Bibliographic record
Abstract
This thesis contains three chapters investigating the gains from trade and the subsequent costs of changes in trade policy. In Chapter 1, I use detailed data linking individual households to the origin country of the goods they buy in order to study the distributional costs of tariffs across US households. I find that tariffs on high-income countries are progressive and anti-urban, whereas tariffs on low-income countries are regressive and anti-rural. In estimating these costs, I propose a model of import substitution which marks a departure from standard assumptions often made in the trade literature, and I illustrate that this proposed model is effective at matching the underlying substitution data while also providing a nuanced analysis of how import substitutability differs across origin countries.Chapter 2 complements the analysis in Chapter 1 by providing an empirical study of the US-China trade war of 2018-2019. I use the same barcode country-of-origin data to provide direct evidence that, in the short run, the cost of tariffs implemented on Chinese imports was borne almost entirely by American consumers. However this paper departs from the previous literature in finding that in the medium-run, the incidence of these policies shifted away from American consumers and towards Chinese producers, with the market share of tariff-affected goods decreasing by almost 50\% eight months after the final tariff increase. I discuss which firms and countries gained and lost the most from this substitution and provide evidence that, due to selection effects, estimates of tariff pass-through using customs data may be biased upward. Chapter 3 provides an analysis and critique of the empirical literature documenting the variety gains from trade. I use detailed scanner data to study how the set of varieties available to US consumers responds to increases in import penetration. I find that while increased trade does lead to increased entry of new varieties, this entry is more than off-set by subsequent exit of existing varieties leaving households with contracted choice sets. I discuss heterogeneity across US cities in terms of their relative entry and exit responses to changes in trade costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.012 |
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".