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) program has been reducing relative income inequality for the past 40 years by providing equal annual payments to nearly all state residents. We examine specifically the direct effects of the PFD on Alaska poverty rates over the past several decades, with a particular focus on vulnerable populations: children, elders, and Indigenous peoples. Since children of all ages may receive a PFD, accurate measurement of the effect of the PFD requires adjusting for the under-reporting of income of children in government surveys generating official poverty statistics. After adjusting individual incomes of US Census and American Community Survey Public Use Microdata Sample (PUMS) households, we find that the PFD reduced Alaska poverty rates from 2.1 to 4.2 percentage points from 1990 through 2019. The effect of the PFD on ameliorating poverty has been even larger for vulnerable Alaska populations. The PFD has reduced poverty rates of rural Indigenous Alaskans from 28 percent to less than 22 percent. The PFD has also played an important role in alleviating of poverty among seniors and children. Aside from the special case of 2020, as much as 50 percent more Alaska children – 15 percent instead of 10 percent – would be living in poor families without PFD income. The poverty-ameliorating effects of the PFD have lessened somewhat since 2000, as the dividend amount adjusted for inflation has been falling.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".