Abstract
This paper employs commuter flow data from the 1990 and 2000 Decennial Censuses, and the 2006–2010 American Community Survey to replicate, evaluate, and extend the delineation of commuting zones first proposed by Tolbert and Killian (Labor Market Areas for the United States, 1987). Commuting zones offer a valuable tool for research on regional economies and have long served rural sociologists, economists, and geographers interested in a representation of the economy that acknowledges a connection between urban and rural areas and the capacity of economic systems to cross state lines. Our delineations provide both an update in the form of new delineations for 2010 and a revised set of 1990 and 2000 delineations that benefit from a consistent methodology across decades. We also provide a set of tools for comparing delineations across methods and over time. In presenting our revised delineations, we shed light on the role of expert opinion in the original delineations, the strengths and weaknesses of the original method, and offer suggestions for further revision of this tool that may better reflect the theoretical conception of commuting zones.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Commuting zones provided units of analysis that represented regional economies. However, a number of rural CZs failed to meet the 100,000 minimum population threshold required for use in a microdata tabulation. These CZs were aggregated to meet the population threshold. The original CZs along with the aggregated CZs are called LMAs. While PUMS files using LMAs would be comprehensive of all geographic areas of the US, they mask nuanced delineations of rural economies.
We also conducted a robustness check using the commuting flow data associated with the longitudinal employer-household dynamics (LEHD) data (Abowd et al. 2009). LEHD Origin–Destination Employment Statistics (LODES) data have apparent advantages over the ACS in that it is based on a larger population than the ACS sample and does not include a margin of error. However, as discussed by Spear (2011) and Graham et al. (2014), the LODES data assign flows to origin–destination pairs based on a stochastic model to protect privacy. In Spear’s analysis, this produces a very large number of very small flows and a configuration that is markedly different from the ACS or prior Census long-form products. In our own analysis, clustering using LODES data produced markedly different results from all other data sources, grouping counties into only 318 clusters as opposed to 610 for the 2006–2010 ACS data and 641 for the 2000 Decennial data. As a result, we feel that the ACS data, with all their flaws, are a better choice for this application. As with all of the elements of this analysis, the results of this robustness check are available from the corresponding author.
The apparent similarity between the 1990 and 2000 data includes two Virginia counties that disappear between decades and two Alaska boroughs that appear between decades.
Cooperative Agreement S-184: “Labor Markets and Labor Differentiation in Nonmetropolitan America.”
Cooperative Agreement S-229: “The Changing Structure of Local Labor Markets in Nonmetropolitan Areas.”
There is considerably less documentation on the method employed for the internal ERS delineation conducted in 2004. ERS on its website states that “The identical methodology was used to develop CZs for all three decades” (Economic Research Service 2015); however, software and hardware limitations would not have been a problem in 2004, and personal communication with ERS staff (July 23rd, 2015) indicates that at least the ‘expert review’ portion of the process was not undertaken for the 2000 delineations. Other differences may also exist.
Hierarchical cluster analysis is highly sensitive to the cutoff point selected which determines the number of clusters in the analysis. For the purposes of this paper, we are trying to develop a mechanism that allows for cross-decade comparisons against the original delineations.
Using the same approach 528 counties required adjudication in 2000 and 579 in 2010.
Higher values for the cutoff produce a smaller number of clusters. Lower cutoff values produce a larger number of clusters. To get the same number of clusters in a larger dataset requires a lower cutoff value.
Authors’ personal communication with ERS staff July 23rd, 2015.
References
Abowd, J. J., Stephens, B. B., Vilhuber, L., Andersson, F., McKinney, K. L., Roemer, M., & Woodcock, S. (2009). The LEHD infrastructure files and the creation of the quarterly workforce indicators. In T. Dunne, J. B. Jensen, & M. Roberts (Eds.), Producer Dynamics: New Evidence from Micro Data. Chicago: University of Chicago Press.
Allegretto, S., Dube, A., & Reich, M. (2009). Spatial Heterogeneity and Minimum Wages: Employment etimates for teens using cross-state commuting zones. Institute for Research on Labor and Employment: Working Paper Series. http://escholarship.org/uc/item/1x99m65f
Armington, C., & Acs, Z. J. (2010). The determinants of regional variation in new firm formation. Regional Studies, 36(1), 33–45. doi:10.1080/00343400120099843.
Autor, D., & Dorn, D. (2009). This job is “Getting Old:” measuring changes in job opportunities using occupational age structure. American Economic Review, 99(2), 45–51.
Autor, D. H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review, 103(5), 1553–1597.
Cotter, D. A. (2002). Poor people in poor places: Local opportunity structures and household poverty. Rural Sociology, 67(4), 534–555.
Davis, E. E., Connolly, L. S., & Weber, B. A. (2003). Local Labor Market Conditions and the Jobless Poor: How much does local job growth help in rural areas. Journal of Agricultural and Resource Economics, 28(3), 503–518.
Dorn, D. (2009). Essays on Inequality, Spatial Interaction, and the Demand for Skills. St. Gallen: University of St. Gallen.
Dupor, B., McCrory, P. (2014) A cup runneth over: Fiscal policy spillovers from the 2009 recover act. Federal Reserve Bank of St. Louis: Working Paper Series. http://research.stlouisfed.org/wp/2014/2014-029.pdf
Economic Research Service. (2015). Commuting zones and labor market areas documentation. United States Department of Agriculture. Retrieved from http://www.ers.usda.gov/data-products/commuting-zones-and-labor-market-areas/documentation.aspx. Accessed date on 26 May 2015
Feser, E. J. (2003). What regions do rather than make: A proposed set of knowledge-based occupation clusters. Urban Studies, 40(10), 1937–1958. doi:10.1080/0042098032000116059.
Feser, E. J. (2005). Benchmark value chain industry clusters for applied regional research. Urbana-Champaign: Department of Urban and Regional Planning and Regional Economics Applications Laboratory (REAL), University of Illinois at Urbana-Champaign.
Feser, E. J., & Isserman, A. (2005). Clusters and rural economies in economic and geographic space. Urbana-Champaign: University of Illinois.
Feser, E. J., Renski, H., & Goldstein, H. (2008). Clusters and economic development outcomes: An analysis of the link between clustering and industry growth. Economic Development Quarterly, 22(4), 324–344. doi:10.1177/0891242408325419.
Fowler, C. S., & Kleit, R. G. (2013). The effects of industry clusters on the poverty rate. Economic Geography, 90(2), 129–154.
Fullerton, A. S., & Villemez, W. J. (2011). Why does the spatial agglomeration of firms benefit workers? Examining the role of organizational diversity in U.S. Industries and Labor Markets. Social Forces, 89(4), 1145–1164. doi:10.1093/sf/89.4.1145.
Gentleman, R., & Lang, D. T. (2007). Statistical analyses and reproducible research. Journal of Computational and Graphical Statistics, 16(1), 1–23.
Gibbs, R. M., & Bernat, G. A. (1980). Rural industry clusters raise local earnings. Rural Development Perspectives, 12(3), 18–25.
Graham, M. R., Kutzbach, M. J., & McKenzie, B. (2014). Design comparison of LODES and ACS commuting data products (No. CES 14-38). CES working papers. Washington D.C. doi:10.1017/CBO9781107415324.004
Henderson, J. R., & McNamara, K. T. (2000). The location of food manufacturing plant investments in corn belt counties. Journal of Agricultural and Resource Economics, 25(2), 680–697. doi:10.2307/40987084.
Hildner, K. F., Nichols, A., & Martin, S. (2015). Methodology and assumptions for the mapping America’ s futures project. Washington D.C.
Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis. New York: John Wiley & Sons.
Killian, M. S., & Tolbert, C. M. (1993). Mapping social and economic space: The delineation of local labor market areas in the United States. In J. Singelmann & F. A. Deseran (Eds.), Inequality in local labor markets (pp. 69–79). Boulder, CO: Westview Press.
Lloyd, C., Shuttleworth, I., & Wong, D. (Eds.). (2014). Social-spatial segregation: Concepts, processes and outcomes. Bristol: Policy Press.
Martin, S., Astone, N. M., Peters, H. E., Pendall, R., Nichols, A., Hildner, K. F., & Stolte, A. (2015). Evolving patterns in diversity. Washington D.C.
McKenzie, B. S. (2013). County-to-County Commuting Flows: 2006-2010. US: Census Bureau.
Minnesota Population Center. (2011). National historical geographic information system version 2.0. Minneapolis, MN: University of Minnesota.
Molloy, R., Smith, C. L., & Wozniak, A. (2011). Internal migration in the United States. Journal of Economic Perspectives, 25(3), 173–196. doi:10.1257/jep.25.3.173.
Nichols, A., Martin, S., Astone, N., & Peters, H. (2015). The labor force in an aging and growing America. Washington, DC: The Urban Institute.
Pendall, R., Martin, S., Astone, N., & Nichols, A. (2015). Scenarios for regional growth from 2010 to 2030. Washington, DC: The Urban Institute.
Spear, B. D. (2011). Improving employment data for transportation planning NCHRP 08-36, Task 098. Cambridge, Massachusetts.
Tickamyer, A., & Bokemeier, J. (1987). Sex differences in labor-market experiences. Rural Sociology, 53(2), 166–189.
Tolbert, C., Blanchard, T., & Irwin, M. (2009). Measuring migration: Profiling residential mobility across two decades. Journal of Applied Social Science, 3(2), 24–38.
Tolbert, C., & Killian, M. (1987). Labor market areas for the United States. Washington, DC: US Department of Agriculture.
Tolbert, C., & Sizer, M. (1996). U.S. commuting zones and labor market areas: A 1990 update. Washington, DC: US Department of Agriculture.
United States Census Bureau. (2010a). Decennial Census Questionnaires for 1990 and 2000.
United States Census Bureau (2010b). Decennial Census of the Unites States.
Voss, P. R. (2007). Demography as a spatial social science. Population Research and Policy Review, 26(5–6), 457–476. doi:10.1007/s11113-007-9047-4.
Zolnik, E. J. (2010). The geographic distribution of U.S. unemployment by gender. Economic Development Quarterly, 25(1), 91–103. doi:10.1177/0891242410386592.
Acknowledgments
This project has been supported by a Cooperative Agreement (No. 58-6000-4-0053) with the Economic Research Service, USDA. We also acknowledge assistance provided by the Population Research Institute at Penn State University, which was supported by an infrastructure grant by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24-HD041025). Leif Jensen was supported by a USDA-funded Hatch Multistate Project W-3001, “The Great Recession, Its Aftermath, and Patterns of Rural and Small Town Demographic Change,” administered through Penn State College of Agricultural Sciences Experiment Station Project Number PEN04504.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fowler, C.S., Rhubart, D.C. & Jensen, L. Reassessing and Revising Commuting Zones for 2010: History, Assessment, and Updates for U.S. ‘Labor-Sheds’ 1990–2010. Popul Res Policy Rev 35, 263–286 (2016). https://doi.org/10.1007/s11113-016-9386-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11113-016-9386-0