Testing the impact of unemployment on self-employment: empirical evidence from OECD countries
Bibliographic record
Abstract
The impact of unemployment on self-employment is rather an ambiguous issue in economics. According to refugee effect approach, there are two counter arguments: the theory of income choice argument suggests that increased unemployment may lead to increased self-employment activities whereas the counter argument defends the view that an increase in unemployment rates may decrease the endowments of human capital and entrepreneurial talent causing a rise in unemployment rates further. The empirical evidence on this issue seems to support both hypotheses. This research presents fresh and more comprehensive evidence on this issue from 28 OECD countries using the ARDL approach to co-integration technique over the period 1986-2013. The empirical results indicate that the first hypothesis holds in the case of Belgium, Canada, Sweden and the UK whereas the second hypothesis is valid in the case of Greece, Luxembourg and Portugal. The empirical results for the remaining OECD countries did not reveal any long-run relationship between the variables in question. The empirical results are also evaluated briefly for policy recommendations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".