ENTREPRENEURSHIP BY NECESSITY AND OPPORTUNITY IN THE MEXICAN STATES DURING THE COVID-19 CRISIS
Bibliographic record
Abstract
The sanitary measures implemented during the COVID-19 pandemic had a significant effect on the labor market, particularly in terms of entrepreneurship. To analyze this effect, a random-effects data panel was used, including observations for the 32 states of the Mexican Republic, covering the period from the second quarter of 2016 to the third quarter of 2021. As the effect is necessarily differentiated, the variable to be explained is the change in the number of employers and self-employed because the first group could be argued to approximate entrepreneurship by opportunity, and the second, entrepreneurship by necessity. Both groups are explained by variables of the state such as economic activity, access to financial products and whether COVID had any effect on the change in the types of entrepreneurship. The main conclusion is that the crisis generated by the pandemic had a positive effect on entrepreneurship out of necessity (NEC) but was not significant when it came to those called by opportunity (OPP). Public and private interventions are proposed to take advantage of and strengthen this new wave of entrepreneurship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".