Trade Reform and Poverty: The Case of Mexico
Bibliographic record
Abstract
We use a two‐step computationally simple procedure to analyse the effects of Mexico's's potential unilateral tariff liberalisation on real incomes. First, we use the CGE model provided by the Global Trade Analysis Project (GTAP) as the new price generator. Second, we apply the price changes to Mexican household data in order to assess the effects of the policy simulation on poverty and income distribution. Although Mexico widely liberalised most of its imports by the mid 90s, one salient feature is its membership in the North American Free Trade Agreement (NAFTA) with Canada and United States. By choosing GTAP as the price generator, we are able to model the differential tariff structure. Even starting with a low level of tariff protection, simulation results show that the impact of tariff reform on welfare will be positive in general for all expenditure deciles. We find that, when we assume non‐homothetic individual preferences, trade liberalisation benefits people in the poorer deciles more than those in the richer ones.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".