Austerity and the path of least resistance: how fiscal consolidations crowd out long-term investments
Bibliographic record
Abstract
What policies do government prioritize when they implement austerity? I argue that governments choose the path of least resistance when they engage in fiscal consolidations. Positive policy feedback protects programmes from retrenchment when they cover large segments of the population. In contrast, policies involving discounting short-term benefits for long-term gains are more exposed to austerity as they are subject to an intertemporal trade-off. Using a compositional dependent variable analysis in 17 OECD countries from 1980 to 2014, I show that austerity, measured with the narrative approach to fiscal consolidations, is associated with a decrease in the proportion of public investment in research and development and gross fixed capital formation and an increase in health care and pensions’ proportion of budgets. The budget shares of human capital investments in education, childcare and active labour market policies and of compensatory transfer-based labour market insurance are resilient to austerity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".