On the sources and value of information: public announcements and macroeconomic performance
Bibliographic record
Abstract
In the context of macroeconomic coordination, studies of the social value of information distinguish sharply between private and public information. However, no information is truly public (that is, common knowledge) or private in the established sense. This paper develops a general approach by allowing for many informative signals each of which incorporates elements of both public and private information. A measure of relative publicity determines a signal's equilibrium use and its social value. Output gaps (and hence social losses) arise when signals differ in their publicity: such differences drive a wedge between price-formation and expectations-formation processes. Turning to the effect of public announcements, and contrary to previous results, it is never socially optimal to withhold information completely, nor is it optimal to release perfectly public (or, indeed, perfectly private) information. Instead, when perfect communication is feasible, limited clarity enhances macroeconomic performance.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".