Does International Trade Cause
Economic Growth? A Survey
Tarlok Singh1
1
Griffith Business School, Griffith University, Brisbane
1. INTRODUCTION
THE effects of international trade on economic growth remain an area of
protracted controversy in both theoretical and empirical research. The
contemporary paradigms of theory have been characterised by a lack of consensus:
the ‘neoclassical trade’ theory supports, while the ‘neoclassical growth’
theory does not recognise; and the ‘new trade’ theory is dubious, while the
‘new growth’ theory supports the positive effects of trade on output and
growth. Mixed support is reinforced by mixed empirical evidence. The timeseries
models testing non-causality, and cross-section and panel data models
examining the macroeconomic cross-country and microeconomic firm (industry)
level effects commonly support positive and significant effects of trade on
output and growth; the evidence is, however, not unambiguous. Rodriguez and
Rodrik (2000) raise sceptical concerns on the strength of the argument for the
beneficial effects of trade. An archival analysis and the historical evidence suggest
that trade openness and growth were not correlated during the interwar
period; were negatively correlated a century ago (Foreman-Peck, 1995; O’Rourke
and Williamson, 1999; Vamvakidis, 2002); and became significantly correlated
only in recent decades (Vamvakidis, 2002; Clemens and Williamson,
2004).
Emerging evidence pioneered by Rose (2004a, 2004b; 2005a, 2005b), casts
a dubious note on the efficacy of GATT⁄WTO in promoting world trade and
reducing trade volatility. It suggests that the insiders trade at no higher levels
than the outsiders. Such evidence in the wake of avowed recognition for the
contributions of GATT⁄WTO in spurring multilateral free trade marks a puzzle
in open economy macroeconomics. While a few studies have attempted to
I am grateful to Glenn Otto of The University of New South Wales, Sydney, Australia, for his
valuable comments and incisive suggestions. I am also grateful to two anonymous referees of the
journal for very useful comments and suggestions; however, I am solely responsible for any error
and omissions that may remain in the paper.
The World Economy (2010)
doi: 10.1111/j.1467-9701.2010.01243.x
Ó 2010 Blackwell Publishing Ltd., 9600 Garsington Road,
Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. 1517
The World Economy
resolve the ‘Rose Paradox’ and support the significant role of GATT⁄WTO in
spurring world trade (Subramanian and Shang-Jin, 2007; Tomz et al., 2007),
the controversy remains unsettled. Rose (2007) argues that the preferential
trade agreements and protectionist tariffs still hamper any real progress by
the WTO to foster free trade. The GATT⁄WTO remains surrounded by a
multitude of preferential trade agreements (PTAs) that are non-discriminatory
for members, but discriminatory against non-members. The PTAs have proliferated
exponentially and spurred several studies evaluating binary choices and
examining their effects on the trade and welfare of non-members of the bloc,
level of Pareto-efficient multilateral free trade and welfare, and the enforcement
of WTO agreements. An issue that remains centre stage is whether the
PTAs supplement or supplant the multilateral trading system and, thus, have
the ‘trade creation’ and ‘building block’ or the ‘trade diversion’ and ‘stumbling
block’ effects. While several studies support the ‘trade creation’ and
‘building block’ effects (Baldwin, 1993, 1995; Ethier, 1998; Laird, 1999;
Clausing, 2001; Glick and Rose, 2002; Lee et al., 2008), a parallel counterstrand
raises apprehensive concerns and contrarily postulates the ‘trade diversion’
and ‘stumbling block’ effects of PTAs (Bhagwati, 1991; Panagariya,
1996; Bagwell and Staiger, 1998; Bhagwati et al., 1998; Karacaovali and
Lima˜o, 2008).
Several factors seem catalytic to the variance in empirical findings and
unresolved controversies on the gains of trade, the role of GATT⁄WTO in
world trade, and the effects of PTAs on multilateral cooperation and trade,
such as the differences in the sample periods and sample countries covered,
frequency of data used, measures of trade openness employed, model specifications
estimated, and the test statistics used for testing the null. The estimating
econometric methodology has itself been evolving and so have been
the conclusions of empirical research. A number of studies have reviewed
the trade–growth empirics and assessed the associated methodological and
measurement issues (Edwards, 1993, 1998; Girma et al., 2004; Lopez, 2005;
Greenaway and Kneller, 2007a; Wagner, 2007). While the studies by
Edwards (1993, 1998) asymmetrically focus on the macroeconomic aspects
of the trade–growth nexus and pre-date the microeconomic research that spurted
since the mid-1990s, the studies by Girma et al. (2004), Greenaway and Kneller
(2007a) and Wagner (2007) provide a distinctively exclusive focus on the microeconomic
aspects of the effects of trade on firm-level efficiency and productivity.
The study by Lopez (2005) attempts to reconcile the microeconomic
and macroeconomic evidence, but it bypasses the analysis of several methodological
issues concerning the trade–growth relationship. Temple (1999)
provides a more generic review of the macroeconomic sources of growth, and
dwells very sparsely on the specific issues surrounding the trade–growth
empirics.
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1518 T. SINGH
This study surveys the literature on the relationship between trade and
growth and differs from previous research on two counts. First, it provides an
extensive account of the macroeconomic and microeconomic evidence, and
distils the debate on the gains of trade from a comprehensively wider and
diverse domain. The review of empirical evidence is juxtaposed with the policy
issues concerning protectionism versus trade liberalisation, and an analytical
account is undertaken of the role of GATT⁄WTO in spurring multilateral
trade. Second, the study undertakes an in-depth analysis of the methodological
and measurement issues. Such an analysis is particularly essential to
gauge the depth of empirical support and determine the strength of the argument
for the gains of trade. The study is organised as follows. Section 2
reviews the macroeconomic and microeconomic evidence. Section 3 dwells
on the policy debate on protectionism versus trade liberalisation, and examines
the role of the GATT⁄WTO in fostering free trade. Section 4 presents a
critical account of the methodological and measurement issues surrounding
the trade–growth empirics. Section 5 sums up the conclusions emerging from
the study.
2. TRADE AND GROWTH: THE EMPIRICAL EVIDENCE
The empirical literature on trade–growth relationships can be classified into
two broad strands of studies: one using time-series models and assessing
mainly the demand-driven effects, and the second estimating cross-section and
panel data models and examining mostly the supply-induced investment and
productivity effects of trade on output and economic growth.
a. Trade–Growth Non-causality: The Time-series Models: The testing of
non-causality and the implied ‘export-led growth’ versus ‘growth-led export’
hypothesis marks one of the major areas of time-series research on trade and
growth. Most studies conducted in the 1960s and 1970s performed unconditional
correlation and static regression analyses (Emery, 1967; Maizels, 1968; Kravis,
1970; Voivodas, 1973), and examined the demand-driven role of trade in affecting
income and growth. Nurkse (1961) postulated that trade served as the ‘engine
of growth’ for a number of developed countries in the nineteenth century, but he
was pessimistic about its similar role for the developing countries in the
twentieth century. Kravis (1970) examines growth in the nineteenth century and
for the 1950s and the 1960s for the developing countries, and raises concerns
about Nurkse’s argument. He instead asserts that growth was mainly the consequence
of favourable internal factors, and the external demand represented an
added stimulus, which varied in importance from country to country and period
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1519
to period.1
Kravis (1970) postulates that a more generally applicable metaphor
would be to describe trade expansion as the ‘handmaiden’ of successful growth,
rather than as an autonomous ‘engine of growth’. The unconditional correlation
and static regression analyses of the 1950s and 1960s are beset with several snags
and limitations. The correlation does not imply causality and it does not control
for the effects of several conditioning factors on growth. Similarly, the standard
regression models assume all regressors to be exogenous, and predict unidirectional
causality from trade to income and growth. These models, a priori, preclude
the possibility of feedback effects and do not provide any information on
the plausible reverse-causation from growth to trade. The development of noncausality
tests since the late 1960s (Granger, 1969; Sims, 1980) facilitated the
analysis of feedback effects, and spurred several studies testing non-causality and
assessing the ‘export-led growth’ versus ‘growth-led export’ hypothesis.
The conventional strand of research estimating static models in levels, and the
dynamic models in (normally) first-differences remained dominant until the late
1980s (Michaely, 1977; Balassa, 1978; Heller and Porter, 1978; Jung and Marshall,
1985; Chow, 1987). The ex post recognition of the limitations of time-series
econometric models (since the 1980s) suggests that the studies estimating the
long-run models in levels did not test stationarity and I(d), where d > 0, properties
of the model series, while those estimating the dynamic models in first-differences
ignored the possible long-run relationship among the level variables. It is
likely that some or all of the variables in a model are individually I(d) and, in
such cases, regressing an I(d) variable on one or more I(d) variables leads to a
‘spurious regression’ problem and complicates the statistical inference.2
The
1
Kravis (1970) argues that trade is one among many factors affecting growth and, as such, it is
unlikely to be a dominant variable in many instances. The exaggeration of the past role of trade has
often served to heighten the contrast drawn with allegedly less favourable world markets, and, thus,
to minimise the potential role of trade for the developing countries. The term ‘engine of growth’ is
not generally descriptive and involves expectations which cannot be fulfilled by trade alone (Kravis,
1970).
2
The classical econometrics indeed suggests differencing the I(d) variables d times to make them
I(0) and stationary. The difference-stationary variables can be used to estimate the model and the
standard asymptotic distribution can be used to draw statistical inferences. The differencing of data,
however, results in a loss of long-run information and it precludes the possibility of estimating a
steady-state relationship among the level variables. While the models estimated on I(0) series did
resolve the problem of non-stationarity, these models could capture only the short-run and ignored
the possible long-run relationship relevant for the steady-state analysis of economic growth. The
causality in these models could arise from purely short-run dynamics. Granger (1988) shows that in
a cointegrated I(1) process, the simple dynamic model suffers from a misspecification problem and
the standard causality tests are not valid. The exclusion of error correction terms from the dynamic
model results in a model misspecification. The failure of classical methodology to provide statistical
inference in the models estimated on non-stationary data in levels and that to model long-run relationship
in the models estimated on stationary data in differences, led to a paradigm shift in estimating
methodology marked by the development of cointegration estimators and error correction
models (ECMs) since the late 1980s.
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1520 T. SINGH
strand of studies testing non-causality based on dynamic models with I(0) series
did not unequivocally differentiate between the ‘short’- and ‘long’-run relationship
and thus ended-up assessing mainly the short-run business cycle, rather than
the long-run steady-state, relationship between trade and growth.
The late 1980s marked the development of more efficient cointegration estimators
and error correction models (ECMs), which fashioned a new approach
to the analysis of the trade–growth nexus. The ECMs resolve the ‘spurious
regression’ problem without losing long-run information, and are useful for distinguishing
between the steady-state equilibrium and short-run dynamic relationship
between trade and growth. Most studies conducted since the 1990s
have used the cointegration estimators and estimated ECMs to re-assess the
relationship between trade and growth (Table 1). The methodological evolution
and the shifting paradigms of econometric modelling have been two of the key
factors catalytic to the variance in empirical findings and unresolved controversies:
some studies support the ‘export-led growth’ hypothesis (Emery, 1967;
Maizels, 1968; Voivodas, 1973; Michaely, 1977; Balassa, 1978; Heller and
Porter, 1978; Williamson, 1978; Fajana, 1979; Tyler, 1981; Feder, 1982;
Balassa, 1984; Kavoussi, 1984; Jung and Marshall, 1985; Ram, 1987; Moschos,
1989; Greenaway and Sapsford, 1994; Bodman, 1996; Henriques and Sadorsky,
1996); some provide only limited support to the ‘export-led growth’ hypothesis
(Chow, 1987; Chen, 1990; Kugler, 1991; Boltho, 1996); and yet some others
yield mixed results (Nishimizu and Robinson, 1984; Kunst and Marin, 1989;
Tybout, 1992; Oxley, 1993).
b. Productivity Effects of Trade on Growth: The Cross-section and Panel
Data Models
(i) Macroeconomic cross-country models
The time-series studies testing non-causality mainly focus on the Keynesian
demand-driven role of trade in affecting income and growth. A parallel strand
of studies using a cross-section, and more recently panel data (since the
1990s), approach examines the productivity and supply-side effects of trade on
output and growth, traversing through the accumulation of capital and TFP
parameter of production technology. Krueger (1978) and Bhagwati (1978,
1988) postulate the productivity effects of trade and argue that the liberal trade
regimes encourage specialisation in industries having scale economies and lead
to an increase in efficiency and productivity in the long run. In the case of
non-OPEC and middle-income developing countries, Tyler (1981) shows that
an increase in manufacturing exports leads to the increase in technological progress.
Feder (1982) estimates separate production functions for the export and
non-export sectors and finds a significant externality effect and high factor
productivity differentials. He argues that the productivity differentials across
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1521
sectors can be due to various factors including a more competitive environment
in which the export sector operates. The studies using Feder’s model show the
positive and significant relationship between exports and growth (Balassa,
1985; Ram, 1987). Chenery (1983) conducts a comprehensive study covering
Western European and North American countries with high growth of TFP relative
to growth of factor inputs, East Asian semi-industrialised countries with
outward-orientation and high growth of both productivity and factor inputs, and
middle-income developing countries with more inward-orientation and low
growth of productivity relative to growth of factor inputs. He finds that the
higher productivity growth observed in the East Asian semi-industrialised
countries reinforces the favourable effects of outward orientation on productivity.
Nishimizu and Robinson (1984) find similar evidence for Japan, Korea,
Turkey and Yugoslavia, and suggest that export expansion leads to higher TFP
growth through economies of scale and competitive incentives, while import
substitution leads to lower TFP growth.
The post-neoclassical endogenous theory of economic growth (Romer, 1986,
1990; Lucas, 1988) provided a more nuanced focus on the productivity effects of
trade, and added an additional dimension examining trade-induced convergence
in income per capita and growth across countries. Several studies have since suggested
positive and significant effects of trade on productivity and growth, and
shown that openness to trade induces convergence in income per capita and TFP
across countries (Dollar, 1992; Ben-David, 1993, 1996; Sachs and Warner,
1995; Harrison, 1996; Edwards, 1998; Vamvakidis, 1999; Alcala and Ciccone,
2004). Ben-David (1996) shows that trade reduces the income gaps among countries;
a majority of trade-based country groups witness significant convergence.
The trade leads to an increase in productivity and growth by providing a wider
range of intermediate inputs (Grossman and Helpman, 1991; Rivera-Batiz and
Romer, 1991a, 1991b) and facilitating an international diffusion of technology
(Benhabib and Spiegel, 1994; Parente and Prescott, 1994; Coe and Helpman,
1995; Eaton and Kortum, 1994, 1996). Alcala and Ciccone (2004) show the economically
significant and statistically robust positive effects of trade on TFP.
The technology is developed and produced in the inventing country and is then
exported and diffused for use as an intermediate input in other countries (Coe
and Helpman, 1995). Eaton and Kortum (1996) develop a model of innovations
and international diffusion of technology to explain the relative productivity and
growth among the OECD countries. Relative productivity depends on the ability
of a country to innovate or adopt a new technology, and they predict that each
country will eventually grow at the same rate.
TFP in developed countries is higher than that in the developing countries,
and the barriers to trade reduce TFP (Parente and Prescott, 1999; Schor, 2004;
Berthold and Teixeira, 2005; Schmitz, 2005). Parente and Prescott (1999) argue
that insider groups in developing countries have stronger monopoly rights than
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1522 T. SINGH
TABLE 1
Effects of International Trade on Productivity and Economic Growth: Macroeconomic Evidence
Author
(Year)
Data and
Sample
Economic
Growth
Openness Other
Variables
Methodology and
Estimators
Main Results and
Conclusions
Emery
(1967)
Cross-section,
50 countries
(average of
1953–63).
GNP. Exports. Current
account.
OLS. Support for export
growth hypothesis.
Maizels
(1968)
Time-series,
1950–62,
9 countries.
GDP. Exports. None. OLS. Support for export
growth hypothesis.
Voivodas
(1973)
Cross-section,
22 countries;
1956–67.
GDP growth. Export share. Country dummies. OLS. Support for export
growth hypothesis.
Michaely
(1977)
Cross-section,
41 countries
(average of
1950–73).
Per capita GNP
growth.
Growth in export
share.
None. Rank correlation. Support for export
growth hypothesis.
Threshold effect.
Balassa
(1978)
Cross-section,
11 countries;
(average of
1960–66 and
1966–73).
GNP growth. Export growth,
Real export
growth.
Labour force,
Domestic
investment and
Foreign
investment ⁄
output.
Rank correlation,
OLS, Production
function.
Support for export
growth hypothesis.
Heller and
Porter
(1978)
Cross-section,
41 countries
(average of
1950–73).
Per capita GNP
growth.
Growth in export
share.
None. Rank correlation. Support for export
growth hypothesis.
Threshold effect.
Williamson
(1978)
Cross-section,
22 countries
(average of
1960–74).
Change in GDP. Lagged exports. Country dummies,
Direct
investment,
Other foreign
capital.
OLS, Linear models. Support for export
growth hypothesis.
Fajana (1979) Time-series,
1954–74,
1 country.
GDP growth. Export share,
Export change ⁄
output.
Trade balance,
Current account.
OLS. Support for export
growth hypothesis.
Tyler (1981) Cross-section,
55 countries.
GDP growth. Export growth. Labour force,
Investment
growth.
OLS, Production
function.
Support for export
growth hypothesis.
Threshold effect.
Feder (1982) Cross-section,
31 semi-
industrialised
countries; 1964–
73 (average).
GDP growth. Export growth,
Export change ⁄
output.
Labour force,
Investment ⁄
Output.
OLS. Support for export
growth hypothesis.
Positive
externality from
export sector to
non-export sector.
Balassa
(1984)
Cross-section,
10 countries.
GNP growth. Export growth. Labour force
growth, Ratio of
output to
domestic
investment.
OLS, Production
function.
Support for export
growth hypothesis.
Kavoussi
(1984)
Cross-section,
73 countries.
GDP growth. Export growth. Labour growth
rate, Capital
growth rate.
Rank correlation,
OLS, Production
function.
Support for export
growth hypothesis.
Threshold effect.
Nishimizu
and
Robinson
(1984)
4 countries,
Annual data;
Korea (1960–
77); Turkey
(1963–76);
Yugoslavia
(1965–78);
Japan (1955–
73). Industrybased
analysis.
TFP growth by
industry
(manufacturing
industries).
Output growth
allocated to
export
expansion;
Output growth
allocated to
import
substitution.
– OLS. Import substitution
regimes seem to
be negatively
correlated with
TFP change,
whereas export
expansion regimes
are positively
correlated with
TFP change.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1523
TABLE 1 Continued
Balassa
(1985)
Cross-section,
43 developing
countries;
Annual data:
1973–79 (after
oil shock).
GNP growth. Export growth,
Ratio of exports
to GNP, Share
of manufactured
goods in total
exports (all in
real terms).
Labour force
growth, Saving
rate, GNP per
capita, Current
account
balances as a
percentage of
GNP,
Investment.
OLS. Significant positive
effects of trade
orientation on
economic growth.
Jung and
Marshall
(1985)
Time-series,
1950–81,
37 countries.
Real GNP (or
GDP) growth.
Lagged real
export growth.
Lagged GNP
(GDP) growth.
Maximum-
likelihood
simultaneous
linear
functions, grangercausality
test.
Limited support for
export growth
causing economic
growth.
Chow (1987) Cross-section and
Time series,
Annual data:
1960–84, 8
Asian NICs.
Growth rate of
manufacturing
output.
Growth rate of
manufacturing
exports.
None. Sims-causality test. No causality for
Argentina, uni-
directional
causality for
Mexico, and
bidirectional
causality for
remaining sample.
Ram (1987) Time-series and
Cross-section,
73 countries,
1960–72 (before
oil shock) and
1973–82 (after
oil shock).
Real GDP growth. Real export
growth.
Labour force
growth,
Investment
growth.
Production function,
OLS, Test for
heteroscedasticity
and
specification bias.
Support for export
growth hypothesis.
Threshold effect.
Kohli and
Singh
(1989)
30 countries
(same sample as
in Feder (1982)
excluding
Taiwan),
1960–70 and
1970–81
(averages).
Growth rate of
GDP.
Growth rate of
exports, Growth
rate of exports
multiplied by
the share of
exports in GDP.
Share of
Investment in
GDP, Growth
rate of labour.
OLS. Support for positive
and significant
effects of exports
on growth.
Kunst and
Marin
(1989)
Time-series,
1965.2–85.4,
1 country
(Austria).
GDP of OECD. Manufactured
exports.
Productivity
(output per
employee in
manufacturing
sector), Terms
of trade.
Unrestricted VAR
and Subset Model
Autoregression.
Support for
productivity
growth causes
exports.
Moschos
(1989)
Cross-section. Real GDP growth. Real export
growth.
Labour force
growth, Real
domestic
investment
growth.
OLS, Production
function.
Support for export
growth hypothesis.
Threshold effect.
Chen (1990) Time-series,
1968–82,
1 country
(Taiwan).
Growth in TFP
and growth in
output in
manufacturing
sector.
Growth in exports
in
manufacturing
sector.
None. Correlation, Rank
correlation, OLS
regression.
Limited support for
the effect of
export growth on
productivity
growth.
Kugler
(1991)
Time-series,
quarterly data:
1970–1987;
6 countries.
Real GDP. Real exports. Total private
consumption,
Gross fixed
business
investment.
Johansen
Cointegration test.
Weak support for
export-led growth
hypothesis: in two
countries. In
remaining four, no
cointegrating
relationship.
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1524 T. SINGH
TABLE 1 Continued
Bonelli
(1992)
1 country, Cross-
section,
1975–85;
Sectoral annual
data for 22
industries.
Total factor
productivity.
Export, Import. None. Cross-section
regressions, OLS,
Demand-side
decomposition of
growth.
Positive association
between export
expansion and
TFP growth. TFP
growth is
explained by
variables related
to export
expansion and
import change.
Dollar (1992) 95 LDCs, 1976–
85.
Per capita GDP
growth.
Index of openness
based on
weighted
average of
distortions and
variability of
real exchange
rate.
Investment rate,
Real exchange
rate variability,
Index of real
exchange rate
distortion.
OLS, Cross-section
regressions.
Trade increases per
capita GDP
growth. Outwardorientation
is
highly correlated
with per capita
GDP growth.
Significant,
negative
relationship
between distortion
in real exchange
rate and growth of
per capita GDP.
Edwards
(1992)
Cross-section,
developing
countries and
developed and
developing
countries; 1960–
82.
Growth of real
GDP per capita.
Deviation from
predicted trade,
Nine indicators
of trade
orientation.
Ratio of gross
investment to
GDP, Proxies
for knowledge
gap, Human
capital,
Government
expenditure,
Political
instability.
OLS, Production
function.
Significant positive
effects of trade on
growth. More
open economies
tend to grow
faster than
economies with
trade distortions.
Marin (1992) 4 OECD
industrialised
countries; Time
series: 1960:1 to
1987:2.
Labour
productivity
(manufacturing
output per
employee).
Export of
manufacturing
goods.
Terms of trade for
manufacturing
goods, OECD
output at
constant prices.
VAR model,
Cointegration
tests, Error
correction model;
Granger-causality
tests.
Support for exportled
growth
hypothesis.
Exports Grangercause
productivity
in all four
countries.
Tybout
(1992)
4 countries; 1976–
88.
TFP. Import
substitution.
– Cross-section,
Production
function.
Trade orientation
affects
productivity. No
clear link between
trade policies and
patterns of entry
and exit.
Knight et al.
(1993)
Panel data: 98
countries
comprising 22
industrial
OECD countries
and 76
developing
countries; 1960–
85.
Real GDP per
worker.
‘Closedness’
proxied by
weighted
average of tariff
rates.
Real GDP per
worker, Average
growth rate of
working-age
population.
Technological
progress and
real investment
to real GDP,
Human capital
investment to
GDP, Public
infrastructure.
Panel data
estimation,
Seemingly
Unrelated
Regressions
estimator.
A high tariff
structure
discourages
imports of capital
goods and leads to
less technology
transfer.
Coefficient on
‘Closedness’ is
negative and
highly significant.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1525
TABLE 1 Continued
Lee (1993) Cross-section:
1960–85 and 81
countries, 21
industrial and
60 developing.
Growth of real
GDP per capita.
Import-weighted
average tariff
rate, Black
market
exchange rate
premium, Share
of total imports
in GDP and
estimate of
openness.
Secondary school
enrolment rate,
Ratio of real
domestic
investment to
real GDP.
OLS, Neoclassical
model,
Cross-country
regressions.
Trade distortions
generate cross-
country
divergences in
growth rates and
per capita income.
Tariff rates and
black market
premia interacting
with estimated
share of imports,
have significant
negative effects on
the growth rate of
per capita income.
Oxley (1993) Time-series,
Annual data:
1833–85,
1 country:
Portugal.
Real GDP (at
1914 prices).
Real exports (at
1914 prices).
None. Granger-causality
tests, Johansen
Cointegration tests
and Error
correction model.
No support for
export-led growth
hypothesis.
Evidence of
reverse causality.
Greenaway
and
Sapsford
(1994)
Time-series
(1957–85) and
Cross-section
(1960–88), 104
countries,
1960–88.
Real GDP growth,
Real GDP per
capita and Real
GDP per
worker.
Growth rates of
export shares in
GDP, i.e. Ratio
of exports to
GDP.
None. OLS regression, DF
and ADF Unit
root tests, White
test, CUSUM and
CUSUMSQ.
Statistically
significant effect
of export share
growth on real per
capita GDP
growth. Threshold
effect.
Harrison
(1994)
C^ote d’Ivoire.
Time-series and
Cross-section,
Annual data:
Sample firms
aggregated into
9 sectors; 1979–
87.
Firm-level capital
productivity and
Total factor
productivity.
Tariff rates,
Import
penetration, and
TFP comparison
before and after
trade reforms
(1985).
Market power
across sector as
measured by
price–cost
margins.
OLS, Instrumental
variable (IV)
estimator;
Generalised Least
Squares, TFP
growth.
Positive association
between trade
policies and
higher
productivity
growth.
Productivity
growth four times
higher in less
protected sectors.
Harrison and
Revenga
(1995)
United States;
Four-digit
manufacturing
sub-sector;
Annual data:
1958 and 1984.
Sector-specific
real output.
Import
competition,
Export shares.
Labour, Material
inputs, Energy
consumption,
Capital stock,
R&D
expenditure,
Rate of
Unionisation.
Modified production
function.
Higher import
competition
associated with
productivity
increases. Export
activity positively
associated with
productivity
growth. No
significant
association
between import
competition and
productivity
growth.
Ó 2010 Blackwell Publishing Ltd.
1526 T. SINGH
TABLE 1 Continued
Sachs and
Warner
(1995)
135 countries,
1970–89; Subsample:
117, 81,
79, 78 and 33
countries.
Real annual per
capita growth in
GDP over
1970–89.
Openness
dummies; using:
Average tariff,
Non-tariff
barriers, State
monopoly on
major exports,
and Black
market
premiums.
Real GDP per
capita, School
enrolment rate,
Ratio of
government
consumption to
GDP, Deviation
of investment
price level from
cross-country
mean, and
political and
social factors.
Cross-section
regressions, OLS.
High and robust
coefficient on
openness dummy
in growth
regression.
Protectionist trade
policies reduce
overall growth.
Openness raises
investment to
GDP ratio.
Bodman
(1996)
Time-series:
Quarterly data:
1960:1–1995:4;
2 countries:
Australia and
Canada.
Labour
productivity.
Exports. None. Cointegration and
Granger-causality
tests.
Support for exportled
growth
hypothesis.
Reverse causality
from productivity
to exports is
rejected for both
countries.
Boltho
(1996)
Time-series:
Japan, Annual
data, 1885–
1913: (i) 1913–
37; (ii) 1952–
73; and (iii)
1973–1990.
Macroeconomic
and
Microeconomic
analysis.
GDP growth and
Output growth.
Export growth
and Shares of
import and
export.
None. Granger-causality,
OLS regression.
No support for
export-led growth
hypothesis.
Support for
growth-led export
hypothesis.
Eaton and
Kortum
(1996)
Cross-section, 19
OECD
countries; 1986–
88 (average).
Technology
diffusion:
Productivity
measured in
terms of real
GDP per
worker.
Share of imports
in GDP.
Human capital,
Distance
between
countries,
Dummy to
capture country
differences,
Research
efforts.
OLS, 2SLS, NLLS
and Generalised
Non-linear Least
Squares.
Each country will
grow at the same
rate, with relative
productivity
determined by
ability to adopt
new inventions.
Ability to tap into
the sources of
invention depends
on human capital,
trade relationships
and proximity to
sources of
innovation.
Frankel and
Romer
(1996)
Cross-country,
150 countries
and a subsample
of 98
countries; 1985.
Per capita income. Exports plus
Imports as a
ratio to GDP.
Population, Area,
Real investment
to GDP;
Working-age
population in
secondary
school, Initial
per capita
income.
Cross-country
income
regressions; OLS,
IV estimator.
Significant effect of
trade on income.
Openness to trade
raises income.
Ó 2010 Blackwell Publishing Ltd.
INTERNATIONAL TRADE AND ECONOMIC GROWTH 1527
TABLE 1 Continued
Harrison
(1996)
Cross-section and
Panel data.
Countries vary
from 17 to 51.
1960–87 and
1978–88.
Annual data.
Real GDP growth,
Share of
investment in
GDP.
Seven different
proxies for trade
openness and
policy.
Capital stock,
Years of
primary and
secondary
education,
Labour, Human
capital, Land.
General production
function,
Spearman rank
correlation, OLS,
Cross-section and
Panel data models.
Half of the measures
do exhibit a robust
relationship with
GDP growth. The
choice of time
period is critical.
A generally
positive
association
between growth
and different
measures of
openness.
Bidirectional
causality between
openness and
growth.
Henriques
and
Sadorsky
(1996)
Time-series,
(i) 1877–1945;
(ii) 1946–91;
and (iii) 1877–
1991; Canada.
Real GDP. Real exports. Real terms of
trade.
VAR, Cointegration
and Grangercausality
tests.
Support for growthled
export
hypothesis. No
evidence for
export-led growth
hypothesis.
Riezman et
al. (1996)
Time-series, 126
countries; 1950–
90.
Real GDP. Exports, Imports. Human capital
growth,
Investment
growth.
Granger-causality,
Forecast Error
Variance
Decomposition.
Unidirectional
causality from
exports growth to
income growth in
30 countries and
from income
growth to exports
growth in 25
countries.
Bidirectional
causality in 65
countries.
Edwards
(1998)
Panel data for
1960–90; 93
advanced and
developing
countries.
Total factor
productivity
growth.
Nine indices of
trade policy.
Initial GDP per
capita, Initial
human capital.
Weighted least
squares,
Instrumental
weighted least
squares.
More open countries
experience faster
productivity
growth.
Frankel and
Romer
(1999)
Cross-section: 150
countries. 1985.
Sub-sample: 98
countries
(averages).
Income per
person.
Actual trade
share,
Constructed
trade share.
Distance,
Relative country
size, Dummies
for a common
border and
being
landlocked.
Two country size
measures:
Population and
Land area.
OLS, IV estimator. Statistically and
economically
significant
relationship
between trade and
income.
O’Rourke
(2000)
10 developed
countries, 1875–
1914.
Growth of real
GDP per capita.
Average tariff
rate.
Saving rate,
School
enrolment.
Population.
Change of
capital–labour
and land–labour
ratios. Deviation
of output.
Country and
time dummies.
Conditional and
Unconditional
convergence
models, Factor
accumulation
models, Panel data
estimation.
Tariffs were
positively
correlated with
growth during
1875–1914.
Tariffs boosted
late nineteenth
century growth.
Ó 2010 Blackwell Publishing Ltd.
1528 T. SINGH
TABLE 1 Continued
Rodriguez
and Rodrik
(2000)
Review of Key
studies
Growth of real
GDP per capita:
1970–89 and
1976–85; TFP
growth: 1980–
90; Income per
person in 1985.
Openness
indicators with
alternative
definitions and
weighting.
Same as used in
the reviewed
studies and
some additional
variables.
OLS, 2SLS,
Weighted least
squares, IV
estimator,
Sensitivity
analysis.
Sceptical concerns
on the strength of
beneficial effects
of trade on
growth, and
contend the view
that integration
into the world
economy is such a
potent force for
economic growth.
Easterly and
Levine
(2001)
Panel data: 73
countries; 1960–
95.
Real per capita
GDP growth.
Ratio of exports
plus imports to
GDP.
Initial income per
capita, Years of
schooling,
Inflation,
Government
consumption,
Black market
exchange rate
premium,
Financial
intermediary
credit.
Generalised Method
of Moments
(GMM) Dynamic
panel estimator.
TFP residual, rather
than factor
accumulation,
accounts for most
of the crosscountry
and crosstime
variations in
income and
growth. Openness
and black market
exchange rate
premium
significantly
correlated with
economic growth.
Lane (2001) Cross-section: 71
low- and
middle-income
debtor
countries; 1970–
95.
Net resource
inflows, to
GDP.
Trade ratio
adjusted for
cross-country
differences in
trade policies;
geographybased
measure
of natural
openness.
Initial GDP per
capita,
Government
consumption,
Country size
(population),
Trade shocks,
Inflation rate.
Numerical
simulations and
Empirical
estimates. Cross-
section
regressions, OLS.
Open economies
exhibit greater
debt to output
ratios. External
liabilities to output
positively
associated with
trade openness.
Trade not only has
direct effects on
allocation and
growth, but also
promotes
convergence.
Wacziarg
(2001;
1998)
Panel data: 57
countries; 1970–
89.
Per capita GDP
growth.
Two trade policy
indices.
Price distortions,
Government
consumption,
Manufactured
exports,
Investment rate,
FDI, Macro
policy quality.
Correlation,
Simultaneous
equation model,
3SLS, Seemingly
Unrelated
Regressions
estimator.
Positive impact of
openness on
growth. Enhanced
technology
transmission and
improvements in
macroeconomic
policy account for
smaller effects.
Irwin and
Tervio
(2002)
Pre-World War I,
Inter-war, Great
Depression,
Early Post-war,
Later Post-war.
Countries vary
each year.
GDP per capita. Trade to GDP
ratio. Bilateral
trade.
Distance,
Population,
Area.
OLS, 2SLS, IV
estimator.
Positive effects of
trade on growth.
Countries that
trade more as a
proportion of their
GDP have higher
incomes even after
controlling for the
endogeneity of
trade.
Ó 2010 Blackwell Publishing Ltd.
INTERNATIONAL TRADE AND ECONOMIC GROWTH 1529
TABLE 1 Continued
Vamvakidis
(2002)
Historical data:
Countries vary
in different time
periods.
Growth of GDP
per capita.
Six proxies for
openness to
trade.
Investment to
GDP, School
enrolment,
Population,
Inflation, Black
market
premium,
Illiteracy rate.
Cross-country
growth
regressions, OLS,
Spearman rank
correlation.
No correlation
between openness
and growth
between 1870 and
1970, with the
exception of the
interwar period.
Positive
correlation
between openness
and growth during
1970–90.
Awokuse
(2003)
Time-series,
Canada;
Quarterly data:
1960:1 to
2000:4.
Real GDP. Real exports. Real terms of
trade,
manufacturing
employment,
capital
formation,
industrial
production.
Johansen
Cointegration test,
Granger-causality
with VECM.
Long-run
relationship
among model
variables, and
unidirectional
Granger-causality
from exports to
GDP. Support for
export-led growth
hypothesis in both
short run and long
run.
Dollar and
Kraay
(2003)
Cross-country:
Number of
countries vary;
1970s to the
1990s.
Growth of real
GDP per capita.
Share of trade in
GDP, Decadal
changes in trade
volume.
Measures of
institutional
quality and
market size.
Cross-section
regressions, OLS,
IV estimator.
Joint role of trade
and institutions in
the long run,
larger role for
trade over shorter
horizons. Both
trade and
institutions are
important in
differences in
growth rates in the
very long run.
Alcala and
Ciccone
(2004)
Cross-section: 138
countries.
GDP per worker
in PPP US$;
TFP.
Imports plus
exports relative
to purchasing
power parity
GDP.
Population, Area,
Institutional
quality.
OLS, 2SLS. Trade has a
significant and
robust positive
effect on
productivity.
Clemens and
Williamson
(2004)
35 countries,
1865–1996.
Sample sizes
vary depending
on the years.
Growth in real
GDP per capita.
Average tariff
rate.
School enrolment,
Railway density,
Primary
products
exports, Energy
consumption,
Average trading
partner tariff
and real GDP
growth,
Distance to
trading partners.
Panel data
estimation, Cross-
section
regressions, OLS,
IV estimator.
High tariffs
associated with
fast growth before
World War II, and
associated with
slow growth
thereafter. Increase
in own tariffs after
1950 hurt or at
least did not help
growth.
Ó 2010 Blackwell Publishing Ltd.
1530 T. SINGH
TABLE 1 Continued
Dollar and
Kraay
(2004)
Approximately
100 countries;
1980s and
1990s.
Per capita income
in bottom
quintile, Annual
growth of per
capita GDP
(average).
Ratio of exports
plus imports to
GDP.
Initial income,
Contract-
intensive
money,
Government
consumption,
Inflation,
Revolutions,
Commercial
bank assets,
Rule of law.
Panel data
estimation, OLS,
IV estimator.
No relationship
between changes
in trade volumes
and changes in
inequality.
Increase in growth
rates that
accompanies
expanded trade,
translates into
proportionate
increases in
income of the
poor. Open trade
regimes lead to
faster growth and
poverty reduction.
Lee et al.
(2004)
Approximately
100 countries;
1961–2000.
Growth of real
GDP per capita.
Imports and
exports to GDP,
Tariff index,
Black market
premium.
Initial GDP per
capita,
Investment to
GDP, Inflation,
M2 to GDP,
Population,
Education, Age
dependency.
Panel data
estimation,
Identification
through hetero-
scedasticity,
GMM, OLS.
Openness has a
positive effect on
growth, even
when controlling
for the effect from
growth to
openness.
Rodrik et al.
(2004)
Cross-section,
137 countries.
GDP per capita in
1995 on PPP
basis.
Integration,
Predicted trade
share.
Geography,
Settler
mortality,
Institutions,
English and any
European
language,
Regional
dummies. Land
area,
Population.
OLS, IV estimator,
Sensitivity
analysis.
Quality of
institutions
‘trumps’
everything. Once
institutions are
controlled for,
conventional
measures of
geography have at
best weak direct
effects: trade is
almost always
insignificant.
Felbermayr
(2005)
Panel data: 108
countries; 1960–
99 (five-year
averages).
Per capita output. Trade share. Lagged output,
Secondary
schooling,
Investment,
Population.
OLS, IV estimator,
Pooled 2SLS
regression,
System-GMM
estimator.
Support for strong
effect of trade on
income.
Konya (2006) 24 OECD
countries;
Annual data:
1960–97.
Real GDP. Real exports of
goods and
services,
Openness.
None. Panel-data based on
SUR systems,
Bivariate and
Trivariate
Granger- causality
tests.
Support for: oneway
causality
from exports to
GDP in some;
one-way causality
from GDP to
exports in others;
two-way causality
between exports
and growth in a
few; and no
causality in either
direction for a
few.
Awokuse
(2007)
Time-series,
Three Transition
economies;
Quarterly data.
Real GDP, Real
GDP growth.
Real exports, Real
imports.
Gross capital
formation,
Labour force.
Johansen
Cointegration test,
ECM, Grangercausality
test.
Support for both
export-led growth
and growth-led
export.
Ó 2010 Blackwell Publishing Ltd.
INTERNATIONAL TRADE AND ECONOMIC GROWTH 1531
those in developed countries, and these reduce TFP. Schor (2004) shows that
tariffs on inputs have a negative marginal effect on productivity and that, along
with higher competition, new access to better inputs contributes to productivity
gains after liberalisation. Berthold and Teixeira (2005) examine the effects of
trade barriers on TFP in the presence of insider groups and monopoly rights in
import-competing industries, and show that these industries use inefficient technology
which adversely affects efficiency and productivity. Low productivity in
developing countries affects product quality and reduces competitiveness in
international markets. Frankel and Romer (1996) show significant effects of
trade on income and argue that openness raises income both by inducing factor
accumulation and increasing output. Landlocked countries and regions are
geographically disadvantaged and likely to trade less, compared to countries
and regions with their own seaports. Frankel and Romer (1999) re-assess the
issue and examine the effects of geography using a gravity model. They find a
statistically and economically significant relationship between trade and
income.
Empirical evidence could be sensitive to the measures of openness. Some studies
use multi-measures of openness (Levine and Renelt, 1992; Lee, 1993, 1995;
Sachs and Warner, 1995; Baldwin and Seghezza, 1996; Harrison, 1996; Edwards,
1998; Wacziarg, 2001; Vamvakidis, 2002; Lee et al., 2004; Rodrik et al., 2004)
(Table 1). Levine and Renelt (1992) use trade volumes, black market premium,
real exchange rate index and the Leamer index, and find no robust and consistent
evidence for the positive relationship between openness and long-run growth.
They instead find a positive and robust correlation between growth and share
of investment in GDP, and between investment share and ratio of trade to
GDP. This implies that the favourable effects of trade traverse through capital
TABLE 1 Continued
Awokuse and
Christopoulos
(2009)
Time series,
5 industrialised
countries.
Real GDP growth. Real exports. Real terms of
trade,
manufacturing
employment,
gross capital
formation, and
industrial
production
index.
Causality tests
Linear VAR
model and
Non-linear
multivariate STAR
(LSTAR and
ESTAR)
estimations.
Non-linear Grangercausality
tests
provide support
for the validity of
‘export-led
growth’ in some,
and growth-led
export hypothesis
in others.
Notes:
GDP stands for Gross Domestic Product; GNP: Gross National Product; Govt.: Government; TFP: Total Factor Productivity; R&D:
Research and Development expenditure; FDI: Foreign Direct Investment; PPP: Purchasing Power Parity; DCs: Developed Countries;
LDCs: Less Developed Countries; SICs: Semi-industrialised Countries; NICs: Newly Industrialised Countries; OECD: Organisation for
Economic Co-operation and Development; EC: European Community; OLS: Ordinary Least Squares; 2SLS: Two-stage Least Squares;
IV: Instrumental Variables; 3SLS: Three-stage Least Squares; NLLS: Non-linear Least Squares; SUR: Seemingly Unrelated Regressions;
DF: Dickey–Fuller; ADF: Augmented Dickey–Fuller; VAR: Vector Autoregression; ECM: Error Correction Model; VECM: Vector Error
Correction Model; GMM: Generalised Method of Moments; STAR: Smooth Transition Autoregression; ESTAR: Exponential Smooth
Transition Autoregression; and LSTAR: and Logistic Smooth Transition Autoregression.
Ó 2010 Blackwell Publishing Ltd.
1532 T. SINGH
accumulation, rather than a more efficient allocation of resources. Trade policies
are important insofar as these policies facilitate the access to investment goods
and encourage the accumulation of capital.
Several studies have shown that trade fosters growth through its favourable
effects on investment and capital stock (Lee, 1995; Baldwin and Seghezza,
1996; Frankel and Romer, 1996; Wacziarg, 2001). Baldwin and Seghezza
(1996) argue that trade induces investment, as the traded goods sector is relatively
more capital intensive than the non-traded sector, investment goods
require imported intermediate inputs, and competition in international markets
lowers the price of capital. Wacziarg (2001) finds that the rate of physical capital
accumulation explains 46 to 63 per cent of the impact of trade policy on
growth, and openness affects growth mainly by raising the ratio of domestic
investment to GDP. Harrison and Revenga (1995) use three measures of trade
orientation – export plus import as a share of GDP, average level of tariffs,
and adjusted dollar index – to capture the extent of distortions in a country’s
relative prices of tradables. They examine the link between trade policies and
foreign investment flows, and argue that trade reforms have been accompanied
by significant increases in investment inflows. Harrison (1996) uses seven measures
of openness and finds that only one positively affects growth when crosssection
data are employed; three reveal a positive association with growth when
data are averaged over five-year periods; and six measures are significant.
Greenaway et al. (1998) use several measures of liberalisation and find liberalisation
and openness do impact favourably on growth of GDP per capita.
Edwards (1998) uses a set of nine indices of trade policy and finds that more
open countries experience faster productivity growth; the results are robust to
the use of openness indicator, estimation technique, time period, and functional
form.
An archival analysis and historical evidence suggest that openness and
growth were not correlated during the interwar period; were negatively correlated
a century ago (Foreman-Peck, 1995; O’Rourke and Williamson, 1999;
Vamvakidis, 2002); and became significantly correlated only in recent decades
(Vamvakidis, 2002; Clemens and Williamson, 2004). Foreman-Peck (1995)
examines the effect of tariffs on the level of output per capita for 18 European
countries (1860–1910), and finds that tariffs were negatively related to output
per capita. O’Rourke (2000) uses a different sample and shows tariffs were
instead positively correlated with growth during 1875–1914. Clemens and
Williamson (2001) find that the positive relationship between openness and
growth was reversed for a number of countries in the period prior to 1950.
Irwin (2002) examines the correlation between tariffs and growth in the late
nineteenth century and argues that correlation does not establish a causal relationship.
Vamvakidis (2002) uses data from 1870 onwards and employs six
proxies of openness, and finds no support for a positive growth–openness
Ó 2010 Blackwell Publishing Ltd.
INTERNATIONAL TRADE AND ECONOMIC GROWTH 1533
connection before 1970; in fact, the correlation is negative for the period
1920–40. He argues that the positive correlation between openness and growth
is only a recent phenomenon.
(ii) Microeconomic firm-level models
The strand of studies following the microeconomic approach examine the
effects of trade on the firm- or industry-level X-efficiency and productivity.
While a number of studies show favourable effects (Condon et al., 1985; Chen,
1990), some find little or no correlation between openness measures (exports,
imports, tariffs and quotas) and productivity (Tybout and Westbrook, 1995). Katrak
(1997) finds a positive, though weak, relationship between technology
imports and firm-level R&D. The degree and extent to which foreign technology
imports encourage domestic R&D depends on the availability of technological
skills and infrastructure in the domestic economy. Keller (2000) uses industrylevel
data for machinery goods imports and productivity for eight OECD countries
(1970–91), and finds that countries benefit more from domestic R&D than
from the R&D of the average foreign country.3
Keller (2002) further reinforces
these results and shows strong productivity effects from both own R&D spending
and R&D conducted elsewhere. The R&D in industry itself contributes about 50
per cent, R&D in other domestic industries about 30 per cent, and R&D in foreign
industries about 20 per cent of the increase in productivity. Singh (2003)
examines the effects of exports on productivity and growth and tests the exportinduced
convergence in 10 manufacturing industries in India. The effects of
exports on TFP are significant in half of the sample industries, while in the
remaining half these are statistically insignificant.
The mid-1990s marked the development of a distinct strand of research based
on micro-theoretic models, and provided new dimensions to the transmission
channels. These models draw on the industry dynamics models, which show a
systematic relationship between ‘entry and exit’ and firm-level productivity differentials
(Jovanovic, 1982; Hopenhayn, 1992; Ericson and Pakes, 1995). The
micro-theoretic models disregard the assumption of representative firms underlying
most traditional models based on the Heckscher–Ohlin framework, and
instead consider intra-sectoral heterogeneity in productivity and export behaviour
as arising from the ‘entry and exit’ decisions of firms in the export market. The
plant-level heterogeneity within the same industry potentially induces the reshuffling
of resources and reallocation of market shares from less efficient to more
efficient firms, and leads to the improvements in productivity. These studies use
longitudinal data and test two mutually-reinforcing hypotheses to explain the
3
The import composition of a country matters only if it is strongly biased toward or away from
technological leaders. The differences in technology inflows related to the pattern of imports
explain about 20 per cent of the total variations in productivity growth.
Ó 2010 Blackwell Publishing Ltd.
1534 T. SINGH
higher productivity and X-efficiency of exporters as compared to non-exporters:
self-selection or market-selection hypothesis and the learning-by-exporting
hypothesis. The self-selection hypothesis suggests the causal effects of firm-level
productivity on exports, while the learning-by-exporting hypothesis shows the
feedback and learning effects of exports on firm-level productivity.
The self-selection hypothesis postulates that the firms with exogenously determined
high levels of productivity self-select themselves into the export markets,
and this hypothesis builds on two propositions. First, the firms entering the export
market incur higher irreversible sunk costs as compared to firms operating in the
domestic market and, therefore, the initial productivity levels for the export-oriented
firms need to be higher than the domestic-oriented firms (Roberts and Tybout,
1997; Bernard and Jensen, 1999; Bernard and Wagner, 1997, 2001; Isgut,
2001; Delgado et al., 2002; Melitz, 2003).4
Second, firms entering a foreign market
are exposed to more intense competition than firms in a domestic market.
Such competition in export markets provides fewer opportunities to inefficient
firms (Aw and Hwang, 1995; Delgado et al., 2002).5
The export markets, thus,
select the most productive and efficient firms from among potential entrants. It is
only the productive and above-average firms that are likely to cope with sunk
costs and face fierce competition abroad, and self-select themselves into the foreign
export market. The patterns of entry, survival and exit in the export market
are related to the firm-level productivity differentials. Melitz (2003) uses a
dynamic industry model with heterogeneous firms and shows that the exposure to
trade will induce only more productive firms to enter export markets (while some
less productive firms continue to produce only for domestic markets) and will
simultaneously force the least productive firms to exit.6
Feenstra (2006) argues
that the gains from trade in monopolistic competition models arise from three
sources: (i) price reductions due to increasing returns to scale; (ii) increased product
variety available to consumers; and (iii) self-selection of firms with only the
most efficient firms surviving after liberalisation.
The learning-by-exporting hypothesis, which is analogous to the learningby-doing
model of Arrow (1962), suggests that internationalisation is a catalytic
4
Tybout (2000) argues that in developing countries: (i) markets tolerate inefficient firms, so the
cross-firm productivity dispersion is high; (ii) small groups of entrenched oligopolists exploit
monopoly power in product markets; and (iii) many small firms are unable or unwilling to grow, so
important scale economies go unexploited. He draws on firm-level studies to assess these conjectures
and finds none to be systematically supported.
5
Even if the competitive pressures in domestic and export markets are similar, the differences in
sunk entry costs explain the productivity differentials between exporters and domestic-oriented
firms (Delgado et al., 2002).
6
Melitz (2003) argues that the firms with different productivity levels co-exist in an industry
because each firm faces an initial uncertainty concerning its productivity before making an irreversible
investment to enter the industry. Entry into export markets is also costly, but the firm’s decision
to export occurs after it gains the knowledge of its productivity.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1535
source of technological innovations and managerial inputs for the exporting
firms.7
Several studies find support for the learning effects of exports, such as
Clerides et al. (1998) for Colombia, Mexico and Morocco; Kraay (1999) for
China; Aw et al. (2000) for Taiwan (but not for South Korea); Castellani (2002)
for Italy; Delgado et al. (2002) for Spain; Mengistae and Patillo (2004) for Kenya,
Ghana and Ethiopia; Bigsten et al. (2004) for four Sub-Saharan African countries;
and Girma et al. (2004) for Great Britain. Clerides et al. (1998) find strong evidence
for the self-selection mechanism, but no evidence for learning effects from
exporting in Colombia, Mexico and Morocco. Aw et al. (2000) find support for
learning-by-exporting in Taiwan, but not in South Korea. The newly exporting
firms in Taiwan outperform the non-exporting firms before entry into the export
market and, in some industries, the exporting firms show productivity improvements
after their entry into the export market. In the case of Korea, the correlation
between exporting and firm productivity is somewhat weak. Delgado et al. (2002)
examine the TFP differences between exporting and non-exporting Spanish manufacturing
firms (1991–96).8
They find significant evidence for self-selection, but
insignificant evidence for learning effects; the learning effects become significant
only when the sample is restricted to young firms. For German manufacturing
firms, Arnold and Hussinger (2005) show Granger-causality from TFP to exporting
and not vice versa. Castellani (2002) observes that only the firms with very
high exposure to export markets experience learning effects and not the firms
below this threshold export intensity. Van Biesebroeck (2005) supports both selfselection
and learning-by-exporting hypotheses in nine African countries. A study
conducted by the International Study Group on Exports and Productivity (2008)
for 14 countries finds that the exporters are more productive than non-exporters
when observed and unobserved heterogeneity is controlled for, and these exporter
productivity premia tend to increase with the share of exports in total sales.
The learning effects are likely to be more pronounced in trade between
countries with wider technological gaps (such as trade between developed and
developing countries), rather than in trade between countries with similar technologies
(such as trade among developed countries or among developing countries).
In self-selection hypothesis, the exporting firms are exogenously more
7
The interaction of export-oriented domestic firms with foreign clients and competitors engenders
positive learning effects. The exporting firms accumulate experience and knowledge and are systematically
more productive and close to the efficiency frontier than the domestic-oriented
non-exporting firms. The exporting is viewed as a learning process and it improves the productivity
premium of exporting firms.
8
Delgado et al. (2002) argue that the productivity distribution of exporters should stochastically
dominate the productivity distribution of non-exporters in the period prior to their entry into the
export market. On the exit side of the export market, the productivity distribution of continuing
exporters stochastically dominates the distribution of exiting exporters. In the presence of learningby-exporting
effects, the differences between productivity levels for exporting and non-exporting
firms should increase after the entry of exporters in the export market.
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1536 T. SINGH
productive from the outset, and exporting contributes to productivity only when
the productivity premium of already productive firms improves after their entry
into the export market. The studies supporting the self-selection hypothesis
numerically overwhelm the studies supporting the learning-by-exporting hypothesis,
and this implicitly provides a stronger support for the effects of productivity
and growth on trade as compared to the effects of trade on productivity and
growth (Table 2). Rodriguez and Rodrik (2000) review the studies by Dollar
(1992), Ben-David (1993), Lee (1993), Sachs and Warner (1995), Harrison
(1996), Edwards (1998), Frankel and Romer (1999) and Wacziarg (2001; 1998),
and raise sceptical concerns on the strength of the argument for the beneficial
effects of trade. They argue that ‘these papers … find little evidence that firms
derive technological or other benefits from exporting per se; the more common
pattern is that efficient producers tend to self-select into export markets. In
other words, causality seems to go from productivity to exports, not vice versa’
(Rodriguez and Rodrik, 2000, p. 317).
3. PROTECTIONISM VERSUS TRADE LIBERALISATION:
A TALE OF TWO PARADIGMS
a. Protectionism and Barriers to Trade: Recognition for the ‘gains of trade’
paradoxically co-exists with the preferences for ‘barriers to trade’ and passion
for protectionism. Developing countries, until the 1980s, have been sceptical
about the gains of free trade, and many pursued protectionist and import-substituting
industrialisation policies. Several factors seem catalytic to the support
for trade barriers, such as: (i) infant-industry protection; (ii) export-pessimism
arising from the apprehensions of adverse terms of trade in the developing
countries (Singer, 1950; Nurkse, 1958; Prebisch, 1959);9
(iii) disbelief in the
market mechanism that stemmed from the Great Depression and failure of the
laissez-faire system; (iv) concerns regarding the decline in real wages and displacement
of workers from jobs due to the increased foreign competition
engendered by trade liberalisation (Krueger, 2004); and (v) the plausible
adverse effects of trade on environmental quality.
9
Singer (1950), Nurkse (1958) and Prebisch (1959) provide a theoretical and generalised argument
for the poor export performance of the developing countries, and suggest that, as a result of the low
income and price elasticities of demand for primary commodities, the developing countries specialising
in the production and export of these commodities have experienced steady and long-run
deterioration in terms of trade. In conjunction with low demand elasticities, the increased supplies
lead to the reduction in prices of primary commodities relative to the prices of manufactured goods
imported by the developing countries. Nurkse (1958) argues that, as a result of both relatively low
income elasticities of demand and the increased substitution of synthetic material for raw material
in the production of manufactured goods in the developed countries, the primary exports are
confronted with a stagnant world demand.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1537
TABLE 2
Effects of International Trade on Productivity and Economic Growth: Microeconomic Evidence
Author (Year) Country Sample Methodology and
Estimators
Main Results and Conclusions
Aw and Hwang
(1995)
Taiwan 2,832 Firms, 1986. Translog production
function, Cross-section.
Support for SS; Higher productivity of
exporters; No support for LE.
Bernard and
Wagner (1997)
Germany 7,624 Firms, 1978–92. Panel data. Support for SS. Exporting firms have
higher productivity.
Roberts and
Tybout (1997)
Colombia Manufacturing plants
in four major
exporting industries.
Panel data, Dynamic Probit
Model, Method of
Simulated Moments and
Maximum-Likelihood.
Sunk costs are significant, and prior
export experience increases the
probability of exporting. Plants that are
large, old and owned by corporations
are all more likely to export.
Clerides et al.
(1998)
Colombia, Mexico
and Morocco
2,800 Firms; 1981–91,
1986–90 and 1984–
91.
FIML of cost function;
Panel data.
Exporting firms are more efficient;
Quitters are less productive; No
support for LE in Colombia and
Mexico; Support for SS and LE in
some Moroccan industries; Spillover
effects from exporters to non-exporters.
Bernard and
Jensen (1999)
USA 50,000–60,000 Firms;
1984–92. Subsamples:
1984–88
and 1989–92.
Panel data. Support for SS. Exporting firms have
higher productivity. No support for LE.
Kraay (1999) China 2,105 Firms; 1988–92. Dynamic panel data. Exporting firms have higher productivity;
Support for LE in established
exporters.
Aw et al. (2000) Korea, Taiwan
(China)
Korea: 39,022 to
88,864 Firms.
Taiwan: 88,000 to
100,000 Firms.
Cross-section. Support for SS; No support for LE in
Korea. LE in some Taiwanese
industries. Productivity is correlated
less strongly with export market
participation in Korea than in Taiwan.
Aw et al. (2001) Taiwan 80,000 to 100,000
Firms; Sample
period: 1981, 1986
and 1991.
Panel data. More productive firms, on average,
survive and, in many cases, their
productivity converges to older
incumbents. Exiting firms are less
productive than survivors. Productivity
differential between entering and
exiting firms is an important source of
industry-level productivity growth.
Isgut (2001) Colombia 6,454 Plants; 1981–91. Panel data. Support for SS. Does not rule out
possibility that successful exporters
learn from participation in export
markets.
Castellani (2002) Italy 2,898 Firms; 1989–94. Cross-section. Support for SS; LE in plants with high
export orientation. Exporting firms
have higher productivity.
Delgado et al.
(2002)
Spain 1,766 Firms; 1991–96. Non-parametric method. Support for self-selection. Higher levels
of productivity of exporting firms than
non-exporting firms; Inconclusive
evidence on LE.
Pavcnik (2002) Chile 4,379 Plants; 1979–86. Semi-parametric method;
Panel data.
Within-plant productivity improvements
can be attributed to liberalised trade in
import-competing sector. In many
cases, aggregate productivity
improvements stem from reshuffling of
resources from less to more efficient
producers.
Wagner (2002) Germany 353 Firms; 1978–89. Panel data; Matching
approach.
No support for LE. Support for positive
effects of starting to export on growth
of employment and wages; weaker
evidence for a positive effect on labour
productivity.
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1538 T. SINGH
TABLE 2 Continued
Baldwin and Gu
(2003)
Canada 236 Manufacturing
industries (at fourdigit
1980 SIC
level); Four periods:
1974–79; 1979–84;
1984–90; and 1990–
96.
Panel data, GMM; System
GMM estimator.
Support for SS and LE. Among nonexporters,
more productive ones and
those whose productivity has recently
been growing more rapidly expand into
export markets and less productive
remain non-exporters.
Girma et al.
(2003)
UK 2,989 Firms; 1991–97. Difference-in- Differences
based on Matched Firms.
Temporary negative contemporaneous
impact of exit on TFP, but more
persistent and sizeable negative effects
on output and employment trajectories.
Head and Ries
(2003)
Japan 1,070 Firms; 1977–89. Ordered regression. Firms that invest abroad and export are
more productive than firms that just
export. Firms that export are larger
than firms that serve domestic market,
but both are smaller than firms that
invest abroad as well as export.
Baldwin and Gu
(2004)
Canada 19,142 Plants; 1984–
90 and 1990–96.
Survey data and Difference
regressions.
Support for SS and LE. Exporters tend to
be the more innovative firms. Plants
that enter into export markets increase
investments in R&D and training.
Entry into export market leads to an
increase in number of advanced
technologies, increases in foreign
sourcing, and improvements in
information available.
Bernard and
Jensen (2004a)
USA 50,000–60,000 Plants;
1983–92.
Panel data: Unbalanced
panel.
Support for SS. Exporting is associated
with reallocation of resources from less
to more efficient plants. Reallocation
effects account for over 40 per cent of
total TFP growth.
Bernard and
Jensen (2004b)
USA 13,550 Plants; 1984–
92.
Panel data; GMM
estimator.
Entry and exit in export market is
substantial, past exporters are apt to reenter,
and plants are likely to export in
consecutive years. Entry costs are
significant and spillovers from export
activity negligible.
Bigsten et al.
(2004)
Cameroon, Ghana,
Kenya and
Zimbabwe
289 Firms:
(1991 ⁄ 93–1994 ⁄ 95).
Panel data; Maximumlikelihood
and GMM.
Support for LE. Significant efficiency
gains from exporting. Little direct
evidence for self-selection.
Criscuolo et al.
(2004)
UK 1980–2000. Cross-section. Productivity growth increased due to
entry and exit. Share of productivity
growth accounted for by entry and exit
increased considerably from 1980s to
1990s. Globalisation and increased
information and communication
technology contribute to productivity
growth.
Girma et al.
(2004)
UK 8,992 Firms; 1988–99. Panel data; Matching
approach.
Support for SS and LE; Exporting
involves sunk costs. Exporters are
more productive and exporting further
increases firm productivity.
Greenaway and
Kneller (2004)
UK 11,225 Firms; 1989–
2002.
Probit model. Support for SS. Sunk costs are important,
and firms have to become more
productive to enter the export market.
LE only in unmatched and not in
matched sample.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1539
TABLE 2 Continued
Greenaway et al.
(2004)
UK 3,662 Firms; 1992–96. Panel data. Positive spillover effects from
multinational enterprises on the
decision to export and export
propensity. Probability of domestic
firms exporting positively influenced by
intensity of foreign R&D expenditure,
and relative importance of production
and export activities of multinational
enterprises.
Hansson and
Lundin (2004)
Sweden 3,275 Firms; 1990–99. Matching and Differencein-Differences
Analysis.
No TFP growth differentials for entrants
into the export market.
Schor (2004) Brazil 4,484 Firms; 1986–98. Panel data. Tariffs on inputs have negative effect on
productivity. Higher competition,
access to better inputs contributes to
productivity gains.
Alvarez and
Lo´pez (2005)
Chile Manufacturing Plants
with 10 or more
workers; 1990–96.
Probit Model, Panel data
Model.
Plants that enter international markets
show superior initial performance,
consistent with self-selection. Existence
of learning-by-exporting for entrants,
but not for those that export
continuously.
Arnold and
Hussinger
(2005)
Germany 389 Firms; 1999–2000. Panel data, Probit model,
Granger-causality,
Matching.
Support for SS. No support for LE.
Greenaway et al.
(2005)
Sweden 3,570 Firms; 1980–97. Matching and Differencein-Differences;
Panel
data.
No evidence of pre- or post-entry
differences in firm-level productivity.
Performance characteristics of
exporters and non-exporters remarkably
similar.
Ruane and
Sutherland
(2005)
Ireland 2,854 Firms; 1991–98. Panel data. Support for SS. Superior characteristics
of exporters relative to non-exporters.
No evidence that enterprises improve
their performance after entry.
Van Biesebroeck
(2005)
9 African
countries
1,916 Firms, with
approximately 200
in each country;
1992–96.
Panel data; GMM
estimator.
Support for SS and LE. Exporting firms
have higher productivity.
Bernard et al.
(2006)
USA Approximately
210,000 Plants in
337 manufacturing
industries; 1987–97.
Panel data; OLS and
Logistic Regressions.
Reallocation of economic activity
towards high-productivity firms as
trade costs fall. Evidence for
productivity growth within firms in
response to decreases in industry-level
trade costs.
Fernandes (2007) Colombia Average of 6,474
Plants per year;
1977–91.
Panel data; OLS; GMM. Liberalisation has positive impact on
plant productivity. Impact is stronger
for large plants and for plants in
industries with less domestic
competition.
Greenaway and
Kneller (2007b)
UK 12,875 observations on
domestic
manufacturing firms.
Probit Model, Panel
Difference-in-
Differences.
Post-entry productivity growth of new
export firms faster than non-export
firms. Potential for learning varies
across industries, depending upon the
extent to which they are already
exposed to international competition
and where R&D intensity is already
high.
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1540 T. SINGH
TABLE 2 Continued
Greenaway et al.
(2007c)
UK 9,292 manufacturing
firms (total 51,668
annual observations);
Of the 9,292 firms,
5,461 are continuous
exporters (58.77 per
cent) and 2,798
never exported
(30.11 per cent); and
434 are starters (4.67
per cent); 1993–
2003.
Pooled Probit Model,
Random-effects Probit,
Fixed effects, GMM,
Dynamic Random-effects
Probit and Dynamic
GMM.
Exporters exhibit better financial health
than non-exporters. Starters generally
display low liquidity and high leverage.
No evidence that firms enjoying better
ex ante financial health are more likely
to export, and strong evidence that
participation in export markets
improves firms’ ex post financial
health.
Alvarez and
Lo´pez (2008)
Chile Manufacturing plants
with 10 or more
workers; 1990–99.
Olley and Pakes (1996)
method to estimate
productivity. Pooled
regressions with OLS and
IV estimators.
Domestic as well as foreign-owned
exporting plants improve productivity
of local suppliers. Higher exporting
activity increases productivity of plants
in domestic sector. No evidence of
spillovers from exporters. Foreignowned
exporters generate positive
productivity spillovers. Support for
backward spillover effects from
domestic exporters.
Andersson et al.
(2008)
Sweden Manufacturing firms
with at least 10
employees. 56,957
firm-level
observations; 1997–
2004.
Panel data Model; GLS, IV
and GMM estimations.
Support for selection operating from
market to market. Productivity
premiums increase in number of
markets and number of products. Firms
that both export and import are more
productive.
Bellone et al.
(2008)
France 23,000 Manufacturing
firms with at least 20
employees; 1990–
2002.
Non-parametric
methodology to compute
TFP, Pooled Regression.
Support for U-shaped productivity
dynamics of new exporters. Pattern
more pronounced for intensively
exporting firms and firms operating in
capital-intensive or high-technology
sectors. Both self-selection and
learning-to-export mechanisms prevail
during the pre-entry period.
Fryges and
Wagner (2008)
Germany All establishments
from mining and
manufacturing
industries that
employ at least 20
persons or in
company that owns
the unit; 1995–2005.
Propensity Score, Dose–
Response functions and
Fractional Logit model;
Pooled Regressions.
Support for causal effect exports on
labour productivity growth, but only
within a sub-interval of the range of
firms’ export–sales ratios. Evidence for
time-varying causal relationship
between labour productivity growth
and the export–sales ratio.
Girma et al.
(2008)
Great Britain and
the Republic of
Ireland
UK firms with fixed or
current assets in
excess of £150,000;
1996–2003.
Irish Manufacturing
plants with at least
10 employees; 2000–
03.
Bivariate Probit Model;
Maximum-likelihood and
3SLS estimators.
Previous exporting experience enhances
innovative capacity of Irish firms and
positive LE effects. No strong evidence
for such direct effects for British firms.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1541
A parallel counter-strand postulates protectionism as a myopically short-term
development strategy in that it encourages X-inefficiency and leads to reduction
in productivity and growth and rise in costs and prices. The immiserising growth
model (Bhagwati, 1958, 1968) shows the possibilities of immiserising tariffs,
where import protection leads to negative real growth. Krueger (2004) asserts
that the job protection case for protectionism comes at the cost of increased inefficiency
and higher prices for consumers, arising from the tariff and non-tariff
TABLE 2 Continued
Greenaway and
Kneller (2008a)
UK 11,225 manufacturing
firms. Taking 1995
as representative, 66
per cent of firms
exported; 1989–
2002.
Probit Model, Propensity
Score-matching and
Difference-
in-Differences.
Spillovers associated with agglomeration
raise probability of export entry.
Survival is driven partly by size and
TFP and partly by industry
characteristics. Exporters larger and
have higher productivity. Regional and
industry agglomeration relevant to
successful entry. Firms receive a
significant boost to productivity in the
year they enter. Firms that exit are
more likely to do so due to loss of
market share than loss of productivity.
Having a sales mix with a larger share
of exports and being in activities with
differentiated products offer some
protection against exit.
Greenaway et al.
(2008b)
Sweden 3,570 manufacturing
firms with more than
50 employees; 1980–
96.
TFP estimated using Olley
and Pakes and Levinsohn
and Petrin methodology,
Multinominal Logit
Regression.
Higher levels of international
competition increase probability of exit
by merger and closedown. If trade is
more intra-industry in character, effect
of import penetration on exit is less.
Probability of exit by switching is
higher in revealed comparative
disadvantage industries. The
geographical source of international
competition is important. Structure of
international competition matters
indirectly as well directly.
International
Study Group on
Exports and
Productivity
(ISGEP) (2008)
14 countries Sample varies across
countries.
Country-by-Country
Analysis using identically
specified and estimated
models; Pooled
Regressions; MetaRegression
Analysis.
Exporters are more productive than nonexporters
and exporter productivity
premia tend to increase with the share
of exports in total sales. Support for SS
for less developed countries and for all
European Union countries. Support for
LE for only one out of 14 countries.
Serti and Tomasi
(2008)
Italy 38,771 Manufacturing
firms with
employment of 20
units or more; 1989–
97.
Semi-parametric technique
to estimate productivity;
Panel data model, OLS,
Propensity Score
Matching and Difference-
in-Differences.
Support for self-selection and exporters
outperform non-exporters. Firms
serving foreign markets have higher
productivity and are larger, more
capital and skilled labour intensive, and
more (labour) cost competitive.
Heterogeneous post-entry effects with
respect to characteristics.
Notes:
SS stands for self-selection or market-selection hypothesis; LE: learning-by-exporting hypothesis; R&D: Research and Development; SIC:
Standard Industrial Classification; TFP: Total Factor Productivity; OLS: Ordinary Least Squares; 3SLS: Three-stage Least Squares; GLS:
Generalised Least Squares; IV: Instrumental Variables; GMM: Generalised Method of Moments; and FIML: Full Information Maximum-
likelihood.
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1542 T. SINGH
barriers. In contrast, trade liberalisation and free trade lead to the rapid and sustainable
growth, rise in living standards and reduction in poverty (Krueger,
2004). Greenaway (2004) postulates that infrastructure investment and trade liberalisation,
which reduce natural and man-made barriers, respectively, are good
for growth. Copeland and Taylor (2004) review the literature on the effects of
trade and growth on environment, and assert there is now a great deal of evidence
supporting the view that rising incomes affect environmental quality in a
positive way. The ‘Environmental Kuznets Curve’ hypothesises an inverseU-shaped
relationship between a country’s per capita income and its level of
environmental quality: increased incomes are associated with an increase in
pollution in poor countries, but a decline in pollution in rich countries. If environmental
quality is a normal good, then the increases in income brought about
by trade or growth will both increase the demand for environmental quality and
increase the ability of the governments to afford costly investments in environmental
protection (Copeland and Taylor, 2004). Frankel and Rose (2005) examine
the effects of trade on environment, and assert that there is little evidence
that trade has a detrimental effect on environment.
b. Multilateral Trade Liberalisation and the Preferential Trade
Agreements: The protectionism of the 1920s and 1930s and failure of multilateral
attempts to foster a cooperative trade-policy environment led to the formation
of the GATT in 1947, which was subsequently transformed into the
WTO in January 1995. GATT⁄WTO emphasises the reduction or removal of tariff
and non-tariff barriers to foster multilateral free trade in goods, services and
capital. The World Bank and the IMF have the lending powers and, thus, are in
a position to enforce liberalisation in (developing) countries seeking financial
assistance, as compared to GATT⁄WTO that works on the principles of most
favoured nations (MFNs) and reciprocal liberalisation. Irwin (1995) argues that
the World Bank and IMF are the autonomous institutions that use lending power
to affect economic policies of the member countries, as compared to GATT that
has no autonomous power, independent leverage or financial sanction. Trade liberalisation
remains a characteristic feature of the IMF loan conditionality for
(developing) countries seeking financial assistance.
(i) GATT⁄WTO and the world trading system
The GATT⁄WTO prescribes the rules that govern the trade policy of the
member countries and help foster multilateral free trade by: (i) facilitating the
reduction or removal of trade restrictions;10
(ii) resolving the prisoners’ dilemma
10
The ‘prisoners’ dilemma’ problem in repeated Nash games on tariff agreements suggests that it
is individually rational to impose and collectively rational to remove the tariffs.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1543
that may arise in unilateral trade (tariff) policy in response to the adverse termsof-trade
(Bagwell and Staiger, 1999, 2002; Tomz et al., 2007), (iii) eliminating
tariff wars and protectionism; (iv) resolving hegemonic and power asymmetries
across negotiating partners; and (v) helping countries to coordinate on efficient
outcomes. In Nash equilibrium, tariffs too high and trade volumes too low, and
hence a trade agreement that facilitates a reciprocal reduction in Nash tariffs
would be mutually beneficial (Bagwell and Staiger, 2002). The reciprocal reduction
in trade barriers spurs higher trade, and MFN principle forbids member
countries from pursuing discriminatory trade policies against one another (Panagariya,
1999). The rules-based approach reduces uncertainty and provides a guaranteed
market access. It also helps alleviate time-inconsistency problems and
policy reversals (Staiger and Tabellini, 1987; Maggi and Rodriguez-Clare, 1998;
Tomz et al., 2007). Irwin (1995) argues that in spite of its small size and uncertain
place as an economic institution, GATT’s long-run impact on the world
economy has been more significant than either that of the World Bank or the
IMF.
While there has been a long-standing recognition and widely-held consensus
on the substantive contributions of GATT⁄WTO to the promotion of world
trade, the emerging evidence pioneered by Rose (2004a, 2004b; 2005a, 2005b)
casts a sceptical note. It suggests that insiders trade at no higher levels than
outsiders.11
Rose (2004a) uses a gravity model of bilateral merchandise trade
and a large panel dataset covering over 50 years and 175 countries, and estimates
the effects of multilateral trade agreements – GATT⁄WTO and
Generalised System of Preferences – on trade. He finds little evidence that the
countries joining or belonging to GATT⁄WTO have different trade patterns
from the outsiders. Using a comprehensive set of over 60 measures of trade
policy, Rose (2004b) finds no support for the significant differentials in terms
of tariff rates and other measures of trade policy between members and outsiders.
Rose (2005a) then compares the effects of three multilateral organisations
on trade: GATT⁄WTO, IMF and the Organisation for Economic Co-operation
and Development (OECD) and its predecessor, the Organisation for European
Economic Co-operation (OEEC). He finds that the membership in the OECD is
consistently associated with a strong positive effect on trade, while the comparable
evidence is weaker for GATT⁄WTO and especially the IMF.12
Rose
11
The GATT conducted eight ‘rounds’ of multilateral trade negotiations before it was subsumed
by the World Trade Organization in 1995: Geneva (concluded in 1947), Annecy (1949), Torquay
(1951), Geneva (1956), Dillon (1961), Kennedy (1967), Tokyo (1979), and Uruguay (1994) (Rose,
2004a).
12
The effects of both IMF and GATT ⁄ WTO membership on trade are usually quite small (indeed,
often negative). The exception is that the effects of GATT ⁄ WTO membership are positive when a
fixed-effects estimator is employed: that is, joining the GATT ⁄ WTO is associated with a tradecreating
effect, though simply belonging to it is not. The OECD, on the other hand, has a robustly
positive effect on trade that is both economically and statistically significant (Rose, 2005a).
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1544 T. SINGH
(2005b) further examines the effects of GATT⁄WTO on the stability and predictability
of trade flows, and finds little evidence that membership in
GATT⁄WTO has a significant dampening effect on the volatility.
The Rose evidence marks a puzzle in open economy macroeconomics. A few
studies have attempted to resolve the ‘Rose Paradox’ and support the significant
role of GATT⁄WTO in spurring world trade (Subramanian and Shang-Jin, 2007;
Tomz et al., 2007). Subramanian and Shang-Jin (2007) argue that the
GATT⁄WTO has served to increase world imports substantially, possibly by
about 120 per cent of world trade. Tomz et al. (2007) suggest that grouping nonmember
participants with non-participants causes a substantial downward bias in
the estimated effects of GATT membership. They argue that, when this misclassification
is corrected, the agreement proved beneficial for both formal
members and non-member participants, which traded at higher levels than the
countries outside GATT. The GATT exerted a positive effect on trade in nearly
all time periods and for most groups of countries (Tomz et al., 2007).
(ii) Preferential trade agreements (PTAs): The system of GATT⁄WTO
remains surrounded by a multitude of economic integration and preferential
trade agreements that are non-discriminatory for members, but discriminatory
against non-members.13
The formation of economic unions and PTAs has proliferated
exponentially in that these have come to constitute a numerically massive
magnitude and encompass a predominant proportion of the globe.14
Panagariya (1999) argues that the aggressive race between the European Union
and United States on the promotion of Free Trade Areas (Agreements) led to
the renewal of efforts for PTAs by and among smaller countries of Africa,
Latin America, South and Central Asia, Central and Eastern Europe and the
13
The GATT’s Article XXIV permits preferential agreements, provided that member countries
eliminate tariffs on substantially all trade between them in a reasonable period of time (Bagwell
and Staiger, 1998). This exception to the principle of non-discrimination was controversial in its
inception and has met with renewed controversy recently, as many GATT members have increasingly
exercised their rights under this article to negotiate preferential trading agreements. These
agreements may take either of two forms. When countries form a free trade area, they eliminate
barriers to internal trade, but maintain independent external trade policies. Under a customs union,
member countries also agree to harmonise their external trade policies, and create a common
external-tariff-setting authority (Bagwell and Staiger, 1998).
14
A common characteristic of Free Trade Agreements (FTAs), Preferential Trade Agreements
(PTAs) and the Regional Trade Agreements (RTAs) is that these Agreements (Areas) are nondiscriminatory
for members, but discriminatory against non-members. The terms FTAs, PTAs and
RTAs are, therefore, used interchangeably throughout the study.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1545
Baltic Republic. The only region which has, so far, remained firmly committed
to the MFN approach to liberalisation is East Asia (Panagariya, 1999).15
The proliferation of PTAs has spurred several concerns and numerous studies
evaluating binary choices and the effects of PTAs on the trade and welfare
of non-members of the bloc, level of multilateral free trade and welfare, and
the enforcement of WTO agreements. An issue that remains centre stage is
whether PTAs supplement or supplant the multilateral trading system and, thus,
have the ‘trade creation’ and ‘building block’ or ‘trade diversion’ and ‘stumbling
block’ effects.16
A number of studies have supported the ‘building block’
effects (Baldwin, 1993, 1995; Ethier, 1998; Laird, 1999; Clausing, 2001; Glick
and Rose, 2002; Lee et al., 2008). Krugman (1991, 1993) envisions that the
PTAs among the ‘natural’ trading partners are likely to generate positive
effects, as the gains from trade creation would outweigh the losses from trade
diversion.17
The process of coalition formation and PTA configuration could
lead to two possible outcomes: First, as per the Vinerian static analysis, the
expansion of existing PTAs would raise intra-bloc, but reduce inter-bloc trade,
and also trade with non-members. The emergence of several PTAs would lead
to inter-bloc trade discrimination and tariff barriers and, thus, would cripple
multilateral cooperation and impede global free trade. Second, as per the postVinerian
dynamic analysis, higher intra-bloc trade would lead to higher income
and growth of the member countries, which, in turn, would lead to expansion
of market size and creation of trade and investment opportunities for nonmember
countries. These growth externalities and spillover effects would transform
PTAs into the ‘building blocks’ of global free trade (Baldwin, 1993,
1995; Laird, 1999; Lee, 2008).
The domino theory of Baldwin (1993, 1995, 1997) postulates that increased
integration among a subset of countries would provide incentives for outsiders
to seek accession, thereby expanding the trading blocs and fostering trade liberalisation.
Baldwin (1997) asserts that the ‘idiosyncratic incidents of regionalism
triggered a multiplier effect that knocked down bilateral import barriers like a
15
Panagariya (1999) provides a description of the three key concepts used by the academic and
policy literature: Preferential Trade Area (PTA), Free Trade Area (FTA) and the Customs Union
(CU). A PTA is a union between two or more countries in which goods produced within the union
are subject to lower trade barriers than the goods produced outside the union. An FTA is a PTA in
which member countries do not impose any trade barriers on the goods produced within the union,
but do so on those produced outside the union. A CU is an FTA in which member countries apply a
common external tariff (CET) on a good imported from outside countries. The CET can differ
across goods, but not across union partners. In policy documents and debates, the acronym FTA is
often used to refer to a Free Trade Agreement or Free Trade Arrangement, rather than Free Trade
Area (Panagariya, 1999). For a review of literature on the preferential trade liberalisation, see
Panagariya (2000).
16
Bhagwati (1991) coins the ‘building block’ and ‘stumbling block’ effects of PTAs.
17
The countries that trade substantially and disproportionately more with each other are termed as
the ‘natural’ trading partners.
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1546 T. SINGH
row of dominos’ (Baldwin, 1997, p. 877). He asserts that almost all empirical
studies of the European and North American arrangements find the positive
impacts of PTA’s on the living standards of members. Egger et al. (2008)
postulate that the likelihood of new RTA membership is influenced by economic
fundamentals, such as the country size, factor endowments, and trade
and investment costs. They find strong effects of endogenous PTAs on
intra-industry trade in a sample of country-pairs covering mainly the OECD
countries. They argue that the PTA-induced increase in trade volumes can be
mainly attributed to an associated growth in intra-industry trade, at least in
developed economies. Ethier (1998) suggests that the new regionalism is a
direct result of the success of multilateral liberalisation, as well as being the
means by which new countries trying to enter the multilateral system compete
among themselves for direct investment. By internalising an important externality,
regionalism plays an important role in expanding and preserving liberal
trading order (Ethier, 1998). Laird (1999) argues that the quantitative estimates
tend to show that trade creation effects outweigh trade diversion effects and
hence are overall welfare-enhancing. Clausing (2001) finds that the Canada–
United States FTA had substantial trade creation effects, with little evidence of
trade diversion. Glick and Rose (2002) examine the effects of currency union
on trade, using a comprehensive panel dataset of over 200 countries (1948–97).
They find that a pair of countries which joined (left) a currency union experienced
near-doubling (halving) of bilateral trade. Lee et al. (2008) use a panel
dataset of 175 countries (1948–99) and examine the effects of regional trading
blocs on global trade. They conclude that on average, they increase global trade
by raising intra-bloc trade, without damaging the extra-bloc trade.
While several studies support the ‘building block’ effects, a parallel counterstrand
raises concerns and postulates ‘stumbling block’ effects (Bhagwati,
1991; Panagariya, 1996; Bhagwati et al., 1998; Romalis, 2007). These arrangements
not only reduce the incentives for MTL, but could also lead to retaliatory
and periodic tariff wars. While an infinite extension of a trading bloc in
that it would encompass the globe and map multilateral free trade seems a theoretical
possibility and is unlikely to materialise, the finite extensions are as
likely to strengthen the retaliatory power of a trading bloc against non-bloc
members and lead to higher bloc-lateral trade barriers, as could induce the
removal of bilateral trade barriers and incite the liberalisation of trade as envisioned
in the domino theory. Krugman (1991) asserts that the enlargement of
trading blocs leads to more retaliatory power for each, which, in a non-cooperative
environment, could lead to higher inter-bloc trade barriers; the welfare
reaches the minimum in a world with two or three CUs. Bhagwati et al. (1998)
assert that the formation of PTAs multiplies by imitation; one PTA leads to
another and so on and so forth, and eventually Gresham’s Law takes over.
Panagariya (1996) argues that when trade liberalisation is discriminatory, as in
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1547
FTA, within the Vinerian framework, the effects of freer trade have a strong mercantilist
bias: a country gains from the liberalisation by the partner, but loses
from its own liberalisation.18
Romalis (2007) finds that the North American
Free Trade Agreement (NAFTA) had a substantial impact on international trade
volumes, but a modest effect on prices and welfare. The NAFTA increased
North American output and prices in many highly protected sectors by driving
out imports from non-member countries.
4. METHODOLOGICAL AND MEASUREMENT ISSUES
The above discussion suggests the evidence is not unambiguous. Several
factors, such as differences in sample periods and countries, estimators and
econometric methodologies employed, frequency of data used, measures of
trade openness employed, model specifications estimated, and test statistics
used for testing the null have a bearing on this. This section explores methodological
and measurement issues that surround the trade–growth empirics.
a. Solow and Stochastic Residuals: A Generated Regressand: The findings
of the studies examining the productivity effects of trade are contingent on the
quality of data on productivity, commonly represented by TFP and is computed
as the residual difference between output and weighted contribution of factor
inputs to output or as the ratio of output to (weighted) factor inputs as,
TFPðtÞ ¼ AðtÞ ¼
YðtÞ
KðtÞa
LðtÞb
" #
; t 2 ½1; :::; TŠ: ð1Þ
A major problem in computing TFP in (1) is that the aggregate data on the
incomes accruing to capital, K(t), and labour, L(t), are commonly beset with
measurement problems in developing economies with large informal sectors.
Consistent time-series data are, in fact, not available in many cases. In the
absence of actual data on factor income shares, a and b, a commonly used
alternative is to use a regression analogue of growth accounting, and estimate
the Cobb–Douglas production technology with unity restriction, a + b = 1,
imposed on its parameters,
18
Panagariya (1996) argues that a country benefits from receiving a preferential (or discriminatory)
access to the partner’s market and is hurt by giving the partner a similar access to its own market.
When the country gives access to the partner on a preferential basis, it loses the tariff revenue collected
on the imports from the partner. The revenue goes to boost the terms of trade of the latter.
The reverse happens when the country receives a preferential access from the partner. On balance,
the country which liberalises the most, is likely to lose (Panagariya, 1996).
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1548 T. SINGH
ln YðtÞ ¼ ln AðtÞ þ a ln KðtÞ þ b ln LðtÞ þ eðtÞ: ð2Þ
In (2), e(t) = l(t) + m(t) with cov½lðtÞ; mðtފ ¼ 0: The estimated parameters, a
and b, from (2) are used to mimic the income shares of capital and labour in
output, and derive the TFP in levels as,
ln TFPðtÞ ¼ ½ln AðtÞ þ eðtފ ¼ ln YðtÞ À ^a ln KðtÞ À ^b ln LðtÞ: ð3Þ
Some of the limitations of computing TFP based on production technology are as
follows. First, since the intercept term in (3) is constant, productivity is virtually
represented by the stochastic residual term of the production technology. All the
estimation problems (such as omitted variables bias, functional-form
misspecification bias, sample-selection bias and simultaneity bias) and the errorsin-variables
bias (such as measurement of capital stock and total employment in
the economy) get reflected in the residuals, e(t), and, thus, embodied in the TFP.
Second, the TFP derived using parameter estimates obtained from the production
technology is apparently a ‘generated’ series with the possibility of
itself containing the standard errors. The use of TFP as a regressand or regressor
in the model is likely to compound the standard errors of the estimated
parameters, and provide a misleading statistical inference. It is likely to commit
a Type I (Type II) error and erroneously overstate (understate) the relationship
between trade and TFP.
Third, the regression or TFP residuals in (3) represent a linear combination
of (normally) non-stationary series of factor inputs, K(t) and L(t), and the output
Y(t). If the TFP residuals are non-stationary and I(d), then it implies a lack
of cointegrating and equilibrium relationship between factor inputs and output
and, thus, ‘spurious estimates’ of the production technology parameters used to
proxy the factor income shares and estimate the TFP. In contrast, if the TFP
residuals are stationary and I(0), then it is difficult to regress TFP on its possibly
I(d) determinants such as trade. A first-difference transformation of the I(d)
determinants of TFP (such as trade) to make them synchronous with I(0) TFP
would result in a loss of long-run information. The short-run model, based on
I(0) variables, loses its relevance for the long-run analysis of economic growth.
Fourth, the OLS parameters, ^a and ^b, are by definition constants and thus,
do not account for possible yearly (quarterly) variations that may occur in the
actual income shares of capital and labour. Besides, the OLS estimates of a
and b and implied TFP are likely to be biased and inconsistent, given simultaneity
between inputs and unobserved productivity.19
19
Van Biesebroeck (2007) asserts that estimating a production function by ordinary least squares
(OLS) is generally not advisable. He argues that the GMM system estimator provides the most
robust productivity level and growth estimates. The disadvantages of OLS are, however, less acute
for productivity growth than for productivity levels. For a review and discussion on the econometric
estimators to estimate productivity, see Van Biesebroeck (2007).
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1549
b. Estimates of GDP and Measures of Trade Openness: The estimates of
GDP in developing countries with a large unorganised sector are beset with
numerous measurement problems (Heston, 1994).20
The differences in the magnitudes
of the informal sector, definitions and coverage of items, and the timings
of revisions commonly characterise the cross-country dispersion in GDP.
Such differentials weaken the conclusions drawn from cross-country growth
models that use a heterogeneous mix of GDP estimates. As regards the measures
of trade openness, the most commonly used measures based on trade volumes
and trade ratios are not perfect proxies for trade policy and access to
international markets. The developed countries with higher trade volumes generally
have smaller trade shares, and the trade ratios in these countries may not
differ discernibly from those in the developing countries with lower trade vol-
umes.21
Measures of openness based on trade policy have been used relatively
sparsely, possibly due to the lack of consistent time-series and cross-section
data. Leamer (1988) argues that in the absence of direct measures of trade barriers,
it is impossible to determine the degree of openness for most countries,
as does Edwards (1997, 1998). Temple (1999) argues that the measures which
are most defensible on theoretical grounds, such as the effective rates of protection,
can be difficult to calculate for a sufficiently large number of countries.
Typically, researchers fall back on simple proxies, such as trade shares in GDP
or a black market exchange rate premium, meant to give some indication of
openness (Temple, 1999).
A major problem concerns the aggregation of tariff and non-tariff barriers
into a single trade policy index (Harrison, 1996; Edwards, 1998; Dollar and
Kraay, 2003). Rodriguez and Rodrik (2000) argue that the simple tradeweighted
tariff averages or non-tariff coverage ratios – which I believe to be
the most direct indicators of trade restrictions – are misleading as indicators of
the stance of trade policy. Dollar and Kraay (2003) argue that the most immediate
candidates of trade policy (average tariff rates and non-tariff barrier coverage
ratios) have obvious drawbacks, and it is not possible to construct very
convincing measures of overall trade policy. Rose (2004b) asserts that all tariff
measures are affected by the well-known fact that tariff revenues divided by
total imports is a downward-biased measure of tariff rates, since highly taxed
imports tend not to be imported. The coverage of non-tariff barriers (NTBs) in
terms of total imports is another measure of trade policy. It is, however, widely
recognised that the presence of NTBs is a potentially poor substitute for the
importance or intensity of the NTBs; hence this measure of trade policy is cer-
20
Heston (1994) argues that the data on per capita GDP are prone to fewer errors as compared to
the data on aggregate GDP, as some of the errors in the estimates of GDP are counterbalanced by
the errors in the estimates of population.
21
Harrison and Revenga (1995) suggest that the large countries generally have smaller trade
shares, and no independent measure of openness is free of methodological problems.
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1550 T. SINGH
tainly measured with error (Rose, 2004b).22
Different measures reflect different
aspects of trade policy and have differential effects on growth. Pritchett (1996)
argues that different measures could be uncorrelated or weakly correlated
among themselves and, thus, different dimensions of trade policy would have
different effects on growth.23
Historically, many tariffs were raised for revenue
purposes and were not necessarily directly protective, though even these revenue
tariffs could have some general equilibrium impact on the economy
(O’Rourke, 2000).
Another commonly used measure has been the deviations of actual from predicted
trade flows (Balassa, 1985; Leamer, 1988; Syrquin and Chenery, 1989;
Edwards, 1992, 1998; Wacziarg, 2001). This, however, basically represents the
stochastic residual term of the model estimated for trade flows, and is likely to
contain omitted variables model misspecification bias. It not only measures
restrictiveness, but also encompasses the unobserved effects of all the factors
omitted from the model for trade flows. Some studies use a black market premium
on foreign exchange to surrogate the efficiency of price system and capture
the effects of economic and policy distortions on growth (Lee, 1993; Barro
and Sala-i-Martin, 1995; Sachs and Warner, 1995; Rodriguez and Rodrik,
2000; Wacziarg, 2001; Vamvakidis, 2002; Lee et al., 2004). Such a premium
reflects the controlled market for foreign exchange and measures the expectations
for the depreciation of exchange rate (Fischer, 1993). The black market
premium, however, captures only a narrow (foreign exchange) dimension of
the macroeconomic and policy distortions, as compared to the distortions
inflicted by tariff and non-tariff barriers to trade.
It is also difficult to disentangle the effects of trade policies from those of
other macroeconomic policies and unequivocally interpret the observed correlations
between trade policies and economic growth. IMF-supported structural
adjustment programmes commonly begin with a devaluation, which results in
high, albeit exceptional, performance of domestic exports. These programmes
are closely followed by stabilisation programmes and the adoption of several
macroeconomic measures to sustain the effects and avoid policy reversals. The
improvements in productivity and growth that are commonly ascribed to trade
liberalisation, in fact, arise from the conglomerate effects of comprehensive
and wide-ranging economic reforms undertaken in almost all the real and
22
Rodriguez and Rodrik (2000) argue that no papers that document the existence of serious biases
in these direct indicators, much less establish that an alternative indicator performs better (in the
relevant sense of calibrating the restrictiveness of trade regimes).
23
Irwin (2002) argues that high tariff measures are imperfect indicators of trade-policy orientation
and may not always reflect protectionist policies. Winters (2004) suggests that the fiscal consequences
of tariff revenue losses are far from inevitable, especially if non-tariff barriers are converted
into tariffs (exemptions are reduced and collections improved); but they can pose a problem
for poorer countries in which trade taxes account for large proportions of total revenue.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1551
financial sectors of the economy; trade liberalisation is only one segment in the
whole spectrum of reforms. Rodrik (1995) argues that the trade-regime indicators
are measured very badly, and trade openness in the sense of a lack of trade
restrictions is often confused with the macroeconomic aspects of policy
regimes.
c. Model Specification, Estimators and Endogeneity: The problem of endogeneity
and non-orthogonality of regressors remains unresolved in the timeseries
models and least addressed in the cross-section and panel data models
(Trefler, 1993; Rodrik, 1995; Harrison, 1996; Edwards, 1993, 1998; Frankel
and Romer, 1999; Temple, 1999; Pritchett, 2000; Wacziarg, 2001; Irwin and
Tervio¨, 2002; Lee et al., 2004; Winters, 2004; Felbermayr, 2005; Frankel and
Rose, 2005; Alvarez and Lo´pez, 2008). The openness to trade and trade policies
may not be determined exogenously, and these may themselves be a function
of productivity and growth. Exports and imports vary with the level of
production and are determined jointly within the system. The endogeneity
between trade (exports) and productivity is reinforced more conspicuously in
the microeconomic self-selection models, which suggest that the firms with
exogenously determined high levels of productivity self-select themselves into
the export market. The trade policy-based measures of openness, such as tariffs,
could be affected by growth and are not completely immune from simultaneity.
In a low growth and recessionary phase, tariff rates may be raised to export
domestic recession and revive economic growth. Since most studies perform
partial equilibrium analysis and use single-equation models, the problem of
endogeneity remains a serious concern for the conclusions drawn from these
studies. The non-orthogonality of regressors to the residual process makes the
least squares estimates biased and inconsistent. Trefler (1993) shows that the
US import flows are 10 times higher than previously estimated, partly because
the previous studies have ignored the simultaneity of trade policy and import
flows.
A commonly used measure to alleviate reverse-causation and possible sources
of endogeneity has been to use some of the lagged regressors as instruments and
estimate the model using an instrumental variables (IV) estimator. The efficiency
of an IV estimator, however, hinges heavily on the quality and validity of
instruments. Besides, when several regressors in a model are instrumented, then
validity requirements for instruments (used for endogenous regressors) become
even more stringent (Staiger and Stock, 1997). It is difficult to find appropriate
instruments that are strongly correlated with endogenous regressors, but uncorrelated
with Gaussian disturbances. Temple (1999) argues that some studies use
initial values, such as regressing growth over 1960–85 on the 1960 secondary
school enrolment rate, to avoid simultaneity. This is not quite as watertight as the
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1552 T. SINGH
researchers seem to think: even if the endogeneity problem is solved, perhaps
some omitted variables affect both growth and initial level of the variables like
schooling.
The microeconomic firm-level models commonly use matching and semiparametric
estimators, such as the estimator developed by Olley and Pakes
(1996), to alleviate endogeneity (Pavcnik, 2002; Bernard and Jensen, 2004a;
Schor, 2004; Van Biesebroeck, 2005; Fernandes, 2007). These account for
unobserved plant heterogeneity by using observable firm characteristics, and
are robust to endogeneity and simultaneity-bias arising from endogenous input
and exit choices.24
Greenaway and Kneller (2007a), however, argue that
matching attempts to reduce heterogeneity have the disadvantage of removing
observations from the dataset and requiring specific assumptions about the nonobservable
factors, such as managerial ability. These estimators, by design,
have remained restricted to only the microeconomic models estimating productivity
in the manufacturing sector, and have not been extended to the economywide
macroeconomic models. Temple (1991) argues that the micro studies
often miss the economy-wide resource allocation effects that may be central to
understanding the effects of trade policy.
Model misspecification bias impinges on the robustness of results and yields
misleading statistical inference. The commonly used bivariate models testing
‘export-led growth’ versus ‘growth-led export’ hypotheses do not control for
the effects of various other conditioning factors on growth. The studies testing
‘export-led growth’ hypotheses commonly provide a solo focus on the role of
exports and erroneously ignore the role of imports (and other factors such as
investment). The goods embody technical know-how, and the imports play a
catalytic role in the transfer and diffusion of foreign technology. An incomplete
analysis aside, this leads not only to omitted variables model misspecification
bias and serial correlation problems, but also to the endogeneity of
trade, as the unobserved factors and several unknowns that affect economic
growth could also contemporaneously affect the orientation to trade. The use
of simultaneous-equation models has recently been uncommon, albeit virtually
abandoned, possibly due to the identification and estimational problems associated
with a large system. The commonly used single-equation models (which
assume all regressors as exogenous) need to be appropriately specified and
instrumented, so as to draw statistically robust and economically meaningful
interpretations.
24
Pavcnik (2002) argues that the selection-bias induced by plant closings and the simultaneity-bias
induced by plant dynamics significantly affect the magnitude of the capital coefficient in the
production function, and that Olley and Pakes’s semi-parametric methodology provides a useful
alternative to the techniques used in previous studies.
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INTERNATIONAL TRADE AND ECONOMIC GROWTH 1553
Most studies do not perform model misspecification tests and examine robustness
to the inclusion of additional regressors. Levine and Renelt (1992) use a variant
of extreme bounds analysis from Leamer (1988) and conduct a sensitivity
analysis. The statistical significance of a majority of regressors is shown to be
sensitive to model specification, and it disappears if the set of regressors is
altered; they find that almost all results are fragile. They suggest that it is important
to provide a formal sensitivity test by systematically varying the right-hand
variables to ensure the robustness of results to variations in the model specification.
Harrison (1996) finds that when three macro variables are included in the
model, the statistical significance of openness measures disappears in half
the cases. Rodriguez and Rodrik (2000) and Irwin and Tervio¨ (2002) examine
the sensitivity of the Frankel and Romer (1999) results. The effect of trade disappears
with the inclusion of geographical latitude (distance from equator) in the
model (Rodriguez and Rodrik, 2000), and the sensitivity of results holds for
different sets of historical data (Irwin and Tervio¨, 2002).
5. CONCLUSIONS
This study has surveyed the literature on the relationship between international
trade and economic growth, and reviewed the role of GATT⁄WTO in
fostering free trade. Most studies support the gains of trade and recognise the
substantive contributions of GATT⁄WTO in fostering free trade; the evidence
is, however, not ubiquitously unambiguous. The macroeconomic evidence provides
dominant support for the positive and significant effects of trade on output
and growth, while the microeconomic evidence lends larger support to the exogenous
effects of productivity on trade, as compared to the effects of trade on
productivity. The studies supporting the self-selection hypothesis overwhelm the
studies supporting the learning-by-exporting hypothesis. The rounds of the
GATT⁄WTO trade negotiations could not vanquish the support for trade barriers
and passion for protectionism. The globally Pareto-efficient multilateral free
trade system of GATT⁄WTO remains surrounded by locally-efficient preferential
(free and regional) trade agreements (areas). The strength of the argument
for the gains of trade needs to be evaluated in juxtaposition with several methodological
and measurement issues surrounding the trade–growth empirics, such
as the estimation of TFP, measurement of trade openness, quality of data, frequency
of data, possible structural breaks and regime-switches in the model series,
construction of trade policy indices, specification of an econometric model,
endogeneity of trade, netting of exports and imports from GDP, disentanglement
of the effects of trade policy from those of other macroeconomic policies, and
the decomposition of the effects of trade into short-run transitory and long-run
permanent components. The econometric methodology that is central to the
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1554 T. SINGH
empirical evidence has itself been evolving and so have been the estimates and
statistical evidence on the relationship between trade and growth.
The most commonly used measures of trade openness based on trade volumes
are highly endogenous, while those based on trade policy are characterised
by several measurement problems and have been used relatively sparsely.
The different measures represent different aspects of trade openness, and have
differential effects on growth. Most studies focus on the partial equilibrium
analysis of trade policy, and ignore the general equilibrium aspects of macroeconomic
policy. It is difficult to disentangle the effects of trade policies from
those of other macroeconomic policies and unequivocally interpret the observed
correlations between trade policies and economic growth. The productivity
improvements that are commonly ascribed to trade liberalisation in the developing
countries, in fact, arise from the conglomerate effects of comprehensive
and wide-ranging economic reforms undertaken in almost all the real and financial
sectors of the economy. Trade is one of the several catalysts of productivity
and growth and hence its contribution is contingent on its weight in the
aggregate economic activity. The hitherto unresolved methodological and measurement
issues characterise a challenging agenda for future research. Future
research needs to resolve these issues and unambiguously determine and crystallise
the strength and robustness of the argument for the gains of trade.
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