Institutional quality and trade in intermediate goods

Abstract

Purpose Recently published studies stress the importance of trade in intermediate goods. The literature on determinants of trade, however, have largely focused on the sources of comparative advantage in determining aggregate trade flows rather than trade in intermediate goods. The purpose of this paper is to examine the role of institutional quality and trade costs to explain the determinants of trade in intermediates. Design/methodology/approach The simple model is based on the model of comparative advantage in the gravity framework used by Eaton and Kortum (2002) and Chor (2010) to relate trade flows of intermediate goods to institutional parameters, factor endowments and geography. The empirical tests use a data set containing 172 countries and 17 industries spanning ten years. Findings The results confirm the theoretical prediction that a country with higher institutional quality has a comparative advantage in institution-intensive goods and trade costs have a negative effect on trade. The author further finds that these effects are stronger in share of trade in intermediate goodsvis-à-visfinal goods. Originality/value To highlight the distinct nature of trade in intermediate goods, the author separates industry trade flows as intermediate input trade and final goods (consumption goods) trade to compare the importance of different sources of comparative advantage among different types of trade flows. Unlike Eaton and Kortum (2002) and Chor (2010) who used cross-sectional data for final goods trade, the ten-year industry-level panel data are used to compare the relative importance of institutions and geography as determinants in trade in intermediate goods compared to final goods trade and capture the macroeconomic time variant factors as well as industry–country pair characteristics. A significant caveat in gravity regression is that an empirical finding may often be driven by omitted variables. Inclusion of a set of country variables such as GDP, production costs and institutional level may still allow omitted variables to bias the estimation. To avoid this problem, the author includes a fixed effect of exporter and importer as well as industry and year, instead of a set of country characteristics.

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