Date of Award

1-1-2010

Document Type

Campus Access Thesis

Department

Moore School of Business

Sub-Department

Economics

First Advisor

Paulo Guimaraes

Abstract

Urban economists refer to the clustering of firms that belong to the same industry across spatial space as localization of economic activity. To determine if an industry is localized, researchers use indicators of localization and tests of statistical significance. While the asymptotic distributions of these tests are well-known, there is little information regarding the small sample size properties of the tests. Often, agglomeration researchers choose tests without knowledge of their properties and relative performance. This paper evaluates the small sample size properties of discrete tests of localization using Monte Carlo simulations to determine which tests are the most reliable and accurate. Based on our results we provide some recommendations on which tests to use and the situations where they perform better. To our knowledge this paper is the first known study to do so.

Rights

© 2010, Matthew Young Milnichuk

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