Trust, Collaboration, and Economic Growth
Bibliographic record
Abstract
We propose a macroeconomic model in which variation in the level of trust leads to higher innovation, investment, and productivity growth. The key feature in the model is a hold-up friction in the creation of new capital. Innovators generate ideas but are inefficient at implementing them into productive capital on their own. Firms can help innovators implement their ideas efficiently but cannot ex ante commit to compensating them appropriately. Rather, firms are disciplined only by the value of their reputations—the present value of their future partnerships. We model trust as a public signal and construct a correlated equilibrium. When trust is high, firms anticipate fruitful collaborations and thus can credibly commit to not expropriating inventors, leading to the more efficient production of new capital. Our model can be used to qualitatively replicate the empirical relation between measures of trust and investment, innovation, and productivity growth—at both the micro and macro level. This paper was accepted by Tomasz Piskorski, finance.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".