Does Income Inequality enter into an Aggregate Model of Voter Turnout? Evidence from Canada and Indian States
Bibliographic record
Abstract
Conflict theory argues that greater income inequality induces greater political and electoral participation. Relative power theory argues that greater inequality leads to political alienation and electoral disengagement. We test these alternatives on time series data by entering the Gini coefficient into an aggregate model of electoral participation in Canada. While ordinary least squares (OLS) results suggest that income inequality is inversely related to voter turnout, time series considerations raise the possibility that this result is spurious. Correction using a linear autoregressive distribution lag (ARDL) model finds no evidence of a relationship, but nonparametric modeling suggests an inverted U-shaped shape that is captured quadratically within the ARDL model. Additional support is found when the nonlinearity hypothesis is tested on a panel of Indian states. Together the results are consistent with the hypothesis that conflict theory operates at low levels of income inequality before growing inequality leads to voter alienation and lower turnouts consistent with relative power theory.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".