When Reelection Increases Party Unity: Evidence from Parties in Mexico
Bibliographic record
Abstract
Abstract It is often argued that when legislators have personal vote-seeking incentives, parties are less unified because legislators need to build bonds of accountability with their voters. I argue that these effects depend on a legislator’s ability to cultivate a personal vote. When parties control access to the ballot and the resources candidates need to cultivate personal votes, they can condition a legislator’s access to these resources on loyalty to the party’s agenda. I test this theory by conducting a difference-in-differences analysis that leverages the staggered implementation of the 2014 Mexican Electoral Reform. This reform introduced the possibility of consecutive reelection for state legislators, increasing their incentives to cultivate personal votes. I study unity in position-taking and voting behaviour of Mexican state legislators from 2012 to 2018. To analyze position-taking, I apply correspondence analysis to a new dataset of over half a million legislative speeches in twenty states. To study voting, I analyze over 14,500 roll-call votes in fourteen states during the same period. Results show that reelection incentives increased intra-party unity, which has broad implications for countries introducing electoral reforms aiming to personalize politics.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".