Abstract
Small area and subnational population projections are important for understanding long-term demographic changes. I provide county-level population projections by age, sex, and race in five-year intervals for the period 2020–2100 for all U.S. counties. Using historic U.S. census data in temporally rectified county boundaries and race groups for the period 1990–2015, I calculate cohort-change ratios (CCRs) and cohort-change differences (CCDs) for eighteen five-year age groups (0–85+ ), two sex groups (Male and Female), and four race groups (White NH, Black NH, Other NH, Hispanic) for all U.S counties. I then project these CCRs/CCDs using ARIMA models as inputs into Leslie matrix population projection models and control the projections to the Shared Socioeconomic Pathways. I validate the methods using ex-post facto evaluations using data from 1969–2000 to project 2000–2015. My results are reasonably accurate for this period. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States.
Highlights
Background & SummaryPopulation projections have a long history in the social and physical sciences as a means of examining demographic change, planning for the future, and to inform decision making in a variety of applications[1,2,3,4,5,6,7]
Scholars typically produce detailed population projections for countries[6,8], but growing demand for small-area demographic analysis, especially as it relates to climate change, highlights the importance of subnational projections[9,10,11,12,13,14]
The lack of rigorous small-area population projections by detailed demographic subgroups has likely hampered our understanding of subnational demographic change in the United States
Summary
Population projections have a long history in the social and physical sciences as a means of examining demographic change, planning for the future, and to inform decision making in a variety of applications[1,2,3,4,5,6,7]. Using a parsimonious cohort-component alternative[15], I overcome the data issues associated with a typical cohort-component projection to produce a set of U.S county-level population projections by detailed demographic characteristics (eighteen age groups, two sex groups, and four race groups) controlled to the five Shared Socioeconomic Pathways (SSPs)[8] and make both the R code and subsequent population projections available for dissemination to a wide audience. These projections can be used to understand small-area demographic change in the United States. Out-of-sample validation reveals errors on par with or better than cohort-component population projection models undertaken at the national and sub-national scale[18,19,20,21,22]
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