Macroeconomic Policy Changes and Its Impact on Trade Unions, an Empirical Study on OECD Countries for the Period 2001-2020
Bibliographic record
Abstract
This study examines the macroeconomic variables affecting trade union rate membership in OECD nations from 2001 to 2020. The Organization for Economic Cooperation and Development (OECD) has 38 of the most industrialized countries globally, which counts more than 80% of the global GDP; analyzing the macroeconomic movements of these countries means that we most likely know the variance of the global macroeconomic changes. We target the effect of employability, expenditure on education, unemployment, inflation, FDI, economic growth, wages, and salaries on trade union participation of employers. To conduct this research, we used data from World Bank, ILO, and OECD for 38 countries during the period 2001-2020, conducting a panel data Fixed Effect non-linear regression model with robust effect considering the non-normality and the possibility of heteroscedasticity of some of the variables. The results show that employers in the industry, the productivity in the service sector, and wages will increase the enrolment in a trade union, but on the other side, an increase of FDI and unemployment rates will decrease the association of employers to be in a trade union.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".