Job Creation and Emplozment in a Time of Crisis
Bibliographic record
Abstract
Serbian economy has been severely affected by the latest global economic crisis. After salient slowdown in the last quarter of 2008, the national economy went into recession that was followed by gradual reductions in GDP and employment, transient fall in the rate of inflation and sustained rise in unemployment. Despite the fact that the corporate sector has even slightly enlarged during the observed period, it is evident that this sector has experienced significant contractions too. These contractions are evident due to permanent decline in firm size, owing to the negative employment growth, and due to deterioration in key business performance indicators. The dynamic of the growing number of enterprises was driven by micro and to some extent by small firms, which have narrow potentials for further growth of employment without significant enlargement of the number of enterprises. The Serbian economy is a vulnerable transition economy that strongly reacts to shocks. In regular conditions, before the global economic crisis, expansion of the corporate sector was not sufficient to absorb majority of workers. Following the background facts, in this chapter we have examined potentials for job creation and destruction by size of enterprises and main sectors of economic activity. For this purpose we have used the nationally representative survey of firm-level data collected during May 2011. We have found that Serbian economy creates 7.6% of new jobs per year. Almost the same percentage of jobs has been destroyed, meaning that job destruction in contracting firms contributes in almost the same proportion to the excess job reallocation as creation of new jobs in expanding firms.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".