Legislative Pensions and Re‐election Seeking: Evidence from Canadian Legislatures
Bibliographic record
Abstract
We use data from Canadian legislatures to examine how legislative pension rules affect the propensity of incumbents to seek re‐election. We predict that legislators with defined‐benefits pensions are more likely to seek re‐election than legislators without pensions. Once the legislator is vested (i.e., qualified) in the pension, however, this incentive disappears; indeed, pensions that accrue value quickly and can be collected at an early age, induce legislators to retire rather than seek re‐election. Difference‐in‐differences estimates bear out these predictions: on average, legislators with defined benefits pensions are 11 percentage points more likely to seek re‐election than legislators without pensions, whereas legislators who on vesting immediately qualify for a pension of 50% of their salary are 11 percentage points less likely to do so. These results show that legislative pensions alter the value that legislators place on re‐election and, in doing so, they affect the accumulation of legislative professionalism and the strength of democratic accountability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".