Information acquisition and funding for public service agencies: imperfect categorizing
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the impact of information acquisition for the purpose of differentiating agencies operating in different localities on the design of optimal funding. Design/methodology/approach This paper is a theoretical study. The focus is on a situation in which agencies providing public services have perfect private information about their cost conditions before the government sets the formula for funding. Findings The authors show that, using a free signal correlated with costs of operation to differentiate agencies situated in different localities, the government can achieve better welfare for households across regions. However, when there exist non-negligible costs involved in the differentiating process, it may pay to acquire information only if the signal acquired is informative enough, i.e., the correlation between the signal and the agencies’ true cost conditions is strong enough. Social implications This paper is of interest to academics and policy makers. Acquiring information for tagging can be viewed as a preliminary screening process. Different types are then endowed with distinctly different incentives to control the costs of operating their agencies. Specifically, when the observed cost signal and the true cost conditions of agencies are positively correlated, the government should optimally be more aggressive in distorting the high-cost type’s effort decision by giving less incentive for the low-cost type agencies to cut costs than in the no-differentiation case, and vice versa. Originality/value This paper is the first study that explores the impact of information acquisition on the design of optimal funding for public service agencies.
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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 | 0.021 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".