How low response among Latino immigrants will lead to differential undercount if the United States’ 2020 census includes a question on sensitive citizenship
Bibliographic record
Abstract
The article presents a model developed to estimate the undercount stemming from lowered response among sub-populations of 1 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="{}^{\text{st}}" display="inline" overflow="scroll"> <mml:msup> <mml:mi/> <mml:mtext>st</mml:mtext> </mml:msup> </mml:math> and 2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="{}^{\text{nd}}" display="inline" overflow="scroll"> <mml:msup> <mml:mi/> <mml:mtext>nd</mml:mtext> </mml:msup> </mml:math> generation Latino immigrants if a question on citizenship is included in Census 2020. The analysis is relevant to census efforts wherever socioeconomic and sociopolitical disparities result in differential census participation. The model is referred to as a “cascade” model because it examines successive causes of undercount in the course of non-response follow-up (NRFU), partial household omission in “complex” households, and omission of low-visibility housing units from the U.S. Census Bureau’s Master Address File (MAF). The analysis also examines the likelihood of enumeration errors from the U.S. Census Bureau’s proposed reliance on administrative records for enumerating non-responding housing units. The model incorporates data from an 8-county survey of Latino immigrants regarding their willingness to participate in Census 2020 if it includes a question on citizenship. It shows that systematic differences in the size of responding and non-responding households will undermine reliability of hot-deck imputation. The conclusion is that adoption of inadequately-tested “modernized” census procedures exacerbates differential undercount of immigrant populations and contributes significantly to geographic disparities in the census count and erodes the reliability demographic profile of areas with higher-than-average concentrations of immigrants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".