Economic Performance and Electoral Volatility : Testing the Economic Voting Hypothesis on Indian States, 1957–2013
Bibliographic record
Abstract
The electoral consequences of variations in economic growth on vote volatility are analyzed on a panel of fourteen Indian states between 1957 and 2013. Two measures of volatility are used: first changes in party vote shares at the assembly level and the state average of volatilities constructed at the constituency level. While the results suggest that both volatilities are reduced by higher income growth rates, volatility at the constituency level is found to be somewhat more sensitive to growth rates. Examination of the periodicity of income growth’s impact finds that the growth rate in the final year of governance has a stronger effect on volatility than does the average income growth rate arising over the entire election cycle. We also find that vote switching responds more to negative rather than positive growth changes and, by decomposing volatility, find that growth changes affect internal vote shifting more than between established parties and new comers. More generally the responsiveness of volatility to set of economic and political characteristics of the state suggests that theories of economic voting have an important role to play in understanding electoral outcomes and hence the process of development
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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".