Cambio tecnológico y desigualdad de ingresos en México
Bibliographic record
Abstract
The objective of the work is to analyze income inequality in Mexico during the third quarter of 2018 and 2021 with information from the National Survey of Occupation and Employment (ENOE) of the National Institute of Statistics and Geography (INEGI). The link between workers' earnings and a technology variable, constructed from the automation probabilities estimated by Frey and Osborne (2017), is examined. Control variables such as gender, schooling, type of employment, regional and sectoral structure of the Mexican economy are considered. A procedure is implemented to correct the sample selfselection bias problem. Estimates indicate that those who perform occupations with a medium and high level of automation earn less than those with a low level of automation. During 2018 the percentages were minus 25% and 30% and during 2021, minus 23% and 28% respectively.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".