A time to make laws and a time to fundraise? On the relation between salaries and time use for state politicians
Bibliographic record
Abstract
Abstract. We study the relationship between the level of compensation and time use for US state legislators. Using survey data on time use, we show that higher salary is robustly associated with legislators spending more time on fundraising. In contrast, higher salary is also robustly associated with less time spent on legislative activities and has no clear relation to time spent on constituent services. The fundraising results are particularly strong for legislators who do not intend to run for higher office. Our results are consistent with an interpretation that higher salary raises the value of office, and politicians respond with more fundraising because increased fundraising raises the chance of getting re‐elected more than does increased legislative activity. Back‐of‐the‐envelope calculations suggest that a $30,000 increase in salary is associated with politicians annually devoting 13 more hours to fundraising and 18 fewer hours to legislative activity. Résumé. Un temps pour faire les lois et un autre pour lever des fonds? Étude du rapport entre le salaire et l’emploi du temps des législateurs d’état. Dans cet article, nous examinons le lien entre le niveau de rémunération et l’emploi du temps des législateurs d’état aux États‐Unis. En nous appuyant sur plusieurs données d’enquêtes relatives à la gestion du temps, nous montrons que plus leur salaire est élevé, plus les législateurs ont tendance à consacrer du temps à lever des fonds, le temps dévolu aux activités législatives ayant, quant à lui, tendance à diminuer sans lien évident avec le temps passé en circonscription au service des citoyens. Les résultats relatifs aux levées de fonds sont particulièrement forts dès lors que les législateurs ne briguent pas de plus hautes fonctions, et sont conformes à l’interprétation selon laquelle un salaire élevé accroît l’intérêt pour une fonction. Et puisque l’augmentation des activités de financement ne se traduit pas par davantage d’activités législatives, mais par des chances de réélection accrues, les politiciens multiplient les levées de fonds. Des calculs rapides et approximatifs suggèrent qu’une hausse du salaire des politiciens de 30 000 $ par an représenterait 13 heures de collecte de fonds en plus, mais 18 heures d’activités législatives en moins annuellement.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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 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".