New firms and labor market entrants: Is there a wage penalty for employment in new firms?
Bibliographic record
Abstract
In this paper, we explore the role of new firms as an entry point to the labor market. Because the vast majority of new firms are short-lived, it is a risky decision to accept employment in a new venture. It can be argued that individuals with little (or no) labor market experience are more willing to accept the high risks associated with employment in new firms. Hence, new firms may work as an entry point to the labor market. Nevertheless, some research concludes that one disadvantage of employment in a new firm is that new firms pay less (Shane, 2009). However, this empirical conclusion is primarily based on literature on the wage penalty of small firms. In this paper, we study whether the wage penalty of employment in a new firm persists if we focus solely on labor market entrants. In the empirical analysis, we employ an employer-employee matched dataset that covers the Swedish population during the period from 1998-2008. We use the Propensity Score Matching (PSM) method to study the wage differences between labor market entrants employed in new and incumbent firms. We find an average wage penalty of 2.9 percent for labor market entrants employed in new firms over the studied period.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".