The Impact of Supply Chains on Firm-Level Productivity
Bibliographic record
Abstract
Firms in a vertical relationship are likely to affect each other’s productivity. Exactly how does productivity spill over across this type of relationship (i.e., through which mechanisms)? Additionally, how does the relative importance of these mechanisms depend on the structure of the supply chain? To answer these questions, we decompose the channels of upstream productivity spillovers—from customers to suppliers—by developing a structural econometric model on a sample of approximately 22,500 supply chain dyads. We find that the “endogenous channel” (i.e., the effect of the customer’s own productivity on the supplier’s productivity) is by far the most important source of spillovers. This is especially true if (i) the supplier has a concentrated customer base, (ii) the supplier and the customer have similar operational characteristics, and (iii) the relationship has medium maturity. In the converse scenarios, we find, it is more important to have a partner with a portfolio of favorable “contextual” characteristics (high inventory turnover, financial liquidity, and asset turnover) than to have a productive partner. The online appendix is available at https://doi.org/10.1287/mnsc.2016.2632 . This paper was accepted by Serguei Netessine, operations management.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".