Measuring the Impacts of Global Trade Reform with Optimal Aggregators of Distortions

Traditional weighted-average measures of trade distortions are widely used in analyzing global and regional reforms, despite well-known deficiencies. This paper develops and applies optimal aggregators for the real-world case of multiple countries...

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Bibliographic Details
Main Authors: Laborde, David, Martin, Will, van der Mensbrugghe, Dominique
Language:English
Published: 2012
Subjects:
GDP
WTO
Online Access:http://www-wds.worldbank.org/external/default/main?menuPK=64187510&pagePK=64193027&piPK=64187937&theSitePK=523679&menuPK=64187510&searchMenuPK=64187283&siteName=WDS&entityID=000158349_20110518163857
http://hdl.handle.net/10986/3428
Description
Summary:Traditional weighted-average measures of trade distortions are widely used in analyzing global and regional reforms, despite well-known deficiencies. This paper develops and applies optimal aggregators for the real-world case of multiple countries and commodities with much more detailed information on trade than on production and consumption. The approach reflects the fact that different aggregators are needed for expenditure on imported goods and for tariff revenues, and allows for incorporation of both intensive and extensive margins of adjustment to reform. Applications confirm that the technique is straightforward enough for widespread use, and point to close to a doubling of the welfare gains at the intensive margin when using the highest possible level of international commodity disaggregation, with larger gains in developing regions than in the industrial countries. The measured income gains increase along the entire path of liberalization, with slightly larger increases in the earlier stages, where the gaps between the responses of the expenditure and tariff revenue aggregators are largest. Sensitivity analysis suggests that, for global trade reform, the ease of substitution between tariff lines is much more important than that between varieties from different countries.