Long‐term policymaking and politicians' beliefs about voters: Evidence from a 3‐year panel study of politicians
Bibliographic record
Abstract
Abstract Politicians are required to make policy decisions that involve short‐term and long‐term tradeoffs, and existing theory largely expects election‐driven myopic thinking to dominate their reasoning when they do so. Direct evidence on this is surprisingly absent, leaving open questions on whether and when politicians do support future‐oriented policies, and what factors, beyond the shadow of elections, influence such choices. Responding to this gap, we report results of a multi‐year survey of more than 1500 elected politicians who faced an original decision task involving short‐term and long‐term solutions to a local policy problem. First, we show that politicians' theories of voting behavior—specifically, their beliefs about whether voters focus on the short or the long term—strongly predict their decisions when facing inter‐temporal policy tradeoffs. Second, we show that politicians are responsive to changes to short‐run costs associated with long‐term policy investments. Finally, we leverage the panel design of our study and find—in contrast to prevalent assumptions—no evidence that politicians' policy choices are related to their proximity to the next election. In doing so, we expand and refine the theoretical framework on inter‐temporal choice by policymakers, and outline a comparative research agenda for studying how politicians think about the future.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".