Local government amalgamations and pre-merger overspending: Central naivety meets local opportunism
Bibliographic record
Abstract
Amalgamation of local governments is an incentive for pre-merger overspending as the costs are transferred to the merged unit of the future. The article updates application of the common pool theory to such opportunistic pre-merger behavior. It studies Norway’s reform of the 2010s and paints a uniquely nuanced picture of pre-merger overspending, comparing fiscal policies before and after enactment of the reform among merging and non-merging municipalities. It provides corroboration that local governments that are about to merge overspend prior to the merger. New insights are gained into local governments’ differential incentives to allocate overspending to capital or current expenditure, and their opportunities to act on these incentives in the final and penultimate years before mergers are implemented. New insights are also gained into the differential incentive structures of junior and senior partners to a merger. Juniors overspend the most on current expenditure, while junior and senior partners overspend equally on capital expenditure. These insights not only have theoretical value but also practical applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".