Statistical Approaches to the Evaluation of the Demand and Supply at the Labour Market Based on Panel Data
Bibliographic record
Abstract
The labour market as any trade market is based on demand and supply. In this case, the demand takes the form of the need to occupy free job positions and to perform work, and the supply represents the availability of unemployed workforce or a desire to change job. Demand and supply are implemented in competition, on the one hand, between workers for occupying a particular job or performing work, and, on the other hand, between employers for engagement of the necessary workforce in terms of quantity and quality. A fair solution of this competitiveness implies observance of a number of conditions. First of all, it is the mobility of workforce and rational distribution of production forces. In modern economy there is a situation, when the labour supply is reducing primarily due to the influence of demographic trends. For certain sectors of economy, the labour shortage becomes especially acute, since it has not only quantitative but also qualitative and structural nature. The decision of structural problems of employment and labour market will contribute to alleviation of labour shortage. In the present article the main factors which form the demand and supply at the labour market at both national and regional level are identified and analyzed. Absolute and relative values of structural unbalanced employment are evaluated in the Mari El Republic by economic activities. The authors have formulated the methodology of the demand and supply evaluation, based on regressive models using panel data, which allowed not only to identify the influence of explanatory variables on the number of employed population, but also to take into account time effects. We have presented forecasting and analytical tools, have described the main preconditions and principles of models’ construction, have carried out approbation of the suggested methods and models and have given the results of calculation of the dynamics and structure of the main indicators in the sphere of employment and the labour market of the region. Analysis and forecasting of socio-economic processes and identifying their relations is an essential condition for dynamic development of the national economy and for the growth of welfare of the citizens. The definite advantage of the suggested approach is its universality and applicability to the evaluation of the dynamics and structure of various socio-economic indicators and their structures.
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".