Trade Liberalisation 16-05-08

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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.


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Trade Liberalisation and Economic Growth

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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).


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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 countrys

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 countrys 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


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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


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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


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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 : ECONOM TER I C METHODOLOGY AND DA TA

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 ++= 3,20 (1)

where git is the growth rate in country i in time period t, the coefficient i

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


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the 5-year period following liberalisations. The coefficient therefore measures

whether the growth rate was significantly different to that before liberalisation. If

liberalisation adds significantly to growth we would expect 0 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 thedate of liberalisation. The construction of this variable is different from the more commonly usedSachs 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 thedata.


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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 +++++= *3,213,20211 (2)

As before, i 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


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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 Xvariables 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 : I N I T I A L EV I D ENCE

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


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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 afterliberalisation

5-10 years afterliberalisation

Mean change in growth 0.87 2.38Standard deviation 3.63 3.88

Minimum -5.84 -3.67Maximum 12.80 18.93No. of countries growth fell 20 13No. 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


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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

ChangeinGD

Ppccapitagrowth Guinea-Bissau

Gambia

Bangladesh

Tanzania

Cambodia

Paraguay

Ethiopia

Mozambique

Guyana

Nicaragua

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. Tradeliberalisation 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


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(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

Guine

a-Biss

au

Gambia

Cape

Verde

Kenya

NewZe

aland

Brazil

UruguayNe

pal

Turke

y

Uganda

Niger

Zambia

Tanzania

Madagascar

Honduras

Venezuela

Maurita

nia Mali

Egypt

South

Afric

a

Jamaica

Philip

pines

Tunisia

Albania

Paraguay

Israel

Benin

SriL

anka

Ecuador

Banglad

esh

Dominic

anRepublic

Mexic

o

coefficient insigificant

estimated effectacross all countries

confidence interval

Note: The figure plots the point estimates of the estimated change in growth from the 5-year periodpre-liberation to the 5-year period following trade liberalisation using a rolling regression of 15countries.

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


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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. Theestimated effect of trade liberalisation using the specification of regression 1 but estimated over thesame sample usedto produce regression 2 is 1.5 percentage points per annum.


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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 Baseregression

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 andWarner (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.


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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 countrys 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


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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


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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 inImports

Change inExports

PrimaryExporter

Imports ofR&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 0.80Note: Trade liberalisation measured using the Wacziarg and Welch (2003) update of Sachs andWarner (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-


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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 Secdaryschool

Tertiaryschool

Naturalbarriers

(landlock)

Naturalbarriers

(latitude)

PoliticalRights

EthnicFractionali

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 problemsassociated with cross-country data on human capital. The suggestion from that paper is that the Barroand Lee (2000) dataset provides fewer errors compared to other commonly used data, at least for theOECD countries. We recognise these problems and assume that these concerns are also minimised byusing the Barro and Lee data for less developed countries. We leave testing the robustness of ourresults to alternative data for a future exercise.7 Estimations were also attempted with the initial level of GDP per capita. These regressions weredropped in favour of human capital owing to the high correlation between these measures. A j-testcould not establish which model we should prefer.


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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 andWarner (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


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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 civilliberties from the same source.


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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).


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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 0.81Note: Trade liberalisation measured using the Wacziarg and Welch (2003) update of Sachs andWarner (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.


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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.


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Table A1

Country Pre-liberalisationgrowth

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 -2.70Cape 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 0.00Madagascar -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 1.58Cambodia 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 3.92Panama -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 9.16Guyana -6.77 5.37 11.60


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Nicaragua -6.55 4.37 18.93

Average -0.51 0.87 2.38

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