The USDA, Economic Research Service’s (ERS) Commuting Zones (CZs) are geographic units of analysis that are intended to reflect the local economy where rural people live and work. Commuting Zones allow users to aggregate county-level data to geographic units of analysis that more closely reflect functional rural labor markets. The information on this page documents the creation and use of CZs in several sections:
- Background
- Scope/Coverage
- Methodology
- Strengths and Limitations
- Data Source
- Recommended Citation
- References
Background
Commuting Zones (CZs) and Labor Market Areas (LMAs) were developed by ERS in the 1980s to facilitate research on how labor markets influence the socioeconomic well-being of workers residing in those markets (Tolbert & Sizer Killian, 1987; Tolbert & Sizer, 1996). A local economy and its labor market are not defined by the nearest county line but by interrelationships between buyers and sellers of labor. Therefore, geographic areas that captured these cross-county interrelationships were needed to understand the variation in socioeconomic conditions across the nonmetropolitan United States. Existing regional delineation systems—such as the U.S. Department of Commerce, Bureau of the Census’s (Census Bureau) Core Based Statistical Areas and the U.S. Department of Commerce, Bureau of Economic Analysis’s (BEA) Economic Areas—did not adequately capture these interrelationships in rural counties.
The 1980 and 1990 delineations include two geographic levels: Commuting Zones and Labor Market Areas. The smaller geographic areas, Commuting Zones, are clusters of counties based on intercounty commuting patterns. The larger geographic areas, Labor Market Areas (LMAs), are neighboring commuting zones grouped using commuting patterns to contain at least 100,000 residents. This minimum population level was necessary to acquire specially tabulated public-use microdata samples from the 1980 and 1990 Decennial Censuses that identify the labor markets in which individuals work.
The process used to create the 1980 and 1990 CZs and LMAs included two steps. Step 1 created preliminary delineations using a hierarchical cluster analysis based on commuting flows between counties. Step 2 utilized expert opinion to revise the preliminary delineations by placing counties into commuting zones that experts deemed more reasonable. This process resulted in 765 CZs and 382 LMAs in the 1980 delineation, while the 1990 delineation contains 741 CZs and 394 LMAs.
The 2000 CZ methodology used only step 1 (the hierarchical cluster analysis) of the previous methodology. Furthermore, LMAs were discontinued due to limited usage. The 2000 delineation contains 709 CZs.
ERS did not publish CZs for 2010, but The Pennsylvania State University researchers (Christopher S. Fowler, Danielle C. Rhubart, and Leif Jensen) evaluated the ERS Commuting Zone methodology to create new CZs for 2010 and used a consistent methodology to create revised CZs for 1990 and 2000 (Fowler et al., 2016). This revised methodology only uses hierarchical cluster analysis to identify CZs. The methodology also includes changes to how the data are processed and to specifications used in the cluster analysis. Fowler later created 2020 CZs using the same methodology (Fowler, 2024). Fowler’s CZ delineations (as well as source code, research, and documentation) are available on the Labor-sheds for Regional Analysis website.
ERS’s 2020 Commuting Zones are based on the consistent methodology that was developed by Fowler, Rhubart, and Jensen. However, the ERS's 2020 methodology adds an additional requirement that all commuting zones be contiguous, so they are not an exact match.
Scope/Coverage
The 2020 Commuting Zones (CZs) data product places the 3,222 counties and census-designated county equivalents in the United States and Puerto Rico into 598 CZs. The Island Areas of American Samoa, Guam, the Northern Mariana Islands, and the U.S. Virgin Islands are not included because county-to-county commuting data are not available for these areas.
In addition to identifying which commuting zone each county belongs to, the 2020 CZs data product assigns unique names to each CZ based on the name of the largest urban area in the CZ. If there are no urban areas in a CZ or if one urban area is the largest in multiple CZs, the CZ is given the name of the largest incorporated place or census designated place in the CZ. County- and CZ-level “containment” measures are also included. “County containment” is the percentage of a county’s resident work force that commutes to work within the CZ. “Average county containment” is the unweighted average of the county containment measure for all counties in a CZ. These are measures used to evaluate how well each CZ captures the labor market of its component counties (Fowler & Jensen, 2020; Fowler, 2024).
The 1980, 1990, and 2000 CZ and Labor Market Area (LMA) delineations are available for counties in the 50 U.S. States and Washington, DC. For the 1980 and 1990 delineations, a CZ code is provided for each county indicating its CZ. Importantly, there is not a separate LMA code in these releases; each county’s LMA is indicated by the first three digits of the CZ code.
A delineation of 2010 commuting zones is not currently available from ERS. Fowler, Rhubart, and Jensen from Penn State published a 2010 CZ delineation, as well as revised CZs for 1990 and 2000, that all follow a consistent methodology. Fowler later created a 2020 CZ delineation using the same methodology. These delineations (as well as source code, research, and documentation) are available on the Labor-sheds for Regional Analysis page.
Methodology
The 2020 Commuting Zones data product is based on the Fowler et al. (2016) and Fowler (2024) methodology but adds an additional constraint that all counties comprising a commuting zone must be contiguous.
The 2020 CZs delineation uses county-level commuting flows data (see the Data Source section) to represent the labor market connection among counties. For every county and census-designated county equivalent, the commuting flows data estimate how many workers reside in County A and commute to County B for work. Commuting zones are created by grouping counties together, based on the strength of the commuter flows between them.
A proportional flow metric is used to represent the strength of the commuting flows between counties. The metric is defined as the sum of the number of people commuting from County A to County B and the number of people commuting from County B to County A, divided by the smaller of County A’s and County B’s work forces. Defined mathematically:
where Cab represents the number of commuters residing in County A and working in County B, Cba represents the number of commuters residing in County B and working in County A, Wa represents the total work force residing in County A, and Wb represents the total work force residing in County B.
The proportional flow metric is calculated for every pair of county combinations in the United States and Puerto Rico. County pairs with a large number of people commuting between them relative to the size of the smaller county are considered to have a stronger commuting connection. Conversely, county pairs with fewer people commuting between the two counties (relative to the size of the smaller county) are considered to have a weaker commuting connection. Using the smaller of the two county work forces as the denominator in the proportional flow metric prioritizes grouping smaller work force counties with larger ones.
The proportional flow of workers commuting between each county combination is used to calculate the distance metric (this metric does not refer to the geographic distance between counties, e.g. miles) for an agglomerative hierarchical cluster analysis. Agglomerative hierarchical cluster analysis is an iterative process that starts with individual counties and gradually groups the counties together into successively larger clusters. This process is implemented using the distance metric between the counties. In our analysis, the distance metric is calculated as 1 minus the proportional flow. The distance metric ranges between 0 and 1, where .001 represents the strongest possible commuting connection between two counties and 1 indicates no commuting connection between two counties. See Fowler (2024) for more details on preparing the data for hierarchical cluster analysis.
Using an unconstrained hierarchical cluster analysis to identify commuting zones results in some CZs that are not contiguous. That is, at least one county in the CZ is not adjacent to the other counties in the CZ. While it is conceivable that strong commuting connections may exist between noncontiguous counties, noncontiguous commuting zones conflict with the conceptual model of what a CZ represents (Fowler, 2024). To avoid having noncontiguous CZs, we use a constrained hierarchical cluster analysis developed by Guénard and Legendre (2022). Our constrained hierarchical cluster analysis uses the Census Bureau's County Adjacency File to limit which counties may be grouped by the cluster analysis to counties that are adjacent to each other. When counties A and B are grouped together, their adjacency characteristics are shared so that all counties adjacent to A are considered adjacent to B and vice versa. The contiguity requirement requires that separate hierarchical cluster analyses must be run for the contiguous United States, Alaska, Hawaii, and Puerto Rico.
The cluster analysis starts by grouping the two adjacent counties with the smallest distance (strongest proportional commuting flow) between them to create the first cluster. Once two counties have been grouped into a cluster, they act as a single unit for the rest of the clustering process. When units are grouped into a cluster, their distance to other counties or clusters must be recalculated. We use the average linkage method to recalculate the distance between units. This method calculates the average distance of each county in one grouped unit to each county in the other grouped unit. Then, the next two adjacent units (counties or clusters) with the smallest distance are combined. This process continues until the maximum distance threshold is reached.
The figure below illustrates how the iterative steps used by the cluster analysis create the Steamboat Springs, CO Commuting Zone (CZ 68). In Step 1, Moffat and Routt Counties are grouped together into a cluster because their distance metric is the lowest of the county pairs (indicating a stronger connection) at 0.69. The connection between the new cluster and Rio Blanco County is then recalculated by averaging the connection between Rio Blanco County and Moffat County and between Rio Blanco County and Routt County. In Step 2, the connection between Rio Blanco County, CO and the cluster is 0.92. Since the connection of Rio Blanco County to the cluster is less than 0.977, Rio Blanco is considered to have a strong enough connection to be added to the cluster. There are no other adjacent counties with a strong enough connection to be added to the cluster, so the final result, shown in Step 3, is the Steamboat Springs, CO Commuting Zone, CZ 68.
Agglomerative hierarchical cluster analysis can continue grouping units—counties and/or commuting zones—together until one large cluster is created. However, we want to identify functional labor markets with strong commuting ties so that researchers can use labor markets to analyze economic and social characteristics throughout the United States. Therefore, we end the clustering process when the connection between two units is too weak for the units to be considered part of the same labor market (i.e., the distance among units is too large for any more combinations to occur).
Following the previous work of our collaborators at Penn State, we set 0.977 as the distance at which two units are no longer considered to be part of the same labor market. This distance was determined by Fowler et al. (2016) to be the point at which their hierarchical cluster analysis on 1990 counties was the most similar to ERS’s published 1990 CZ delineation.
Strengths and Limitations
One of the greatest strengths of the ERS Commuting Zones (CZs) is that the CZs are designed to identify connections between small, rural counties and more populous counties. The CZs are intended to identify the counties to which rural residents are most likely to commute to work, shop, recreate, or consume other goods and services. The CZs create functional units of observation rather than units that are defined by political boundaries, such as counties. Additionally, CZs can be used with a variety of Federal and other data sources by aggregating widely-available county-level data to the CZ geographic unit.
Another advantage of CZs is that they include all the territory within the United States and Puerto Rico. Conversely, the U.S. Office of Management and Budget’s (OMB) widely-used Core Based Statistical Areas—metropolitan and micropolitan statistical areas—only include counties with strong commuting connections to urban cores with populations of 10,000 or more. All other counties are considered part of one large noncore area. This delineation results in a significant number of U.S. counties being excluded from the OMB’s delineation, whereas every county is placed in a labor market in the CZs data product.
While CZs are intended to represent real-world labor markets, the documentation for the original ERS CZs does not specifically define the term “labor market”. This lack of a clear definition makes it difficult to evaluate whether CZs successfully represent the concept. Fowler et al. (2020) identified measures of how well labor market delineations capture three different concepts often associated with labor markets. The most intuitive of these concepts is “containment”—the share of workers residing in a defined area that also work within that area. The CZs data product includes measures of county containment and average county containment so that users can evaluate the quality of each CZ and each county’s fit within its CZ. County containment is the percentage of county resident workers who commute within the CZ. Average county containment is the unweighted average value of county containment within each CZ.
Analyzing these containment measures provides additional insight into the strengths and limitations of ERS’s 2020 CZ data product. The map below shows average county containment within each of the 2020 CZs. The map illustrates that the 2020 Commuting Zones generally perform better on the containment measure in the western half of the United States than in the eastern half. This finding may be because residents in the more densely populated eastern United States may live close enough to multiple relatively large cities—which act as employment centers—to accept a job from multiple commuting destinations. Therefore, another limitation of the commuting zones is that they seek to identify discrete labor markets, when, in reality, labor markets overlap.
Another limitation of commuting zones is that it is difficult to explain why some counties are in one commuting zone and not another. Hierarchical cluster analysis is an iterative process where every grouping that is made is influenced by groupings made before it and will influence the groupings that come after it. To fully explain why a particular county gets placed in a particular commuting zone, one needs to calculate the proportional flows between all counties and track the clustering process to the point where the county was added to the cluster. Furthermore, the proportional flow metric always takes the perspective of the county with the smaller work force. This process means that there are many counties that are part of a CZ because a smaller county in that CZ sends and receives a relatively large number of workers to and from that county, though that county may have a stronger commuting connection with another CZ.
There are several limitations associated with the original CZs methodology that were kept for consistency over time. There are methodological decisions that had no documented justification other than that the decisions seemed to be reasonable to the researchers at the time. The most significant of these decisions was selecting 0.98 as the maximum average distance between units, at which point the hierarchical cluster analysis stopped grouping units together. Choosing a higher threshold would have resulted in fewer, larger CZs and choosing a lower threshold would have resulted in more, smaller CZs.
The selection of a seemingly arbitrary distance threshold may stem from the fact that the specific labor market concept that CZs were intended to capture was never explicitly defined (see Fowler and Jensen (2020) for more information on defining labor markets). Had the labor market concept been explicitly defined using measurable characteristics, it may have been possible to select a distance threshold that optimized the CZs’ ability to represent it . Instead, the distance threshold used in the 2010 and 2020 Commuting Zones (0.977) was selected to optimize the similarity between Penn State’s replication of the 1980 and 1990 CZs and the original CZs published by ERS (Fowler et al., 2016).
Another methodological limitation of the 1980 and 1990 CZ delineations is that they are not replicable. Their creation included a step in which an ERS researcher evaluated the results of the hierarchical cluster analysis and used expert judgement to reassign counties to CZs that the researcher deemed more appropriate. Given the limitations of the hierarchical cluster analysis described above, this step likely improved the final CZ data product by moving counties that were very weakly connected to the commuting zones assigned by the cluster analysis to other commuting zones with which the counties had stronger connections. While this step likely improved the CZs delineation, these decisions were made solely on the basis of expert knowledge, without documenting rules of thumb or guidelines followed to make these decisions.
Finally, there is a limitation associated with the data used to create the 2010 and 2020 CZ delineations. The delineations use county-to-county commuting flows data from the Census Bureau’s 5-year American Community Survey (ACS) estimates. The ACS is based on a relatively small sample of the U.S. population, particularly in counties with small populations. This means that ACS commuting flow estimates may not closely reflect real-world commuting flows in some counties due to sampling error. While data reliability is a concern, Fowler and Cromartie (2023) found that sampling error had little effect on the classification of the vast majority of counties and census tracts in other classification schemes.
Data Source
U.S. Department of Commerce, Bureau of the Census, 2016–2020 5-Year American Community Survey commuting flows. [Accessed June 2, 2025.]
U.S. Department of Commerce, Bureau of the Census, 2020 Census Demographic and Housing Characteristics File (DHC). [Accessed September 12, 2025.]
U.S. Department of Commerce, Bureau of the Census, 2024 County Adjacency File. [Accessed September 12, 2025.]
Recommended Citation
U.S. Department of Agriculture, Economic Research Service. 2020 Commuting Zones, January 2026.
References
Fowler, C.S. (2024). New Commuting Zone delineation for the U.S. based on 2020 data. Scientific Data, 11(975). https://doi.org/10.1038/s41597-024-03829-5
Fowler, C.S. & Cromartie, J. (2023). The role of data sample uncertainty in delineations of core based statistical areas and Rural Urban Commuting Areas. Spatial Demography, 11(2), 6. https://doi.org/10.1007/s40980-023-00118-4
Fowler, C.S. & Jensen, L. (2020). Bridging the gap between geographic concept and the data we have: The case of labor markets in the USA. Economy and Space, 52(7), 1395–1414. https://doi.org/10.1177/0308518X20906154
Fowler, C.S., Rhubart, D.C., & Jensen, L. (2016). Reassessing and revising commuting zones for 2010: History, assessment, and updates for U.S. ‘labor-sheds’ 1990–2010. Population Research and Policy Review, 35(2), 263–286. https://doi.org/10.1007/s11113-016-9386-0
Guénard, G. & Legendre, P. (2022). Hierarchical clustering with contiguity restraint in R. Journal of Statistical Software, 103(7), 1–26. https://doi.org/10.18637/jss.v103.i07
Tolbert, C.M. & Sizer Killian, M. (1987). Labor market areas for the United States (Report No. AGES870721). U.S. Department of Agriculture, Economic Research Service. https://doi.org/10.22004/ag.econ.277959
Tolbert, C.M. & Sizer, M. (1996). U.S. Commuting Zones and Labor Market Areas: A 1990 update (Staff Paper No. AGES-9614). U.S. Department of Agriculture, Economic Research Service. https://doi.org/10.22004/ag.econ.278812