A General Empirical Law of Public Budgets: A Comparative Analysis
Bibliographic record
Abstract
We examine regularities and differences in public budgeting in comparative perspective. Budgets quantify collective political decisions made in response to incoming information, the preferences of decision makers, and the institutions that structure how decisions are made. We first establish that the distribution of budget changes in many Western democracies follows a non‐Gaussian distribution, the power function. This implies that budgets are highly incremental, yet occasionally are punctuated by large changes. This pattern holds regardless of the type of political system—parliamentary or presidential—and for level of government. By studying the power function's exponents we find systematic differences for budgetary increases versus decreases (the former are more punctuated) in most systems, and for levels of government (local governments are less punctuated). Finally, we show that differences among countries in the coefficients of the general budget law correspond to differences in formal institutional structures. While the general form of the law is probably dictated by the fundamental operations of human and organizational information processing, differences in the magnitudes of the law's basic parameters are country‐ and institution‐specific.
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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 | 0.007 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".