About

Welcome to the Penn State Commuting Zones / Labor Markets data repository.

This site serves two functions:

  1. A data repository where researchers can access delineations of labor markets from a variety of sources (Bureau of Economic Analysis, Census, Economic Research Service, Federal Communications Commission, etc…). These include Commuting Zones, Core-Based Statistical Areas, Economic Areas, and Partial Economic Areas. All delineations come with associated fit statistics referencing the quality of a specific county or labor market as described in a paper currently under review by Fowler, Jensen, and Rhubart.
  2. A visualization tool for comparing different delineations with each other and over time.

Labor markets are geographically defined areas where individuals can both live and work. While the ‘ideal’ labor market may mean different things to different people, it is generically a self-contained economic unit with dense economic activity within its borders and little economic activity crossing its borders.

A number of researchers and Federal agencies have delineated labor markets including the Census Bureau, The Bureau of Economic Analysis, and the US. Department of Agriculture’s Economic Research Service. In each case these units are designed to give researchers access to observations suitable for regional analysis where smaller units like counties would be measuring parts of the same functional region.

While labor market delineations have been available since at least the 1960’s they have rarely been subjected to scrutiny as to how well they capture meaningful economic regions. Economic activity is not static and rarely fits neatly within an administrative boundary so no delineation will be perfect. Moreover, economic activity varies spatially over time, so a delineation that works well for one time period may not work well for another.

The data and visualization presented here are an effort to alleviate three problems:

  • First, it aggregates delineations from a range of agencies and research teams covering the years 1980 to 2010 (where available). It makes these delineations available with consistent, county-level crosswalks for each decade 1980 to 2010 in .txt, .xls, and .shp format for both statistical and GIS analysis.
  • Second, it offers a comparison of delineations across method and over time with consistent fit statistics describing delineations in the aggregate and individual observations within specific delineations. These additional fit statistics allow researchers to A) Pick among extant delineations for one that is suitable to their analysis, and B) Consider the areas where the selected delineation is particularly strong or weak as this may introduce bias into subsequent analysis.
  • Third, it provides a script that researchers can use to test the efficacy of their own delineations against the fit of extant delineations to identify points of agreement and difference.

The data presented here and the analysis that underlies the fit statistics are discussed in detail in an associated paper currently under review. Users of this data should acknowledge its source as:

Fowler, C. S., Jensen, L., & Rhubart, D. (2018, September 26). Assessing U.S. Labor Market Delineations for Containment, Economic Core, and Wage Correlation. https://doi.org/10.17605/OSF.IO/T4HPU

This research was undertaken by Christopher S. Fowler, Leif Jensen and Danielle Rhubart. Michael Martin provided research assistance in preparing the files for this web site. Research was undertaken with support from the USDA ERS through cooperative agreement #58-6000-4-0053 with additional support from Penn State’s Population Research Institute which is supported by an infrastructure grant by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025).

While it relies on work done by a range of researchers and Federal Agencies including the Census Bureau, The Economic Research Service, and the Bureau of Economic Analysis, the results presented here are entirely a product of the named authors and do not reflect the opinions or output of those agencies.