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Globalisation and Technology Intensity
as Determinants of Exports1
Ray Barrell and Olga Pomerantz
NIESR 2 Dean Trench Street
Smith Square
London SW1P 3HE
Abstract
This paper augments traditional equations for estimating export demand with a
measure of technology intensity of output, and several variables capturing the impact
of regional integration and global trade liberalisation programmes. Using data for a
panel of 20 OECD countries it is shown that the augmented long run relationships
cointegrate and can be embedded into equilibrium correction form. The effects of
technology and trade liberalisation were found to be stronger at times than the impact
of competitiveness and together these variables help explain large changes in export
shares in the presence of relatively little shifts in competitiveness.
JEL Classification: C23, F15, F10, O30
1 An earlier version of this paper was presented at an ESRC MMF workshop on 5
th February 2007 in
Trinity College, Cambridge. We would like to thank Ron Smith, Kevin Lee, Hashem Pesaran and other
participants for their comments and Iana, Liadze, Dawn Holland, Amanda Choy and Simon Kirby for
inputs into this paper.
1
Introduction
As globalisation has progressed over the past several decades, world trade growth has
outpaced global production by a considerable margin, expanding by about 30 per cent
faster than world GDP since the early 1970s. While import penetration has increased
uniformly across all OECD countries, the impact of globalisation on export
performance of individual countries has been more nuanced. Specifically, many
countries in the OECD have witnessed substantial fluctuations in market share for
their exports over the past several decades and these movements cannot be explained
by changes in relative export prices alone.
A conventional model for estimating export demand functions one country at a time,
(Senhadji and Montenegro,1999), predicts a cointegrating relationship between export
volumes, relative export prices and a measure of export market demand. We study 20
OECD countries in this paper, and residual analysis of single cointegrating equations
for export volumes as a function of demand and relative price failed to reject the
presence of a unit root for all but one country, which suggests that there is no
cointegrating relationship between the aforementioned variables. This has been noted
already by Hooper et. al (2000) and elsewhere and sophisticated econometric
techniques are then used to proceed with estimation in the presence of non-
stationarity. However, these results fail to explain what else, besides relative prices,
can explain changes in export market shares in the industrialised countries over the
past several decades, and it is our intention to fill this gap with measures of
technology capacity and of trade liberalisation.
This question of technology intensity and its impact has attracted substantial attention
in theoretical literature and has produced a number of empirical studies. The models
of Krugman (1983) and Grossman and Shapiro (1987) among others, establish a
theoretical link between international technological competitiveness and international
trade. Many empirical studies have attempted to validate and quantify these
theoretical findings, but most of the analysis has focused on single country or
industry, making broad international comparison difficult. Our empirical analysis –
using a panel of 20 OECD member countries – augments the existing models for
estimating export demand equations with a measure of a country’s technology
intensity of output relative to the OECD average, to capture the impact of the
proliferation of new technologies as a determinant of export volumes, while making
international comparison possible.
Recognising the importance of regional integration and global trade liberalisation
efforts in changing patterns of trade, we also include several variables to capture the
completion of the European Single Market, North American Free Trade Agreement,
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and the integration of China into the world trade system. We do not include the
impacts of generalised tariff reductions as these are less likely to have impacted on
trade shares, but will have increased the overall level of demand for exports as they
increase imports. Thus, the analysis presented here is based on a model that
incorporates changes in competitiveness, technological intensity of production and
international trade agreements as determinants of export shares across most
industrialised countries.
The inclusion of additional variables into the cointegrating set suggests that there is a
structural relationship between export shares, competitiveness, technological intensity
of output and measures of trade liberalisation in all the countries in the sample.
Following the panel estimation and incorporating the latest econometric techniques,
we find that export price elasticities under this specification are smaller than those
obtained with standard models. Furthermore, the effects of technology and trade
liberalisation were found to be stronger at times than the impact of competitiveness
and together these variables help explain large changes in export shares in the
presence of relatively little shifts in competitiveness.
The remainder of the paper is structured as follows. In section II, we undertake a brief
survey of relevant findings from other studies for context. In Section III we discuss
the general structure of our results as well as the econometric model used in the
analysis. Section IV consists of. an analysis of empirical findings and major
conclusions are summarised in section V. Details on the data and procedures are
contained in the Appendices.
II. Theoretical and empirical work on the determinants of trade
This section provides a brief survey of the existing studies for the purposes of
comparing our results to the existing findings, as well as to place the current research
in the context of existing literature. The study of the determinants of trade has
received attention for many years. Generally, the available research can be
categorised into two strands: macroeconomic studies seek to estimate trade elasticities
to forecast the implications for exchange rates and current account fluctuations, while
microeconomic studies disaggregate trade data to tease out the relative importance of
quality, product composition and geographical proximity in determining the volume
of trade for a particular industry or country.
While much has been written as regards trade elasticities and exchange rate
movements, the following two studies are broadly representative both of the
econometric methodologies and economic findings. Senhadji and Montenegro (1999)
estimate export demand elasticities for a large number of developing and industrial
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countries and find the average long-run price and income elasticities to be -1 and 1.5,
respectively, with Asian countries facing the highest income and price elasticities in
the sample. The authors fail to find evidence of cointegration between export
volumes, relative price and export demand and use statistical techniques that can deal
with nonstationarity. Hooper et. al (2000) focus their analysis on the G-7
industrialised countries and find their long-run income elasticities to be nearly
identical to the results in the above study, ranging from 0.8 for the United States to
1.6 for Italy. However, price elasticities were found to be as low as -0.2 for Germany
and as high as -1.6 for the United Kingdom. Barrell and Pain (1997) study export
demand equations in a variety of European countries and validly impose a unit
coefficient on demand. They also find price elasticities vary from -0.5 for the UK to
over -1.9 for Sweden. Together, these studies suggest a range of estimates of income
and price elasticities for exports over the past several decades.
The other strand in recent literature uses the new trade and endogenous growth
theories expounded by Krugman (1983), Grossman and Helpman (1995) and
Fagerberg (1996) to argue that product variety and technology are at least as
important as price competitiveness in explaining varying income elasticities across
countries. The results of this strand of research suggest that income elasticities in
traditional export equations are biased upward because income is highly correlated
with factors that are common across countries and that change at the same rate over
time such as technology and product variety. Earlier studies such as those by
Anderton (1999), Fagerberg (1988), Greenhalgh (1990) and Magnier and Toujas-
Bernate (1994) estimate an average elasticity of technology and product variety
competitiveness of approximately 0.3 depending on industry and country. By contrast,
Carlin et al. (2001) suggest that the estimated coefficients of patent and R&D
competitiveness are not significantly different from zero. Madsen (2004) augments
existing specifications with a measure of stocks of external patents and shows them to
be a good proxy for measuring the stock of technology and product variety in a large
sample of OECD countries. The resulting elasticities are broadly in line with earlier
findings.
The role of international agreements and European integration and the global
reduction of trade barriers has been largely a separate trend of research as these
studies are usually preoccupied with a particular agreement’s impact on domestic
constituents. This is particularly prevalent in the studies considering the impact of
NAFTA on US production and trade. Studies which quantify the impact of European
integration usually analyse the impact on intra-EU trade, largely abstracting from
broader issues.
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III. Modelling export demand equations
In this section we discuss the model for estimating export demand equations for
industrialised countries using a panel of 20 OECD countries2. We highlight the
changes in trade dynamics over the past several decades and explain the motivation
for the chosen variable set. We test explicitly for common parameters across
countries, constructing pooled mean group (PMG) estimates, and we extend our
econometric analysis to allow for correlated cross equation errors (CCE) as in Pesaran
(2006) to obtain more precise parameter estimates. This section concludes with a
discussion of cointegration tests on the final specification of the panel.
The analysis is based on quarterly data from 1978Q1 to 2004Q4 for volumes of
exports of goods and services. The sample period was chosen largely out of data
availability and quality considerations. Reliable data on export prices for competitors
outside Europe, particularly those in East Asia, is available only from the late 1970s.
The data on volumes, relative prices, demand and technology composition of output
are computed in natural logarithms.
Following the methodology described in Senhadji et al (1998), we model trade
volume equations as demand relationships, where the total level of exports depends
on the level of a demand indicator for the relevant economies and on relative prices.
This approach was followed for European trade equations in Barrell and te Velde
(2002) who discuss standard macroeconomic demand relationships for estimating
trade volumes. We estimate our equations in a panel of dynamic equilibrium
corrections, with a long run embedded in an adjustment process.
This specification follows the Armington approach to exports that is based on a
structural demand equation where the goods produced in one country are imperfect
substitutes for the goods produced elsewhere. We may write the long run equation as
XVOL = f ( S, RPX) (1)
where XVOL, the volume of exports of goods and services, depends on S, a country
specific export market demand measure, and on RPX, export prices relative to prices
in destination countries. The competitor group includes all exporters to the same
market. S – the size of the export market of a country i – is defined as an average of
import volumes of a country’s trading partners weighted by the share of the exporting
country’s total exports. The size of a country’s export market and the relative export
2 The countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece,
Ireland, Italy, Japan, The Netherlands, South Korea, Spain, Sweden, Switzerland, U.K, and the U.S.
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prices capture important changes in the dynamics of trade patterns across countries
and over time, such as the integration of China into the world trade system.
Following Pain and Wakelin (1998), we test whether the long run coefficient on S is
equal to one in all countries in the panel, in which case equation (1) becomes a market
share equation. Estimating this equation freely we generally find that long run
coefficient on the demand indicator is not one, with several OECD countries
sustaining noticeable changes in market share for exports over the past several
decades, and these fluctuations cannot be fully explained by movements in relative
export prices. We can write the long run of the simple share equation as
log(XVOLt) = α0 +log(St) + α1 log(RPXt) + wt (2)
To ascertain the presence of a cointegrating relationship described by equation (2), the
residuals in the export demand equation were tested for the presence of a unit root. All
tests were computed with a lag length of 4, which is reasonable for quarterly data. The
left hand side of Table II.1 presents the summary of several different test statistics for
the presence of a unit root under this specification. The results do not suggest the
existence of a structurally stable long run relationship in our data under the basic
specification for any of the countries except perhaps Austria. This failure to find a
cointegrating long run relationship in the basic specification lends further justification
to the need for other explanatory variables. These may be either stochastic time series
variables or shift variables that change the intercept of the equation.
Several forces have affected patterns of exports from the industrialised countries over
the past several decades. A sequence of world trade liberalisation measures following
the Kennedy, Tokyo, and Uruguay rounds reduced tariffs on goods and removed non-
tariff barriers. As importantly, increasing world trade pushed the limits of the General
Agreement on Tariffs and Trade, better known as GATT, and resulted in a move from
a multilateral trade agreement to an international organisation, WTO, whose
agreements are permanent, binding and enforceable; and whose scope extends beyond
goods to services, intellectual property rights, and investment. European integration
has deepened as the Common Market moved well beyond a free trade area to one
where goods and factors are mobile, and competition rules and standards for
production have become common to all countries. China embarked on a series of
economic reforms which reduced non-tariff trade barriers and integrated China into
the world economy. As importantly, rapid advancements in the Information and
Communication Technology (ICT) industries changed the size and the production
process of many traded goods. Light and highly portable goods are produced in long
manufacturing ‘strings’ that do not need a common country location to link up
together into a production process (Arndt and Kierzkowski, 2001). International trade
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liberalisation, regional integration, the rise of China and rapid technological
advancements have affected trade patterns since the 1970s.
We augment the basic model structure to capture the impact of European regional
integration, removal of trade barriers and technological advancement on trade. The
new variables are constructed based on the timeline of major agreements and
unilateral developments. The details can be found in Appendix 1 at the end of the
paper.
The regional variables cover North America and Europe but not Asia, as APEC seems
to have had less impact on trade than its counterparts. The effect of the European
Single Market (ESM) is captured by a variable equal to one prior to 1987Q2 which
gradually declines to zero in 1992Q4, the formal completion of the Single Market
Programme. EMU is a dummy variable which equals to 1 from 1999Q1 for most
countries, but later for Greece, with the official introduction of single currency in
Europe and is zero prior to 1999. The North American Free Trade Agreement
(NAFTA) variable is created to account for the impact of gradual elimination of
tariffs on the goods shipped between US, Canada and Mexico. As NAFTA agreement
represented an expansion of the earlier Canada-U.S. Free Trade Agreement of 1988,
we model the NAFTA variable to be equal to one before 1989q1 and then gradually
reduce to zero by 1998.
Variables affecting global trade are constructed in a manner similar to those for
regional agreements. WTO models the impact of the significant deepening in global
trade relations, symbolised by the introduction of the World Trade Organisation in
1995, with formal rules governing trade of goods, services, intellectual property and
investment as well as an effective enforcement mechanism (Crowley, 2003). WTO is
gradually reduced from one in 1995Q1 when the major outcomes of the Uruguay
round of trade liberalisation measures came into effect, to zero by the end of the
sample period, as the majority of transitional quotas and tariffs from that round were
formally abolished in January 2005. The most important introduction of market based
trade in the last 40 years has been by China, and the variable representing this
liberalisation, CHINA, is gradually reduced from one in 1978Q1 when first trade
reforms were implemented to zero in 1991Q1 when mandatory export planning was
abolished completely.
We include a measure of a country’s technology intensity of output relative to the
OECD average, TECHS, to capture the impact of the proliferation of new
technologies as a determinant of export volumes. The variable is constructed as a
share of a country’s high- and medium-high technology production in total output,
relative to the prevailing OECD average. The individual country and industry data
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comes from the Groningen Growth and Development Centre, 60-Industry Database
(2006), which itself is based on the OECD STAN database. The definitions of high-
and medium-high technology industries are fully compatible with the OECD STAN
classification.
We augmented the cointegrating equation (2) with the stochastic time series variable
for TECHS and with the global shift variables CHINA and WTO, and also added
regional integration variables ESM, EMU and NAFTA to obtain:
log(XVOLt-1) = α0 + log(St-1) + α1 log(RPXt-1)
+ α2 log(TECHS t-1) + α3WTOt-1 + α6CHINAt-1
+α4ESMt-1 + α5EMUt-1 + α7NAFTAt-1 +vt (3)
Unit root tests of the long run specification augmented by the trade and technology
variables, reported in right hand side panel of Table II.1, suggest that all the countries
in the sample, with one possible exception, may have stable long run relationships
Table II.1 Cointegration tests on the long run export demand relationships Augmented Dickey-Fuller tests, 4 lags
Simple
t-statistic
Australia -0.915 -4.390***
Austria -2.920 -5.052***
Belgium -0.648 -3.032
Canada 0.469 -3.551*
Denmark -2.275 -3.753**
Finland -1.609 -4.351***
France -1.585 -3.938***
Germany -2.080 -4.585***
Greece -2.775* -3.563**
Ireland -1.803 -3.079*
Italy -0.497 -4.640***
Japan -0.469 -3.475**
Netherlands -0.922 -3.737**
Portugal -2.467 -4.845***
South Korea -2.363 -3.228*
Spain -1.862 -3.642**
Sweden -2.361 -4.232**
Switzerland -1.564 -3.298*
UK -0.731 -3.096*
US -1.041 -3.534**
Simple & global
variables
t-statistic
Notes:*, **, and *** indicate significance at the 10%, 5% and 1% level, respectively.
For simple regression, the appropriate critical values are -2.567, -2.862, -3.434 at the 10%, 5%
and 1% level, respectively.
When global variables are added, the relevant critical values for the test -3.045, -3.338, -3.900
at the 10%, 5% and 1% level, respectively.
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Given the evidence of a stable long run relationship, we construct a panel of 20
country equations as a system of seemingly unrelated regression equations (SURE)
and estimate the system by the Generalized Least Squares (GLS) techniques. In the
augmented specification, the results of the standard Wald test indicate that a unit
coefficient on S can be imposed in the panel context as this is not rejected at the 10
per cent level. Large cross section-time series panels, such as the one considered here,
may exhibit cross correlations between errors, and estimating the covariance structure
of the error terms is difficult. Even assuming no common auto correlations the
number of covariances rises with N(N-1)/2 where N is the number of members of the
cross section. The number of parameters to estimate in an unrestricted covariance
matrix rises quickly under any form of GLS technique, and the panel becomes
impossible to estimate. The SURE method of estimation imposes a restricted
covariance matrix.
The results presented in the following section are based on a system of equations for
Y of the standard equilibrium correction form:
dlog(Yit ) = λi [log(Yit-1)- ai -bilog(Xit-1)]+cidlog(Xit)+didlog(Yit-1) + ωit (4)
where i varies from 1 to N, the number of elements of the cross section, t varies from
1 to T, the last time period, and X represents a vector of determining variables. We
explicitly test for common parameters using the pooled mean group estimator (PMG).
PMG estimates impose shared behaviour across countries in the parameter estimates
of λi, and bi, whilst allowing ci, and di to vary between countries. Our non-stochastic
regressors are encompassed in ai.
Since exports generally compete in one international market, price elasticities and
technology content of output may have common, but unobserved, effects across
countries. As first argued by Pesaran and Smith (1995), aggregating in the presence of
cross sectional dependence will result in inconsistent and highly misleading estimates.
Pesaran (2006) proposes to filter the individual-specific regressors by means of
weighted cross-section aggregates and demonstrates that doing so eliminates common
unobserved factors. Our trade liberalisation variables may be likened to these
unobserved factors. The unobserved common factors may include some that prevent
individual equations cointegrating, and hence it is best to test for cointegration in each
equation after they have been removed. This technique is particularly appropriate in
the current context, as the common correlated effects (CCE) estimator has been
shown to be consistent for any number of unobserved factors, which do not constitute
the focus of the current study. In our case the CCE estimator consists of the weighted
cross-section aggregates of the relative prices and the technology composition of
output, in addition to the weighted mean of dependent variable. Including the cross-
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section weighted averages of the relevant regressors the panel is estimated using
SURE, but the covariances associated with the PMG estimates are removed from the
error matrix, allowing for a more general specification.
Each cross section equation is modelled using equilibrium correction approach, and is
described by:
∆log(XVOLt) = β0 + λ[log(XVOLt-1) – log(St-1) - α1 log(RPXt-1) – α2 log(TECHS t-1)
– α3WTOt-1 –α4ESMt-1 – α5EMUt-1 – α6CHINAt-1 – α7NAFTAt-1]
+ β1∆log(St) + β2 ∆log(RPXt )+ωt (5)
where λ reflects the speed of adjustment in response to shifts in the long run
relationship, and the changes in demand and relative prices are modelled as dynamic
effects. We estimated the full panel with all variables included where relevant, and
proceeded one equation at a time to delete insignificant variables. Our panel was
balanced because with a relatively small cross section unbalanced panels can produce
biased results with CCE estimators. We also tested to see if we could impose
commonalities across the equations, and apart from the coefficient on the demand
variable, these restrictions were not accepted. Hence the final panel has a degree of
diversity. It would have been acceptable to remove our shift dummies and replace
them with the CCE cross section averages for the variables. We would have produced
the same parameters on other variables in this case, but we would not have been able
to explain the change in trade shares except to the extent it was driven by
competitiveness and technology. Hence we include the full set of shift variables as
they contribute to our understanding of the causal process driving export penetration.
To ensure that our analysis is based on a structurally reasonable description of the
data, the final panel with estimated parameters is tested for cointegration using
residual-based approaches discussed in Breitung et. al (2005). Since non-stationary
dynamic terms could obscure non-cointegration of the long run, the long run structure
is tested separately from the stationarity of the errors on the full panel. The results of
both sets of tests, reported in Table II.2, suggest that there exists a long run structural
relationship between a country’s export share of the world total and its relative export
prices, technology intensity of its output and select trade augmenting globalisation
variables. From the economic theory, we assume a single cointegrating vector behind
the panel even when coefficients differ marginally between countries, as is permitted
with the Breitung tests. We also require that individual country regression cointegrate
in order to ensure that the overall panel test is valid. Detailed results of individual
country ADF tests are presented in Appendix 2.
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Table II.2 Cointegration tests on final panel of export demand equations
Method Statistic Probability Method Statistic Probability
Null: Unit root (assumes common unit root process) Null: Unit root (assumes common unit root process)
Levin, Lin & Chu -3.614 0.000 Levin, Lin & Chu -3.402 0.000
Breitung t-statistic -10.990 0.000 Breitung t-statistic -5.443 0.000
Null: Unit root (assumes individual unit root process) Null: Unit root (assumes individual unit root process)
Im, Pesaran and Shin W-stat -15.286 0.000 Im, Pesaran and Shin W-statistic -9.832 0.000
ADF - Fisher Chi-square 320.693 0.000 ADF - Fisher Chi-square 180.462 0.000
PP - Fisher Chi-square 1219.260 0.000 PP - Fisher Chi-square 352.598 0.000
Null: No unit root (assumes common unit root process) Null: No unit root (assumes common unit root process)
Hadri Z-statistic -2.893 0.998 Hadri Z-statistic 3.635 0.000
Full panel Long run only
The Hausman technique was used to test for the presence of endogenously determined
right-hand side variables because it is possible that a sustained increase in the
technological composition of output can have an impact on the relative export prices.
The results suggest that in all but two countries, the right hand side variables are not
jointly determined. In the case of Portugal and Ireland – where residuals from the
auxiliary regression were found to have explanatory power in the original regression –
the relative export price variables are instrumented, using domestic prices and a
measure of the output gap.
IV. Estimation results and analysis
Having established the long run structure of the underlying data and detailed the
econometric model that forms the basis of our analysis of cross-country export
performance over the past several decades, we turn to the analysis of empirical
estimates. Table IV.1 presents our results in detail. All countries have a
competitiveness effect of the right sign and it is significant in all cases, albeit only at
10 per cent in Greece. The technology indicator enters in most countries, but is absent
in the US, Australia, Canada and Greece because the composition of high to low
technology industries in these countries changed at the same rate as the whole of the
OECD. Hence this variable was a constant in these countries. Regional and global
variables turn up in the places we might expect them. In addition to the standard
formulation our measure of relative export prices for Canada uses US domestic
consumer prices as a proxy for competitors’ domestic price because so much of
Canadian exports is US bound, and hence the relative competitor group is US
producers rather than other exporters to the US. In the case of Belgium, the equation
is augmented with an explicit measure of German and French competitiveness
indicator because much of these countries’ trade passes through Belgium’s ports en
route to other destinations.3
3 It was also important to include a measure of population growth as a determinant of export volumes
for Ireland as the size of the country increased particularly rapidly over the sample period.
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Table IV.1 Unrestricted parameter estimates from export shares panel Error
correction RPX TECHS ESM NAFTA CHINA WTO EMU ∆ RPX ∆ S
-0.210 -0.517 -0.122 0.296 -0.102 0.756
(5.371) (6.392) (3.646) (5.768) (2.116) (9.948)
-0.277 -0.655 0.324 -0.065 -0.239 0.745
(6.519) (5.844) (4.913) (2.682) (5.843) (6.512)
-0.565 -0.382 0.275 0.054 0.046 0.914
(9.802) (10.789) (6.430) (4.170) (2.207) (6.145)
-0.578 -0.505 0.236 -0.112 0.141 -0.023 -0.114 0.973
(10.353) (15.449) (6.730) (13.885) (8.402) (2.843) (3.105) (10.180)
-0.083 -1.086 1.521 0.593 0.956
(2.909) (2.102) (3.197) (3.823) (7.199)
-0.290 -0.804 -0.096 -0.250 0.172 -0.073 -0.357 0.651
(4.405) (5.174) (2.050) (3.453) (1.820) (1.898) (4.060) (2.711)
-0.261 -0.252 -0.327 0.185 0.538 0.677* 0.429
(4.954) (1.826) (5.453) (4.478) (8.632) (4.671) (4.274)
-0.616 -0.452 0.572 -0.081 0.128 0.206 -0.059 1.449
(7.614) (4.754) (8.962) (2.614) (2.491) (3.416) (2.007) (4.774)
-0.573 -0.242 0.393 0.082 -0.046 0.787
(9.611) (5.393) (10.168) (5.937) (2.452) (6.255)
-0.248 -0.383 0.310 -0.088 0.356 0.165 0.549
(4.699) (3.228) (3.142) (2.913) (4.763) (4.188) (3.864)
-0.369 -0.597 0.308 -0.057 0.074 0.159 0.761
(7.590) (4.782) (2.781) (2.476) (2.185) (4.551) (3.864)
-0.145 -1.319 0.783 -0.320 -0.329 1.060
(5.007) (3.842) (4.450) (3.993) -(2.848) (7.044)
-0.527 -0.213 0.234 -0.116 0.085 0.767
(8.698) (4.497) (4.025) (8.464) (3.623) (8.511)
-0.211 -0.274 0.150 -0.181 -0.153 0.383
(4.918) (3.339) (2.165) (5.066) (3.708) (4.075)
-0.157 -1.141 0.729 -0.201
(4.735) (3.125) (16.123) (4.385)
-0.291 -1.201 0.630 -0.274 -0.348 -0.226
(5.679) (11.883) (2.053) (5.298) -(6.345) (4.330)
-0.518 -0.685 0.245 -0.325 0.422
(7.817) (14.482) (9.448) (4.564) (2.637)
-0.286 -0.363 0.157 -0.051 0.166 0.931
(6.597) (5.032) (5.710) (1.669) (5.719) (8.251)
-0.231 -1.352 -0.676 -0.503 1.506
(6.539) (16.357) (13.612) (4.837) (6.794)
-0.495 -0.469 -0.156 0.350 -0.423 -0.167
(8.475) (1.902) (1.768) (3.077) (3.936) (3.063)
Switzerland
South Korea
Greece
Austria
Ireland
Spain
Australia
Denmark
Belgium
Portugal
Netherlands
US
UK
Germany
France
Japan
Italy
Canada
Finland
Sweden
The results of the unrestricted panel estimation in Table IV.1 suggest that the long run
price elasticities range from a low of -0.21 for the Netherlands to the high of -1.35 for
South Korea. In general, the price elasticities stay in the range of -0.5, which is about
half of those reported in the analyses which use income and relative prices as the sole
determinants of export volumes. Our results are in line with the findings of Madsen
(2004) and others who consider changes in technology and other factors as important
determinants of changes in trade shares. We find export price elasticities above 1 in
only five countries in our sample. Four of these – Ireland, Spain, Portugal and South
12
Korea – underwent a period of rapid industrialisation during the sample period. The
only exception is Japan, where the price elasticity above unity corroborates earlier
findings. Many Japanese firms placed manufacturing facilities in the nearby low-cost
countries such as the Philippines and Malaysia, long before their European and
American counterparts began doing so. In periods of currency appreciation production
is shifted overseas relatively quickly. As a result, Japanese firms have not lost market
share per se, but Japan’s exports have. In line with Anderton (1999), whose
conclusions are confined to UK and Germany, our analysis confirms that traditionally
strong continental exporters such as the Rhineland and Scandinavian countries have
relatively small export price elasticities as compared to the Anglo-Saxon economies
such the UK and the US.
The coefficients on the technological intensity of output are notably higher in the
rapidly industrialising countries as compared to all others, with the exception of
Japan. The average value of the coefficient on TECHS in Ireland, Spain and Portugal
is over 0.7, while a comparable figure for all other countries except Japan is 0.3. It is
hardly surprising that the rapid advancement of the former group of countries over the
sample period could not have been driven by competitiveness alone and that
technological advancements must have played a crucial role in the industrialisation
process. At an early stage of our investigation we considered whether relative prices
and the technology share were jointly determined but we found no evidence for this
and treat the TECHS variable as weakly exogenous. We found that correlation
coefficients between relative export prices and technology intensity of output were
small and statistically insignificant over the entire sample period in Germany, Japan,
the US for instance. In Finland, where we might expect the two series to move
together we observed the lowest correlation between the series in our sample. The
technology indicator is absent in Korea, despite its rapid rise over time. This in part
reflects the nature of Korean production, especially prior to 1996, when the country
joined the OECD and liberalised its price structure in the process; this is discussed in
Barrell et al (1998). Trade competitiveness elasticities were found to be high, as in
this study, but profitability and technology were not found to be immediate
determinants of exports. Up until the 1990s Korean production was organised along
state controlled lines, and the factors that affected it were more limited than in other
countries in our sample. In general, these findings support the conclusions of previous
research efforts, and extend the results to a large group of countries.
Table IV.2 details the contributions of individual components such as the technology
intensity of output (TECHS), competitiveness (COMP) and integration variables
(GLOBE) to explaining the changes in export shares over 9-year periods. The table is
computes as the per cent change in the trade share for the country, decomposed into
13
the contributions from each of the explanatory variables from the long run equation.
We may write this as
∆log(XVOL/S) = α2 ∆log(TECHS) + Σi-3-7 αi ∆GLOBEi+ Competitiveness (6)
Where ∆ represents the change from the start to the end of the period for the variable
concerned. For illustration purposes, the impacts of regional integration and
globalisation variables have been combined under the heading of GLOBE, which was
constructed using the relevant coefficients of the underlying variables. The last
column includes competitiveness effects and the remaining dynamics that may not
have worked themselves out over a nine year period.
The decomposition of changes in export shares illustrates that competitiveness effects
alone cannot explain these fluctuations. There are few obvious parallels among
countries as each has a unique combination of factors behind the changes in export
shares over each period. In the last decade the US has lost share in part because of
regional integration and the WTO, but prior to 1996 it had gained share because of
improvements in competitiveness. Any impacts China might have had on trade shares
after 1991 are captured by competitiveness effects, although before that date
deregulation had additional effects. The US gain in competitiveness prior to 1996 was
in part at the expense of Japan where the rise of China in the 19080s and early 1990s
resulted in a loss of trade share not driven by relative prices. At the same time
competitiveness losses also helped reduce the Japanese share of world trade.
In Europe competitiveness losses have been continually reducing the French trade
share, with a noticeable additional effect from regional and global indicators in the
last period. The German trade share suffered for all reasons around unification in
1990, but after 1996 improvements in the technology mix of production have been a
major factor behind strong export performance. UK trade share losses over the last
three decades appear to be strongly associated with a rising real exchange rate.
Extensive export share gains in Ireland are mostly explained by substantial
technological advancements over the sample period and were helped further by the
country’s integration into the European Single Market. Thus, despite significant loss
of competitiveness, particularly in the last decade, Ireland’s exports have performed
well. In the case of Italy and the UK, the observed loss of export share is due largely
to loss of competitiveness, although the losses are ameliorated somewhat in the UK
by technological advancement. As expected, given the transformation of Nokia,
technological composition of output explains much of the Finnish export
performance. The integration of China into the world trade system has boosted South
Korean exports as manufacturers of the newly industrialised country found
unsaturated markets for their products relatively close to home.
14
Table IV.2 Decomposition of the changes in the export shares Per cent change
∆ XVOL/S β2∆ TECHS β3∆ GLOBE COMP ∆ XVOL/S β2∆ TECHS β3∆ GLOBE COMP
Australia France
78-86 20.89 - - 20.89 78-86 -4.72 -2.96 - -1.75
87-95 -12.20 - -4.96 -7.24 87-95 1.18 -0.89 8.29 -6.21
96-04 -15.57 - -17.30 1.73 96-04 -9.77 3.80 -12.24 -1.33
Belgium Germany
78-86 -4.66 5.41 -4.16 -5.92 78-86 3.02 3.39 - -0.37
87-95 2.88 -0.13 -0.17 3.19 87-95 -20.20 -4.69 -6.24 -9.26
96-04 -12.93 4.20 -11.42 -5.72 96-04 8.28 11.33 -3.36 0.31
Canada Greece
78-86 3.21 - -10.25 13.46 78-86 6.58 - -18.90 25.48
87-95 3.10 - 5.49 -2.39 87-95 4.85 - 12.43 -7.59
96-04 -25.92 - -28.88 2.95 96-04 13.89 - 12.76 1.13
Denmark Ireland
78-86 3.35 - - 3.35 78-86 35.14 37.05 - -1.91
87-95 -0.76 - 1.14 -1.90 87-95 58.41 41.21 21.15 -3.95
96-04 -21.09 10.11 -11.56 -19.64 96-04 34.75 48.43 - -13.68
Finland Italy
78-86 7.93 11.27 -7.16 3.82 78-86 -5.01 - 15.08 -20.08
87-95 2.10 9.71 -0.63 -6.98 87-95 11.74 - 14.94 -3.20
96-04 9.21 17.75 -19.83 11.29 96-04 -35.90 - -18.77 -17.13
Japan Spain
78-86 -1.72 33.82 -30.59 -4.96 78-86 18.87 6.71 21.05 -8.89
87-95 -31.53 -3.42 -19.56 -8.55 87-95 12.93 -3.70 49.89 -33.27
96-04 -12.23 -12.97 - 0.74 96-04 0.98 3.40 17.52 -19.94
Netherlands Switzerland
78-86 1.01 2.25 -4.79 3.55 78-86 -7.51 - 2.86 -10.37
87-95 3.93 -0.05 8.80 -4.81 87-95 -28.24 - -16.32 -11.92
96-04 0.24 -1.58 - 1.82 96-04 -10.87 - -11.98 1.10
Austria UK
78-86 6.10 1.11 10.69 -5.70 78-86 -3.80 7.56 - -11.37
87-95 4.27 -0.13 6.37 -1.97 87-95 -11.60 -0.04 6.62 -18.18
96-04 5.06 6.57 - -1.51 96-04 -13.98 7.23 - -21.21
Portugal US
78-86 39.38 14.45 19.79 5.14 78-86 11.93 - 0.00 11.93
87-95 27.15 6.73 48.18 -27.75 87-95 10.99 - 2.43 8.56
96-04 -14.20 -7.56 - -6.64 96-04 -17.12 - -17.67 0.55
Sweden South Korea
78-86 -1.81 3.93 2.65 -8.40 78-86 66.76 - 43.43 23.32
87-95 -8.11 4.04 -6.54 -5.61 87-95 24.28 - 24.76 -0.48
96-04 6.23 1.41 - 4.82 96-04 54.53 - - 54.53
V. Concluding remarks
Our research finds evidence of a cointegrating relationship between export shares,
relative export prices, technological intensity of output, as well as measures of
regional integration and global trade liberalisation. This finding makes it possible to
analyse trade dynamics in a large group of countries within one study. As importantly,
we have shown that a significant proportion of cross-country export performance is
not related to competitiveness, but can be traced to technological advancements and
regional integration processes. We introduce a relatively simple measure of
technological progress relative to competition and show that this measure largely
15
explains changes in export shares, particularly in countries like Ireland and Finland.
Where the change in technology intensity of output has been flat relative to the OECD
average, as is the case in the US, technological progress or lack thereof cannot be
used to explain loss of export shares over the most recent decade. As importantly,
price elasticities of exports are in the range suggested by microeconomic studies,
which make extensive use of disaggregated industry data.
This study suggests several extensions to expand and deepen the scope of the analysis
presented here. The issue of the determinants of relative export prices needs to be
examined on a country by country basis and joint determination of export volumes
and relative export prices should be explored explicitly. Given substantial changes in
trade liberalisation agreements and other changes in the world trade system, it is
worth estimating the panel over sub-periods. Finally, the rise of China as a major
exporter may be modelled explicitly.
16
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18
Appendix 1. Chronology of Key Events in International Trade Relations
Date Key Output Events
1973– 1979 Tokyo
Round codes
The 7th round, launched in Tokyo sees General
Agreement on Tariffs and Trade (GATT) reach
agreement to start reducing not only tariffs but trade
barriers as well.
1978– 1984 Trade
reforms in
China
Mandatory export planning in China reduced to 60% of
exports, with procurement prices fixed by the Foreign
Trade Corporations and target quantities assigned to
the producing enterprises.
1986– 1994 Uruguay
Round and
WTO
GATT trade ministers launch the Uruguay round,
embarking on the most ambitious and far-reaching
trade round so far. Foremost is the Agreement
Establishing the WTO. Agreements to allow increasing
access for textile and clothing from developing
countries and reductions in agricultural subsidies,
services, intellectual property, were made.
1988 Further trade
liberalization
in China
Mandatory export planning sharply reduced.
Retention ratios for foreign exchange increased for
enterprises that exceeded their targets
1991 Export
liberalization
in China
Mandatory export planning in China abolished
Jan 1995 WTO and
Stage 1 of
ATC
WTO is created in Geneva to replace GATT.
Agreement on Textiles and Clothing (ATC) succeeded
the Multi Fibre Arrangement (MFA) with 3 successive
stages for integration of textiles and clothing products
into the rules of the GATT 1994. Stage 1 in 1995 raised
quotas and set a 16% growth rate on remaining quotas.
Jan 1998 Stage 2 of
ATC
Cumulative 33% of 1990 volume to be integrated with
a 25% growth rate on remaining quotas
2001 DDA and
Accessions
of China and
Taipei
Doha Ministerial Conference agreed on the Doha
Development Agenda which paid special attentions to
assist developing countries strengthen their capacity to
participate more fully in international trade. China and
Taipei formally joined the WTO.
Jan 2002 Stage 3 of
ATC
Cumulative 51% of import volume to be integrated
with a 27% growth rate on remaining quotas
Jan 2005 Completion
of ATC
Cumulative 100% of import volume to be integrated
19
Appendix 2. Cointegration tests results Augmented Dickey-Fuller tests, 4 lags
Final panel tests with actual estimates
Including dynamic terms
t-statistic probability t-statistic probability
Australia -4.401 0.001 -4.967 0.000
Austria -3.859 0.003 -5.773 0.000
Belgium -3.044 0.034 -4.110 0.002
Canada -2.723 0.074 -4.663 0.000
Denmark -3.454 0.011 -6.261 0.000
Finland -3.921 0.003 -4.128 0.001
France -4.187 0.001 -4.975 0.000
Germany -3.288 0.018 -4.474 0.000
Greece -3.285 0.018 -3.668 0.006
Ireland -2.687 0.080 -4.292 0.001
Italy -3.044 0.034 -5.254 0.000
Japan -2.724 0.074 -4.212 0.001
Netherlands -3.540 0.009 -4.524 0.000
Portugal -3.296 0.018 -4.524 0.000
South Korea -4.029 0.002 -3.680 0.006
Spain -3.389 0.014 -4.103 0.002
Sweden -4.135 0.001 -4.205 0.001
Switzerland -2.700 0.078 -5.069 0.000
UK -3.600 0.007 -3.517 0.009
US -3.204 0.023 -3.525 0.009
Average -3.426 -4.496
Long run only
Appendix 2A. Test results for serial correlation LaGrange Multiplier tests, 4 lags
F-statistic probability
Australia 1.219 0.308
Austria 6.896 0.000
Belgium 1.790 0.138
Canada 2.537 0.045
Denmark 1.435 0.229
Finland 2.109 0.086
France 1.862 0.124
Germany 0.906 0.464
Greece 5.479 0.001
Ireland 1.997 0.102
Italy 2.133 0.083
Japan 2.286 0.066
Netherlands 1.955 0.108
Portugal 0.474 0.755
South Korea 0.597 0.666
Spain 0.248 0.910
Sweden 1.405 0.238
Switzerland 4.091 0.004
UK 1.068 0.377
US 1.250 0.295
20
Appendix 3. Changes in export demand shares and technology intensity of
output TECHS XVOL/S TECHS XVOL/S
TECHS XVOL/S TECHS XVOL/S
-10
-5
0
5
10
15
1979
1982
1985
1988
1991
1994
1997
2000
2003
-15
-10
-5
0
5
10
15
AUTECHS AUXVOL/S
-10
-6
-2
2
6
10
1979
1982
1985
1988
1991
1994
1997
2000
2003
-4
-2
0
2
4
BGTECHS BGXVOL/S
-10
-5
0
5
10
15
20
1979
1982
1985
1988
1991
1994
1997
2000
2003
-8
-4
0
4
8
CNTECHS CNXVOL/S
-10
-5
0
5
10
15
1979
1982
1985
1988
1991
1994
1997
2000
2003
-8
-4
0
4
8
12
DKTECHS DKXVOL/S
-20
-10
0
10
20
1979
1982
1985
1988
1991
1994
1997
2000
2003
-10
-5
0
5
10
15
FNTECHS FNXVOL/S
-10
-6
-2
2
6
10
1979
1982
1985
1988
1991
1994
1997
2000
2003
-6
-4
-2
0
2
4
FRTECHS FRXVOL/S
-10
-5
0
5
10
15
1979
1982
1985
1988
1991
1994
1997
2000
2003
-12
-8
-4
0
4
8
GETECHS GEXVOL/S
-12
-7
-2
3
8
13
18
1979
1982
1985
1988
1991
1994
1997
2000
2003
-20
-10
0
10
20
GRTECHS GRXVOL/S
21
TECHS XVOL/S TECHS XVOL/S
-4
1
6
11
16
21
1979
1982
1985
1988
1991
1994
1997
2000
2003
-4
0
4
8
12
16
IRTECHS IRXVOL/S
-6
-4
-2
0
2
4
6
1979
1982
1985
1988
1991
1994
1997
2000
2003
-15
-10
-5
0
5
10
15
20
ITTECHS ITXVOL/S
-6
-2
2
6
10
1979
1982
1985
1988
1991
1994
1997
2000
2003
-20
-10
0
10
20
30
JPTECHS JPXVOL/S
-6
-2
2
6
10
1979
1982
1985
1988
1991
1994
1997
2000
2003
-3
0
3
6
NLTECHS NLXVOL/S
-6
-2
2
6
10
1979
1982
1985
1988
1991
1994
1997
2000
2003
-8
-4
0
4
8
OETECHS OEXVOL/S
-12
-7
-2
3
8
13
1979
1982
1985
1988
1991
1994
1997
2000
2003
-10
0
10
20
30
PTTECHS PTXVOL/S
-12
-7
-2
3
8
13
1979
1982
1985
1988
1991
1994
1997
2000
2003
-4
0
4
8
12
SDTECHS SDXVOL/S
-10
-5
0
5
10
15
1979
1982
1985
1988
1991
1994
1997
2000
2003
-15
-5
5
15
25
35
SKTECHS SKXVOL/S
22
TECHS XVOL/S TECHS XVOL/S
-10
-6
-2
2
6
10
1979
1982
1985
1988
1991
1994
1997
2000
2003
-10
-5
0
5
10
SPTECHS SPXVOL/S
-15
-10
-5
0
5
10
15
1979
1982
1985
1988
1991
1994
1997
2000
2003
-15
-5
5
15
SWTECHS SWXVOL/S
-15
-10
-5
0
5
1979
1982
1985
1988
1991
1994
1997
2000
2003
-6
-4
-2
0
2
4
UKTECHS UKXVOL/S
-5
-3
-1
1
3
5
1979
1982
1985
1988
1991
1994
1997
2000
2003
-5
-3
-1
1
3
5
7
9
USTECHS USXVOL/S
23
Appendix 4. Data description and sources
XVOL – export volumes of goods and services of a country i – are taken as reported
as part of the national accounts by National Statistics Offices.
S – the size of the export market of a country i – is defined as an average of import
volumes of a country’s trading partners weighted by the share of the exporting
country’s total exports. The weights are based on an actual trade matrix which is
updated every five years. The trade matrix is constructed for all countries using the
IMF’s Direction of Trade Statistics, various years. This measure of the size of a
country’s export market captures important changes in the dynamics of trade patterns
across countries and over time, such as the integration of China into the world trade
system.
RPX – relative export price – this measure uses home country export prices relative to
a weighted average of the prices of competing countries on world markets. The data
on non-commodity export prices is sourced from the OECD Economic Outlook
database.
ESM – describes the establishment of the European Single Market. This variable is
equal to one prior to 1987Q2 which gradually declines to zero in 1992Q4, the formal
completion of the Single Market Programme.
EMU – is meant to capture the impact of the European Monetary Union. It is a
dummy variable which equals to 1 from 1999Q1, in line with the official introduction
of single currency in Europe and is zero prior to 1999. For Greece, which joined EMU
later, the start date is 2001Q1.
NAFTA – the North American Free Trade Agreement variable is created to account
for the impact of gradual elimination of tariffs on the goods shipped between US,
Canada and Mexico. As NAFTA agreement represented an expansion of the earlier
Canada-U.S. Free Trade Agreement of 1988, the NAFTA variable is equal to one
before 1989Q1 and is reduced to zero by 1998Q1.
WTO – this variable captures the impact of the significant deepening in global trade
relations, symbolised by the introduction of the World Trade Organisation in 1995,
with formal rules governing trade of goods, services, intellectual property and
investment as well as an effective enforcement mechanism. WTO equals 1 up to and
including 1994Q4 and is gradually reduced from one in 1995Q1 when the major
outcomes of the Uruguay round of trade liberalisation measures came into effect, to
zero by the end of the sample period, as the majority of transitional quotas and tariffs
from that round were formally abolished in January 2005.
CHINA – this variable is meant to capture to integration of China into the world
trading system. It is gradually reduced from one in 1978Q1 when first trade reforms
were implemented to zero in 1991Q1 when mandatory export planning was abolished
completely.