Other processing exports
2005
Total exports
a. Ordinary exports
b. Processing exports
c. Other processing exports

World

Japan
(1)

S. Korea &Taiwan (2)ASEAN-5(3)

East Asia(4=1+2+3)

Hong Kong

UnitedStates

EU-15

100.0
47.1
48.2
4.7

17.2
9.8
7.3
0.1

4.7
2.7
2.0
0.0

5.1
3.6
1.4
0.1

27.0
16.0
10.7
0.2

24.0
9.6
14.0
0.4

18.5
5.8
12.7
0.0

13.3
6.8
6.5
0.0

17.2
8.9
4.3
4.0

100.0
41.3
54.7
4.0

11.0
4.4
6.5
0.1

6.8
3.2
3.4
0.2

6.3
2.9
3.2
0.2

24.1
10.5
13.1
0.5

16.3
3.3
12.2
0.9

21.4
6.9
13.9
0.6

17.3
7.4
9.5
0.4

20.9
13.2
6.0
1.7

40

Rest of theWorldTable-1 (Contd.)
Panel C: China’s trade account balance – 1993 and 2005 (in billions of U.S. dollars)Partner
ASEAN-5 East Asia
Hong
United
EU-15
U.S.+
Rest ofWorld Japan (1) S. Korea &Trade categories
Taiwan (2)(3)(4=1+2+3) Kong
StatesEU-15
the World
1993
-22.8
Trade account balance
-12.2
-7.5
-14.0
-1.3
11.6
6.3
-3.5
-3.8
14.4
0.9
a. Ordinary trade
5.2
0.7
0.3
-0.1
7.7
0.0
-2.0
-1.5
5.7
-11.4
b. Processing trade
7.9
-1.3
-9.4
-0.6
5.7
9.7
4.2
-0.3
19.5
-12.4
-2.0
-25.2
-6.9
-4.9
-0.6
-1.7
-3.4
-5.8
c. Other processing trade
-10.8
2005
Trade account balance
102.0
-16.4
-99.8
-23.8
112.3
114.3
61.4
-45.8
-140.1
287.9
a. Ordinary trade
35.4
-2.5
-12.9
2.0
21.6
26.9
14.4
-14.0
-13.4
62.9
b. Processing trade
142.5
4.5
-69.3
-13.3
85.1
92.9
60.4
-17.9
-78.1
238.4
c. Other processing trade
-75.9
-18.5
-17.7
-12.4
5.6
-5.6
-13.4
-13.9
-48.6
-13.4
Hong Kong is included in China’s imports from East Asia since inbound imports from Hong Kong are largely from other East Asian economies, generally
intended for further processing into finished exports in China. However, China’s exports via Hong Kong, largely finished exports, are generally destined for the
U.S. and EU-15 markets. Following Kwan (2002), China’s bilateral trade surplus against Hong Kong is therefore considered as China’s bilateral trade surplus
against the U.S. and EU-15. Feenstra and Spencer (2005, p.1) noted that both the “processing exports by foreign-owned firms” and “other processing exports by
Chinese-owned firms” were largely produced under contractual arrangements with foreign multinationals, whereas the “ordinary exports by local firms” did nothave these arrangements. Trade balance on account of the “processing trade” is thus related to foreign affiliates of multinationals, while trade balance on accountof both the “ordinary trade” and the “other processing trade” is related to Chinese-owned local firms. EU-15 includes Austria, Belgium, Denmark, Finland,
France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and United Kingdom. ASEAN-5 includes Indonesia, Malaysia,
Philippines, Singapore and Thailand.
Source: Updated from Rahman and Thorbecke (2007) and China (2006).

41

Table 2
Panel unit root tests
Panel A: Levin-Lin-Chu pooled augmented Dickey-Fuller (ADF) tests
Test Statistic Levin-Lin-Chu ADF Specifications for deterministics
Variables
and autoregressive ordert δ* test
Real exports (ordinary)-11.336***
Constant and trend; AR(1)Real exports (processing)-16.065***
Constant and trend; AR(1)-6.897***
Constant and trend; AR(1)Real GDP ( GDPit )-2.749***Constant; AR(4)RMB RER ( RER cit )Panel B: Pesaran cross-sectionally augmented Dickey-Fuller (CADF) tests
Test Statistic
Specifications for deterministics
CADF t test
Variables
and autoregressive order
Real exports (ordinary)-2.522**
Constant and trend; AR(2)Real exports (processing)-2.599**Constant and trend; AR(2)-4.231***
Constant and trend; AR(2)Real GDP ( GDPit )RMB RER ( RER cit )-0.679Constant; AR(2)A three-step procedure is followed to obtain Levin-Lin-Chu panel unit root tests. First, ∆yit and yit −1 areregressed on ∆y it − L ( L = 1, L , pi ) for generating orthogonalized residuals êit and vˆit −1 respectively. Second,
the ratio of long run to short run innovation standard deviations for each cross-sectional unit is estimated.
Finally, all cross-sectional and time series observations are pooled to estimate: e~it = δv~it −1 + ε~it , where ~eit and~v
are the normalized residuals estimated in step 1. The estimate of the average standard deviation ratio is
it −1

then used to adjust tδ statistic from the above estimation to derive adjusted tδ* statistics. By contrast, Pesaran
CADF t statistic is defined as t = N −1Σt i , where ti is the cross-sectionally augmented Dickey-Fuller
statistic for the i th cross-section unit given by the t-ratio of the coefficient of yit −1 in the CADF regression.

RERcit represents the bilateral real exchange rate of Chinese renminbi vis-à-vis country i . The intra-regional
RER flexibility ( RERwt ) between China and other East Asian countries that supply intermediate goods toChina is a time series variable. Since the variable RERwt does not vary across cross-sections, we obtain boththe augmented Dickey-Fuller and the Philips-Perron unit root test statistics. The p-values of the unit root test
statistics are about 0.90. The results indicate that RERwt is a unit root process, regardless of the number ofhigher-order autoregressive terms and/or a drift term included in the estimated regression. ‘*’, ‘**’ and ‘***’denote 10%, 5% and 1% statistical significance, respectively.

42

Table 3
Estimates of the autoregressive parameter of individual data generation processes (DGPs)
Name of the DGPs
OLS
GMM-SysWithin
GMM-Diff
Ordinary real exports ( EX1it )0.978***
0.928***
0.719***
0.811***(0.013)(0.043)(0.063)(0.091)
Processing real exports ( EX 2it )0.979***
0.912***
0.754***
0.360***(0.009)(0.027)(0.060)(0.097)
Real GDP ( GDPit )0.997***
0.974***
0.771***
0.521***(0.001)(0.008)(0.043)(0.157)RMB RER ( RER cit )0.999***
0.786***
0.282***-0.070(0.018)(0.081)(0.094)(0.132)
Weighted RER ( RER wt ).881***n/an/an/a(.091)The data series EX 1it and EX 2it represent China’s bilateral ordinary and processing exports respectively tocountry i , GDPit real gross domestic product of importing country i , RERcit the bilateral real exchange rate
between China and country i , RERwt the intra-regional RER flexibility between China and countries that
supply intermediate goods to to China. The OLS and within estimates of the parameter are biased upwards
and downwards respectively. GMM first-difference estimates are further biased than the within estimates.
The consistent system GMM estimator is obtained by estimating a system combining both the differenced
equations and the levels of the univariate dynamic panel model. The moment conditions are
E ( y it − s ∆eit ) = 0 for t = p + 1, L , T and s ≥ 2 , for the differenced equations, and E (eit ∆yit −1 ) = 0 fort = p + 1, L , T , for the levels equations of the model. The above moment conditions give rise to the extended
instrument matrix used to obtain the system GMM estimator. Only the OLS estimate of persistency of
RERwt is reported, since the variable does not vary cross-sectionally. ‘*’, ‘**’ and ‘***’ denote 10%, 5% and
1% statistical significance, respectively.

43

Table 4
Estimation of autoregressive and distributed lag model for China’s processing exports to 33
countries, 1992-2005 (Benchmark results; dependent variable: China’s bilateral processing
exports)
Fully specified model

Independent Variables
Lagged real exportsi(t-1)
GDP of importerit
GDP of importeri(t-1)
GDP of importeri(t-2)
Bilateral RMB RERci,tBilateral RMB RERci,(t-1)
Bilateral RMB RERci,(t-2)
Intra-regional RER
flexibilitywt
Intra-regional RER
flexibilityw(t-1)
Intra-regional RER
flexibilityw(t-2)

1
Pooled OLS
0.988***(0.073)2.466***(0.487)-3.037***(0.770)0.591(0.452)-0.784***(0.191)
0.523**(0.252)0.267*(0.157)

2
Fixed-Effect0.774***(0.084)2.480***(0.484)-2.382***(0.739)0.646(0.451)-0.799***(0.187)0.380(0.231)
0.082(0.155)

3
GMM1
0.791***(0.069)2.674***(0.556)-2.515***(0.715)0.014(0.392)-0.718***(0.186)
0.419*(0.246)0.254**(0.115)4GMM2
0.776***(0.073)2.560***(0.508)-2.091**(0.797)-0.297(0.521)-0.754***(0.182)0.429(0.263)0.160(0.165)-1.691***(0.436)-1.368***(0.270)-1.088***(0.224)-1.306***(0.368)0.770**(0.351)

0.763**(0.340)0.598*(0.295)0.533(0.340)-0.799**(0.324)-0.738***(0.249)

Hypothetical
model
5
GMM2
0.663***(0.090)2.494***(0.574)-1.465**(0.704)-0.844*(0.482)-0.584***(0.160)0.178(0.167)
0.236**(0.088)-0.439*-0.715*(0.217)(0.352)-2.94***-2.91***-2.81***
m1
-0.29
-0.28
-1.11
m2
0.111
Hansen J Statistic
0.009
0.590
(21)
P-value (d.f.)
(10)
(28)
No. of Groups
33
33
33
33
33
Estimation Period
1992:2005
1992:2005
1992:2005
1992:2005
1992:2005
No. of obs.
396
396
396
396
396
Hong Kong export price index is used to deflate the nominal dollar value of the processing exports. Columns 14 report estimation results of the fully specified model, while column 5 reports estimation results of the
hypothetical model, which excludes RERw,( t − j ) by assumption. The coefficient of lagged real exportsi(t-2) isalways statistically insignificant. Both GMM1 and GMM2 are one-step system GMM estimates that are
obtained by estimating a system of the differenced equations and the levels equations of the model. While
GMM1 assumes that RER and RERw are exogenous, GMM2 assumes that they are predetermined in thecimodel. m1 and m 2 are tests for first-order and second-order serial correlation in the first-differenced residuals,
asymptotically distributed as N(0,1) under the null of no serial correlation. Hansen J statistic is the test for overidentifying restrictions, asymptotically distributed as χ 2 under the null of instrument validity. Asymptotic
standard errors, asymptotically robust to cross-section and time-series heteroscedasticity, are reported inparentheses. Significance tests: *** p<0.01, ** p<0.05, * p<0.1.

44

Table 5
Estimation of autoregressive and distributed lag model for China’s ordinary exports to 33
countries, 1992-2005 (Benchmark results; dependent variable: China’s bilateral ordinary
exports)
Fully specified model

Independent Variables
Lagged real exportsi(t-1)
Lagged real exportsi(t-2)
GDP of importerit
GDP of importeri(t-1)
GDP of importeri(t-2)
Bilateral RMB RERci,tBilateral RMB RERci,(t-1)
Bilateral RMB RERci,(t-2)
Intra-regional RER
flexibilitywt
Intra-regional RER
flexibilityw(t-1)
Intra-regional RER
flexibilityw(t-2)

1
Pooled OLS
0.657***(0.104)0.288***(0.096)1.795***(0.466)-2.185***(0.718)0.449(0.422)-0.861***(0.128)0.526***(0.154)0.414***(0.119)2
Fixed-Effect
0.504***(0.096)
0.198**(0.087)1.823***(0.487)-1.796***(0.674)0.039(0.404)-1.001***(0.139)0.417***(0.147)0.233*(0.140)3
GMM1
0.572***(0.078)0.342***(0.089)1.768***(0.628)-1.703**(0.793)0.026(0.289)-0.821***(0.160)0.500**(0.188)0.411***(0.109)4
GMM2
0.537***(0.105)0.384***(0.103)1.564**(0.635)-1.369(0.943)-0.120(0.358)-0.889***(0.150)0.480***(0.169)0.352**(0.131)-0.853***(0.315)-1.028***(0.246)-0.455**(0.214)-0.676**(0.328)0.409*(0.225)0.348(0.231)0.436*(0.224)

0.398*(0.225)-0.904***(0.243)-1.119***(0.217)

Hypothetical
model
5
GMM2
0.545***(0.080)0.301**(0.111)1.850***(0.598)-1.820**(0.741)0.130(0.315)-0.749***(0.118)0.204(0.142)0.559***(0.136)-0.701***-0.937***(0.164)(0.218)-1.71*-1.84**-1.68*
m1
-0.08
-0.34
0.06
m2
0.009
0.289
0.087
Hansen J Statistic (d.f.)
(10)
(28)
(21)
No. of Groups
33
33
33
33
33
Estimation Period
1992:2005
1992:2005
1992:2005
1992:2005
1992:2005
No. of obs.
396
396
396
396
396
Hong Kong export price index is used to deflate the nominal dollar value of the ordinary exports. Columns 14 report estimation results of the fully specified model, while column 5 reports estimation results of the
hypothetical model, which excludes RERw,( t − j ) by assumption. As in Table 3, both GMM1 and GMM2 are
one-step system GMM estimates that are obtained by estimating a system of the differenced equations and thelevels equations of the model. While GMM1 assumes that RER and RERw are exogenous, GMM2
ci
assumes that they are predetermined in the model. m1 and m 2 are tests for first-order and second-order serial
correlation in the first-differenced residuals, asymptotically distributed as N(0,1) under the null of no serial
correlation. Hansen J statistic is the test for over-identifying restrictions, asymptotically distributedas χ 2 under the null of instrument validity. Asymptotic standard errors, asymptotically robust to cross-section
and time-series heteroscedasticity, are reported in parentheses. Significance tests: *** p<0.01, ** p<0.05, *p<0.1.

45

Table 6
Estimates of long-run export demand elasticities
Specifications and estimations methods

Income ofimporting

Bilateral RMB
real exchange

Panel A: China’s processing exports to 33

country β̂rate ξ̂(ψ̂ )2.497***(0.772)
1.011***(0.153)−1.131***(0.374)−0.257(0.321)−4.503***(0.572)−5.432***(1.002)

0.975***(0.180)−0.934**(0.481)−8.465**(4.558)0.219(0.727)1.064***(0.103)−1.175***(0.338)1.049**(0.397)−6.022***(0.696)−8.376(5.743)0.946***(0.269)−0.715(1.607)−15.389(31.130)

countries—1992-2005(1) Based on within estimates of the dynamic
regression, Eq. 3.1(2) Based on GMM system estimates of the
dynamic regression, Eq. 3.1 (both the RERvars are treated strictly exogenous)(3) Based on GMM system estimates of the
dynamic regression, Eq. 3.1 (both the RERvars are treated predetermined)
Panel B: China’s ordinary exports to 33()()

Intra-regional
RER flexibility

countries—1992-2005(1) Based on within estimates of the dynamic
regression, Eq. 3.1(2) Based on GMM system estimates of the
dynamic regression, Eq. 3.1 (both the RERvars are treated strictly exogenous)(3) Based on GMM system estimates of the
dynamic regression, Eq. 3.1 (both the RERvars are treated predetermined)

Significance tests: *** p<0.01, ** p<0.05, * p<0.1.
Long-run estimates (3) in both panels (A) and (B) are based on consistent system GMM estimates, whereas
the estimates (1) and (2) are reported for comparison. For the dynamic estimates in each case, refer tocolumns (2-4) in Tables 4 and 5 respectively. Standard errors, obtained by using delta method, are reported
in parentheses.

46

Table 7
Trade effect of a fixed exchange rate system in East Asia
(Estimates are shown for China’s processing exports to 33 countries)
Importer
IDs

Importingcountryyiyi*

y i* − y i(in million
USD) [1](in million
USD) [2](in million
USD) [3]t-ratio[4]( y i / y i* )
[5]

101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131

Argentina
179
243
63.6
2.4
0.74
Australia
2112
2508
396.1
3.7
0.84
Austria
241
396
154.2
5.6
0.61
Belgium
1520
1691
170.7
2.2
0.90
Brazil
692
769
76.3
1.1
0.90
Canada
2183
2651
468.3
2.7
0.82
Denmark
511
669
158.0
4.8
0.76
Finland
749
785
36.7
0.7
0.95
France2812
3912
1099.7
4.0
0.72
Germany,FR
8065
9809
1744.03.3
0.82
Greece
235
321
86.8
2.8
0.73
Hong Kong
42306
48473
6167.81.7
0.87
Iceland
8
13
5.1
1.9
0.61
Indonesia
793
844
51.3
0.9
0.94
Ireland
712
667
-45.2
-0.7
1.07
Italy
1599
2290
690.8
9.0
0.70
Japan
27891
42850
14958.8
8.8
0.65
Korea Rep
7241
9439
2198.87.2
0.77
Luxembourg326
161-164.8-1.6
2.02
Malaysia
2189
2388
199.6
1.9
0.92
Mexico
972
965
-7.6
-0.1
1.01
Netherlands
6175
5487
-687.8-1.0
1.13
New Zealand
219
273
54.3
4.3
0.80
Philippines
918
878
-40.0
-0.5
1.05
Portugal
110
156
46.2
5.4
0.70
Russia
636
932
296.7
5.0
0.68
Singapore
4565
5313
748.3
2.8
0.86
Spain
997
1384
386.4
6.4
0.72
Sweden
470
722
252.4
6.6
0.65
Taiwan pro
4075
6533
2457.510.5
0.62
Thailand
1397
1472
75.5
1.2
0.95
United
132
4726
6084
1358.76.1
0.78
Kingdom
133
United States
46779
52286
5507.81.8
0.89
Overall
174400213365
38965
2.35
0.81
*
For each cross-section i , y i and y i represents respectively the actual and the potential volumes of China’s
processing exports. The potential volume is estimated under the assumption that China shares a fixedexchange rate system with the rest of East Asia and that the regional currency is perfectly flexible vis-à-vis*

the rest of the world. Both y i and y i are respective averages over the 1994-2005 period. The t-ratios are
based on nonparametric bootstrap method.

47

Table 8
Estimation results of the fully specified model under alternative variable definitions and w/wo control for the supply side effect1
Processing

2
Processing

3
Ordinary

4
Ordinary

5
Processing

6
Processing

7
Processing

8
Ordinary

9
Ordinary

10
Ordinary

0.802***(0.071)0.781***(0.073)0.570***(0.112)0.547***(0.109)0.695***(0.075)0.719***(0.087)0.741***(0.088)0.413***(0.093)0.442***(0.100)0.461***(0.104)0.013(0.057)2.647***(0.521)

0.042(0.059)2.561***(0.511)

0.362***(0.103)
1.571**(0.626)

0.378***(0.106)
1.581**(0.631)0.123(0.073)2.849***(0.519)0.129*(0.070)2.946***(0.540)0.099(0.073)2.851***(0.543)0.387***(0.107)2.089***(0.627)0.405***(0.106)2.229***(0.625)0.435***(0.109)2.225***(0.676)

RER ci-0.749***(0.184)-0.751***(0.182)-0.903***(0.150)-0.886***(0.149)-0.659***(0.210)-0.606***(0.220)-0.665***(0.228)-0.698***(0.166)-0.646***(0.171)-0.691***(0.180)

RER w-1.082***(0.378)No-1.325***(0.371)No-0.289(0.301)
No-0.663*(0.329)No-2.280***(0.548)
FDI stock

USMPI

USCPI

USMPI

HKXPI

-1.957***(0.483)Grossfixed C. F.
HKXPI-1.774***(0.262)
FDI stock

USCPI-2.073***(0.424)GDP ofexporter
HKXPI-1.724***(0.288)GDP ofexporter
HKXPI-2.019***(0.354)Grossfixed C. F.
HKXPIDependent variable:
Bilateral real exports
Independent variables
yit −1y it − 2
GDPit

Proxy for supply
shift effectDeflator usedLong-run effect of
RER w (ψ̂ )HKXPI-5.376**-8.962**
Not
Not-14.175*-16.474-13.933*
Not
Not
Not
(2.986)(4.646)Significant Significant(7.912)(11.173)(8.846)Significant Significant Significant-2.94***-2.92***-1.94*-1.83*-2.99***-3.02***-3.06***-1.60-1.61*
-1.56
m1
-0.07
-0.17
0.26
-0.24
-0.93
-0.72
-0.56
-0.45
-0.37
-0.44
m2
Hansen J Statistic (d.f.)
0.588
0.585
0.318
0.380
0.501
0.712
0.484
0.794
0.474
0.524
(28)
(28)
(28)
(28)
(26)
(27)
(27)
(27)
(27)
(27)
No. of Groups
33
33
33
33
33
33
33
33
33
33
Estimation Period
1992:
1992:
1992:
1992:
1992:
1992:
1992:
1992:
1992:
1992:
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
No. of obs.
396
396
396
396
396
396
396
396
396
396
The table reports the one-step GMM estimators of the dynamic panel model (3.1). A stacked system of the differenced equations and the levels equations of
the model is estimated in every case and an extended instrument matrix is used to obtain the estimators. Both RER and RERw are treated to becipredetermined (weakly exogenous) in the model. When the model is augmented by including proxy for the supply shift effect, only the lagged (t-2) term ofproxy variables is used in the estimation. This was necessary because these proxies are too endogenous. The long-run coefficients of other variables areomitted for reporting convenience. See footnotes to Table 4 for interpretations of m1 , m 2 and Hansen J statistics. Standard errors, asymptotically robust to
cross-section and time-series heteroscedasticity, are reported in parentheses. Significance tests: *** p<0.01, ** p<0.05, * p<0.1

48