The Impact of the Regional Trade Agreements on Inflow of Mexican Foreign Direct Investment: An Empirical Approach
Bibliographic record
Abstract
This article aims to empirically evaluate, based on the annual time series from 1970 to 2018, the hypothesis that regional trade agreements (RTA) had a positive impact on the Mexican Foreign Direct Investment (FDI) inflow in this period. We use the North American Free Trade Agreement (NAFTA) to evaluate its impact in Mexico, which entered into force in 1994. In this context, our interest variable is the dummy d_1994, where from 1994 to 2018 it is equal to 1. We tested several empirical approaches based on ARDL models, robust least-squares methods as well as GMM methods. We also use Granger causality tests and impulse response tests based on the VAR system. All empirical models show that the estimated coefficient from the dummy variable, d_1994, is statistically significant and presents a positive sign. Moreover, the causality tests show that the variable d_1994 granger causes FDI as a proportion of GDP, and the impulse response tests validate the tested hypothesis as well.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".