Curva de Beveridge, Vacantes y Desempleo: Chile 1986-2002.II
Bibliographic record
Abstract
In this paper we provide a vacancyindex for Chile for the period 1986 to the second quarter of 2002. This index is calculated using the number of jobs offered in newspapers’ advertisements of the 5 main urban areas. This information, in addition with employment and labor force data, is used to draw and estimate a Beveridge Curve (CB). We used this curve to analyze the sources of unemployment volatility. The stability of the curve along the sample is a signal of the anticyclical trayectory of the unemployment. On the other hand, shifts in this curve allow to conclude that unbalanced shocks have affected the labor market. The main conclusions of this paper are: i) the index is reasonably unbiased, despite of the fact that some statistical problems were not completely corrected; ii) there is no evidence to reject the stability of the CB (or the anticyclical behavior of the unemployment) at the national level during the sample, including the recent period; iii) in the context of an impulse-response analysis, a transitory innovation of vacancies has permanent impact on employment; iv) the index may be considered as a leader indicator of GDP (in one quarter) and employment (in half a year); v) these aggregated results are strongly influenced by the high weight of Santiago (the Capital) over the total; vi) outside the Capital, the CB seems to have been more unstable, specially in Concepción-Talcahuano and Temuco (both located in the South of the country), suffering a shift in the early 90s. However, it is not possible to conclude that these displacements are caused by sample problems in the data, by sectorial shocks or by non-linearity in the employment-vacancies elasticity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".