Pair-Wise Approach to Test the Regional Convergence Hypothesis in Mexico
Bibliographic record
Abstract
This paper assesses the gross domestic product (GDP) per capita convergence of the 32 Mexican States in the period 1940-2010 through the method proposed by Pesaran (2007), which is based on the convergence stochastic criterion. One of the main advantages of this method is not only on a model of leading economy, also on a pair-wise approach that considers all possible gap pairs of per capita logarithms of all the Mexican States analyzed in the sample. According to this method, all the differences or output gaps of the States must be stationary around a constant mean. Most results provide evidence against the hypothesis of convergence especially for the total sample from 1940 to 2010 and the first period from 1940 to 1985. However, mixed evidence of this hypothesis was observed in the second period from 1986 to 2010. Additionally, the test results applied to a set of States considered as the richest suggest these findings are not due to the unique behavior of these States.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".