A rising tide that lifts all boats: Long‐term effects of the Alaska Permanent Fund Dividend on poverty
Bibliographic record
Abstract
Abstract Although not designed as a social program to redistribute income, the Alaska Permanent Fund Dividend (PFD) has been reducing poverty by providing equal annual payments to nearly all state residents for over 40 years. We examine direct effects of the PFD on Alaska poverty rates since 1990, using US Census and American Community Survey Public Use Microdata Sample records to adjust for under‐reporting of children's PFD income in official statistics. We estimate that the PFD reduced the number of Alaskans with incomes below the US poverty threshold by 20%–40%. We measure only a small effect on income distribution: a 0.02 reduction in the Gini coefficient. The effect of the PFD has been even larger for vulnerable populations. The PFD has reduced poverty rates of rural Indigenous Alaskans from 28% to less than 22%, and has played an important role in alleviating poverty among seniors and children. Aside from the special case of 2020, up to 50% more Alaska children—15% instead of 10%—would be living in poor families without PFD income. The poverty‐ameliorating effects of the PFD have lessened somewhat since 2000, as dividend amounts adjusted for inflation have been declining.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".