Abstract
Microdata from U.S. decennial censuses and the American Community Survey are a key resource for social science and policy analysis, enabling researchers to investigate relationships among all reported characteristics for individual respondents and their households. To protect privacy, the Census Bureau restricts the detail of geographic information in public use microdata, and this complicates how researchers can investigate and account for variations across levels of urbanization when analyzing microdata. One option is to focus on metropolitan status, which can be determined exactly for most microdata records and approximated for others, but a binary metro/nonmetro classification is still coarse and limited on its own, emphasizing one aspect of rural–urban variation and discounting others. To address these issues, we compute two continuous indices for public use microdata—average tract density and average metro/micro-area population—using population-weighted geometric means. We show how these indices correspond to two key dimensions of urbanization—concentration and size—and we demonstrate their utility through an examination of disparities in poverty throughout the rural–urban universe. Poverty rates vary across settlement types in nonlinear ways: rates are lowest in moderately dense parts of major metro areas, and rates are higher in both low- and high-density areas, as well as in smaller commuting systems. Using the two indices also reveals that correlations between poverty and demographic characteristics vary considerably across settlement types. Both indices are now available for recent census microdata via IPUMS USA (https://usa.ipums.org).
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Data for the featured indices are freely available at https://usa.ipums.org.
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The authors have not made code available but can upon request.
Notes
The Census Bureau’s urban/rural classifications have evolved over time (Ratcliffe, 2015), but for the 2000 and 2010 censuses, the general procedure was to define “urban areas” as groups of relatively dense neighboring (or nearby) blocks with combined populations of at least 2,500 each (Ratcliffe et al., 2016). The Census then classified all residents of urban areas as “urban” and all other population as “rural.” OMB metropolitan area definitions have also evolved, but since 2003, the OMB has delineated “metropolitan statistical areas” as one of two types of CBSAs along with “micropolitan statistical areas.” Each CBSA consists of a set of central counties, where a substantial population resides in the same core urban area(s), combined with any outlying counties, where a substantial proportion of workers commute to or from the central counties. To qualify as a metropolitan area, a CBSA must contain an urban area with at least 50,000 residents, while the largest urban area in a micropolitan area has between 10,000 and 50,000 residents (https://www.census.gov/programs-surveys/metro-micro/about.html).
Since 2003, the OMB has designated certain places within each CBSA as “principal cities,” typically the largest incorporated place within a CBSA along with other places of similar size. Prior to 2003, the OMB instead used the term “central city” to denote a similar concept.
IPUMS USA recently added a variable, PCTMETRO, that gives the percentage of each PUMA’s population living in metro areas, which analysts can use to produce a binary metro/nonmetro classification like that of the ERS.
The complete CBSA specifications include additional information distinguishing central and outlying counties as well as central/principal cities, but neither the IPUMS METRO variable nor the ERS metro/nonmetro classifications convey all this information, nor could they with great precision at the PUMA level. This additional CBSA information also pertains mainly to a second dimension of urbanization, accessibility/remoteness, and still reveals little about another key dimension, concentration.
In Coombes and Raybould’s model, settlement size is associated with the size of an urban area (a concentrated settlement) and accessibility is associated with proximity to large settlements. In our model, “size” is associated with the size of an entire commuting system, encompassing both urban areas and lower-density areas that are “accessible” to the urban core as determined by commuting flows.
Although 2010 census tracts nest exactly within 2010 PUMAs, not all 2000 census tracts nest within 2000 PUMAs. Also, the 2005–2011 ACS PUMS files use 2000 PUMA definitions, but DENSITY summarizes 2010 tract densities for those samples, so it is necessary to associate 2010 tracts with the 2000 PUMAs for those samples.
For Virginia “independent cities” that lie outside of CBSAs, we combine the populations of the independent cities with the populations of their neighboring counties.
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Acknowledgements
This work was supported by grants from the National Institutes of Health (R01HD043392, P2C HD041023). David Van Riper provided helpful feedback throughout the research process, and John Cromartie provided helpful comments as a conference discussant.
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Support for this work was provided by IPUMS USA (NIH R01HD043392) and the Minnesota Population Center (NIH P2C HD041023).
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Jonathan Schroeder developed and computed the indices, produced the figures, and led the writing. José Pacas initiated the research, developed the illustrative models, produced the tables, and contributed to the writing.
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The authors have released a similar version of this manuscript through the Minnesota Population Center Working Paper Series: #2019-05, https://doi.org/10.18128/MPC2019-05. An earlier version appears in the 2019 conference proceedings of the Population Association of America under the title “Getting ‘Rural’ Right: Poverty Disparities Across Two Dimensions of Rurality,” including a third author, David Van Riper.
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Notice of Prior Versions: The authors have released a similar version of this manuscript through the Minnesota Population Center Working Paper Series: #2019-05, https://doi.org/10.18128/MPC2019-05. An earlier version appears in the 2019 conference proceedings of the Population Association of America under the title “Getting ‘Rural’ Right: Poverty Disparities Across Two Dimensions of Rurality,” including a third author, David Van Riper.
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Schroeder, J.P., Pacas, J.D. Across the Rural–Urban Universe: Two Continuous Indices of Urbanization for U.S. Census Microdata. Spat Demogr 9, 131–154 (2021). https://doi.org/10.1007/s40980-021-00081-y
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DOI: https://doi.org/10.1007/s40980-021-00081-y