Is Voluntary Profiling Welfare Enhancing?1
Bibliographic record
Abstract
Although consumer profiling advocates tout benefits from personalization, consumer advocacy groups oppose profiling in online markets because of concerns about privacy and price discrimination. Policies such as opt-out or opt-in that provide consumers the option to voluntarily participate in profiling are the favored compromise. We compare voluntary profiling to no profiling and show that voluntary profiling leads to some counterintuitive results. Consumers that do not participate in profiling and some that participate are worse off under voluntary profiling. Neither social welfare nor aggregate consumer surplus is necessarily higher under voluntary profiling; even when voluntary profiling leads to an increase in social welfare, it may come at the expense of consumer surplus. If the seller cannot price discriminate and charge only a uniform price for everyone or the seller can only charge different prices based on the consumer’s participation status, then aggregate consumer surplus under voluntary profiling is higher and a reduction in privacy cost has a positive impact on all consumers as well as the seller. However, when personalized pricing is possible, reducing privacy cost alone may reduce aggregate consumer surplus. The primary reason for these results is that voluntary profiling allows the seller to identify high valuation consumers that have no incentive to participate and set a higher price for them (compared to no profiling) while simultaneously benefitting from the profile information of low valuation consumers that participate. However, a positive privacy cost mitigates the participation incentives of even low valuation consumers and hence sellers’ ability to engage in price discrimination.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".