Measuring the Localization of Economic Activity: A Random Utility Approach

The recent index proposed by Ellison & Glaeser (1997) is now well established as the preferred method for measuring the localization of economic activity. We build on McFadden's Random Utility (Profit) Maximization framework, to develop a parametric version of this measure that is more consistent with the theory originally proposed by Ellison and Glaeser (EG). Given that our method is regression based, it goes beyond the descriptive nature of the EG index, allowing us to evaluate how the localization measure behaves with changes in the determinants that drive firms' location decisions.

Modeling industrial location decisions in U.S. counties

Given its sound theoretical underpinnings, the RandomUtilityMaximizationbased conditional logit model (CLM) serves as the principal method for applied research on industrial location decisions. Studies that implemented this methodology, however, had to confront the underlying Independence of Irrelevant Alternatives (IIA) assumption and were unable to fully accommodate this problem. This paper shows that by taking advantage of an equivalent relation between the CLM and Poisson regression likelihood functions one can more e.ectively control for the potential IIA violation in complex choice scenarios where the decision-maker confronts a large number of spatial alternatives. The paper also provides an illustration, demonstrating the advantages of this relation in investigation of location determinants of new manufacturing plant births in the U.S. counties.

Modeling industrial location decisions in U.S. counties

Given its sound theoretical underpinnings, the Random Utility Maximization-based conditional logit model has been the methodological basis for applied research on industrial location decisions. However, in practice, the implementation of this methodology presents problems. A notable one is the underlying Independence of Irrelevant Alternatives (IIA) assumption. In this paper we show that by taking advantage of an equivalence relation between the likelihood function of the conditional logit model and the Poisson regression [Guimarães, Figueiredo and Woodward (2002)] one can more effectively control for the potential IIA violation resulting from omitted attribute characteristics. We also provide an empirical illustration, wherein we exemplify how that relation can be helpful to investigate the location determinants of new manufacturing plants in the United States counties.

Vertical Disintegration in Marshallian Industrial Districts

This paper uses a novel measure and detailed plant-level Portuguese data to reexamine the Marshallian hypothesis that specialization and the vertical disintegration of firms should be greater in areas where an industry concentrates. Our measure of firm specialization and vertical disintegration employs a Herfindhal index constructed with occupational shares for all workers within the firm. Controlling for firm size and sector of activity, we find that vertical disintegration is around three percent higher in areas where industries agglomerate. Sensitivity tests reveal that this positive relation is remarkably robust across different specifications.Vertical Disintegration of Firms; Agglomeration; Localization Economies

Location Modelling and the Localization of Portuguese Manufacturing Industries

The recent index proposed in Ellison & Glaeser (1997) is now well established as the preferred method for measuring localization of economic activity. We critically review this index and build on the McFadden’s Random Utility (Profit) Maximization framework to develop an alternative measure that is more consistent with the theoretical construct underlying the original work of Ellison and Glaeser. Given that our method is regression based it goes beyond the descriptive nature of the EG index and allows us to evaluate how the localization measure behaves with changes in the systematic forces that drive firms’ location decisions. JEL classification: C25, R12, R39

Firm-Worker Matching in Industrial Clusters

In this paper we use a novel approach and a large Portuguese employer-employee panel data set to study the hypothesis that industrial agglomeration improves the quality of the firm-worker matching process. Our method makes use of recent developments in the estimation and analysis of models with high-dimensional fixed effects. Using wage regressions with controls for multiple sources of observed and unobserved heterogeneity we find little evidence that the quality of matching increases with firm’s clustering within the same industry. This result supports Freedman’s (2008) analysis using U.S. data.agglomeration, matching, fixed-effects

Location Modelling and the Localization of Portuguese Manufacturing Industries

The recent index proposed in Ellison & Glaeser (1997) is now well established as the preferred method for measuring localization of economic activity. We critically review this index and build on the McFadden’s Random Utility (Profit) Maximization framework to develop an alternative measure that is more consistent with the theoretical construct underlying the original work of Ellison and Glaeser. Given that our method is regression based it goes beyond the descriptive nature of the EG index and allows us to evaluate how the localization measure behaves with changes in the systematic forces that drive firms’ location decisions. JEL classification: C25, R12, R3

Firm-worker matching in industrial clusters

In this paper we use a novel approach and a large Portuguese employer-employee panel data set to study the hypothesis that industrial agglomeration improves the quality of the firm-worker matching process. Our method makes use of recent developments in the estimation and analysis of models with high-dimensional fixed effects. Using wage regressions with controls for multiple sources of observed and unobserved heterogeneity we find little evidence that the quality of matching increases with firm's clustering within the same industry. This result supports Freedman's (2008) analysis using U.S. data