Foreign direct investment’s role in strengthening metro manufacturing: A case study of the U.S. state of Georgia

ABSTRACT

Though concern has arisen in recent years over manufacturing job losses in major U.S. metropolitan areas, this study demonstrates that foreign manufacturing firms still tend to locate in such areas. Using longitudinal data from the National Establishment Time-Series data set for the state of Georgia, we examine intrastate spatial patterns of manufacturing foreign direct investment (FDI) employment and associated underlying location factors. We find a strong spatial concentration of manufacturing FDI employment in metropolitan areas over time, particularly in the Atlanta metropolitan statistical area (MSA), the largest metropolitan area in the state. The results of the panel regression analysis suggest that existing agglomerations of innovative firms and institutions, a pool of highly educated workers, highly developed transportation networks, and critical links to domestic and international markets in metropolitan areas are key attraction factors for manufacturing FDI.

Acknowledgments

This article extends the analysis in Jeong-Il Park’s unpublished doctoral dissertation, Foreign Direct Investment and Sustainable Local Economic Development: Spatial Patterns of Manufacturing Foreign Direct Investment and Its Impacts on Middle Class Earnings, completed in the School of City and Regional Planning, Georgia Institute of Technology, Atlanta, Georgia, May 2014.

Notes

1. Right-to-work states secure the right of employees to decide for themselves whether or not to join or financially support a union. The following states have adopted right-to-work as of 2010: Alabama, Arizona, Arkansas, Florida, Georgia, Idaho, Iowa, Kansas, Louisiana, Mississippi, Nebraska, Nevada, North Carolina, North Dakota, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, and Wyoming.

2. The global Moran’s I values range from −1 (indicating perfect dispersion) to +1 (perfect correlation). A zero value indicates a random spatial pattern.

3. The definition of high-tech manufacturing for this article is taken from the Bureau of Labor Statistics report by economist Daniel Heckler (2005), which is based on the proportion of employment of science, engineering, and technician occupations in an industry. The report defines high-tech industries as those industries with a concentration of science, engineering, and technician occupations that is at least two times the average for all industries. High-tech industries meeting that criterion are then broken out into three levels.

4. Though there are 111 counties in Georgia that had at least one manufacturing FDI employment, the other 48 counties had no employment between 1990 and 2010. This research includes all 159 counties in the panel models after log(x + 1) transformation. To check the sensitivity of the models, it estimates coefficients of the intrastate locational factors model with only 111 counties (groups) for the same periods. The results show that the coefficients do not change much and there are no reverse signs.

5. Measure of the localization economies varies in previous studies. Whereas Coughlin et al. (1991) measured agglomeration economies by manufacturing density as state manufacturing employment per square mile of state land excluding federal land, studies by Smith et al. (1994) and Coughlin and Segev (2000) included a manufacturing intensity variable, measured by the percentage of a county’s labor force that is employed in the manufacturing sector. In addition, the localization economies was measured by the number of manufacturing plants in Woodward’s (1992) study. In this research, the localization economies were measured by absolute size of employment in the manufacturing sector in a given county for a specific year.

6. In addition to FTZ and county property tax rates (PROTAX), other types of spatially targeted incentives, such as job tax credit tiers, enterprise zones, and opportunity zones, are available in Georgia. However, some of these variables are difficult to hard to obtain, so those variables are not included in the locational factors model.

Additional information

Notes on contributors

Jeong-Il Park

Jeong-Il Park is an assistant professor in the Department of Urban Planning in Keimyung University, Daegu, Republic of Korea. He received his PhD in city and regional planning from the Georgia Institute of Technology in 2014. His research focuses on vertical mixed-use urban industrial spaces, green industries and firm locations, and job accessibility.

Nancey Green Leigh

Nancey Green Leigh, FAICP, is Associate Dean of Research for the College of Design at the Georgia Institute of Technology, Professor of City and Regional Planning, and a Fellow of the American Institute of Certified Planners. She specializes in economic development planning with a focus on urban industrial land and economies, brownfield redevelopment, and the impact of robotics diffusion on local economies and employment.