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Record W3124036397

The Micro and Macro of Disappearing Routine Jobs: A Flows Approach

2014· article· en· W3124036397 on OpenAlexafffund
Guido Matías Cortés, Nir Jaimovich, Christopher J. Nekarda, Henry Siu

Bibliographic record

VenueResearch Explorer (The University of Manchester) · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversity of British ColumbiaYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDemographicsDemographic economicsDownloadMicro levelUnemploymentWagePolarization (electrochemistry)Labour economicsEconomicsPer capitaDeveloping countryMacro levelDemographyEconomic growthPopulation
DOInot available

Abstract

fetched live from OpenAlex

The U.S. labor market has become increasingly polarized since the 1980s, with the share of employment in middle-wage occupations shrinking over time. This job polarization process has been associated with the disappearance of per capita employment in occupations focused on routine tasks. We use matched individual-level data from the CPS to study labor market flows into and out of routine occupations and determine how this disappearance has played out at the "micro" and "macro" levels. At the macro level, we determine which changes in transition rates account for the disappearance of routine employment since the 1980s. We find that changes in three transition rate categories are of primary importance: (i) that from unemployment to employment in routine occupations, (ii) that from labor force non-participation to routine employment, and (iii) that from routine employment to non-participation. At the micro level, we study how these transition rates have changed since job polarization, and the extent to which these changes are accounted for by changes in demographic composition or changes in the behavior of individuals with particular demographic characteristics. We find that the preponderance of changes is due to the propensity of individuals to make such transitions, and relatively little due to demographics. Moreover, we find that changes in the transition propensities of the young are of primary importance in accounting for the fall in routine employment.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
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 head0.064
GPT teacher head0.242
Teacher spread0.178 · 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 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

Citations2
Published2014
Admission routes2
Has abstractyes

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