Population projections for U.S. counties by age, sex, and race controlled to shared socioeconomic pathway

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 re...

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Veröffentlicht in:Scientific data 2019-02, Vol.6 (1), p.190005-190005, Article 190005
1. Verfasser: Hauer, Mathew E.
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Sprache:eng
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Zusammenfassung: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. Design Type(s) source-based data analysis objective Measurement Type(s) population characteristics Technology Type(s) computational modeling technique Factor Type(s) age • biological sex • ethnic group • Socioeconomic Factors Sample Characteristic(s) Homo sapiens • United States of America Machine-accessible metadata file describing the reported data (ISA-Tab format)
ISSN:2052-4463
2052-4463
DOI:10.1038/sdata.2019.5