Who does (not) benefit from entrepreneurship programs?
Bibliographic record
Abstract
Research Summary We evaluate a technology entrepreneurship training program by comparing career decisions among applicants accepted into the program with unaccepted applicants who are program finalists. We find that program participation is associated with an increased likelihood of subsequent entrepreneurship but that this is not uniform across participants; the estimated relationship between program participation and subsequent entrepreneurial activity is disproportionately lower for applicants with ex‐ante resources and capabilities in entrepreneurship, measured by prior entrepreneurship experience. Moreover, we only observe this reduced impact of the program on subsequent entrepreneurial activity for participants that have prior experience in founding a technology company as opposed to other forms of entrepreneurial activity. This suggests the program is more effective for individuals that have otherwise limited access to technology entrepreneurship opportunities. Managerial Summary Given the increasingly competitive landscape for entrepreneurship education programs, it is important to understand when and for whom they have the greatest impact. Using 5 years of data from a technology entrepreneurship training program, we show that individuals with a higher predisposition toward the type of entrepreneurship being taught by the program, measured by prior technology entrepreneurship experience, are less likely to benefit from training. Our findings imply that individuals who enter programs with the skill set being taught benefit less from the program at the margin, and that individuals without prior experience can be trained in entrepreneurship. These patterns have implications for entrepreneurial program strategy, individuals considering entry into entrepreneurial careers, and firms deciding whether to develop entrepreneurial capabilities in‐house or acquiring them externally.
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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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 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 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".