The Road to Power: Partisan Loyalty and the Centralized Provision of Local Infrastructure
Bibliographic record
Abstract
Because they yield durable and visible benefits to voters, public infrastructure expenditures are an attractive instrument for politicians to build enduring electoral support in their constituencies. Static models of special-interest politics typically predict that public spending should be targeted at swing voters, at the expense of voters who display strong partisan loyalty. Yet static theories are not well-suited to capture the implications of long-run relationships between political parties and their loyal supporters. To address this limitation, I set out a simple dynamic probabilistic voting model in which a government allocates a fixed budget across electoral districts that differ in their loyalty to the ruling party. The model predicts that the contemporaneous geographic pattern of spending depends on the way the government balances long-run ‘machine politics’ considerations with the more immediate concern to win over swing voters. To assess the empirical relevance of both forces, I analyze rich data on road spending from a panel of electoral districts in Québec. Empirical results exploiting the province’s linguistic fragmentation provide robust evidence that partisan loyalty is a key driver of the geographic allocation of spending, in contrast with the standard ‘swing voter’ view.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".