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Record W3174177202

Trade agreements and decent work in Mexico: the case of the automotive and textile industries

2021· preprint· en· W3174177202 on OpenAlexaboutno aff
Juan Carlos Moreno‐Brid, Rosa Gómez Tovar, Joaquín Sánchez Gómez, Lizzeth Gómez Rodríguez

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryDynamismLiberalizationFree tradeBusinessInternational tradeWork (physics)Competition (biology)EconomicsInternational economicsMarket economyEngineering
DOInot available

Abstract

fetched live from OpenAlex

The study examines the effects of trade liberalization on employment and the labour market in Mexico's manufacturing industry. The analysis places special emphasis on assessing the extent to which the industry's distinct trade performance is accompanied by an improvement in labour conditions with the objective of ensuring decent work. For this purpose, the study applies the framework of decent work indicators developed by the International Labour Organization (ILO), in combination with input-output analysis, to explore selected links between international trade and certain indicators of decent work in two industries of Mexico's manufacturing sector: automotive and textile. We chose these two industries because of the key differences in their organizational structures, their roles in global value chains (GVC) and their dynamism in recent decades. With the policy shift towards trade liberalization in recent decades, the automotive industry has come to be regarded as the jewel of Mexico's export market. The textile industry, in contrast, suffered a severe shock as trade liberalization brought about increased competition in Mexico's domestic market, despite the industry increasing its participation in GVCs. A key contribution of the study was to construct a set of relevant time series indicators of decent work for these two industrial activities in Mexico, based on ILO guidelines and official data. Taking into account this set of indicators, as well as Mexico's labour market regulatory reforms and their links to trade agreements, including the Agreement between the United States of America, the United Mexican States and Canada (USMCA), the study finds important differences in these two industries' advance towards decent work, which can be partly explained by their distinct performances in international trade. Based on these results, the study offers some policy recommendations to help achieve a more robust pace of progress towards decent work.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.038
GPT teacher head0.304
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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