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Record W1572609329 · doi:10.32468/espe.5505

The minimum wage in Colombia 1984-2001 : favoring the middle class with a bite on the poor

2007· article· es· W1572609329 on OpenAlexaff
Carlos Arango, Angélica Pachón

Bibliographic record

VenueEnsayos sobre Política Económica · 2007
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsBank of Canada
Fundersnot available
KeywordsMiddle classWageMinimum wageClass (philosophy)EconomicsGeographyDemographic economicsLabour economicsComputer scienceMarket economy

Abstract

fetched live from OpenAlex

This paper exploits the long history of the minimum wage in Colombia in order to see whether it has im-proved the living conditions of low income families and reduced income inequality. This paper also ex-plores how the minimum wage may have distorted market outcomes in the process. We fi nd signifi cant negative minimum wage effects on both the likeli-hood of being employed and hours worked among household heads. The negative effect is strongest for women, the young and less educated people. For non-head household members, we fi nd that a rise in the minimum wage signifi cantly increases labor participation, reduces hours worked, and increases the likelihood of being unemployed. Most impor-tantly, we fi nd evidence that the minimum wage ends up being regressive, improving the living con-ditions of families in the middle and the upper parts of the income distribution but causing net losses for those at the bottom.

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)0.000
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 score0.348
Threshold uncertainty score0.691

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.035
GPT teacher head0.236
Teacher spread0.201 · 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

Citations11
Published2007
Admission routes1
Has abstractyes

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