Bibliographic record
Abstract
This study examines the association between efficient labor investment and a firm’s life cycle. Investing in labor is an important corporate decision that ensures proper resource allocation and control over costs, which in turn influences a firm’s financial performance at different stages of its life cycle. Based on a sample of U.S. firms from 1989 to 2019, a regression model incorporating 117,462 firm-year observations is used to estimate a measure of labor investment efficiency, while one incorporating 66,940firm-year observations is used to examine the relationship between labor investment efficiency and firm life-cycle stages. The study shows that mature firms are more negatively associated with labor investment inefficiency than other stages of the firm’s life cycle. Thestudy observesaU-shaped patternof laborinvestment efficiencyacrossthestagesof thefirm’s life cycle. That is, absolute values of abnormal net hiring, which indicate inefficiency in labor investment, are higher (lower) during the introduction, growth, shake-out, and decline (mature) stages. The results remain constant after controlling for various factors, including firm size, leverage, cash flow and sales volatilities, using alternative proxies for labor investment efficiency, as well as considering the potential impact of other non-labor investments and labor intensity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".