Outsourcing and Downstream R&D under Economies of Scale
Bibliographic record
Abstract
Abstract While the significance of economies of scale in outsourcing has been well-documented, the consequences of outsourcing on firms' R&D efforts, when outsourcing is driven by scale economies, remains unexplored. We investigate a simple model where downstream competitors outsource to a common upstream supplier out of their incentive to better exploit scale economies. We find that the efficiency gain of outsourcing in virtue of economies of scale helps to internalize R&D spillovers, which tends to enhance outsourcing firms' R&D incentives. On the other hand, outsourcing also mitigates downstream competition, which imposes an ambiguous impact on outsourcing firms' R&D incentives. The aggregate effect is that outsourcing enhances R&D investments only when R&D spillovers are sufficiently large, or when upstream economies of scale are sufficiently small.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".