Innovation and Growth with Financial, and other, Frictions
Bibliographic record
Abstract
The generation and implementation of ideas, or knowledge, is crucial for economic performance. We study this process in a model of endogenous growth with frictions. Productivity increases with knowledge, which advances via innovation, and with the exchange of ideas from those who generate them to those best able to implement them (technology transfer). But frictions in this market, including search, bargaining, and commitment problems, impede exchange and thus slow growth. We characterize optimal policies to subsidize research and trade in ideas, given both knowledge and search externalities. We discuss the roles of liquidity and financial institutions, and show two ways in which intermediation can enhance efficiency and innovation. First, intermediation allows us to finance more transactions with fewer assets. Second, it ameliorates certain bargaining problems, by allowing entrepreneurs to undo otherwise sunk investments in liquidity. We also discuss some evidence, suggesting that technology transfer is a significant source of innovation and showing how it is affected by credit considerations.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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; 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".