Bibliographic record
Abstract
People are often unaware that their personal data can serve as valuable inputs for economic activities in secondary data markets. However, whether secondary monetization of personal data determines privacy preferences remains unclear. I examine whether privacy decisions are motivated by the data recipient’s ability to benefit from trading individuals’ data with a third party. A large online laboratory experiment involving personally identifiable psychometric data is implemented with real data-sharing consequences and monetary benefits. I find that individuals decrease their willingness to share data—both in terms of their likelihood of participating in the data market and the prices demanded for such participation—when the recipient’s ability to monetize the data through secondary trade is salient. Strategic responses to updated beliefs about the recipient’s gain from the trade are ruled out via the chosen price elicitation. I find that increased data exposure (to more recipients) does not explain the significant revealed disutility from secondary monetization. These findings are also robust to controlling for the risk exposure differences between data recipients and third parties. This paper was accepted by Anindya Ghose, information systems. Funding: This project was funded in part by the Institute for the Social Sciences, Cornell University. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03423 .
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".