Bibliographic record
Abstract
Little work has been done to assess the accuracy of computable general equilibrium (CGE) models of trade policy. The first essay addresses the question of simulation accuracy in the context of a model of the U.S.-Canada Free Trade Agreement. I adjust the model for macroeconomic shocks that are not part of the original experiment and then develop and apply a method for measuring the performance of the model. I conclude that while the model is effective at simulating changes in sectoral. trade flows, it is far less successful at simulating changes in employment and output by sector. The second essay addresses similar questions of CGE accuracy, but with respect to the role of returns to scale. I incorporate new estimates of returns to scale by sector and test the influence these parameters have on the outcome of the model using the methodology developed in the first essay. I conclude that while the returns to scale does play some role in the welfare results reported, that role is a relatively modest one. I also find that relatively high returns to scale worsen the model's ability to simulate changes in trade flows. The third essay treats a different topic: the role of donations from political action committees (PACs) in the child labor and trade debate. While the motivation to sponsor legislation prohibiting the importation of goods produced by child labor has been studied elsewhere, the role of political contributions in the process has not. I first present a short history of such legislation. Next I survey the work that has already been done in the literature. I then use various estimation techniques to study the role of PAC contributions on the decision to sponsor the Child Labor Deterrence Act of 1995. I conclude that there is weak evidence to support the hypothesis that sponsorship of such legislation is favorably influenced by donations from labor-oriented PACs and negatively by contributions from business-oriented PACs.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".