Beyond Stocks and Surges: The Demographic Impact of the Unauthorized Immigrant Population in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it