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Record W2219187693

The Barriers to Occupational Mobility: An Aggregate Analysis

2014· article· en· W2219187693 on OpenAlexaff
Giovanni Gallipoli, Guido Matías Cortés

Bibliographic record

Venue2014 Meeting Papers · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCounterfactual thinkingTask (project management)Current Population SurveySample (material)PopulationEconometricsGravity equationSet (abstract data type)Aggregate (composite)Demographic economicsVariable (mathematics)Computer scienceEconomicsPsychologyDemographyGeographySocial psychologyMathematicsSociology
DOInot available

Abstract

fetched live from OpenAlex

This paper analyzes the barriers to occupational mobility using a theoretical framework that parallels that of the gravity models commonly estimated in the trade literature. The model provides an equation linking flows of workers across occupation pairs to a set of source and destination occupation characteristics, and to the transition costs faced by workers. The equation is estimated using data from the matched monthly Current Population Survey (CPS) from 1994 to 2012. The main proxies for the transition cost investigated in the paper are related to the task content of occupations, specifically task distance (the degree of dissimilarity in the mix of task requirements across the occupation pair) and a set of indicator variables for transitions that involve changes across major task groups. Task-related variables are found to play a substantial role in increasing the cost of switching between occupations. In a counterfactual scenario where workers are able to switch occupations without bearing any task-related costs, occupational mobility rates for the majority of the occupations in our sample would increase by between 7 and 30 percentage points.

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)0.004
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.026
GPT teacher head0.224
Teacher spread0.198 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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