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Paperback | $26.50 Short | £18.95 | ISBN: 9780262620512 | 384 pp. | 6 x 9 in | October 1985

Sources of International Comparative Advantage

Theory and Evidence


This is the first book to present a clear empirical picture of the international exchange of goods and of the resources that account for the exchanges that occur. Using tables, graphs, and econometric data summaries, it describes the patterns of trade and the patterns of resource supplies of fifty-nine countries and explains these trade patterns in terms of the abundance of eleven resources.

The author's scientific goal is to leave the reader with a clear impression of the empirical validity of a central result of trade theory—the Heckscher-Ohlin theory of international comparative advantage, in which a country's factor endowments (land, labor, capital) play a crucial role in determining trade patterns. The theory is fully articulated and carefully linked to the empirical analysis. The econometric methods and the results should create a standard by which other data analyses will be judged in the future.

Chapters cover theories of international trade, testing the theories of international comparative advantage, formation of the trade aggregates, data preview, econometric methods, estimates of the trade dependence model, and counterfactuals. Appendixes present the effects of factor market distortions, statistics, and detailed discussions of the statistical results.

About the Author

Edward E. Leamer is Chauncey J. Medberry Professor of Management, Professor of Economics, and Professor of Statistics at the University of California, Los Angeles. He is the author Macroeconomic Patterns and Stories and other books.


"Leamer's new book represents one of the most serious attempts at linking economic theory with observations that I have ever encountered. The underlying intellectual honesty in approaching all econometric problems involved is most impressive and should serve as a shining example to all applied econometricians."
Jan Kmenta, Professor of Economics and Statistics, University of Michigan.