Abstract
This article develops a demographic method to estimate the civilian noninstitutional population for counties and county equivalents in the U.S. While these data provide the key sampling frame for national labor market surveys and denominators for labor market prevalence rates, the data are thus far unavailable for small areas. I develop a modified cohort component method to produce novel, monthly estimates of the civilian noninstitutional population for all U.S. counties using publicly available data on population and vital statistics with minimal modifications. The resulting population data may be used by researchers and policymakers to study within-year population dynamics as they relate to economic and demographic factors. I further extend the method to produce short-term population projections that include the most current vital statistics. The method compares favorably to existing annual, midyear estimates by the U.S. Census Bureau, but is prone to error in areas with fewer vital events.
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Notes
The ACS provides limited information on the civilian noninstitutional population through Table S1811, which tabulates data on disability status. Coverage is limited to relatively few counties in both the 1-year and 5-year ACS data. I provide a comparison with the ACS CNP16 data in the Online Appendix.
These data are also called the evaluation estimates and span April 2010 through July 2020. The evaluation estimates are postcensal estimates based on the 2010 enumeration and do not incorporate information from the 2020 enumeration.
The NCHS data-use restrictions forbid publication of counts or death rates based on fewer than 9 deaths.
The American Community Survey (ACS) 5-year estimates also provide the same information; however, the 5-year estimates also lack timeliness and require additional assumptions when comparing across ACS 5-year samples. Additionally, the ACS tabulates the data by age using unconventional age groups that would require more assumptions to arrive at estimates of the population ages 16 plus.
An alternate data source on the intra-year distribution of birthdays is the American Community Survey (ACS) microdata, which records each respondent’s quarter of birth. While the ACS provides a more current measure than historical births data, the geographic coverage is loosely comparable to that of the NCHS births data. The lowest identifiable geography in the microdata is the Public Use Microdata Area (PUMA) and covers geographic areas with a minimum population of 100,000 residents. Further research may examine whether the quarter of birth estimates from the ACS provide more reliable population estimates than the historical births data.
The Population Estimates Program produces separate migration rates for two age groups, under 65 and 65 plus. The distinction between these two groups is the data source used to compute the migration rates. For the population under 65, the Census Bureau uses data on IRS tax filings; whereas, the 65 plus migration rates come from Medicare enrollment records from the Centers for Medicare and Medicaid Services (CMS). Since the CMS data are non-public, I use the total net migration rates produced by PEP for the entire population ages 15 plus.
For areas with extremely small GQ populations, the Beers (1945) formula occasionally returns negative counts for the single age 16 population. In these cases, I bottom code the GQ population for age 16 as zero. Since the populations in question are so small, this edit has no substantive effect on the subsequent prevalence rate calculations.
The Census Bureau plans to release intercensal series for 2010–2020 in 2023.
The same approach is used by U.S. Bureau of Economic Analysis (2022) to adjust the population data to prepare per capita personal income time series.
An alternative control series is the monthly national CNP16 projections from the U.S. Census Bureau. I opt to use the projected CNP16 estimates from the CPS, as they represent the most current data available for CNP16.
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I am grateful for comments from Sean Wilson, Tom Krolik, Luke Rogers, Larry Sink, and discussions at the Bureau of Labor Statistics and the Census Bureau. All views are my own and do not necessarily reflect those of the U.S. Bureau of Labor Statistics, the Department of Labor, or the United States.
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Forrester, A.C. Estimating the civilian noninstitutional population for small areas: a modified cohort component approach using public use data. J Pop Research 41, 5 (2024). https://doi.org/10.1007/s12546-023-09322-x
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DOI: https://doi.org/10.1007/s12546-023-09322-x