Investigating the Dynamic Effects of Counterfeits with a Random Changepoint Simultaneous Equation Model
Bibliographic record
Abstract
Using a unique panel dataset and a new model, this article investigates the dynamic effects of counterfeit sales on authentic-product price dynamics.We propose a Bayesian random-changepoint simultaneous equation model that simultaneously takes into account three important features in empirical studies:(1) Endogeneity of a market entry, (2) Nonstationarity of the entry effects and (3) Heterogeneity of the firms' response behaviors.Besides accounting for the endogeneity of counterfeiting, the proposed methodology improves the estimation of dynamic effects under heterogeneous response times by firms.We identify both a temporary negative short-term effect and a stable positive long-term effect of counterfeit sales on the authentic prices.Such effect estimates are biased in the OLS model and attenuated in a standard IV model.The findings help to unify two strands of I.O.theories on the pricing effects of competition.Finally, our analysis identifies considerable heterogeneity in authentic firms' response behaviors (both response time and magnitude), and the hierarchical structure of our model enables a study of the drivers of the heterogeneity.This study casts managerial insights on effective brand protection and management strategies that can be tailored to each type of firms.The method illustrated provides a new approach to use field data to study the determinants of a firm's response time, an important dimension of management strategy.In particular, firms with more human capital or less diversification from infringed markets were faster in responding and differentiating from counterfeits.The proposed framework can be widely applied to study dynamic and heterogeneous causal effects of marketing variables.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".