When Gray is Good: Gray Markets and Market‐Creating Investments <sup/>
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it