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,

2

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

3

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.

4

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.

5

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

6

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

7

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.

8

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-

9

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.

10

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.

11

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

References

Anderton, B. (1999), “Innovations, product quality, variety and trade performance: an

empirical analysis of Germany and the UK,” Oxford Economic Papers, 51,

152-167

Arndt, S.W. and Kierzkowski, H. (2001) Fragmentation: New Production Patterns in

the World Economy, Oxford: Oxford University Press.

Banerjee, A., J.Dolado, J.W.Galbraith, and D.F.Hendry (1993), Co-Integration,

Error-Correction, and the Econometric Analysis of Non-Stationary Data,

Advanced Texts in Econometrics. Oxford University Press.

Barrell, R.J. Anderton, R., Lansbury, M.L., and Sefton, J. (1998) ‘FEERs for the

NIEs’ in Exchange Rate Policies in Emerging Asian Countries, edited by

Stefan Collignon, Yung Chul Park and Jean Pisani-Ferry, Routledge. London

Barrell, R. and D. Holland (2006), “Interest rates and the real economy,” National

Institute Economic Review, No.197, July 2006

Barrell, R., and N. Pain (1997), “Foreign Direct Investment, Technological Change,

and Economic Growth Within Europe”, Economic Journal, 107, pp. 1770-

1776

Barrell, R, and te Velde, D.W.(2002) ‘European integration and manufactures import

demand, an empirical investigation of 10 European countries. German

Economic Review

Carlin, W., A.Glyn, J. Van Reenen (2001), “Export market performance of OECD

countries: an empirical examination of the role of cost competitiveness,”

Economic Journal, 111, 128-162

Crowley, M. (2003), “An introduction to the WTO and GATT,” Economic

Perspectives 4Q/2003, Federal Reserve Bank of Chicago

Fagerberg, J. (1996), “Technology and competitiveness,” Oxford Review of Economic

Policy, 12, 39-51

Greenhalgh, C. (1990), “Innovation and trade performance in the United Kingdom,”

Economic Journal, 100, 105-118

Groningen Growth and Development Centre, 60-Industry Database, September 2006,

http://www.ggdc.net

Grossman, G. and C. Shapiro (1987) “Dynamic R&D Competition,” Economic

Journal, 97, 372-387

Hooper, P., K. Johnson and J. Marquez, (2000), “Trade elasticities for the G-7

countries,” Princeton Studies in International Economics, No. 87

Krugman, P. (1983), “new theories of trade among industrial countries,” American

Economic Review, Papers and Proceedings, 73, 343-347

Madsen, J. (2004), “Technological revolutions, innovations and trade performance,”

EPRU Working Paper Series, 2004-12

17

Magnier, A. and J. Toujas-Bernate (1994), “Technology and trade: empirical evidence

from the major five industrialised countries,” Weltwirtschaftlishes Archiv, 130,

494-520

Nordås, H.K. (2004), “The Global textile and clothing industry post the agreement on

textiles and clothing,” WTO Discussion Paper No. 5

Pain, N. and Wakelin, K. (1998), ‘Export performance and the role of foreign direct

investment’, The Manchester School, Supplement, No. 60, pp. 62-88.

Pesaran, M.H. (2006) ‘Estimation and inference in large heterogeneous panels with a

multifactor error structure,’ Econometrica, 74(4): 967-1012, July 2006

Pesaran, M.H., Shin YC, Smith R. (1999) ‘Pooled mean group estimation of dynamic

heterogeneous panels’, Journal of the American Statistical Association, 94

(446): 621-634

Pesaran M.H., Smith R. (1995) ‘Estimating long-run relationships from dynamic

heterogeneous panels’ Journal of Econometrics 68 (1): 79-113

Senhadji, A. and C. Montenegro (1999), Time Series Analysis of Export Demand

Equations: A Cross-Country Analysis, IMF Staff Papers, Vol.46, No.3

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.