Job Creation in Colombia Versus the USA: ‘Up‐or‐out Dynamics’ Meet ‘The Life Cycle of Plants’
Bibliographic record
Abstract
One of the most striking contrasts between the anatomies of the business sectors in higher‐ versus lower‐income economies is the overwhelming dominance of very small production units in the latter. Contrasting manufacturing sector data for Colombia and the USA, we show that weaker ‘up‐or‐out’ dynamics are behind this pattern and behind weaker average lifecycle growth. Dampened growth dynamics, not only in terms of upward mobility but also for exit and downward mobility, characterize both micro‐establishments and young establishments in Colombia relative to their US counterparts. These patterns lead to a more dominant role of small older businesses in accounting for employment. Since dynamic selection among startups is a crucial driver of productivity growth in the USA, our findings point to a shortage of high‐growth entrepreneurship and a relative high likelihood of long‐run survival for small, likely unproductive plants, as two key elements at the heart of the development problem. We also show that analysis of establishment lifecycle dynamics based solely on cross‐sectional data substantially underestimates lifecycle growth.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".