Bibliographic record
Abstract
This dissertation is composed of three chapters. The first, jointly authored with Jin Huang and Yuxin Chen, both from NYU-Shanghai, explores the economic role of price trackers. The second, jointly authored with Jin Huang, studies limited-time offers in the search market. Finally, the third one studies content design in the influencer market. In the first chapter, we investigate how the presence of online price trackers changes consumers' behaviors and market equilibrium by providing historical price information. We build a theoretical framework and find that a monopolist seller uses a high-low cyclical dynamic pricing when consumers are unaware of past prices. With the rise of price trackers, more consumers become informed about historical prices, which results in lower regular prices and more frequent sales. We study the economic role of limited-time offers in the second chapter. We highlight that consumers need time to investigate each product, and firms can endogenously direct the consumer search order by advertising limited-time offers, inducing potential consumers to sample its product early. We show that the length of limited-time offers depends on the reservation values of products to the consumer. A firm with a higher reservation value product gives a shorter sale in equilibrium, and is investigated earlier in a consumer's endogenous search order. This implies that search competition with limited-time offers leads to a socially efficient search order in equilibrium. In the last chapter, I aim to understand how new firms grow and establish themselves through their product design. I answer this question with theoretical modeling and empirical findings on the online influencer market. I collect new blog post data from WeChat Official Account, and use machine learning methods to categorize the topics and advertising in posts. I find that new entrants to the influencer market begin their careers by specializing in niche topics with minimal advertisements. Over time, they cover a wider range of topics, and the extent of advertising grows. To understand the underlying mechanism, I develop a theoretical model of dynamic reputation and content design. And I employ a shock to the reputation of influencers to cross-validate the model.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".