When Gray is Good: Gray Markets and Market‐Creating Investments <sup/>
Bibliographic record
Abstract
Gray markets arise when an intermediary buys a product in a lower‐priced, often emerging market and resells it to compete with the product's original manufacturer in a higher priced, more developed market. Evidence suggests that gray markets make the original manufacturer worse off globally by eroding profit margins in developed markets. Thus, it is interesting that many firms do not implement control systems to curb gray market activity. Our analysis suggests that one possible explanation lies at the intersection of two economic phenomena: firms investing to build emerging market demand, and investments conferring positive externalities (spillovers) on a rival's demand. We find that gray markets amplify the incentives to invest in emerging markets, because investments increase both emerging market consumption and the gray market's cost base. Moreover, when market‐creating investments confer positive spillovers, each firm builds its own market more efficiently. Thus, firms can be better off with gray markets when investments confer spillovers, provided the spillover effect is sufficiently large. These results provide a perspective on why firms might not implement control systems to prevent gray market distribution in sectors where investment spillovers are common (e.g., the technology sector) and, more broadly, why gray markets persist in the economy.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".