The Center for Community Solutions strongly opposes a lawsuit aimed at omitting millions of U.S. residents from the United States census based on their immigration status. Proponents of the lawsuit hope to omit residents without legal citizenship status by adding a citizenship question to the 2030 decennial census. We agree with the opinions of the Census Bureau’s experts, who oppose gathering data on citizenship status. Census research estimates that adding questions around citizenship status would lead to missing data, due to fear of disclosure, for roughly 9 million households that have at least one noncitizen member. This would unnecessarily bias and jeopardize the accuracy of critical data that can only be found in the census. As a nonpartisan think tank that firmly believes in advocating for effective policy supported by quality data, we see this as a threat to successful policymaking.
Census research estimates that adding questions around citizenship status would lead to missing data, due to fear of disclosure, for roughly 9 million households that have at least one noncitizen member.
This lawsuit is largely being driven by efforts to influence how the 435 congressional house seats are apportioned to states by population size. Per the Permanent Apportionment Act of 1929, the number of representatives apportioned to each state is adjusted every ten years, based on the decennial census. This allows for equal representation as state populations fluctuate. Per the 14th Amendment, those population counts are to be made by the “whole number of persons in each state,” regardless of citizenship status. Proponents of this lawsuit want to change who “counts” in this apportionment, ignoring actual population counts and only tallying U.S. citizens, breaking with a 234-year-old precedent.
Manipulating population counts to discount people who aren’t citizens, and/or adding a citizenship question to the decennial census that would lead to undercounting immigrant populations, would underrepresent populations in urban areas and overrepresent populations in rural, largely white regions. This is largely because Immigrant populations tend to be concentrated in cities and urban areas. This would also skew how funding (such as 2.8 trillion dollars in 2021) for things like infrastructure spending and disaster relief is allocated.
To visualize what this might look like, take a hypothetical scenario of 1,000 households.
Ignoring people in the data doesn’t make them go away
It does, however, disenfranchise them. It obscures their truths. It makes it harder for everyone to trust our public data. But they still exist. Cherry-picking data makes data useless. Just imagine how meaningful are your 5K results if you ignore the fastest runners? How effective are the new airbags in your car if the manufacturer disregards failed deployments in their testing? How recommended is your toothpaste if they only ever count the one dentist who dislikes it? You’re still an average runner in a dangerous car using toothpaste as good as any other, regardless of what your “data” says.
Data must be as unbiased and comprehensive as possible to be useful and representative.
Thanks to the journalist Hansi Lo Wang, whose extensive reporting on this topic was helpful for this article, and whose continued reporting on the preservation of meaningful data is critical.
