Bibliographic record
Abstract
This dissertation studies the use of the US antidumping (AD) legislation. In the first chapter, I use panel data on AD petitions filed by US industries from 1980 to1995 to study the determinants of antidumping filings. I argue that a negative binomial model is better suited to study the industry's decision to petition than the poisson model employed in the previous literature. I find that contrary to the past findings, import penetration, one of the International Trade Commission's material injury criteria, is not an important factor. I also find that a larger workforce, lower price cost margin, and a higher capital intensity increases an industry's probability of petitioning. In the second chapter I study the stock market response to AD petitions filed by US firms. The main question I study is why so few firms petition for import relief. It is known that at lest in the short run, petition itself can restrain imports, lead to higher prices and hence higher profits. Given this fact, what restrains more firms from filing for protection? I use an event study to analyze the impact of petitioning on the market value of a firm to analyze the puzzle. For some industries, firms experience a decline in their market value at the time of petition. Therefore, it is possible that firms fear that petitioning would signal cost inefficiency on their part. In turn, this concern may act as a deterrent to filing AD petitions. I test the hypothesis of a negative signal by comparing the market response of an AD petition for petitioning firms and non-petitioning firms producing the same product. The main aim of third chapter, based on joint work with Sumeet Gulati, is to evaluate whether the Softwood Lumber Agreement (SLA), signed between US and Canada in May 1996, had a significant economic impact on the industrial users (rather than producers) of lumber in the US. Firm's daily stock prices are used in an event study to analyze market's response to the signing of SLA. I find that the SLA imposed significant economic costs on the users of lumber. The fourth chapter is a case study of the chemical industry. Restricting imports by imposing antidumping duties protects domestic firms from predatory pricing by foreign firms, and reduces competition in the domestic market. I look at the cases filed by the chemical industry to illustrate this possibility.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".