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Abstract
It is expensive to prepare a database for a large CGE model -- so we should plan that the database will be used for many (perhaps unanticipated) purposes. For this and other reasons it is wise to construct a database with as much regional and sectoral disaggregation as possible. But a model with too many sectors and regions is annoyingly slow to solve. Thus it is common practice to aggregate a large 'master' database before use. Can we make results computed with aggregated data more closely resemble those from disaggregate data? A CGE database consists mainly of matrices of flow values, and elasticities (mostly pertaining to CES nests). Having defined many-to-one mappings from the (many) old sectors/regions to the (fewer) new sectors/regions, it is easy to aggregate the flows matrices by simply adding. Aggregated elasticities are usually constructed as weighted averages of the disaggregate elasticities -- using as weights the flow values associated with each elasticity (that is, the total cost of inputs to that nest). However, we present examples where this simple weighted averaging yields odd results. We focus on the case where a number of users (or nests) each combine (using the CES) the same set of inputs (but with different cost shares). Seeking a better method of averaging elasticities, we propose as a criterion that aggregated CES elasticities imply a local own-price substitution response that is close to the average of the own-price responses in the corresponding disaggregated nests. We see that elasticity aggregation bias arises from aggregating nests, rather than aggregating inputs to nests. We develop some simple formulae, which imply that the aggregate elasticity should be K (0
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