Why do Firms Live Longer than Others? The Elixir of (Eternal) Life of Blue Chip American Companies
Bibliographic record
Abstract
Purpose: Why do certain companies live longer than others? The average lifespan of a listed north and South American company is over 33 years and in Europe the average age of a company is 52 (Note 1). In 1288, Stora Enso a big pulp and paper company from Sweden issued its first share. According to credit rating agency Tokyo Shoko Research, in Japan, there are more than 20,000 companies with more than 100 years’ old. Through a sample of blue ship American listed oldest companies and quarter panel data from 1988-2013 this article identifies more than 8 significant explanatory variables and ascertains relevant factors related with longevity.Methodology: A new robust standard errors for panel regressions with cross-sectional dependence based on Driscoll-Kraay estimator is applied. This method (stata xtscc) is heteroskedasticity consistent and the standard error estimates are robust to general forms of cross-sectional and temporal dependence surpassing the deficiencies of traditional panel data statistical approaches.Findings: The sample of blue ship companies and panel regressions with Driscoll-Kraay estimator shows that the most relevant factors to induce longevity are related with growth opportunities perspective and horizon, cash liquidity, profitability and shareholders remuneration whether from dividends or repurchases, capital structure, strong claims-compliance-liability structure department, innovation and firm size.Originality: This paper’s topic considers for the first-time age as a dependent variable and not a control one. Also, the large time period of study, including quarterly observations is new, as well as the original approach to estimation applied to this theme, considered as an alternative to traditional panel data methods.Practical implications: With these determinants identified, professionals and academics can use them as benchmarking and a recipe to endure and assuring bigger lifespan for other mature and young companies.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".