About
Oregon law directs state agencies to collect data on Race, Ethnicity, Language, and Disability (REALD) using a standardized framework, so that health and social outcomes can be understood and addressed equitably across Oregon's communities. As REALD data are being collected in more contexts, the necessity is growing for REALD population estimates to serve as denominators to calculate rates, prevalence, coverage, or for other program needs.
To fill that gap, the Portland State University Population Research Center partners with the Oregon Health Authority's REALD & SOGI Unit and the Oregon Secretary of State to produce county-level population estimates by REALD category, built from the U.S. Census Bureau's American Community Survey (ACS). These estimates give state agencies, counties, and community organizations a consistent, methodologically grounded basis for research and planning.
What's Available
Estimates are published for Oregon counties, broken out by:
- Age and sex
- Race and ethnicity, both federal (OMB) and Oregon REALD categories, according to a single imputed primary race.
- Disability status, overall and by six specific disability types (hearing, vision, cognitive, ambulatory, self-care, and independent living)
- Language spoken at home and limited English proficiency (LEP) status
Each dataset includes both the estimate itself and its standard error, so that users can assess the statistical reliability of an estimate before relying on it — this matters most for smaller counties and smaller subpopulations, where estimates are less precise.
Datasets are released periodically as new ACS data becomes available, and are organized by:
- The ACS 5-year survey period the estimates are drawn from (e.g., 2020–2024)
- The REALD standard in effect at the time (the categories and definitions used were updated in 2024; older releases use the prior 2020 standard)
Download the Data
Summary results are available as Excel reports:
[Release pending -- check back soon!]
Full datasets are available on our GitHub repository:
github.com/PSU-Population-Research-Center/census_reald24
Data is provided as Stata (.dta) files, organized into dated releases. Full download instructions, file naming conventions, and a complete list of available years are in the repository README and on the Releases page.
Using the Data Responsibly
A few things to keep in mind when working with these estimates:
- These are estimates, not counts. Like all data from the American Community Survey, they carry sampling uncertainty. Always check the standard error alongside any estimate, especially for smaller counties or smaller populations.
- Standards have changed over time. REALD race/ethnicity categories were revised in 2020 and again in 2024. Estimates published under different standards are not always directly comparable, even where a category name appears unchanged.
- REALD imputation methodology is not fixed. The methodologies used to impute REALD status and primary REALD identity based on ACS characteristics is an area of ongoing research and results produced under the same REALD standards may not be comparable between data releases.
- Some totals won't sum to the county population. Disability status is only assessed for the non-institutionalized population, and language is only assessed for people age 5 and older, so these subpopulation totals are narrower than the full county population.
Methodology
The full technical methodology — including how REALD categories are imputed from ACS survey responses, and how survey estimates are translated from regional to county-level geography — is documented in detail in our GitHub repository and in the accompanying methodology documentation referenced there.
Funding Acknowledgement
- Oregon Health Authority IGA
#179509, "County-level REALD population estimates" - Oregon Secretary of State IGA #16500-00006522, "Census and demographic data for ORS 251.167 language access"'
Questions?
For questions about the data, methodology, or how to cite this work, please visit the GitHub repository or contact the Portland State University Population Research Center.