Between Election Rivalry and the Agency Costs of Government: The Effectiveness of Party Competition Across Indian States, 1957–2018
Bibliographic record
Abstract
Two dimensions of the intensity of interparty rivalry are used to test the hypothesis that greater interparty competition enhances government efficiency. Using data from a set of 14 large Indian state governments between 1957 and 2018, we find confirmation for two political rivalry hypotheses. The first is that the ex-post size of the first versus second place seat share winning margin is a useful metric of the (in)effectiveness of rival party policing of incumbent spending behavior. The second is the hypothesis that excessive spending by the incumbent governing party is decreased by the expectation of greater election contestability and that contestability is related to the expected effective number of competing parties ( ENPSeats) nonmonotonically. Our analysis suggests that contestability across Indian States reaches a maximum when the incumbent faces an expectation of ENPSeats that is closer to 5 than to Duverger's 2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".