Abstract
Shift-share is a widely-used technique for the analysis of regional economies. As a methodology, shift-share is comprised of traditional accounting-based models, Analysis of Variance models, and information-theoretic models. The purpose of this paper is to present and demonstrate the usefulness of two probabilistic forms of shift-share models. These highly flexible variance partitioning methods are but one example of the broader class of models used in the analysis of aggregate, tabular data within planning, geography and regional science. Further, probabilistic shift-share provides a major advance over traditional accounting-based methods because it allows the researcher to quantitatively test hypotheses about changes in employment or value added by region or sector. Also, the casting of shift-share analysis in this light offers proof of the adequacy of these models.
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