Self–Employment, Wealth and Start–up Costs: Evidence from a Financial Crisis
Bibliographic record
Abstract
Abstract Using individual-level data from three uniquely comparable surveys (Survey of Health, Ageing and Retirement in Europe, English Longitudinal Study of Ageing and Health and Retirement Study) in Europe and the United States, as well as the World Bank’s Doing Business data, this paper empirically zeroes in on the impact of start-up costs on the self-employment–wealth relationship. The longitudinal nature of the data enables us to investigate the potential effects of the last global financial crisis. Results confirm the strong positive relationship between the entrepreneurial choice and wealth as well as the negative effect that stems from the increase in start-up costs. Interestingly, although there is no strong evidence that wealth in itself played a bigger role during the crisis, we find that the negative impact of start-up costs on the entrepreneurship–wealth relationship proved to be significantly pronounced during the last crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".