Profitability Performance And Firm Size-Growth Relationship
Bibliographic record
Abstract
<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt; tab-stops: -.5in; mso-hyphenate: none;"><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; letter-spacing: -0.15pt; color: black; font-size: 10pt; mso-themecolor: text1;">In this study, we intend to examine empirically </span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; color: black; font-size: 10pt; mso-themecolor: text1;">how a firm&rsquo;s profitability performance would impact its <span style="letter-spacing: -0.1pt;">growth process </span>and what implications follow for the validity of Gibrat&rsquo;s law. The basic thesis that is tested in this study is that smaller firms, being more constrained in obtaining outside funds for growth, can possibly show a higher propensity to growth when their internally generated profits are high. To this end, we apply a dynamic <span style="mso-bidi-font-style: italic;">model to panel data</span> on a sample of firms in the USA<span style="letter-spacing: -0.15pt;">.</span> <span style="letter-spacing: -0.15pt;">We first </span>investigate the size-growth relationship for the whole sample, and then, these firms are classified into three profitability performance groups on the basis of the average size of profits as percentage of stock-holders&rsquo; equity. <span style="letter-spacing: -0.1pt;">The empirical results emanating from this study are mixed, with the dominant result that in many cases, larger firms grow faster, violating Gibrat&rsquo;s law. Moreover, the results do not lend visible support to the hypothesis that higher profitability confers a growth advantage to the smaller firms.</span></span></p>
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".