Self-employment and unemployment relationship in Romania – Insights by age, education and gender
Bibliographic record
Abstract
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 (Citation2011), 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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".