Bibliographic record
Abstract
This thesis consists of three separate papers which examine different aspects of the economics of online commerce. The first paper studies a natural experiment in the release of a new ad targeting feature onto an online advertising platform. The experiment affects the specificity of advertising assets in certain geographic ad markets. The paper finds evidence that the additional specificity negatively affects auction participation in the treated areas, an effect that has not been anticipated by the incumbent theoretical literature. The paper also finds evidence that despite negatively affecting auction participation, the additional specificity leads to higher revenue growth for the online platform in the treated areas. The paper’s results highlight the importance of considering entry and exit decisions in theoretical models of specificity choices by market designers. The second paper uses a proprietary data set from Google to find that online trade between two US states or two Canadian provinces is 6.7 times higher than trade between a US state and a Canadian province. This finding is surprising given that in the online environment, information costs and business-to-business transactions involving intermediate inputs are largely absent. When disaggregating the data by sectors of economic activity, the study finds that the largest US-Canada border effects occur for services whose consumption is tied to a particular location and goods that face large regulatory hurdles at the border. The third paper analyzes geographical patterns of cross-country Internet transactions using proprietary data from Google. The paper finds the effect of distance on online trade to be around -0.53. The study also finds that cultural characteristics, such as shared languages or religions, have a large impact on e-commerce, while economic ties, such as a common currency, have an insignificant effect. The paper underlines the importance of accounting for selection into trade in worldwide gravity estimations and identifies two exclusion restrictions that can be used when examining online trade flows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".