Business Dynamics and Productivity Growth with an Application to Taiwanese Electronics Firms
Bibliographic record
Abstract
Empirical progress on understanding the consequences of various sorts of business dynamics-- whether from a technological change, job creation and destruction, or firm governance and management perspective-- requires reliable ways of characterizing business dynamics and of measuring the impacts of various sorts of business dynamics on business and industry and national productivity growth. We review the literature on characterizing and on measuring the impacts of business dynamics on productivity growth. We introduce the business dynamic status categories used in virtually all of the studies of others: classifications of continuing, entering and exiting. This classification can be implemented with just two periods of data. We also propose an alternative dynamic classification for businesses utilizing three periods of data. For the empirical portion of this paper, like many others, we adopt the basic approach of Foster, Haltiwanger and Krizan. We implement this decomposition using data for Taiwanese electronics firms. We first use the conventional dynamic status classification approach for firms and then use our suggested new FNN approach. We demonstrate the value of the FNN classifications. 1.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".