Frankel & Romer (1999)

Question 1

What was Frankel & Romer (1999) about?

What are the findings of the paper?

Answer 1

• Examining the correlation between trade and income cannot identify the direction of causation between the two.
• Countries’ geographic characteristics, however, have important effects on trade, and are plausibly uncorrelated with other determinants of income.
• This paper therefore constructs measures of the geographic component of countries’ trade and uses those measures to obtain instrumental variables estimates of the effect of trade on income. The results provide no evidence that ordinary least-squares estimates overstate the effects of trade. Further, they suggest that trade has a quantitatively large and robust, though only moderately statistically significant, positive effect on income.

Question 2

What was the OLS strategy?

What are the limitations of OLS?

Answer 2

OLS: regression of log income per person on constant, trade share, and two size measures. regression shows statistically economically significant relationship between trade/income. point estimate implies that increase in the share of one percentage point is associated with increase of 0.9 percent in income per person.

The simple OLS construct by regressing income on trade share of GDP cannot test for gains of trade. This is because trade share may be endogenous. – Countries with high income for other reasons may trade more.

Question 3

Why trade policy could not be used as an instrument?

Answer 3

Using trade policy as an instrument does not solve the endogeneity problem, as endogeneity persists. (OVB: government fiscal and monetary policy stability, freer domestic markets, etc.)

Question 4

Why is IV better than OLS estimation

Answer 4

First, countries that adopt free- trade likely to adopt other policies raise income. Second, countries that wealthy other reasons trade likely have better infrastructure & transportation. Third, countries that poor other reasons low trade lack institutions and resources to tax domestic activity, thus rely on tariffs finance G spending. Fourth, increases in income coming from sources other than trade may increase the variety of goods that households demand and shift the composition of their demand away from basic commodities toward more processed, lighter weight goods.

=> lead to positive correlation between trade and the error term in an OLS regression, and thus to upward bias in the OLS estimate of trade’s effects.

Question 5

What was the instrument?

Are the identifying assumptions satisfied?

Answer 5

Use geographic characteristics to form a gravity equation to instrument for trade openness.

IV Identifying assumptions:

1. Random assignment : countries’ geographic characteristics are not affected by their incomes, or by government policies and other factors that influence income.
2. Relevance: geography must have a strong effect on trade openness for a country. (As we will see, this is manifested from both international (distance) and domestic trade (area))
3. Exclusion Restriction: The only channel through which geographical characteristics affect trade should be exclusively through trade & income [this is not mentioned in lecture or reading]

Note that IV regression coefficient turned out to be larger than OLS coefficient., and the authors blamed sampling error

Question 6

Criticize Frankel & Romer (1999)

Answer 6

• 1. Robustness: The estimates are somewhat sensitive to changes in sample and specification of estimation equation. (e.g. IV estimates are no longer significant if we include continent dummy variables as controls)
• 2. Omitted Variable Bias: There are time-invariant country characteristics that are correlated with both geography and income per capita.