Brand Management and Strategies Against Counterfeits
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
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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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 | 0.001 | 0.002 |
| 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