Trade creation and diversion revisited: Accounting for model uncertainty and natural trading partner effects

Abstract

The effect of preferential trade agreements (PTAs) on trade flows is subject to model uncertainty stemming from the diverse and even contradictory effects suggested by the theoretical PTA literature. The existing empirical literature has produced remarkably disparate results and the wide variety of empirical approaches reflects the uncertainty about the 'correct' set of explanatory variables that ought to be included in the analysis. To account for the model uncertainty that surrounds the validity of the competing PTA theories, we introduce Bayesian model averaging (BMA) to the PTA literature. Statistical theory shows that BMA successfully incorporates model uncertainty in linear regression analysis by minimizing the mean squared error, and by generating predictive distributions with optimal predictive performance. Once model uncertainty is addressed as part of the empirical strategy, we find strong evidence of trade creation, trade diversion, and open bloc effects. Our results are robust to a range of alternative empirical specifications proposed by the recent PTA literature.In this paper we apply Bayesian model averaging (BMA) to the PTA literature to re-examine model uncertainty. BMA is specifically designed to incorporate model uncertainty into the estimation process and is firmly rooted in statistical theory. It is a methodology that explores the model space without restrictions, weighs each model according to quality, and provides a probability distribution for each coefficient estimate. Raftery and Zheng (2003) show that BMA maximizes predictive performance while minimizing the total error rate when compared to any individual model. The rapidly growing list of economics applications using BMA include policy evaluations (e.g. Brock et al., 2003), monetary policy (e.g. Levin and Williams, 2003), macroeconomic forecasting (e.g. Garratt et al., 2003), economic growth (e.g. Fernandez et al., 2001), and international economics (e.g. Chen and Rogoff, 2006). The issue of model uncertainty surrounding PTA effects is well known in the PTA literature. Seldom do papers present less than a dozen different PTA regression specifications. We show that BMA overturns the fundamental Ghosh and Yamarik result by identifying a number of PTAs that exert decisive effects on trade flows. Since Ghosh and Yamarik, the PTA literature has evolved to introduce a number of innovations that address omitted variable bias. We show that our main finding of measurable PTA effects on trade flows is robust, even when the Ghosh and Yamarik (2004) dataset is updated to include additional years, additional PTAs, and alternative fixed-effect specifications. 1 Our methodological extensions include a full account of multilateral resistance (see, for example, Anderson and van Wincoop,

Determinants of the exports of Hungary: Trade theory and the gravity model

“…Empirical analyses focused on several factors influencing trade, such as borders (McCallum (1995); Wei (1996); Anderson-van Wincoop (2001); Mayer-Zignago (2005)), factor endowment (Rahman (2003); Schumacher (2003); Sohn (2005) ;Eicher et al (2012); Shirotori et al (2010)), common language and colonial linkages (Hutschinson (2002); Head et al (2010); Oh et al (2011);Felbermayr-Toubal (2006); Martinez-Zarzoso (2003)); institutions and corruption (AndersonMarcouiller (2002);de Groot et al (2004)); free trade agreements and regional integrations ((Martinez-Zarzoso (2003); Baier-Bergstrand (2007); Carrere (2006); Soloaga-Winters (2001) ;Jugurnath et al (2007); Coulibaly (2009) ;Anderson-Yotov (2011); Rose-van Wincoop (2001); Vicard (2009) ;Frankel-Wei (1998)). …”

“…Antonucci and Manzocchi (2006) and Yip (2012) called this variable relative factor endowment. We followed Eicher et al (2012) …”

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The Role of Regional Economic Integrations for Trade Margins: A Case of Croatia

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