Exploring the Relationship between Economic Growth and Employment in the Czech Republic and Belgium
Bibliographic record
Abstract
The paper addresses the issue of employment policy development and implementation in the selected European countries with a similar economic structure and population, namely the Czech Republic and Belgium. The existing approaches used by Ministries of Labour and Social Affairs are based either on drawing subsidies from EU structural funds within the frameworks of various Operational Programmes or direct job creation that is realized as a consequence of GDP growth. The retrospective observation of the development of such macroeconomic indicators as GDP per capita and employment rate in Belgium reminded us the Okun’s law. This encouraged us to verify the inverted version of the latter and conduct a time series analysis with the use of ARIMA model. The conducted calculations revealed the existence of determined relationship between GDP per capita and employment rate, namely with GDP per capita increase by 2% corresponds to an increase in employment by 1%. This relationship applies vice versa as well. The obtained result may be considered as an extension of the classical Okun’s law theoretical framework. The main aim was to explore these relationships and on the basis of comparative analysis between macroeconomic indicators in the Czech Republic and Belgium to suggest recommendations aimed at development of employment policies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".