Análisis de las condiciones estructurales de la industria automotriz en México ante la entrada en vigor del T-MEC
Bibliographic record
Abstract
espanolEl objetivo del articulo es presentar un analisis integral sobre los elementos estructurales que caracterizan a la industria automotriz localizada en Mexico. En principio, se estudia la composicion de la industria, asi como las ventajas estructurales que ofrece la economia nacional a la localizacion de un mayor numero de unidades productivas que participan activamente en las cadenas globales de valor; en particular, la cercania geografica con el mercado de Estados Unidos, las condiciones de acceso a dicho mercado, las bajas tasas salariales, asi como las politicas de promocion al ingreso de inversion extranjera y de conformacion de conglomerados industriales (clusters automotrices). De manera posterior, la metodologia de analisis considera las variables fundamentales que influyen sobre el funcionamiento de la industria, tales como produccion, ventas, exportaciones, empleo, salarios, inversion extranjera directa y consumo. En general, los resultados obtenidos dan cuenta de una industria con vocacion exportadora que depende, de manera significativa, del mercado externo. En este sentido, el valor del estudio esta en la coyuntura que enfrenta la industria ante la entrada en vigor del Tratado Mexico, Estados Unidos, Canada (T-MEC). Sin duda la industria automotriz instalada en Mexico esta atravesando por un momento delicado, que exhibe la debilidad del mercado interno, asi como les efectos derivados del confinamiento provocado por el COVID-19. Dado que el T-MEC inicio operaciones el pasado 1 de julio de 2020, es relevante conocer las condiciones que esta presentando la industria, para realizar los ajustes necesarios y estar en capacidad de generar los elementos que permitan retomar la tendencia positiva que se registro despues de la crisis de 2009 y que se mantuvo casi hasta 2018, cuando la variacion anual fue cercana a 0%. EnglishThe objective of the article is to present a comprehensive analysis of the structural elements that characterize the automotive industry located in Mexico. In principle, the composition of the industry is studied, as well as the structural advantages that the national economy offers to the location of a greater number of productive units that actively participate in global value chains; in particular, the geographic proximity to the US market, the access conditions to said market, the low wage rates, as well as the policies to promote the entry of foreign investment and the creation of industrial conglomerates (automotive clusters). Subsequently, the analysis methodology considers the fundamental variables that influence the industry operation, such as production, sales, exports, employment, wages, foreign direct investment and consumption. In general, the results obtained show an industry with an export vocation that depends, in a significant way, on the foreign market. In this sense, the value of the study is in the situation faced by the industry at the entry into force of the United States, Mexico and Canada Agreement (USMCA). Undoubtedly, the automotive industry installed in Mexico is going through a delicate moment, which shows the weakness of the domestic market, as well as the effects derived from the confinement caused by COVID-19. Given that the T-MEC began operations on July 1, 2020, it is relevant to know the conditions that the industry is presenting, to make the necessary adjustments and be able to generate the elements that allow to resume the positive trend registered after the 2009 crisis and that lasted almost until 2018, when the annual variation was close to zero percent.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".