Do Higher Salaries Lead to Higher Performance? Evidence from State Politicians
Bibliographic record
Abstract
We study the impact of politician salary on electoral competitiveness and political performance using new data on US state legislators and governors over the last sixty years. Higher salary is associated with statistically significant, but economically small, increases in electoral competitiveness and legislative productivity, the latter measured with bill-passing and missed roll-call votes. Salary has no effect on politician quality, corruption, or fiscal policy. To address the possible concern of salary changes being correlated with politicians' outside options, we implement a spatial discontinuity design using legislative district pairs straddling state borders and find modest impacts of salary, similar as in our other research designs. The impact of politician salary is weakest in states with strong political parties, suggesting that parties may reduce entry. Despite small impacts on performance, higher salary is significantly correlated with behavior on another margin, namely time-use: time-use data suggests that politicians in higher wage states spend greater time on fund-raising and on constituent services, but no more time on legislative activities. Our results lend caution to common claims that increasing politician salary would significantly increase the quality of US state government.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".