Trade Liberalisation and Economic
Growth
C.W. Morgan and S. Kanchanahatakij
Abstract
There has been a long held belief that there is an association of economic growth
with increased levels of international trade. However more recent work, such as
Rodriguez and Rodrik (1999) has questioned this hypothesis and the re-opening
of the debate has identified two key areas of contention. One is the extent to
which the effects of openness are conditional on factors omitted from the core
regression relationship and hence how the hypothesis is tested. The other is the
meaning and measurement of openness and liberalisation. This paper addresses
both these areas by exploring the nature of heterogeneity in growth performance
among liberalising countries using a difference in difference approach. The results
show that while in aggregate there appears to be a positive but small impact of
trade liberalisation on growth this masks a huge range of responses. Empirical
analysis of this heterogeneity shows that a one-size-fits-all policy is not
necessarily the most effective and suggests a case-by-case approach is more
appropriate.
Key Words: trade liberalisation, economic growth, heterogeneity
WYN MORGAN is Associate Professor and Research Fellow in CREDIT and SUNTI
KANCHANAHATAKIJ is a postgraduate student in the School of Economics,
University of Nottingham, UK. The authors are grateful for the helpful comments
of an anonymous referee but all remaining errors are the authors’ own.
Trade Liberalisation and Economic Growth
1
1 INTRODUCTION
There has been a long-held belief that there is an association between economic
growth and increased trade. Subsequently the benefits of an economy becoming
‘open’ have been promoted in both academic and policy making circles (see for
example Krueger, 1997). However, the views over how to measure openness and
the degree to which a country alters its degree of openness (via liberalisation of
trade policies) are less concordant. In addition, recent scepticism has arisen over
the validity, or at least the generality, of the hypothesis that links openness to
growth.
Focussing first on the hypothesis itself, evidence has been raised at the empirical
and theoretical level that questions whether the relationship between openness
and growth is necessarily always positive. At the very least, the debate is not
settled. Theoretical models predicting a positive association (River-Batiz and
Romer, 1991; Grossman and Helpman, 1991 and Devereux and Lapham, 1994))
can be contrasted with those yielding the opposite (Redding, 2002). Similar
contradictions exist empirically. While many papers such as Edwards (1998),
Wacziarg (2001) and Greenaway et al (2002) estimate a positive relationship,
others find the opposite even when using similar measures of openness (Rodrik
and Rodriguez, 2000; Clemens and Williamson, 2002; Vamvakidis, 2002).
The sensitivity of the growth outcomes from greater openness has led some to
suggest that effects are conditional on some other factor omitted from the
regression model. For trade liberalisation a large set of variables have been put
forward to explain the proposed heterogeneity, including education, existing
levels of development, the strength of domestic institutions, macroeconomic
stability and measures to tackle corruption (Winters, 2004).
Trade Liberalisation and Economic Growth
2
Alternative explanations of the sensitivity of empirical findings have concentrated
on issues surrounding the measurement of openness. Concerns have been raised
over the ability of some measures to capture particular aspects of a country’s
trade policy (Edwards, 1998; Rodriguez and Rodrik, 1999), as well as the
suitability of a single measure of openness/trade liberalisation to proxy something
as complex and multi-faceted as a country’s trade regime (Edwards, 1998;
Greenaway et al., 2002).
In this paper we test for the influence of both the measurement of trade
liberalisation as well as conditionality in contributing to growth heterogeneity
amongst a sample of 37 liberalising countries. Despite the fact that in aggregate
there appears to be a positive relationship between liberalisation and growth, this
masks significant departures for individual countries. The question we ask is: can
this heterogeneity be explained? If it cannot, then as Pritchett (2000) and
Bhagwati and Srinivasan (2002) suggest case study evidence may offer greater
returns than further cross-country analysis.
To investigate measurement we recognise that openness is multidimensional and
therefore unlikely to be adequately captured by single measures (Edwards,
1998). We therefore combine information about the timing of trade liberalisation
with additional trade policy variables as well as volume measures of openness.
For conditionality we explore the effects of human capital as well as indicators of
natural barriers and institutional quality.
In summary, therefore, the current paper will offer new evidence to enrich the
debate around trade policy and its effects on growth and does so not only through
the hypotheses it tests but also via the estimation techniques employed. The rest
of the paper is structured as follows. Section Two offers a discussion of the
literature to motivate our approach. Section Three discusses our econometric
method while the data set analysed and extent of heterogeneity in our data is
Trade Liberalisation and Economic Growth
3
provided in Section Four. Further evidence is outlined in Section Five which
explains heterogeneity in growth performance in the sample. Section Six offers a
summary and conclusions.
2: LITERATURE REVIEW
Focussing on liberalisation and economic growth, Rodriguez and Rodrik (1999) re-
opened the debate in both developed and developing country settings. Put
simply, they argued that the case for a positive relationship between the two had
been too strongly stated and the relationship was not robust. The root of the
analysis can be traced to a number of papers beginning in the 1950s and 1960s
with the switch from inward oriented (IO) to outward oriented (OO) strategies to
promote growth (Krueger, 1978).
Once this had been accepted as a clear policy preference for many countries, the
debate moved on to look at the nature of an OO strategy and in particular the
relationship between exports and growth (see inter alia Greenaway and Sapsford
(1994) and Greenaway et al (1997) for summaries of these works). In essence,
the literature suggests export growth and economic growth are positively
correlated. Those economies that are more open are more likely to have a better
economic performance than those that are closed.
As the body of empirical work grew, more attention was focussed on the
measurement of variables and the estimation techniques employed. A key
element is the extent to which a country needs to trade before it can benefit in
the manner suggested. Establishing this in an economy creates problems for the
empirical researcher in itself as there are many potential measures that can be
Trade Liberalisation and Economic Growth
4
used. However, Edwards (1998) believes there is a robust relationship regardless
of the openness measure employed. 1
If this is accepted, and Rodriguez and Rodrik (1999) provide arguments that cast
doubt on it, the next stage is to establish exactly when an economy moves from
being closed to being open. As with measuring openness, picking up liberalisation
of policy is not straightforward and many measures exist. Greenaway et al.
(2002) employ three different measures of liberalisation within a dynamic panel
data framework and their results suggest liberalisation positively affects growth of
real GDP per capita but only modestly and with a lag.
Perhaps unsurprisingly this has led some to suggest that the effect liberalisation
is conditional on other factors omitted from the regression. A large set of
variables have been put forward to explain heterogeneity including education, the
existing levels of development, the strength of domestic institutions,
macroeconomic stability and measures to tackle corruption (Winters, 2004).
A second area of contention within the literature has been choice of estimation
technique. Several approaches have been taken although we focus only on the
principal two, namely cross-country studies and time-series analysis. The cross-
country literature has two strands, namely the ‘with-without’ and ‘before-after’
strands. The former relies on identification of the effects of trade liberalisation
using between country variation in the data examples being World Bank (1990)
and Mosley et al (1991). By contrast, the ‘before-after’ approach relies on within
country variation before and after a reform episode. This approach, which we
adopt, has a long history and was originally adopted by Papergergiou et al (1991)
in a work that achieved much comment (e.g. Greenaway, 1993). Alternatively,
1 Others have argued that a robust relationship can not even be demonstrated for the same measure. Clemens and Williamson (2002) and Vamvakidis (2002) for example, find that the estimated
Trade Liberalisation and Economic Growth
5
World Bank (1990) and Mosley et al (1991) both compare the countries in a
‘with-without’ framework for a period before and a period after a specific date.
Finally, there is some suggestion that this approach is likely to produce more
robust findings. Wacziarg and Welch (2003) find that the effect of the timing of
trade liberalisation on growth is significant and positive when looking at cross-
time changes to growth, but is not robust when comparing its effect between
countries.
The time-series literature searches for evidence of a change in behaviour over
time of a country that attempts trade policy reform. Indeed, Greenaway et al
(1997) test for evidence of a smooth transition process after a liberalisation
episode as opposed to a more discrete structural break and Baldwin and Forslid
(1999) explore how liberalisation can be viewed as a process taking several
years. Clearly, though such analysis is highly dependent on a long run of data and
since many countries are only recent liberalisers (post 1990) data are not often
available.
3: ECONOMTERIC METHODOLOGY AND DATA
Motivated by the argument in Wacziarg and Welch (2002) that the between-
country approach yields results that are sensitive, we focus on changes in within-
country growth associated with trade liberalisation. To estimate the cross-time
effects of liberalisation we estimate a regression of the following form:
itoiit Dg εγα ++= 3,20 (1)
where git is the growth rate in country i in time period t, the coefficient αοi
captures fixed time effects in country i, and the D is a time dummy equal to one
when the country starts to liberalise. Three time periods are used: the 5-year
period before liberalisation, the 5-year period in which liberalisation occurred and
relationship between tariff rates and growth may vary across time, switching from positive to
Trade Liberalisation and Economic Growth
6
the 5-year period following liberalisations. The γ coefficient therefore measures
whether the growth rate was significantly different to that before liberalisation. If
liberalisation adds significantly to growth we would expect γ0 to be positive and
significant.
This is equivalent to a first-difference regression in growth or a difference-in-
difference regression in the level of income. It is similar to that used by Easterly
(2001) and Kneller (2006). Specifications of this type are also common in the
evaluation literature (see Blundell and Costa Dias (2000) for a review).
The methodology pre-empts a particular type of measure of trade liberalisation,
the date at which liberalisation took place. As discussed already we consider the
effect of additions to this measure below. The liberalisation dates we use are
drawn from the Sachs and Warner (1995) index (from here S-W) updated by
Wacziarg and Welch (2002).2 Wacziarg and Wallack (2004) cross-check the
dating of liberalisation in S-W with case-study evidence on major trade policy
changes in developing countries and find a close agreement.3 It would appear
that therefore this indicator is robust to concerns raised by Harrison and Hanson
(1999) and Rodriguez and Rodrik (1999).
According to the S-W data 37 countries switch from being closed to open from
1970 onwards. To control for the fact that liberalisation is often not conducted in
a single year but spread across adjacent years we average the data across 5-year
periods.4 In addition we are interested in measuring the medium-term growth
effects of liberalisation rather than the short-term adjustments; the evidence
negative. 2 Of the two measures available within Sachs and Warner (1995) we use here that which refers to the date of liberalisation. The construction of this variable is different from the more commonly used Sachs and Warner index of openness to international trade and details can be found in that paper. 3 Further evidence on this point can be found in Wacziarg & Welch (2002). 4 These concerns are in addition to those usually given in growth studies for period averaging of the data.
Trade Liberalisation and Economic Growth
7
from Greenaway et al (2002) suggests such J-curve effects are completed after 5
years. The specific year of trade liberalisation is therefore condensed into one of
the periods 1970-74, 1975-79, 1980-84, 1985-89, 1990-94, 1995-98. A similar
approach is adopted by Wacziarg (2001) and Kneller (2006), while in addition
Winters (2004) argues in favour of searching for medium-term rather than long-
term growth effects from trade liberalisation.
We can then use the base framework to extend the specification to test whether
any heterogeneity can be explained or not using the following regression.
ititititoiit XDDSHOCKgg εγγββα +++++= − *3,213,20211 (2)
As before, αοi captures fixed time effects in country i, and D is a time dummy
equal to one when the country starts to liberalise. Heterogeneity is captured by
the interaction between this and X (which we describe below). This variable
captures whether cross-country differences in the change in post-liberalisation
growth differs with X. In this sense it adds back some element of the between
country variation in growth rates compared to Wacziarg and Welch (2002) and
Kneller (2006).
We are also careful to control for other sources of variation in growth over time.
Easterly, Kremer, Pritchett and Summers (1993) and Rodrik (1999) find that the
wide dispersion in growth rates and their low persistence across time can be
explained by external shocks. The correlation between the average rate of GDP
growth for the 1960s with that of the 1970s is below 10 per cent, and rises to
just over 20 per cent for the 1970s and 1980s. In contrast, the correlation for
determinants of growth such as investment over the same time periods remains
between 80 and 90 per cent. This high correlation is similar for many other
Trade Liberalisation and Economic Growth
8
determinants of growth. It is possible then, that external shocks mask the effects
of policy reform on growth.
To control for the effect of shocks we include a measure of the change in the
terms of trade multiplied by the openness of the country (exports + imports
/GDP) (which is labelled as SHOCK in (2)) and the effect of adjustment to past
shocks through the inclusion of lagged GDP growth. A similar measure is used by
Rodrik (1998). With this measure the same external shock will be expected to
affect more strongly countries that are more exposed to world markets.
Several X variables are explored. These are grouped according to whether they
account for measurement or conditionality issues, although there may be some
overlap in this. To control for measurement issues we use the ratio of trade taxes
in GDP and measures of the volume of trade (export plus imports) in GDP, (taken
from the World Bank). To capture conditionality we use measures of human
capital from Barro and Lee (2000); for natural barriers we control for whether the
country is landlocked and its latitude (World Bank data); and to proxy
institutional quality we use a measure of political rights (from the Fraser
Institute) and an index of ethnic linguistic fractionalisation (Easterly and Levine,
1997). Motivations for their use are given in Section 5.
4: INITIAL EVIDENCE
In Table 1 we report summary statistics on the effect of liberalisation of changes
in the rate of growth. Even from this it is clear that the effect while positive at the
mean, differs considerably across countries. Comparing the 5-year period after
liberalisation with the 5 years before shows that the average increase in the rate
of growth was 0.87 of a percentage point per annum. However the range of
outcomes is large: in the period following liberalisation the change in growth rates
Trade Liberalisation and Economic Growth
9
differ by up to 18.64 percentage points (the difference between the maximum
and minimum values). The size of this range is not a function of particular
countries in the sample either; growth increased in 28 countries relative to the
pre-liberalisation period and fell in the other 20. It quickly becomes clear why
the estimated effects of trade in previous studies are sensitive to the selection of
countries included within the sample.
The growth outcomes are more positive over the longer term - a comparison of
growth rates 5-10 years after liberalisation with those in the 5-year period
leading up to liberalisation, shows that the post-liberalisation increase in growth
was 2.4 percentage points per annum. Again there are some countries for which
growth fell however, although at 13 this is a smaller number than before. Of
these 10 had also experienced declining growth rates in the earlier period. Even
in this longer time horizon there is still a wide dispersion of growth rates, ranging
from –1.4 per cent per annum in Guinea-Bissau to 12.4 per cent in Nicaragua.
Indeed, in 20 countries the level of GDP per capita 10 years after liberalisation
was lower than that in the pre-liberalisation period (it rose in the other 26).
Table 1: Summary Statistics
0-5 years after liberalisation
5-10 years after liberalisation
Mean change in growth 0.87 2.38 Standard deviation 3.63 3.88
Minimum -5.84 -3.67 Maximum 12.80 18.93
No. of countries growth fell 20 13 No. of countries growth rose 28 35
Another way to demonstrate the heterogeneous outcomes of trade liberalisation
is to plot the change in growth over time (in this case measured relative to the
period before trade liberalisation). This is done in Figure 1. Rather than present
all 48 countries we rank them according to the change in growth in the final
Trade Liberalisation and Economic Growth
10
period and then choose the three countries that saw the biggest changes in
growth, both negative (Guinea-Bissau, Gambia and Bangladesh) and positive
(Mozambique, Guyana and Nicaragua), as well as the countries ranked 12th
(Tanzania), 23rd/24th (Cambodia, Paraguay) and 36th (Ethiopia) fastest. As can
been seen from Figure 1 the dispersion across countries is large and passes
through zero, although for the median country the effect is positive. Even taking
the 25th and 75th percentiles the effect of trade liberalisation on growth is clearly
not positive for all countries.
Figure 1: Change in Growth over Time of Selected Countries
-10
-5
0
5
10
15
20
25
1 2 3
Time Period
Cha
nge
in G
DPp
c ca
pita
gro
wth Guinea-Bissau
GambiaBangladeshTanzaniaCambodiaParaguayEthiopiaMozambiqueGuyanaNicaragua
Note: The time periods are: 1 = 5-year period leading up to trade liberalisation; 2 = 5-year period in which trade liberalisation occurred; 3 = period 5-10 years after trade liberalisation. Trade liberalisation measured using the Wacziarg and Welch (2003) update of Sachs and Warner (1992).
In Figure 2 we explore further the sensitivity to the choice of countries included in
the sample. This figure plots the estimated effect on growth in the 5-year period
after liberalisation using a rolling regression of 15 countries as ordered in the
same way as Figure 1 (i.e. from the slowest to the fastest). That is the first point
estimate gives the change in growth countries ordered 1 to 15, the second the
point estimate for countries ranked 2 to 16 on so on. Just as Levine and Renelt
Trade Liberalisation and Economic Growth
11
(1992) demonstrate the sensitivity of the growth effects of openness to changes
in the set of conditioning variables, we demonstrate a similar outcome from small
changes in the observations. Within Figure 2 we also plot the point estimate for a
regression across all 48 countries (reported as regression 1 in Table 2 below) and
take two standard errors either side of this to provide a confidence interval. The
ordering of countries, along with the change in growth relative to the pre-
liberalisation period can be found in Table A1 of the Appendix.
Figure 2: Plot of Estimated Growth of Trade Liberalisers
-2
-1
0
1
2
3
4
5
6
Guinea
-Biss
au
Gambia
Cape V
erdeKen
ya
New Zea
land
Brazil
Urugua
yNep
al
Turkey
Ugand
aNige
r
Zambia
Tanza
nia
Madag
asca
r
Hondu
ras
Venez
uela
Maurita
nia MaliEgy
pt
South
Africa
Jamaic
a
Philipp
ines
Tunisia
Albania
Paragu
ayIsr
aelBen
in
Sri Lan
ka
Ecuad
or
Bangla
desh
Domini
can R
epub
lic
Mexico
coefficient insigificant
estimated effect across all countries
confidence interval
Note: The figure plots the point estimates of the estimated change in growth from the 5-year period pre-liberation to the 5-year period following trade liberalisation using a rolling regression of 15 countries.
According to the results from this exercise the effect of trade liberalisation on
growth can be negative, zero or positive depending on the countries selected and
can range substantially outside the average effect estimated across all countries
of 1.6 percentage points. The growth effect for the first fifteen (slowest growing)
countries in the sample is -1.5 percentage points and the last fifteen (fastest
growing) 5.3 percentage points. As the figure also makes clear this result does
not appear to be driven by the inclusion of any particular country. The line is
Trade Liberalisation and Economic Growth
12
relatively smooth as countries are added to and subtracted from the sample used
for estimation.
Figure 2 also illustrates the range of countries for which the post liberalisation
change in growth is insignificantly different from zero. This holds for some 10
regressions, suggesting there are 25 countries which if included together produce
insignificant growth effects. This ranges from New Zealand to Madagascar in
Table A1. Either way, it is possible to see why the effect of trade policy
liberalisation on growth has been found to vary across time. Even in a period in
which it is positive at the average it is not positive for all countries.
Finally, in regression 2 of Table 2 we demonstrate that these results are not due
to some omitted factor such as external shocks and persistence. The results, from
regression 1 show the mean effect of trade liberalisation was to raise the average
rate of growth by 1.6 percentage points per annum over a 10-year period.
Comparing this to regression 2 shows however that omitting external shocks,
rather than biasing downward the estimated effect of trade liberalisation,
operates in the opposite direction. Now the estimated effect of trade liberalisation
is to raise growth by an estimated 1.2 percentage points per annum.5
The other covariates included in the regression have the expected relationship
with GDP per capita growth. Positive shocks to the terms of trade are associated
with increases in growth, while the low persistence of growth across short time
horizons is evident from the coefficient on the lagged growth term. For external
shocks the estimated effect of a one standard deviation decrease from the mean
will reduce growth by 0.5 percentage points. This is smaller than the standard
deviation of growth, which is 2.4, suggesting that while they go some way to
5 The reduction in sample size between the two regression has little bearing on this finding. The estimated effect of trade liberalisation using the specification of regression 1 but estimated over the same sample used to produce regression 2 is 1.5 percentage points per annum.
Trade Liberalisation and Economic Growth
13
improving our ability to explain variations in growth (the adjusted R2 increases
from 0.34 to 0.78) there is much left to explain.
Table 2: Base Regression Results
Regression No. 1 2 3
X-variable Base regression
Extended base Trade taxes
Liberalisation 1.600 1.190 2.747
Effect (3.34)* (2.52)* (2.70)*
Liberalisation* 2.888 -0.432
X-variable (1.95)+ (1.72)+
External shock 4.876
(2.53)**
Lagged growth -0.214 -0.169
(13.84)** (9.21)**
Constant -0.484 -0.148 -0.228
(1.23) (0.38) (0.49)
Fixed Effects Yes Yes Yes
Obs 142 127 91
R-squared 0.34 0.78 0.81
Note: Trade liberalisation measured using the Wacziarg and Welch (2003) update of Sachs and Warner (1992). ** denotes significance at the 1% level; * denotes significance at the 5% level; + denotes significance at the 10% level.
5: EXPLAINING HETEROGENEITY IN GROWTH
The above evidence clearly suggests that growth responses to trade liberalisation
differ markedly across countries. A reasonable question is whether this
heterogeneity can be explained: is it countries with certain characteristics that did
better and others worse? Do some measures better capture this variation than
others? Or is it due to an inability of a zero-one variable to capture fully the
characteristics of a multidimensional variable such as openness to trade? Drawing
on the previous literature we examine a number of possible explanations of
heterogeneity that include measurement of trade liberalisation and conditionality.
Trade Liberalisation and Economic Growth
14
Measurement and Trade Taxes: While the difficulties of measuring the trade
orientation and policy of a country over time have long been recognised in the
empirical literature (Edwards, 1993, 1998; Greenaway et al., 1988; Winters,
2004). According to Wacziarg and Wallack (2004), the Wacziarg and Welch
(2002) update of the Sachs and Warner (1995) indicator we have used thus far
has useful information on the timing of trade liberalisation. However, it has
limitations also; for example, it does not contain information on the extent of the
trade liberalisation that took place or across which dimensions of policy (Romer
and Finklestein, 1999). In this sense one explanation for the large variation in
growth outcomes we observe in the data is the inability of a 0/1 indicator to
capture fully such differences across countries. Here we follow Edwards (1998)
and use information from other aspects of a country’s openness to trade, namely
the ratio of trade taxes to GDP and several measures of the volume of trade. We
begin with trade taxes.
Trade taxes are perhaps the most direct measure of trade policy (Rodriguez and
Rodrik, 1999) and have a relatively long history of use in empirical work. They
have not always had the expected negative relationship with growth however.
While Lee (1993), Harrison (1996) and Edwards (1998) find a negative
relationship, Rodriguez and Rodrik (1999) found the reverse, and Clemens and
Williamson (2002) and Vamvakidis (2002) found that the relationship transits
between positive and negative according to the time period under study.
We explore its effects in regression 3 (Table 2) by interacting the liberalisation
indicator with the ratio of revenues from trade taxes in GDP. Despite the drop in
the number of observations their combination does appear to have some
explanatory power and in the direction expected. Countries that have high levels
of trade taxes have lower levels of growth following liberalisation. A one standard
deviation reduction in trade tax revenues from the mean adds 1.9 percentage
Trade Liberalisation and Economic Growth
15
points to growth per annum. This would tend to reinforce the view that 0-1
indicators of trade liberalisation miss important differences in the extent to which
trade liberalisation took place across countries.
Measurement and Trade Variables: While interested in the effects of trade
liberalisation on growth we often have better measures of trade outcomes such as
the volume of trade. In principle these outcomes should reflect underlying
policies, albeit often imperfectly (Rodriguez and Rodrik, 1999). Outcome
measures also have a long history of use in the empirical literature.
The benefits typically listed when the effects of greater openness to international
trade are discussed include greater levels of competition (and therefore greater
product variety and reductions in inefficiency), economies of scale and technology
transfer. For this reason the measure of volume of openness often differs across
studies according to the central hypothesis under test. These range from Edwards
(1993) who uses the ratio of exports plus imports to GDP, to extensions of Coe
and Helpman (1995) to include increasingly refined measures of the components
of total imports most likely to embody foreign R&D by Xu and Wang (1999), Coe,
Helpman and Hoffmeister (1997) and Mayer (2001). Or on the export side it has
been argued that export led growth was important for the success of the East
Asian economies in the post war period.
We deploy four measures of trade volumes. The first are the change in imports
and change in exports (regressions 4 and 5 in Table 3). To capture technology
transfer effects we use an indicator of whether the country was an exporter of
primary products (regression 6, Table 3). It might be expected that if the
structure of trade is dominated by primary products and away from manufactured
goods the post liberalisation growth effects may be relatively small. To investigate
this we separate countries into those that imported the most foreign R&D through
Trade Liberalisation and Economic Growth
16
capital goods using information from Henry, Kneller and Milner (2003). Results
are shown in regression 7, Table 3.
Table 3: Regression Results
Regression No. 4 5 6 7 X-variable Change in
Imports Change in Exports
Primary Exporter
Imports of R&D
Liberalisation 0.448 0.964 0.673 0.036 Effect (0.81) (1.75)+ (1.08) (0.04)
Liberalisation* 0.139 0.052 1.198 2.404 X-variable (2.41)* (0.80) (1.26) (1.99)+
External shock 1.958 2.538 11.087 3.380 (1.31) (1.65)+ (1.78)+ (1.80)+
Lagged growth -0.187 -0.120 -0.236 -0.161 (13.82)** (13.35)** (13.76)** (11.49)**
Constant -0.253 -0.164 -0.162 -0.402 (0.67) (0.80) (0.42) (0.82)
Fixed Effects Yes Yes Yes Yes Obs 126 126 127 85
R-squared 0.80 0.79 0.79 0.80 Note: Trade liberalisation measured using the Wacziarg and Welch (2003) update of Sachs and Warner (1992). ** denotes significance at the 1% level; * denotes significance at the 5% level; + denotes significance at the 10% level. Overall we find much stronger evidence from the import side. Successful
countries witnessed the largest changes in imports and imported higher quality
intermediates from abroad. Countries for which the change in exports was
greatest did not necessarily grow more quickly post liberalisation. Using the
import variable, a one standard deviation increase in imports from the mean
would increase the average rate of GDP per capita growth by 1.04 percentage
points per annum (s.d. = 7.50), for high quality imports the effect of a one
standard deviation change is 1.2 percentage points (s.d. = 0.49).
Conditionality and Human Capital: A common explanation for the non-robustness
of the relationship between trade policy variables and growth is conditionality on
some third factor. Common amongst this list of omitted factors is human capital.
Support for this can be found in Miller and Upadhyay (2000). The authors interact
a measure of the stock of human capital with a measure of openness (exports-to-
Trade Liberalisation and Economic Growth
17
GDP ratio) and find that the coefficient of the interaction term is positive and
statistically significant, while those of the human capital stock and the measure of
openness are negative and positive respectively and statistically significant. From
this the authors conclude that countries must reach a critical level of openness
before human capital contributes positively to TFP. Below this level of openness,
the contribution of human capital to TFP is negative. When they divide their
sample of countries into lower, middle and high-income groups, they find that
only low income countries conform to this ‘threshold effect’.
To measure human capital we follow standard practice in using Barro and Lee
(2000) data. In regression 8 (Table 4) we include a measure of the mean years of
secondary schooling in the population aged over 25 and in regression 9, Table 4,
the mean years of tertiary level education in the population aged over 25.6 It
would appear that countries with higher levels of both secondary and tertiary
education benefited most from trade liberalisation. A one standard deviation
increase in schooling from the mean (s.d. 29.35 and 11.65) increased post-
liberalisation growth by 0.93 and 0.79 respectively.7
Table 4: Regression Results
Regression No. 8 9 10 11 12 13
X-variable Sec’dary school
Tertiary school
Natural barriers
(landlock)
Natural barriers
(latitude)
Political Rights
Ethnic Fractionali
sation Liberalisation 0.293 0.344 1.353 1.219 2.113 1.957
Effect (0.31) (0.49) (2.64)* (2.51)* (2.17)* (2.22)*
Liberalisation* 0.032 0.068 -1.153 -0.007 -0.263 -0.016
X-variable (1.75)** (0.37)+ (0.83) (0.27) (1.08) (0.97)
6 de la Fuente and Domenesch (2006) provide an excellent discussion of the measurement problems associated with cross-country data on human capital. The suggestion from that paper is that the Barro and Lee (2000) dataset provides fewer errors compared to other commonly used data, at least for the OECD countries. We recognise these problems and assume that these concerns are also minimised by using the Barro and Lee data for less developed countries. We leave testing the robustness of our results to alternative data for a future exercise. 7 Estimations were also attempted with the initial level of GDP per capita. These regressions were dropped in favour of human capital owing to the high correlation between these measures. A j-test could not establish which model we should prefer.
Trade Liberalisation and Economic Growth
18
External shock 3.591 4.234 2.751 2.894 2.763 2.983
(2.32)* (2.44)* (1.84)+ (1.94)+ (1.88)+ (1.81)+
Lagged growth -0.162 -0.206 -0.220 -0.220 -0.224 -0.214
(12.50)** (13.60)** (13.38)** (13.38)** (13.89) (13.25)**
Constant -0.121 -0.164 -0.134 -0.144 -0.130 -0.240
(0.30) (0.42) (0.35) (0.37) (0.33) (0.97)
Fixed Effects Yes Yes Yes Yes Yes Yes
Obs 118 122 127 127 127 120
R-squared 0.80 0.80 0.78 0.78 0.79 0.78
Note: Trade liberalisation measured using the Wacziarg and Welch (2003) update of Sachs and Warner (1992). ** denotes significance at the 1% level; * denotes significance at the 5% level; + denotes significance at the 10% level.
To establish which of these measure of schooling we prefer statistically we use a
J-test (Davidson and MacKinnon, 1981), where preference between these non-
nested hypotheses is established on the basis of whether the maintained model
can explain the variation of the data of the competing model (Greene, 2003).
Given the high correlation between these two variables (correlation = 0.79) it is
perhaps of no surprise that in this case we cannot establish statistical preference.
Conditionality and Natural and Institutional Barriers to Trade: Other conditional
factors discussed by Winters (2004) and others include geographic and
institutional factors. The geographic variables explored within productivity
regressions have included latitude, as well as climatic measures such as whether
a country is tropical and the level of rainfall. These variables might be best
thought of as capturing a number of different effects. Most obvious amongst
these is the direct effect of climate on public health and the quality of human
resources. It might also capture the effect of appropriate technology however, the
idea being that the technical frontier does not move out evenly across its surface
but is biased towards certain factors of production (Acemoglu and Zilibotti, 2001).
The returns to technology may therefore differ when the choice of input mix of
countries differs. To argue that technological improvements are country-specific
is a rather extreme view; instead Basu and Weil (1998) suggest that there are
Trade Liberalisation and Economic Growth
19
certain groups of countries that share the same technology. Finally, Kneller
(2005) has argued that distance summarises the effect of international
technology transfer through channels such as international trade and FDI. We use
two measures of natural barriers to trade, whether the country is landlocked and
its latitude (regression 10 and 11, Table 3, respectively). As can be seen neither
is a good predictor of which countries were successful following trade
liberalisation and which were not. In both cases the point estimate is
insignificantly different from zero.
Our measures of institutional barriers are an index of political rights taken from
Fraser Institute (regression 12) and a measure of ethnic linguistic fractionalisation
(regression 13). In both cases institutional quality is not a good predictor of which
countries performed best in the post liberalisation period, neither of the
interaction terms being statistically significant.8
Which is the preferred model?: In summary it would appear that countries that
benefited most from trade liberalisation were those that had; lower levels of trade
taxes, higher levels of human capital (measured either at secondary or tertiary
levels) and that imported most. The final question we consider is whether we can
identify which of these factors best explains heterogeneity in the effect of trade
liberalisation. Again we use a J-test to test between these non-nested models.
Testing the model with trade taxes against that with human capital (measured at
the secondary level) we find we cannot reject the null hypothesis in either case,
although this is close in the case of human capital where rejection of the
alternative hypothesis is at the 13 per cent level only.
8 Similar results are found when we replace the measure of political rights with an index of civil liberties from the same source.
Trade Liberalisation and Economic Growth
20
In contrast, when we test the human capital model versus the import model we
cannot reject the alternative hypothesis in either case. Both models appear to
capture different aspects of the variation in growth in the data. Finally, when we
compare the import model with the trade tax model we find again that we cannot
discriminate between the models. When the import model is used as the null
hypothesis we cannot reject the test statistic on the alternative as it is
insignificant at the 10 per cent level (t-stat 1.62), and when we use the trade tax
model as the null hypothesis we can reject the alternative (t-stat 1.03).
From this set of results it would appear that in general the human capital and
trade tax models pick up similar variation in the data, while the import model
identifies different variation. In regression 15 we therefore report results from a
regression which includes both the import and human capital interaction terms
and in regression 16 the trade tax and import interaction terms. When we nest
the models we find that the human capital and imports interaction terms are
significant in regression 15 whereas the common effect of trade liberalisation is
insignificant. In contrast in regression 16 the common trade liberalisation effect
only is significant, although that for trade taxes lies just outside standard
significance levels. Despite this the J-tests prevent us from concluding strongly in
favour of either model.
Both imports and the level of human capital are known to be positively correlated
with GDP per capita. As a final test of the results we therefore examined whether
similar results were achieved by replacing imports and human capital with the
level of GDP per capita in the period before trade liberalisation. The results
generated suggested that this was indeed the case. A one standard deviation
increase higher level of GDP per capita pre-liberalisation increased post-
liberalisation growth by 2.93 percentage points (s.d. = 3.51).
Trade Liberalisation and Economic Growth
21
Table 5: Trade Measures Regressions
Regression No. 15 16 X-variable
Liberalisation -1.229 2.063 Effect (1.24) (1.70)+
Liberalisation* 0.036 Secondary school (2.00)*
Liberalisation* 0.151 0.108 Imports (2.52)* (1.03)
Liberalisation* -0.407 Trade taxes (1.62)
External shock 2.448 3.592 (1.57) (1.57)
Lagged growth -0.124 -1.157 (12.39)** (9.08)**
Constant -0.230 -0.288 (0.60) (0.62)
Fixed Effects Yes Yes Obs 117 90
R-squared 0.82 0.81 Note: Trade liberalisation measured using the Wacziarg and Welch (2003) update of Sachs and Warner (1992). ** denotes significance at the 1% level; * denotes significance at the 5% level; + denotes significance at the 10% level.
6: SUMMARY AND CONCLUSIONS
The continuing debate over the impact of liberalisation on growth centres on two
key issues: first, the reliability of data generally and liberalisation indicators more
specifically and secondly the degree of conditionality in the relationship and thus
the approach taken in testing the hypothesis. This paper has attempted to
address both these issues using robust indicators of liberalisation and testing the
effect of policy change in a large sample of liberalising developing countries.
Using five year averages of the period before, during and post liberalisation we
are able to explore the effects of liberalisation by adopting a difference-in-
difference approach.
Trade Liberalisation and Economic Growth
22
A key focus for the paper has been in trying to explain the apparent
heterogeneity in the relationship between liberalisation and growth across
countries. Our results suggest a number of conclusions. First, simple 0-1
indicators miss important variation in the extent of trade liberalisation across
countries and that helps to explain the relative improvements in growth. Second,
there are other factors that also appear important for explaining heterogeneity in
the effect of liberalisation on growth rates. These are the level of human capital
and structure of trade. In particular there is some evidence to suggest that the
nature of imports is important; those liberalisers who increased imports of goods
with high R&D levels experience higher growth. Taken together these points
would tend to counsel against generalising from case study evidence.
However there would appear to be a correlation of the factors that explain the
deviation of growth performance with GDP per capita in the pre-liberalisation
period. This suggests that the countries that were successful post-liberalisation
were those that were relatively successful pre-liberalisation. This severely limits
the usefulness of the conclusions that can be drawn from such a regression
exercise and would tend to support the approach advocated by Bhagwati and
Srinivasan (2002) in which a case-by-case method is deemed to be the most
fruitful way of identifying the true impact of trade policy reform on economic
growth.
Trade Liberalisation and Economic Growth
23
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Table A1
Country Pre-liberalisation growth
Change in Growth (lib period – pre-lib
period)
Change in Growth (post-lib. period – pre-
lib period) Guinea-Bissau 5.11 -3.97 -3.67
Gambia 1.32 -2.07 -2.70 Cape Verde 3.45 -2.90 -2.06
Kenya 2.14 -3.55 -1.62 New Zealand 2.52 -2.21 -1.59
Brazil 2.63 -2.71 -1.32 Uruguay 3.31 0.17 -1.27 Nepal 2.24 0.61 -1.00 Turkey 2.35 0.17 -0.63 Uganda 3.16 -2.64 -0.53 Niger 1.19 -4.37 -0.21
Zambia -0.90 -1.74 -0.18 Tanzania 0.77 -0.91 0.00
Madagascar -0.40 -2.18 0.17 Honduras 0.86 -1.05 0.45 Venezuela -0.95 2.52 0.71 Mauritania 0.68 -0.93 1.03
Mali -2.17 1.40 1.03 Egypt. 1.90 -0.47 1.25
South Africa -1.10 -0.85 1.28 Jamaica -0.35 2.44 1.40
Philippines -1.97 1.93 1.48 Tunisia 1.37 -1.27 1.56 Albania 0.81 -5.84 1.58
Cambodia 4.41 1.74 Paraguay -1.66 2.61 1.75
Israel 1.07 0.90 1.80 Guinea 0.80 1.83 Benin 0.08 0.04 2.07
Sri Lanka 1.65 2.59 2.27 Ecuador -1.04 1.02 2.37
Bangladesh 1.07 1.81 2.78 Dominican Rep. 2.07 -1.95 2.86
Mexico -1.74 1.30 3.72 Cameroon -2.14 -4.21 3.92 Panama -3.00 7.81 4.45 Ethiopia -1.72 1.67 4.73
Argentina -2.63 7.65 4.84 Cote d'Ivoire -1.04 -1.82 4.84
Peru -2.06 4.10 4.93 Guatemala -3.67 3.37 4.98 Costa Rica -2.49 3.57 5.16
Bolivia -3.87 2.76 5.54 Ghana -4.58 6.28 5.83
Trinidad & Tobago -4.40 4.89 7.03 Mozambique -8.26 12.80 9.16
Guyana -6.77 5.37 11.60
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Nicaragua -6.55 4.37 18.93 Average -0.51 0.87 2.38