The Dynamics of Digital Privacy: Economic Framework, Field Evidence and Experiment
Bibliographic record
Abstract
This dissertation consists of three chapters on understanding consumer digital privacy decisions, combining large-scale empirical analysis of user choices, insights from a controlled field experiment, and a foundational review of economic principles. Chapter 1 provides novel field evidence that consumers’ digital privacy choices exhibit structural state dependence, where past decisions influence subsequent ones. Analyzing an individual-level consumer panel from Alipay, a natural experiment shows that accepting a previous data request decreases the probability of rejecting subsequent requests by 15%. This effect diminishes over time and is more pronounced when immediate preferences for an app are weak, highlighting temporary intra-platform externalities that incentivize platforms to encourage consumer-friendly data request designs. Chapter 2 establishes a conceptual and economic framework that guides the dissertation's empirical work on privacy. It moves beyond the standard framing of a cost-benefit tradeoff to emphasize the critical role of data externalities. The empirical literature thus far has focused on this direct cost-benefit assessment, examining how privacy regulations have affected various market outcomes. However, an increasing body of theory work emphasizes externalities related to data flows. These externalities, both positive and negative, suggest benefits to the targeted regulation of digital privacy. Chapter 3 investigates whether increasing privacy salience enhances user trust and engagement, or creates unintended consequences. Through a large-scale field experiment on Alipay involving over 8,000 users, we find that informing users about privacy tools causally increases direct visits to these tools while boosting overall platform trust and engagement. However, the information does not alter subsequent data consent decisions for third-party apps, suggesting these choices are contextual and distinct from platform-level trust. The engagement increase is driven by intrinsically privacy-sensitive users, while privacy tool usage increase is concentrated among low-knowledge users. Overall, these results indicate that platforms can strategically use information to enhance user trust and engagement without necessarily impacting contextual data sharing in their ecosystem. This dissertation contributes to the digital privacy literature by exploring its dynamics through the integrated lenses of an economic framework, large-scale field evidence on state dependence, and a field experiment on information provision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".