international trade and development. lecture outline (1)what do we include in a growth model?...

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.



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!!!



Following a survey of empirical work L&R decide upon 4 explanatory variables,

where INV=investment share of GDP; 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.

),,,( 1960 GPOPSECGDPINVfGDP

1960GDP



(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.

Year

GDP Growth of initially high GDP country

Growth of initially low GDP country



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.


(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.


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).


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.


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.



The base specification is,

titiLIBtiGDPINVtiPOPtiTTIiSCHiytiy ,,6,)/(5,ln4,ln365,265,ln1,ln



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.



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).



(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…….


Greenaway et al (2001) cont…Using Sachs and Warner:


Greenaway et al (2001) cont…using Dean et al


Greenaway et al (2001) cont…Using SAL



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!!!


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.


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.


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.


Irwin and Tervio (2002)…cont

For each bilateral trade between country ‘i’ and country ‘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!!

ji,



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.

The predicted values of trade from this first stage are calculated for each observation, represented by . This essentially represents a constructed trade share which is determined in large part by the bilateral trade estimate.

iipopciareaciTcciT )log(3)log(2)ˆ(10

iT~



The final growth equation to be estimated is represented by,

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,

iwipopdiareadiTddipopiGDP )log(3)log(2)~(10)/log(

iuipopbiareabiTbbipopiGDP )log(3)log(2)(10)/log(


Irwin and Tervio (2002) – regression used to calculate the instrument


Irwin and Tervio (2002) – Growth models, OLS and 2SLS using IV



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?


Dollar and Kraay (2004) paper on Trade and Development, Poverty and Inequality

Initially estimate a difference model based on the standard growth equation:

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.

is log-level of per capita GDP k years ago (the lag of growth in previous period).

cttcctXktcycty '2,10

ktcy ,


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.


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,

),()(),('2)2,,(1, ktcctkttktcXctXktcyktcyktcycty


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).


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.

International Trade and Development Table from Dollar and Kraay (2004) – Standard Errors in Brackets OLS IV IV IV

Initial Income 0.419 0.783 0.765 0.96

(0.071)*** (0.297)*** (0.367)** (0.397)**

Changes in Trade Volume

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/GDP

-1.164

(-1.009)

log(1+Inflation Rate)

-0.142

(-0.152)

Revolutions -0.025

(-0.084)

F-Statistic for First-Stage Regressions

Lagged Growth

Openness

12.46

17.49

8.09

16.17

8.56

10.62

Observations 187 187 153 173


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.


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.

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/

http://ksghome.harvard.edu/~drodrik/

international trade and development. lecture outline (1)what do we include in a growth model?...

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