Economic performance and electoral volatility: Testing the economic voting hypothesis on Indian states, 1957–2013
Bibliographic record
Abstract
The consequences of variations in economic growth for vote volatility are analyzed on a panel of 14 Indian states between 1957 and 2013. Two measures of volatility are used: changes in party vote shares at the assembly level and changes in the state average of vote volatilities constructed at the constituency level. While the results find that both vary inversely with income growth rates, volatility at the constituency level is found to be more sensitive to growth rates. Examination of the periodicity of income growth’s impact finds that growth in the final year of governance has a stronger effect on volatility than does the average growth rate arising over the incumbent’s tenure. We confirm for Indian states that vote volatility responds more to negative changes than positive changes in the growth rate and, by decomposing volatility we find, contrary to most studies, that growth rates affect internal vote shifting more than shifting between exiting parties and newcomers. The responsiveness of volatility to economic and political characteristics of the state reinforces the hypothesis that theories of economic voting have an important role to play in understanding electoral volatility and may provide a more insightful way of approaching the political business cycle.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".