Sources of Firm Life-Cycle Dynamics: Differentiating Size vs. Age Effects
Bibliographic record
Abstract
What determines firm growth over the life-cycle?Exploiting unique firm panel data on internal organization, balance sheets and innovation, representative of the entire Canadian economy, we study recent theories that examine life-cycle patterns for firm growth.These theories include organizational capital accumulation and management practices, financial frictions, learning about demand, and recent endogenous growth models with incumbent innovation.We emphasize the importance of differentiating between pure age effects of these theories and effects on size conditional on age.Our stylized facts highlight both empirical successes and shortcomings of current theory.First, models of organizational capital and innovation are broadly consistent with firm size correlations conditional on age but have difficulties matching the life-cycle dynamics of firm organization and innovation.Second, among theories we analyze, organizational capital and management practices are the most important determinants to explain intensive margin firm growth over the life-cycle.Third, although less important to explain intensive margin firm growth, financial frictions are an important determinant of firm exit, conditional on firm age.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".