Bibliographic record
Abstract
In a market economy, firms are continuously exposed to economic shocks that affect their performance and results. In response to these shocks, firms react by reallocating their productive factors, such as capital and labor, to more productive uses. We estimate the job flows over a twenty-year period in Uruguay, exploring firm and worker characteristics. We use panel data from social security administrative records that match employers and employees in formal firms between 1996 and 2015. Job flow levels and their cycles are consistent with international evidence. Entry and exit of firms from the market play an important role, explaining about 30% of the total number of jobs created and destroyed for the whole period with high heterogeneity across industries, firm age, and firm size. In particular, the smallest firms are not as relevant in explaining net growth as political and popular beliefs would suggest, and it is start-ups that have the main role in job creation in Uruguay. Despite representing only 5% of total employment, they created more than one-quarter of new jobs and maintained this role in a fully saturated regression. Among worker characteristics, we found no differences in job flows by gender, but female workers gain participation in the period; there are bigger flow rates among workers under 25 and workers in the first and third wage terciles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".