Economic Convergence in the Mexico-United States Cross-border Region: A Post-crisis Analysis 2010–2019
Bibliographic record
Abstract
This research aims to estimate the σ and β convergence hypotheses for the period following the 2008 crisis (2010–2019) in the states of the Mexico-United States border region. The objective is to determine whether the disparities in per capita income have decreased during that period. Panel and spatial panel methodologies are employed to confirm the β-convergence hypotheses, taking into account heterogeneity, spatial dependence, and the endowment of human capital as conditioning factors. The σ-convergence is estimated using standard deviation. The results indicate the presence of σ-convergence and conditional β-convergence. However, a higher endowment of human capital does not necessarily lead to increased convergence rates due to existing differences between the two economies. In conclusion, there is a need to formulate public policies in Mexico that promote educational attainment among the population residing in the northern border region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".