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Record W4293525112 · doi:10.5539/ijef.v14n9p89

Deindustrialization, Domestic Savings, and Labor, in Mexico and Central America

2022· article· en· W4293525112 on OpenAlexvenueno aff
Luis René Cáceres

Bibliographic record

VenueInternational Journal of Economics and Finance · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsDeindustrializationEconomicsProductivityPer capitaContext (archaeology)Investment (military)Panel dataLabour economicsEconomyMacroeconomics

Abstract

fetched live from OpenAlex

This study analyzes the determination of the deindustrialization process experienced by Mexico and the Central American countries, and the repercussions on employment, domestic savings, investment, and economic growth. The methodology consists of estimating VAR models with panel data from the 1990-2018 period. The results indicate that the percentage occupied by the manufacturing sector in GDP increases in the face of shocks to exports, investment, and the ratio of female to male salaried employment, and does not have a significant response to the increase in remittances. A measure of relative deindustrialization is introduced, defined as the ratio of the value-added of the manufacturing sector to imports, whose responses to shocks to exports and remittances are negative, but its response to salaried employment is positive. Faced with shocks to the ratio of female to male salaried employment, the responses of the domestic saving rate, per capita international reserves, and labor productivity was positive, but the response of the trade deficit was negative. These results show that deindustrialization must be analyzed in the context of the labor market of the countries and consider gender aspects.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2022
Admission routes1
Has abstractyes

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