This paper utilizes a many-country, many-product Ricardian trade model to evaluate the usefulness of measures of revealed comparative advantage (RCA) in academic and policy analyses. I find that, while commonly used indexes are generally not consistent with theoretical notions of comparative advantage, certain indexes can be usefully employed for certain tasks. I explore several common uses of RCA indexes and show that different indexes are appropriate when attempting to (a) evaluate the differential effect of changes in trade barriers across producers of different products, (b) identify countries who are relatively close competitors in a given market, or (c) recover patterns of relative productivity.

A widely used class of quantitative trade models implicitly assumes that patterns of comparative advantage take a specific form such that they have no influence over the effect of trade barriers on aggregate trade flows and welfare. In this paper, I relax this assumption, developing a framework in which to analyze the role of interactions among countries' patterns of comparative advantage in determining the aggregate effects of trade barriers. My model preserves much of the tractability of standard aggregate quantitative trade models while allowing for the effects of any pattern of comparative advantage, across many products and countries, to be taken into account. After fitting my model to product-level trade data, I find that the composition of trade flows is quantitatively important in determining the welfare gains from trade and the aggregate effects of trade barriers. A key finding is that the welfare gains from trade tend to be larger and more skewed in favor of low-income countries than an aggregate model would suggest.

This paper utilizes a many-country, many-product Ricardian trade model to evaluate the usefulness of measures of revealed comparative advantage (RCA) in academic and policy analyses. I find that, while commonly used indexes are generally not consistent with theoretical notions of comparative advantage, certain indexes can be usefully employed for certain tasks. I explore several common uses of RCA indexes and show that different indexes are appropriate when attempting to (a) evaluate the differential effect of changes in trade barriers across producers of different products, (b) identify countries who are relatively close competitors in a given market, or (c) recover patterns of relative productivity.JEL Classification: F10, F13, F14, F15

A widely used class of quantitative trade models implicitly assumes that patterns of comparative advantage take a specific form such that they have no influence over the effect of trade barriers on aggregate trade flows and welfare. In this paper, I relax this assumption, developing a framework in which to analyze the role of interactions among countries' patterns of comparative advantage in determining the aggregate effects of trade barriers. My model preserves much of the tractability of standard aggregate quantitative trade models while allowing for the effects of any pattern of comparative advantage, across many products and countries, to be taken into account. After fitting my model to product-level trade data, I find that the composition of trade flows is quantitatively important in determining the welfare gains from trade and the aggregate effects of trade barriers. A key finding is that the welfare gains from trade tend to be larger and more skewed in favor of low-income countries than an aggregate model would suggest.

Gravity estimation based on sector-level trade data is generally misspecified because it ignores the role of product-level comparative advantage in shaping the effects of trade barriers on sector-level trade flows. Using a model that allows for arbitrary patterns of product-level comparative advantage, I show that sector-level trade flows follow a generalized gravity equation that contains an unobservable, bilateral component that is correlated with trade costs and omitted by standard sector-level gravity models. I propose and implement an estimator that uses product-level data to account for patterns of comparative advantage and find the bias in sector-level estimates to be significant. I also find that, when controlling for product-level comparative advantage, estimates are much more robust to distributional assumptions, suggesting that remaining biases due to heteroskedasticity and sample selection are less severe than previously thought.JEL Classification: F10, F14, C13, C21, C50