State Pension Accounting Estimates and Strong Public Unions
Bibliographic record
Abstract
ABSTRACT Concerns are commonly raised that strong public unions extract generous pension benefits from state governments and are the cause of states' burdensome pension obligations. Prior research (Anzia and Moe 2015) finds evidence supporting such concerns. Consistent with incentives to minimize such perceptions, our findings suggest that state pension plans with stronger public unions select higher discount rates to improve reported funding levels. While riskier asset allocations are used to support the higher discount rates (which equal the expected return on the plan assets), most of the higher rates appear opportunistic. In addition, consistent with a desire to avoid drawing attention to persistent plan underfunding, our evidence indicates that stronger union plans are less likely to select longer amortization periods to recognize pension deficits when underfunding is larger. We do not, however, find evidence for asset smoothing periods being used to delay the recognition of investment losses on plan assets. Together, our findings suggest that stronger union plans take steps to make their pension obligations look less burdensome to the public.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.002 |
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 teacher head, 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".