Document Type

Thesis - Open Access

Award Date

2023

Degree Name

Master of Science (MS)

Department / School

Economics

First Advisor

Joseph Santos

Keywords

Agglomeration Economics, Cluster Strength, Clusters, Industrial Clusters, Wage growth, Wages

Abstract

In this thesis, I examine the relationship between clusters (i.e., the grouping of competitive, interconnected industries within a geographical area) and wages, building upon the work of Marshall (1890) and Porter (2003) on the importance of clusters for regional economic development. I seek to answer two research questions. First, after accounting for robustness tests, do clusters continue to affect wages positively? Second, is labor force productivity the only channel through which this relationship occurs? In my analysis, I employ ordinary least squares, two-stage least squares, and fixed effects regression analyses using panel data from 2009 to 2014 for every U.S. county. My main variable of interest in cluster strength, which U.S. Cluster Mapping (2020) defines as the “percentage of trade labor in a strong cluster.” Using my regression model, I find that cluster strength positively and statistically correlates to the average private wage. However, the increase is not as significant as previously documented in the literature. Furthermore, I perform additional regression analyses on labor force productivity and patents to reveal that, in the short run, there is no correlation between cluster strength and labor force productivity or patents when using a fixed effects regression. Through my conceptual framework, review of the literature, and empirical findings, I suggest that increases in competition between firms, rather than solely labor force productivity, drive the positive relationship between clusters and wages.

Publisher

South Dakota State University

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Rights Statement

In Copyright