Octávio Figueiredo

Home-field advantage: location decisions of Portuguese entrepreneurs

An investors home bias in industrial location decisions may stem from personal factors, social capital, other non-transferable assets, and imperfect information about the urban and regional environment. This paper explores the distinction between home-base and non-home location decisions in Portugal. We reach two important conclusions. First, the introduction of a variable accounting for prior base of economic activity significantly improves the statistical results. Second, we find that the weighting of distinct location attributes differs between home and non-home locations. Notably, non-home location choices are strongly governed by agglomeration economies and proximity to major urban centers, possibly replicating prior location decisions to economize on search costs. The results also enable us to quantify the investors willingness to opt for a possible home- field advantage; for example, entrepreneurs accept over three times higher labor costs to compete in their resident area of business.  2002 Elsevier Science (USA). All rights reserved.

Accounting for Neighboring Effects in Measures of Spatial Concentration

A common problem with spatial economic concentration measures (e.g. Gini, Herfindhal, entropy and Ellison-Glaeser indices) is accounting for the position of regions in space. While they purport to measure spatial clustering, these statistics are confined to calculations within individual areal units. They are insensitive to the proximity of regions - to neighboring effects. Clearly, economic clusters may cross the boundaries of the regions. Yet with current measures, any industrial agglomeration that traverses boundaries will be chopped into two or more pieces. Activity in adjacent spatial units is treated in exactly the same way as activity in far-flung, non-adjacent areas. This paper shows how some popular measures of spatial concentration relying on areal data can be modified to account for neighboring effects and spatial autocorrelation. With a U.S. application, we also show that the new instruments we propose are useful and easy to implement.

Measuring the Localization of Economic Activity: A Parametric Approach

The index proposed by Ellison and Glaeser (1997) is now well established as the preferred method for measuring the localization of economic activity. In this paper we develop an alternative localization measure that is consistent with the theoretical framework originally proposed by Ellison and Glaeser. Our measure follows directly from the Random Utility (Profit) Maximization (RUM) location decision model. Because the distributional assumptions in our model are fully compatible with RUM, we are able to offer a more efficient measure of industry clustering.