Profit Effects of Consumers’ Identity Management: A Dynamic Model
Bibliographic record
Abstract
We consider a nondurable good monopolist that collects data on its customers in order to profile them and subsequently practice price discrimination on returning customers. The monopolist’s price discrimination scheme is leaky in the sense that an endogenous fraction of consumers choose to incur a privacy cost to conceal their identity when they return in the following periods. We characterize the Markov perfect equilibrium of the game under two alternative customer profiling regimes: full information acquisition (FIA) and purchase history information (PHI). In both cases, we find that, contrary to what could be expected, the monopolist’s aggregate profit is not monotonically increasing in the level of the privacy cost, but a U-shaped function of it, leading to ambiguous profit effects: a reduction in privacy costs increases the fraction of customers who choose to be anonymous (detrimental profit effect), but it also softens the firm’s introductory price, reducing the pace at which prices targeted to new customers fall over time (positive profit effect). When comparing results under FIA and PHI, we find that market expansion is faster, and more customers conceal their identity under FIA than under PHI. Equilibrium profits are also higher in the FIA case. Although equilibrium profits are U-shaped functions of the privacy cost in both profiling regimes, they tend to be globally decreasing with the privacy cost under PHI and globally increasing under FIA. This paper was accepted by Eric Anderson, marketing. Funding: This work was supported by Fundação para a Ciência e a Tecnologia [Grants NORTE-01-0145-FEDER-028540 and POCI-01-0145-FEDER-006890]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4511 .
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".