Financial Market Imperfections: Does it Matter for Firm Size Dynamics?
Bibliographic record
Abstract
Recent theoretical work by Cooley and Quadrini (2001) highlight the role of financial frictions in models of firm dynamics. This paper investigates empirically the implications of the Cooley and Quadrini (2001) model for the determinants of firm size dynamics with special emphasis on financial frictions. Previous studies examine firm size dynamics conditional on a firm's age. This paper augments the firm size dynamics by considering the additional role of financial frictions, via a firm's debt-to-asset ratio. This paper utilizes data from the T2-LEAP, an administrative tax longitudinal database, which contains balance sheet and employment information on the universe of all incorporated manufacturing firms in Canada for the period 1985-1997. This paper examines the firm size dynamic relationship using dynamic panel data methodology discussed in Arellano (2002). Care is taken to control for the firm unobservables to get consistent estimates of the model. Another issue is the possibility of endogeneity between firm size and the debt-to-asset ratio. Findings show that accounting for firm unobservables is important and financial friction do matter to some firm size dynamics relationships
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".