Bibliographic record
Abstract
In this paper, I provide a theory for brand‐protection strategies to reduce counterfeiting under weak intellectual property rights. My theoretical framework has general implications for endogenous sunk cost investments as a means of deterring counterfeiters. My model incorporates two layers of asymmetric information that counterfeits can incur: counterfeiters fooling consumers and buyers of counterfeits fooling other consumers. Brands have a number of tools at their disposal to maintain a separating equilibrium in the face of counterfeits. One of the theoretical predictions of this study is that counterfeit entry induces incumbent brands to introduce new products. This helps to explain the innovation strategies that authentic firms employ in response to entry by counterfeiters in practice. Authentic prices rise if and only if the counterfeit quality is lower than a threshold level. In addition, the model demonstrates how authentic producers could invest in self‐enforcement to increase counterfeiters' incentives to separate themselves from brands. Better channel management through company stores and other costly devices are forms of nonprice signals and complement a company's own enforcements against counterfeits. These predictions are validated using unique panel data collected from Chinese shoe companies covering the years 1993–2004. Data further reveal that companies with worse relationships with the government invest more in various self‐enforcement strategies, which are effective in reducing counterfeit sales, and that the set of strategies are complements rather than substitutes for each other.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".