Laboratory markets in counterfeit goods: Hong Kong versus Las Vegas
Bibliographic record
Abstract
“Black markets” represent an extreme challenge to empirical researchers due to the almost insurmountable obstacle of obtaining high-quality data. The dearth of high-precision data precludes not only empirical analysis—including the quantification of various elasticities—but also the informed policy analysis that results from the integration of empirical results with government, market, and social institutions. We propose and conduct a controlled laboratory market in counterfeit goods on several groups of subjects in Hong Kong and Las Vegas. The data generated in the experiments are used to estimate a random-effects model of individual choice behavior. The main empirical findings are that subjects in Hong Kong are more likely to purchase the counterfeit good than are subjects in Las Vegas; the price and penalty elasticities are substantially larger (in absolute value) in Las Vegas than in Hong Kong; and that in both locations the price effects of legitimate and counterfeit goods are asymmetrical in the monetary price and expected penalty cost. An equal increase in the price of an authentic good and the expected penalty cost of a counterfeit good increases the probability that a consumer will purchase the authentic good.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".