Profitability in Swedish Micro Firms: A Quantile Regression Approach
Bibliographic record
Abstract
The purpose of this study is to identify a function for the profitability of Swedish micro firms in the sectors of health, transport, trade and metal. In order to understand how micro firms relate to key variables, such as firm size, growth of sales, productivities, lagged profits, asset turnover and firm’s age, OLS (Ordinary Least Squares), and the more robust quantile regression techniques, are used to estimate micro-firm profitability. Data from 2007 is used for this purpose. The results show that growth (competitive condition) and total factor productivity (comparative advantage) have a significant positive effect on micro-firm profitability, and that size (diminishing returns states) is found to have a rather significant negative effect on micro-firm profitability. The results also indicate a strong relationship between microeconomic theory suggestions and micro-firm profitability for the all micro firms except those in the metal sector. Moreover, the quantile regression approach provided a better understanding, regarding the dynamics of the factors that affect profitability, and provided more interesting results than OLS normally do.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".