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Transportation Infrastructures and Socioeconomic Statuses: A Spatial Regression Analysis at the County Level in the Continental United States, 1970–2010

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Abstract

There is a large body of literature examining transportation impacts on population and employment growth. However, the possible impacts that transportation infrastructures have on socioeconomic statuses are less clear. This study fills the gap in the literature by associating education and income—two socioeconomic status measures—with transportation infrastructures. In specific, this study examines the associations of railroads, highways, and airports collectively with high school, Bachelor’s degree, graduate degree, and income change in the continental United States for the period between 1970 and 2010. Data come from various sources, such as National Transportation Atlas Database, Decennial Census, Cartographic Boundary Shapefiles, and Land Developability Index. Standard regression and spatial analysis are conducted at decade levels and at the entire study period to test the consistency of the associations between transportation infrastructures and education and income. The study shows that railroads have a distributive and highways have a facilitative association with both education and income. Airports behave as a growth factor with education and as a facilitator with income. The findings clearly show the increased complexity of the roles performed by transportation infrastructures and do not show straightforward behaviors as has been considered for a long time. This study provides new insights into the role of transportation infrastructures for transportation planning and decision making.

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Acknowledgements

We thank Mary Emery, Jeffrey Jacquet, Meredith Redlin, and Songxin Tan for providing comments on earlier drafts of this article. This research was supported in part by the National Science Foundation (Award # 1541136), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Award # P2C HD041025-16), and the U.S. Department of Transportation (Awards # DTRT12GUTC14-201307 and # DTRT12GUTC14-201308).

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Authors and Affiliations

  1. South Dakota State University, Brookings, SD, 57007, USA

    Bishal B. Kasu

  2. Department of Agricultural Economics, Sociology, and Education, Population Research Institute, and Social Science Research Institute, The Pennsylvania State University, 112E Armsby, University Park, PA, 16802, USA

    Guangqing Chi

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  1. Bishal B. Kasu
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Correspondence to Bishal B. Kasu.

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Appendix

Appendix

See Figs. 6, 7, 8 and 9.

Fig. 6
figure 6

Moran’s I for 1970–2010 change in high school graduate. LNHS = natural log of change in high school graduate. The first-order Rook’s contiguity weight matrix is used

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Fig. 7
figure 7

Moran’s I for 1970–2010 change bachelor degree. LNBD = natural log of change in bachelor’s degree. The first-order Rook’s contiguity weight matrix is used

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Fig. 8
figure 8

Moran’s I for 1970–2010 change in graduate degree. LNGD = natural log of change in graduate degree. The first-order Rook’s contiguity weight matrix is used

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Fig. 9
figure 9

Moran’s I for 1970–2010 income change. LNINCM = natural log of income change. The first-order Rook’s contiguity weight matrix is used

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Kasu, B.B., Chi, G. Transportation Infrastructures and Socioeconomic Statuses: A Spatial Regression Analysis at the County Level in the Continental United States, 1970–2010. Spat Demogr 7, 27–56 (2019). https://doi.org/10.1007/s40980-018-0045-4

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