Do Startup Employees Earn More in the Long Run?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Evaluating the attractiveness of startup employment requires an understanding of both what startups pay and the implications of these jobs for earnings trajectories. Analyzing Danish registry data, we find that employees hired by startups earn roughly 17% less over the next 10 years than those hired by large, established firms. About half of this earnings differential stems from sorting—from the fact that startup employees have less human capital. Long-term earnings also vary depending on when individuals are hired. Although the earliest employees of startups suffer an earnings penalty, those hired by already-successful startups earn a small premium. Two factors appear to account for the earnings penalties for the early employees: Startups fail at high rates, creating costly spells of unemployment for their (former) employees. Job-mobility patterns also diverge: After being employed by a small startup, individuals rarely return to the large employers that pay more.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it