radeT and Openness - Brown 2840/Trade_and_Openness.pdfradeT and Openness Econ 2840. ... Instrument...

Trade and Openness

Econ 2840

Background

Economists have been thinking about free trade for a longtime. This is the oldest policy issue in the �eld.

Simple correlations:

Richer countries have higher trade/GDP

maybe that is just because of demand or industry structure,rather than e�ect of trade policy

Sachs and Warner (1995) and Wacziarg and Welch (2008)construct measures of trade openness (i.e. policy) based onmultiple dimensions: tari�s, manipulation of the exchangerate, government monopolies on exports, etc.in cross-section, more open countries are richerputting openness in a growth regression, it has a positivecoe�cientAlso, episodes of trade liberalization are accompanied by fastergrowth, on average

Wacziarg and Welch: growth 1.5% higher followingliberalization

That Doesn't Prove Causality!

Omitted Variable Bias: maybe good institutions lead to bothfree trade and higher income

looking at changes in trade openness does not �x this. Maybesame institutional change that led to more trade led to bettereconomic outcomes for other reasons.

We need instruments, natural experiments, etc.

Frankel and Romer �Does Trade Cause Growth?� (1999)

Instrument for trade with �predicted trade share� from agravity equaitonGravity Model:

old empirical speci�cation for trade that �ts the data well,even without a great theoretical foundationanalogy to gravitational attraction

For creating their instrument, they use data on bilateral tradefor 63 countries (instrument then applied to more countriesthan that.dep. var. is log of this (see next slide)trade share is total bilateral trade (imports plus exports)between countries i and j, divided by GDP in country i

regress this on distance, population in each country, area ofeach country, dummy for landlocked, and a �common border�dummy interacted with all of the aboveEstimates reasonable: distance lowers trade; common borderraises it; bigger own population lowers trade share; biggerother population raises it; bigger area lowers trade.

The Bilateral Trade Equation

Zero Trade Shares

One issue in F&R approach: in their regression they take thelog of trade share, so they have to drop zeros.

Helpman, Melitz, and Rubenstein (2008): this is not right.Zero trade is not missing � it is no trade!

No trade is in fact common � see next slide.

The right thing to do is estimate seperate equations forextensive margin (engage in trade or not) and intensive margin(volume of trade, conditional on trading).

E�ect of distance etc. on these margins may be di�erent.

I don't know if this matters much for F&R paper.

Helpman, Melitz, and Rubinstein (2008)

Predicted Trade Shares

Use estimates from Table 1 to predict bilateral trade shares inlarger sample of countries (150)

Sum up within a country to get its own predicted trade share

predicted trade share not a function of own income,institutions, etc.

Figure 1 and Table 2 show that predicted trade share doespredict actual trade share

Actual versus Constructed Trade Share

The Relation between Actual and Constructed Overall Trade

More Investigation of Constructed Trade Share

F-stat on constructed trade share in column (3) above is 13.1

Figure on next page shows the scatter of actual and predictedtrade shares conditional on population and area

big outliers are Singapore and Luxembourg

Dropping these (second panel) lowers the F-stat to 10.1

Partial Association between Actual and Constructed TradeShare

The Big Result

Findings

Trade raises income.

IV is bigger than OLS (see next slide).

Why controlling for area and population? Do they a�ect GDPthrough other channels? Maybe because popualtion isendogenous? Might be interesting to think about.

OLS vs. IV

Why is IV bigger?

If you thought that trade openness biased OLS up, then IVshould be smallerof course, measurement error biases the other wayIn any case, it is good to investigate

Their approach: regress trade share on instrument. Then take�tted values and residuals from that.

For each of these, partial out other stu� from RHS orregression and plot result against income

�rst panel gives scatter underlying IV coe�cient; second panelgives the part of OLS that is not in IV

Partial Associations Between Income and the Trade Share

Di�erence Between IV and OLS

They conclude: no smoking gun

Maybe the problem is indeed measurement error: trade volumeis a bad measure of total interactions among countries. Leavesout exchange of ideas, travel, etc.

IV cleans up meausrement error and so raises coe�cient.

Of course, if that is right, then then trade was not the wholestory.

What are the Channels?

A priori, seems like trade should a�ect productivity. But couldalso a�ect growth/income through capital accumulation, etc.

Use methodology of development accounting

Aggregate Production: Yi = Kαi (AihiLi )

1−α

Human Capital Aggregate: hi = exp(φ(si )) where s is schooling

Rewrite the production function this way (why: because K/Y isconstant in a steady state):

Yi = (Ki/Yi )α/(1−α)eφ(si )AiLi

divide by L and take logs:

ln(Yi/Ni ) =α

1−α ln(Ki/Yi ) + φ(si ) + ln(Ai )

Regress each of these terms on trade share. Coe�cients haveto add up to coe�cient from Table 3! (neat trick � see Wong,2007, for more of this).

Trade and the Components of Income

result: biggest part of the e�ect is through productivity (A) �but weak sign�cance

Rodriguez and Rodrik (NBER Macro Annual 2000)

Critique of F&R

what we should care about is �trade policy,� not �trade volume�

F&R instrument is di�culty in engaging in trade. This is likean �indiscriminate� trade policy

actual trade policy is targetted at e.g. market failures (infantindustry protection)

This policy might be good for growth even if indiscriminatepolicy was bad.

Of course, real world trade policy could also be worse thanindiscriminate policy

for example if trade restriction resulted from rent seeking; thenwe get not only the lost gains from trade, but also the directcosts of rent seeking

Second critique is regarding instrument: It could be spuriouslycorrelated with other geographic features that are bad foroutput (see table next slide).

Frankel-Romer Regressions with Additional GeographicVariables

Feyrer's Papers: F&R in the Time Dimension

Problem with F&R is that geography is so correlated withhistory, tropics, etc. that it is hard to sort out causality

Ideal natural experiment is to look at changes in tradingdistance or costs between countries

Two natural experiments:

switch from sea freight toward air freight in post WWII period

some country pairs become much closer (Germany-Japan);others no change (US-UK)

closing and re-opening of the Suez Canan (1967 and 1975).

similarly some pair distances change a lot (Kenya-UK) whileothers don't

Both cases, �nd that trade a�ects income

This could be looked at within countries as well � probablymore cool experiments out there.