Modelling labour demand in Poland
Bibliographic record
Abstract
The aim of the paper is to construct a long-term model of labour demand in Poland, in which the explanatory variables are the average gross salary and gross value added. Additionally, the authors attempt to detect labour hoarding. The study adopted the production approach, which used autoregressive distributed lag model with an ARDL-ECM error correction mechanism. The model parametres were estimated on the basis of quarterly data on the average number of persons employed, the average monthly gross salary and gross value added, all of which related to the period from the first quarter of 2002 to the fourth quarter of 2018. The data used in the study came from Statistics Poland publications. The proposed approach estimated the actual demand for labour. In the analysed period, a long-term relationship between the average employment, the average monthly gross salary and gross value added was observed. Employment was decreasing as the average salary was growing, and its increase was connected with the production growth. Moreover, short-term deviations of the value of the actual employment from the value of employment estimated by the model were observed on the labour market, which indicates labour hoarding could have been taking place. However, due to an insufficient number of observations, the occurrence of this phenomenon could not be fully confirmed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".