Pandemic Patterns: California is Seeing Fewer Entrances and More Exits
Bibliographic record
Abstract
Since the COVID-19 pandemic began, fewer people have been moving into California from other states and more have been leaving. At the end of September 2021, entrances to California were 8% lower than at the end of March 2020.1 Exits, following a dip in the first half of 2020, stood 12% higher at the end of September 2021 than at the end of March 2020 — representing a return to a steady pre-pandemic rate of increase of approximately 4% per year since 2016. Net domestic migration, defined as the difference between entrances and exits, went from 40,000 net exits per quarter prior to the pandemic to 80,000 afterward. This brief uses data through the end of September 2021. These trends are present throughout the state. Since the end of March 2020, new entrances to the state have dropped in 40 of 58 California counties, and when Californians move, they are slightly more likely to leave the state than they were before the pandemic began (true for nearly every county). But the Bay Area stands out, for several reasons. Since the end of March 2020, new entrances to Bay Area counties have dropped more quickly than in other parts of the state. Before the pandemic, San Francisco County was the only net receiver of population from other US states. Today, all California counties lose population to domestic migration. In addition, whereas in every other economic region the move rate fell since the pandemic began, Bay Area residents moved (to any destination) at higher levels (up 0.3 percentage points, to 4.2%), driving a 21% increase in Bay Area exits.This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".