Author: Chi Wong
Regional Labor Markets
Jobseekers, employers, educators, program administrators, and policymakers all need good data and information to help make economic and labor market decisions. Most of the data collected and reported relies on political boundaries that can seem disconnected from how our economic markets work. For example, using data from a single county may not accurately speak to where a business draws its labor from; as LEAD has posted in previous articles , only a small proportion of NC’s workers live and work in the same county. Another geographic example is data reported for Metropolitan Statistical Areas (MSAs), which represent a federal attempt at creating multi-county regions. However, the MSA definition may not always align with existing labor sheds and not all counties are included in MSAs - leaving many of the state’s counties unattached from their neighbors. To rectify this, LEAD initiated research to better identify the connections between counties that define our state’s labor markets.
Methodology
LEAD researched many existing models of regional economic geography before settling on the USDA’s Commuting Zone methodology. This methodology values bi-directional commuting relationships. In other words, where some models value the primary central hub of a labor market (e.g. a large central city) and look for commuting relationships to it, the Commuting Zone approach values commuters in both directions. In our update to this methodology, we used the Census’ Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) data set as our measure of commuting.
Counties are joined based on the highest percentage of cross commuting to form initial clusters. From there, cross commuting percentages are calculated between those initial clusters and other counties. More joining happens between clusters and counties based on those percentages, and the process repeats between clusters and other clusters, or clusters and counties. The process ends when a designated stopping point – in North Carolina’s case, 25 commuting zones – are created. The stopping point is set through statistical tests, common sense (i.e., is the distance too great for a regular commute), and external validity via comparison to the USDA’s commuting zones.
Map/Analysis
Below is a map of interconnected labor markets produced using the latest LODES data (2018):
The percentages (and colors) represent the share of workers in each market that live and work within those areas (henceforth described as “hub share”). Many of these zones span across multiple counties and/or MSAs, substantiating the need for alternative economic geographies mentioned in the introduction for this post. As evidenced by their darker coloring, the areas in Research Triangle (Raleigh-Durham-Chapel Hill), Charlotte-Mecklenburg, and Asheville have high hub shares. These hub shares provide evidence for existing policies that treat these counties as economic regions. Finally, the map shows interstate interconnected markets in the southwest (with northern Georgia), in the northern piedmont (with Danville, VA), and in the northeast (with Virginia Beach, VA). These regions imply the potential for interstate collaboration with respect to economic development.
Applications/Future Research
This analysis can inform the conversation about North Carolina’s economic geography and hopefully promote greater regional collaboration. For example, it may be beneficial for counties to collaborate, rather than compete, on business recruitment projects if they share a labor market. These counties benefit from the jobs created by adding a new employer to the region.