International Trade and
Development
International Trade and Development
Lecture Outline
(1) What do we include in a Growth model?
(2) Evidence of the relationship between increased
trade (globalisation) and growth – relate to evidence of
poverty/inequality and development.
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(1) What do we include in a Growth model?
Based on the work of Levine and Renelt (1992, AER,
82(4)).
Prior to this paper, empirical papers were not
controlling for appropriate factors.
Levine and Renelt found that many explanatory
variables were not robust to changes to the
inclusion/exclusion of other variables – some even
changed sign!!!
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(1) What do we include in a Growth model?
Following a survey of empirical work L&R decide upon 4
explanatory variables,
GDP  f (INV , GDP1960, SEC, GPOP)
where INV=investment share of GDP; GDP1960 is initial level
of GDP per capita in 1960 (when data begins from); SEC is
the initial secondary-school enrollment rate; GPOP is the
average annual growth rate of the population growth.
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(1)
What do we include in a Growth model?
(Q)
Why these variables?
Expect higher initial GDP level in 1960 to have a negative effect on real GDP/capita
growth – testing the hypothesis that a poor country, ceteris paribus, tends to grow
faster than a rich country.
GDP
Growth of initially high GDP
country
Growth of initially low GDP country
Year
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(1) What do we include in a Growth model?
Expect higher initial level of human capital to positively effect
real GDP/capita growth – consistent with Solow growth model
and more importantly the endogenous growth models.
Increase in INV/GDP expected to positively effect real
GDP/capita growth – tests the Solow model and any other
economic growth model. Investment is a key driver of growth.
Those countries with high rates of population growth have lower
growth rates – finding is not robust to all models.
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(2)
The Empirical Debate: Trade and Development
We will discuss a number of papers on, the relationship between trade and
GDP, on the effect of trade ‘openness’ on GDP and finally the effect of trade
on poverty and inequality.
Look at Greenaway et al (2002), Irwin and Tervio (2000) and Dollar and
Kraay (2004).
First 2 papers focus on the impact of international trade on growth/income
of countries using samples of countries over a number of years.
Dollar and Kraay (2004) focuses on link between trade and poverty/income
inequality.
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The key concern though when estimating the link
between trade and growth is the issue of endogeneity
which prevents a correct estimate of the impact trade
has on growth – I.E.
“THE POSITIVE CORRELATION BETWEEN
TRADE AND INCOME COULD MEAN THAT
COUNTRIES WITH HIGHER INCOMES
ENGAGE IN MORE TRADE RATHER THAN
COUNTRIES WITH MORE TRADE HAVING
MORE INCOME” (Taken from Irwin and Tervio,
2000).
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Greenaway et al (2001)
Focus on the issue of trade liberalisation and its impact on GDP
growth – so not looking at impact of trade liberalisation on
poverty or income distribution or development.
Reason for the paper is the inconclusive evidence from previous
work – argue that reason for debate is (i) inappropriate
methodology and (ii) how trade liberalisation is measured.
Previous research can be criticised for not using panel techniques
that control for (i) country-specific effects, (ii) time-effects and
(iii) an unobserved error effect that varies across both countries
and years (time) which is assumed to be uncorrelated.
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Greenaway et al (2001) cont…
The Core growth model is based on the empirical work of
Levine and Renalt (1992) and theoretical developments by
Romer (1990).
Robust variables included in most core growth models are:
(i) investment, (ii) population growth, (iii) initial human capital,
(iv) initial GDP per capita.
Greenaway et al also add to this list of explanatory variables, (v)
terms of trade variable and (vi) trade liberalisation proxies.
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Greenaway et al (2001) cont…
The base specification is,
 ln yi,t  1 ln yi,65  2SCHi,65  3 ln TTIi,t  4 ln POPi,t  5 ( INV / GDP)i,t  6LIBi,t  i,t
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Greenaway et al (2001) cont…
Problem with this specification is that it is inappropriate if
looking for dynamic effects. Include lagged changes in real
GDP/capita growth as well.
Expect an improvement in terms of trade to positively effect real
GDP/capita growth.
Importantly for the paper, is testing the hypothesis of whether
trade liberalisation effects real GDP/capita growth in any way.
If effect +ve then supports the pro-trade liberalisation group.
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Greenaway et al (2001) cont…
Issue of how to proxy trade liberalisation, with the suspicion that different
proxies can have different effects.
(1) Use a before and after Structural Adjustment Loan (SAL) variable
(simple binary variable) – as this loan at least ‘signal intent’ of trade
liberalisation.
(2) Timing of liberalisation is measured using actual information on levels
and changes in tariffs, quotas, exchange rate misalignment and export
impediments/promoters. Through this can identify the year when
liberalisation has taken place. Is modelled by a binary variable as is the SAL –
based on Dean et al (1994).
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Greenaway et al (2001) cont…
(3) A measure of ‘closed-ness’ and ‘openness’ in trade
in terms of average tariff level. If average tariff<39%
then open, if above 39% then closed. Very arbitrary
but even Sachs and Warner (1995) who come up with
the measurement recognise this – Does just give
another measure of openness/close-ness though.
Results…….
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Greenaway et al (2001) cont…Using Sachs and Warner:
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Greenaway et al (2001) cont…using Dean et al
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Greenaway et al (2001) cont…Using SAL
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Greenaway et al (2001) cont…
Finding is that when the simple dummy trade liberalisation terms are included
that in post-liberalisation period growth in real GDP/capita is greater than in
the pre-liberalisation period.
Implication is that trade liberalisation (all 3 definitions used) is a good thing
for growth but the effect is not that great.
However, the simple dummy variable approach represents an average effect
over a number of years. By switching on the liberalisation proxy for the year
liberalisation is introduced only and then turning it off again we get a direct
growth impact estimate in the first year only, with lags picking up the impact
of reforms in subsequent years.
There are however indications of first and second order serial correlation!!!
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The second-order serial correlation indicates a mis-specified model.
Standard way to get around this issue is to include instruments in the model
which here takes the form of lagged changes in real GDP/capita – from
Columns 3 in the above tables we see 2nd order serial correlation is resolved –
not significant
This is an example of the instrumental variables (IV) approach
Column 3 represents the best model in the paper. The signs on the
explanatory variables are all expected and most remain significant.
There is an element of sensitivity regarding which trade liberalisation measure
is used as to its impact on growth in real GDP/capita.
However, the evidence does give a more consistent picture that trade
liberalisation has a positive impact on growth which may take time to work
through (the lagged trade liberalisation terms) into a growth impact.
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Irwin and Tervio (2002)
Flag the endogeneity issue of trade and GDP and the use of an
instrument that is expected to effect trade but not GDP.
The instrument is relatively new to the literature and was first
used by Frankel and Romer (1999).
They construct an instrument that takes into account the
geographical location of a country and in particular the
country’s distance from trading partners.
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Irwin and Tervio (2002) cont…
They go through the process of constructing the instrument itself by OLS
regression.
Actual trade of country ‘i’ with country ‘j’ as a share of GDP of country ‘i’ is
regressed onto a number of factors that includes distance between country ‘i’
and country ‘j’.
This regression will give predicted values for each country ‘i’ trade with
country ‘j’/GDP ratio.
Explanatory factors include populations of the countries, area of the two
countries, whether landlocked or not.
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Irwin and Tervio (2002)…cont
For each bilateral trade between country ‘i’ and country ‘j’  i, j
can be calculated. These are then
summed to yield a predicted value of country i’s trade share with all sample countries j – this is the
instrument.
From this prediction we construct the instrument necessary to solve the endogeneity problem in the
growth equation.
If the growth model uses (actual trade/GDP) then the coefficient in the growth equation on this term
will be biased upwards.
Reasons are things like richer countries being able to afford better infrastructure that means more trade
is physically possible (e.g. ports, airports, road structure, railway structure).
Technically it means that there is a positive correlation between the error term in the growth equation
and the trade term.
Need to use IV estimation to get over this issue – this is where the constructed instrument comes into
play!!
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Irwin and Tervio (2002)…cont
Instead of the growth equation being estimated through OLS, they use a two stage least squares
(2SLS) approach.
The T hat term represents the predicted trade of country ‘i’, that is a function of geographical
distance between trading partners.
In the first stage of this model, trade/GDP is regressed onto the instrument and a couple of
other variables, i.e.
Ti  c0  c1(Tˆi )  c2 log( areai )  c3 log( popi )  i
The predicted values of trade from this first stage are calculated for each observation, represented
~ . This essentially represents a constructed trade share which is determined in large part by
by T
i trade estimate.
the bilateral
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Irwin and Tervio (2002)…cont
The final growth equation to be estimated is represented by,
~
log( GDPi / popi )  d0  d1(Ti )  d2 log( areai )  d3 log( popi )  wi
where the trade term is constructed from our regressions and does NOT represent
actual trade/GDP – we’ve determined what causes trade using a variable that cannot
theoretically effect GDP.
Compare the coefficient on the trade term with that for the coefficient in the OLS
growth estimation represented by,
log( GDPi / popi )  b0  b1(Ti )  b2 log( areai )  b3 log( popi )  ui
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Irwin and Tervio (2002) – regression used to calculate the
instrument
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Irwin and Tervio (2002) – Growth models, OLS and 2SLS using IV
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Irwin and Tervio (2002)…cont
Conclusions are that the effect of trade on income/capita of a country is larger using
2SLS than OLS (the coefficients on the trade variable are larger) – although frequently
less significant.
Conclusion is that trade does positively effect growth even taking into account the
endogeneity issue.
Criticism of this type of model follows from Rodrik and Rodriguez (2000) with the
model NOT robust to including distance from equator – when Irwin and Tervio
include this variable in the GDP equation the trade term becomes everywhere
insignificant with the coefficient frequently negative!
(Q) Is there an economic explanation for why latitude can be included in growth equations?
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Dollar and Kraay (2004) paper on Trade and Development, Poverty
and Inequality
Initially estimate a difference model based on the standard growth equation:
yct   0  1 yc,t  k   2' X ct  c   t  ct
where LHS is log-level of per capita GDP in country c at time t. Could use
log-level GDP, but then would clearly not be taking into account population
size.
yc,t k
is log-level of per capita GDP k years ago (the lag of growth
in previous period).
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The matrix ‘X’ is a set of control variables measured in
averages. Volume of trade (exports+imports as a % of
GDP) is included in ‘X’.
The 3 error terms are: (i) an unobserved country effect
that does not change over time, (ii) an unobserved error
term that changes over time but which is common to
all countries (e.g. oil prices), (iii) an unobserved error
effect that varies across both countries and years (time)
which is assumed to be uncorrelated.
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Most studies do not concentrate on levels of GDP
but instead on changes in GDP over periods of time.
This represents a regression of growth in GDP onto
lagged growth in GDP and on changes in the set of
explanatory variables in the X matrix and is
represented formally by,
yct  yc,t  k  1( yc,t  k  yc,t  2k )   2' ( X ct  X c,t  k )  ( t   t  k )  (ct  c,t  k )
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The differenced growth equation has a number of desirable
characteristics:
(1) whilst levels of trade (volume of trade) reflect more the
geography of a country, changes in trade volume reflect
something else since the geography of a country does not
change.
(2) institutional characteristics of a country also do not change
considerably over time (e.g. racial composition, historical legacy
of colonialism etc…)
(3) The difference growth model allows “appropriate lags of
the right hand side variables as instruments” (Dollar and Kraay,
2004, f38).
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The identifying assumption is that while trade volumes may be
correlated with lagged shocks to GDP growth, trade volumes
are not correlated with future shocks to GDP growth.
“In practice, this means that when we regress growth in the
1990s on(to) (i) growth in the 1980s and (ii) the change in trade
volumes (changes in trade as % of GDP) between the 1980s and
1990s, we can use the level of trade volumes in the 1970s as an
instrument for trade openness”, (Dollar and Kraay, 2004, f39),
brackets and italics added.
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Table from Dollar and Kraay (2004) – Standard Errors in Brackets
Initial Income
Changes in Trade
Volume
OLS
IV
IV
IV
0.419
0.783
0.765
0.96
(0.071)***
(0.297)***
(0.367)**
(0.397)**
0.252
0.475
0.514
0.543
(0.095)***
(0.175)***
(0.187)***
(0.210)***
Contract Intensive
Money
0.232
-0.41
Government
Consumption/G
DP
-1.164
(-1.009)
log(1+Inflation Rate)
-0.142
(-0.152)
Revolutions
-0.025
(-0.084)
F-Statistic for FirstStage Regressions
Lagged Growth
Openness
12.46
8.09
8.56
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Interpretation of Table:
The point estimate on Trade volume in the OLS reads that a
100% increase in trade share of GDP would raise GDP in the
country by 25% over a decade.
When controlled for instruments on trade volume (See the
significant F-Statistics for Lagged Growth and Openness), the
size of the trade volume coefficient increases to near 0.5!!!
Conclusion: Greater involvement in trade is related to faster
growth in developing nations.
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Cautionary Comment:
Researchers want to test whether lowering trade barriers to international trade significantly
increases growth, once other factors have been controlled for – implications if evidence
supports this hypothesis is that governments should “dismantle their barriers to trade”
(Rodrik and Rodriguez, 2000, pp. 3).
Remember Rodrik and Rodriguez (2000) argue that applied pieces of work are using
‘inappropriate indicators of trade policy’ to systematically bias quantitative results in favour
of finding a statistically significant relationship between trade and growth.
Dollar and Kraay (2004) use change in the volume of trade as a regressor in the change in
growth model and identify the model by assuming that level of trade has no correlation with
change in GDP in the future.
However is this an appropriate identifier? Can this assumption be made?
Remember, GDP and trade are clearly determined by one another and there is question of
finding a variable(s) that determines change in trade but not a change in GDP.
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References
Levine, R., and Renelt, D., (1992), “A Sensitivity Analysis of Cross-Country Growth Regressions”
The American Economic Review, Vol. 82, No. 4 (Sep., 1992), pp. 942-963
Dollar, D., and Kraay, A., (2004), “Trade, Growth and Poverty”, Economic Journal, Vol 114, pp.
f22-f49.
Frankel, J., and Romer, D., “Does trade cause growth?”, American Economic Review, Vol 89, pp.
379-399.
Greenaway, D., Morgan, W., and Wright, P., (2002), “Trade liberalisation and growth in developing
countries”, Journal of Development Economics, Vol 67, pp. 229-244.
Irwin, D.A., and Tervio, M., (2002), “Does trade raise income? Evidence from the twentieth
century”, Journal of International Economics, Vol 58, pp. 1-18.
Rodriguez, F., and Rodrik, D., (2000), “Trade policy and economic growth: a skeptic’s guide to the
cross-national evidence”, Working Paper (available upon request).
Winters, A., (2004), “Trade liberalization and economic performance: An overview”, Economic
Journal, 114: F4-F21.
See Dani Rodrik’s website at http://ksghome.harvard.edu/~drodrik/