Rank Effects in Bargaining: Evidence from Government Formation
Bibliographic record
Abstract
Abstract Theories of multilateral bargaining and coalition formation applied to legislatures predict that parties’ seat shares determine their bargaining power. We present findings that are difficult to reconcile with this prediction, but consistent with a norm prescribing that “the most voted party should form the government”. We first present case studies from several countries and regression discontinuity design-based evidence from twenty-eight national European parliaments. We then focus on 2,898 Spanish municipal elections in which two parties tie in the number of seats. We find that the party with slightly more general election votes is substantially more likely to appoint the mayor. Since tied parties should (on average) have equal bargaining power, this identifies the effect of being labeled the most voted. This effect is comparable to that of obtaining an additional seat, and is also present when a right-wing party is the most voted and the second and third most voted parties are allied left-wing parties who can form a combined majority. A model where elections both aggregate information and discipline incumbents can rationalize our results and yields additional predictions we take to the data, such as voters punishing second most voted parties that appoint mayors.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 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".