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Record W2991633665 · doi:10.1080/1331677x.2019.1689837

Self-employment and unemployment relationship in Romania – Insights by age, education and gender

2019· article· en· W2991633665 on OpenAlexfundno aff
Adriana Grigorescu, Speranța Pîrciog, Cristina Lincaru

Bibliographic record

VenueEconomic Research-Ekonomska Istraživanja · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
FundersOntario Ministry of Research, Innovation and Science
KeywordsUnemploymentDemographic economicsLabour economicsPsychologyEconomicsSociologyEconomic growth

Abstract

fetched live from OpenAlex

We check on the short term if self-employment in Romania influences unemployment and vice versa. Age, education and gender characteristics treat both variables, and self-employment considers both cases with and without employees. The objective is to look at the job creation and unemployment reduction in quarterly variation during the 1999Q1–2017Q3 period. On autoregressive models, we apply the Toda and Yamamoto (1995) procedure, detailed by Giles (), to assess for Granger Causality. We found for unemployment rates a push effect in the self-employment rate for adults and youth with low education level to self-employment without employees’ rate for adults and self-employment with employees’ rate for old adults. We establish a ‘Schumpeter’ effect for the adult with a low level of education self-employment to unemployment, for adults’ males with tertiary education and self-employed, and older adults self-employed without employees to unemployment. We conclude that unemployment work as an inclusion mechanism for some vulnerable groups but inefficient for others. Self-employment with employees is less diversified, indicating a high-risk aversion and low start-up effect. In general, the labour market presents a unidirectional flexibility effect.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.090
GPT teacher head0.320
Teacher spread0.230 · 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.

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

Citations20
Published2019
Admission routes1
Has abstractyes

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