The Gains From Trade: Standard Errors with the CES Monopolistic Competition Model

As currently implemented, the workhorse econometric models of international trade (monopolistic competition, Heckscher-Ohlin, and gravity) do not rigorously incorporate product prices into their estimating equations. They are thus of limited value for assessing the gains from trade liberalization. We model general equilibrium product price effects using the CES monopolistic competition model. We then estimate the model and, mimicking computable general equilibrium (CGE) models, use the model to estimate the compensating variation associated with trade liberalization. Unlike CGE models, we offer standard errors as well. We precisely estimate large gains from liberalization. Going well beyond CGE models, we present extensive specification testing aimed at isolating the violence done by the model to the data. We find (1) the good fit of the model is largely driven by a data identity, (2) the model inappropriately assumes unit income elasticities, (3) CES demand only partially captures the rich price effects in the data, (4) there is substantial heterogeneity across industries in the performance of the model (one size does not fit all), and (5) there is missing trade. These observations point to the types of modifications needed before the CES monopolistic competition model can usefully be applied to policy questions. Finally, we report evidence that all the popular models of trade are mis-specified. ∗We are indebted to Gene Grossman, Elhanan Helpman, Bo Honore, and Jim Harrigan as well as workshop participants at the NBER, Harvard, and Princeton. Research is supported by the NSF (SES9515107) †University of Toronto and SAS Institute ‡University of Toronto and CIAR In the mid 1970s, as negotiations for the Tokyo Round of tariff reductions gathered momentum, a spate of papers appeared offering assessments of the potential effects of such an agreement. Weaknesses in the prevailing input-output methodology led Robert Stern to solicit new approaches from 2 promising junior faculty members. The first proposed a sound econometric strategy, but arrived at results that would generously be called ‘mixed. ’ The second proposed a linearized computable general equilibrium model that yielded remarkably plausible results. The junior faculty in this apocryphal story are Ed Leamer and Alan Deardorff. We relate this story because it marks a watershed. Deardorff’s Michigan Model along with developments by John Whalley, Rick Harris and others foretold the ascendancy of computable general equilibrium modeling over econometrics in discussions of international trade policy. One goal of this paper is to re-establish the role of standard econometric methods for estimating the welfare gains from world-wide tariff liberalization. Our starting point is the CES monopolistic competition model (Dixit and Stiglitz 1977; Helpman and Krugman 1985) with its closed-form predictions about compensating variation and bilateral trade. The bilateral trade prediction was initially examined by Lawrence (1987) and Saxonhouse (1989). Its popularity rose with the careful analysis of Harrigan (1993, 1996). Most of the related econometric literature has focussed on the prediction’s gravity-style terms rather than its price terms. Harrigan (1993), Haveman et. al (1999), and Anderson (1999) are exceptions. However, these authors all simplify the price term, thus making the analysis less useful for our aim of estimating welfare gains. One cannot assess the impact of tariff liberalization without knowing how economic agents respond to product prices. Yet when tariffs and hence product prices differ across countries the bilateral trade and welfare predictions of the CES monopolistic competition model are complicated e.g., Brown (1991). Indeed, prevailing wisdom has it that they are far too complicated to be examined econometrically. Computable general equilibrium (CGE) techniques must be used (e.g., Harris 1984). While there may be many reasons for wanting to use CGE models, this is not one of them. We show that estimation of the model