Research Note—Channel Structure with Knowledge Spillovers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We study two main questions in this paper: (1) How do spillovers of knowledge created by manufacturers' investments in process innovation affect channel structure and effort investment incentives? (2) What are the interactions between organizational incentives to form joint ventures and strategic alliances with competitors, and coordinate decisions vertically with downstream channel members? We focus on situations where spillovers are involuntary, firms' innovative activities are nonoverlapping, and firms benefit directly from the results of competitors' innovations. Under these conditions, we find that spillovers in process knowledge increase the likelihood of observing decentralized channel structures. Surprisingly, decentralized manufacturers invest more in process innovation than perfectly coordinated manufacturers do when spillovers are large. Moreover, in industries where large spillovers exist, horizontal cooperation among manufacturers induces higher levels of process innovation investments than channel coordination does. From a public policy perspective, however, the desirability of such cooperative arrangements among competitors depends on channel structure: joint ventures among decentralized manufacturers are more likely to meet the regulators' criteria of raising effort investments than cooperation among integrated manufacturers would be. Investment incentives are best provided when firms share their process knowledge and are buffered from subsequent price competition by independent retailers.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it