Bibliographic record
Abstract
Abstract What explains the variation in countries’ propensity to engage in austerity policy? Economic and political country-level factors are the paramount explanations in the literature. Nevertheless, variation in fiscal preferences at the executive level remains underexplored, except for ideology. Moreover, budget decisions are endogenous to the state of the economy, thus casting doubt on standard measures based on the debt and/or deficit ratio. This article contributes to the literature in two ways. First, I turn to the individual level of analysis and suggest that leaders with business experience are more likely to pursue a balanced budget and tend to implement fiscal consolidation policies based on spending cuts. Second, I ease concerns about individuals’ self-selection into office by relying on fiscal adjustments that are weakly orthogonal to the economic cycle. The statistical analysis of a panel of 17 OECD countries between 1978 and 2014 confirms the theoretical expectations. The results are robust to a variety of specification and statistical methodologies and hold for a subset of as-if random leadership transitions following close elections. A case study of Brian Mulroney's governments in Canada (1984–93) further illustrates the argument.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".