GTAP Resources: Resource Display
| GTAP Resource #1324 |
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"How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements? " by Hertel, Thomas, David Hummels, Maros Ivanic and Roman Keeney Abstract With the proliferation of Free Trade Agreements (FTAs) over the past decade, demand for quantitative analysis of their likely impacts has surged. The main quantitative tool for performing such analysis is Computable General Equilibrium (CGE) modeling. Yet these models have been widely criticized for performing poorly (Kehoe, 2002) and having weak econometric foundations (McKitrick, 1998; Jorgenson, 1984). FTA results have been shown to be particularly sensitive to the trade elasticities, with small trade elasticities generating large terms of trade effects and relatively modest efficiency gains, whereas large trade elasticities lead to the opposite result. Critics are understandably wary of results being determined largely by the authors’ choice of trade elasticities. Where do these trade elasticities come from? CGE modelers typically draw these elasticities from econometric work that uses time series price variation to identify an elasticity of substitution between domestic goods and composite imports (Alaouze, 1977; Alaouze, et al., 1977; Stern et al., 1976; Gallaway, McDaniel and Rivera, 2003). This approach has three problems: the use of point estimates as “truth”, the magnitude of the point estimates, and estimating the relevant elasticity. First, modelers take point estimates drawn from the econometric literature, while ignoring the precision of these estimates. As we will make clear below, the confidence one has in various CGE conclusions depends critically on the size of the confidence interval around parameter estimates. Standard “robustness checks” such as systematically raising or lowering the substitution parameters does not properly address this problem because it ignores information about which parameters we know with some precision and which we do not. A second problem with most existing studies derives from the use of import price series to identify home vs. foreign substitution, for example, tends to systematically understate the true elasticity. This is because these estimates take price variation as exogenous when estimating the import demand functions, and ignore quality variation. When quality is high, import demand and prices will be jointly high. This biases estimated elasticities toward zero. A related point is that the fixed-weight import price series used by most authors are theoretically inappropriate for estimating the elasticities of interest. CGE modelers generally examine a nested utility structure, with domestic production substitution for a CES composite import bundle. The appropriate price series is then the corresponding CES price index among foreign varieties. Constructing such an index requires knowledge of the elasticity of substitution among foreign varieties (see below). By using a fixed-weight import price series, previous estimates place too much weight on high foreign prices, and too small a weight on low foreign prices. In other words, they overstate the degree of price variation that exists, relative to a CES price index. Reconciling small trade volume movements with large import price series movements requires a small elasticity of substitution. This problem, and that of unmeasured quality variation, helps explain why typical estimated elasticities are very small. The third problem with the existing literature is that estimates taken from other researchers’ studies typically employ different levels of aggregation, and exploit different sources of price variation, from what policy modelers have in mind. Employment of elasticities in experiments ill-matched to their original estimation can be problematic. For example, estimates may be calculated at a higher or lower level of aggregation than the level of analysis than the modeler wants to examine. Estimating substitutability across sources for paddy rice gives one a quite different answer than estimates that look at agriculture as a whole. When analyzing Free Trade Agreements, the principle policy experiment is a change in relative prices among foreign suppliers caused by lowering tariffs within the FTA. Understanding the substitution this will induce across those suppliers is critical to gauging the FTA’s real effects. Using home v. foreign elasticities rather than elasticities of substitution among imports supplied from different countries may be quite misleading. Moreover, these “sourcing” elasticities are critical for constructing composite import price series to appropriate estimate home v. foreign substitutability. In summary, the history of estimating the substitution elasticities governing trade flows in CGE models has been checkered at best. Clearly there is a need for improved econometric estimation of these trade elasticities that is well-integrated into the CGE modeling framework. This paper provides such estimation and integration, and has several significant merits. First, we choose our experiment carefully. Our CGE analysis focuses on the prospective Free Trade Agreement of the Americas (FTAA) currently under negotiation. This is one of the most important FTAs currently “in play” in international negotiations. It also fits nicely with the source data used to estimate the trade elasticities, which is largely based on imports into North and South America. Our assessment is done in a perfectly competitive, comparative static setting in order to emphasize the role of the trade elasticities in determining the conventional gains/losses from such an FTA. This type of model is still widely used by government agencies for the evaluation of such agreements. Extensions to incorporate imperfect competition are straightforward, but involve the introduction of additional parameters (markups, extent of unexploited scale economies) as well as structural assumptions (entry/no-entry, nature of inter-firm rivalry) that introduce further uncertainty. Since our focus is on the effects of a PTA we estimate elasticities of substitution across multiple foreign supply sources. We do not use cross-exporter variation in prices or tariffs alone. Exporter price series exhibit a high degree of multicolinearity, and in any case, would be subject to unmeasured quality variation as described previously. Similarly, tariff variation by itself is typically unhelpful because by their very nature, Most Favored Nation (MFN) tariffs are non-discriminatory in nature, affecting all suppliers in the same way. Tariff preferences, where they exist, are often difficult to measure – sometimes being confounded by quantitative barriers, restrictive rules of origin, and other restrictions. Instead we employ a unique methodology and data set drawing on not only tariffs, but also bilateral transportation costs for goods traded internationally (Hummels, 1999). Transportation costs vary much more widely than do tariffs, allowing much more precise estimation of the trade elasticities that are central to CGE analysis of FTAs. We have highly disaggregated commodity trade flow data, and are therefore able to provide estimates that precisely match the commodity aggregation scheme employed in the subsequent CGE model. We follow the GTAP Version 5.0 aggregation scheme which includes 42 merchandise trade commodities covering food products, natural resources and manufactured goods. With the exception of two primary commodities that are not traded, we are able to estimate trade elasticities for all merchandise commodities that are significantly different form zero at the 95% confidence level. Rather than producing point estimates of the resulting welfare, export and employment effects, we report confidence intervals instead. These are based on repeated solution of the model, drawing from a distribution of trade elasticity estimates constructed based on the econometrically estimated standard errors. There is now a long history of CGE studies based on SSA: Systematic Sensitivity Analysis (Harrison and Vinod, 1992; Wigle, 1991; Pagon and Shannon, 1987) Ho |
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If you have trouble accessing any of the attachments below due to disability, please contact the authors listed above.
Public Access  GTAP Resource 1324 (180.3 KB) Replicated: 0 time(s) GTAP Working Paper No. 26 (288.8 KB) Replicated: 0 time(s)Restricted Access No documents have been attached. Special Instructions Due to size restrictions this application is a mini version of the application presented in the paper. This mini version is used in GTAP short courses and is a RunGTAP archive. To Load: 1. Copy the zip file into a folder 2. Open RunGTAP 3. Select File | Version Archive | Load ZIP 6. Select zip file 7. Choose version name and Click "ok" 8. Click "OK". In the top blue line of RunGTAP (above the menu) you should see the following: "RunGTAP: "Version name"/"experiment loaded". Make sure that "Version name" is the name you specified. |
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Last Modified: 9/15/2023 2:05:45 PM
