MétaCan
Menu
Back to cohort
Record W3160669027 · doi:10.1016/j.jmoneco.2024.103684

Wage employment, unemployment and self-employment across countries

2024· article· en· W3160669027 on OpenAlex
Markus Poschke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Monetary Economics · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsMcGill University
FundersUniversity of TorontoSocial Sciences and Humanities Research Council of CanadaUniversity of AlbertaGeorgetown UniversityInternational Growth CentreUniversität St. GallenFlorida International UniversityUniversity of Miami
KeywordsUnemploymentEconomicsMatching (statistics)Labour economicsWageProductivityEfficiency wage

Abstract

fetched live from OpenAlex

Poor countries have low wage employment and high self-employment. This paper shows that they also have high unemployment relative to wage employment, and that self-employment increases with this ratio. To understand the sources of these patterns, I build a search and matching model with choice between job search and self-employment and with learning about matches, and calibrate it to match all transition rates between wage employment, unemployment and self-employment as well as separation hazards by job duration, separately for all 37 countries with available data. Quantitative analysis of the model shows that labor market frictions affect self-employment as much as unemployment. Labor market frictions also reduce aggregate output, not only by raising unemployment, but also by worsening the average quality of both wage employment matches and active self-employment projects.

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.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.237
Teacher spread0.215 · 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