Beyond Stocks and Surges: The Demographic Impact of the Unauthorized Immigrant Population in the United States
Bibliographic record
Abstract
Stock estimates of the US unauthorized foreign-born population are routinely published, but less is known about this population's dynamics. Using a series of residual estimates based on 2000 Census and 2001-2022 American Community Survey (ACS), I estimate the components of change for the unauthorized immigrant population from 2000 to 2022 by region and country of origin. Further, I develop and present novel measures of expected duration in unauthorized status and demographic impact of unauthorized entries (i.e., person-years lived in unauthorized status). Results reveal dramatic changes over the last two decades. In the early 2000s, the unauthorized immigrant population was dominated by Mexicans who tended to remain in the United States for extended periods of time and whose demographic impact on the US population was substantial. After the 2007-2008 Great Recession, a new pattern emerged. Unauthorized migrants now arrive from across the globe, including Central America and Asia (up through 2018), and most recently from Europe, Africa, Canada, Venezuela, and other parts of South America. These new unauthorized immigrants are more likely to arrive on temporary nonimmigrant visas (which typically allow a foreigner to live and work in the United States for six years) and, with the exception of Venezuelans, spend less time in unauthorized status. Overall, the demographic impact of this new type of unauthorized migration is lower than it was two decades ago.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".