available for the last decade (48 percent of observations), we restrict our attention to the dependent variable with the most nonmissing cells, _Closure._ We substitute _GSP Fraction_ for _GSP_ and use OLS as above. Because _GSP Fractson_ is a continuous variable, our IV method is now standard two-stage least squares instead of the treatment effects approach used in the previous section. In all of these regressions we expect _GSP Fraction's_ coefficient to be positive, increasing trade barriers. All use the same set of controls as in equation (2), again lagged by one year (except _Landlocked)._ Models 7 and 8 in Table 4 show OLS and IV estimates for the regression of _Closure_ on _GSP Fraction._ The results from the previous sections stand even if we adopt a more nuanced measure of GSP benefits. The more a country's total exports benefit from (and depend on) United States GSP duty-free status, the more closed to imports it should be. Just as expected, the coefficient of _GSP Fractson_ is positive and significant in both columns. Dropping the average beneficiary from the GSP program increases its imports by 1.7 percent of its GDP in the OLS estimation. This is a more modest and conservative impact than the previous results (models 3 and 6), because GSP does not cover a country's entire export portfolio and because beneficiaries do not rely wholly on the United States alone as an export market. In other words, the impact of GSP removal would be greatest for countries like the Dominican Republic, for whom 75 percent of its 1990 global exports went to the US, with 13 percent of these qualifying for GSP duty-free treatment.

We should note that the _Irend_ variable (measuring global trade policy patterns) is no longer significant in these regressions, while all the other variables maintain the same signs as in the earlier regressions.

# 5.4 Non-Tariff Barriers

Two additional, but subjective, measures of trade barriers offer a useful comparison. Both assess the overall restrictiveness of trade policies, including non-tariff as well as tariff barriers, grouping countries into ordered categories, annually but onily for the last few years. The first is the International Monetary Fund's (IMF) "Trade Restrictiveness Index" _(TRIX,t)._ It weights NTBs more than tariffs (IMF [1998]), using public and private information at the IMF's disposal.<sup>34</sup> With 10 representing the most protectionist policy (e.g., India) and 1 the least (e.g., Hong Kong), the average for our dataset is 4.6. The second is the trade policy component (O'Driscoll, Holmes, and O'Grady [20021) of the Heritage Foundation's "Index of Economic Freedom" _(HF,,t)._ This draws purely on published sources but factors in customs corruption as well as formal tariffs and NTBs. It goes from the most open score of 1 (e.g., Hong Kong) to 5 (e.g., Bangladesh), averaging 3.8. In the fourth set of regressions, we test how our hypothesis fares with these subjective measures of trade barriers.

> 34We thank Anne McGuirk and Dustin Smith at the IMF's Trade Policy Division for providing these data.

35'These two measures are only moderately correlated _(r_ = 0.47, n = 463). Both are likewise correlated with _Duties_ and _Tariff_ only moderately _(r_ = about 0.6), with _Closure_ even less _(r_ = about 0.25).

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