Data-driven insights into the effects of demographic ageing on Lithuania's labour market sustainability
Bibliographic record
Abstract
Purpose The purpose of this article is to examine how population ageing affects labour market. Utilising Lithuania as a case study, present research seeks to draw broader conclusions that are applicable to other developed economies experiencing rapid demographic ageing. Design/methodology/approach The study employs vector autoregression methodology to assess the impact of the ageing of the Lithuanian labour force and its relationship with labour market indicators, including labour productivity, labour force participation of persons aged 25–54 and the unemployment rate of persons aged 15–24. The data retrieved from the State Data Agency have been utilised to conduct a comprehensive analysis of the most recent period for which data is available at the time of writing, spanning from the first quarter of 2002 to the third quarter of 2024. Findings It was found that ageing does not have a statistically significant impact on selected Lithuanian labour market indicators. However, the results of the study may be influenced by the short research sample, Lithuania's accession to the European Union, the financial crisis of 2007–2008 and the COVID-19 pandemic. Practical implications The present study offers practical insights to facilitate the navigation by policymakers of the challenges posed by a rapidly ageing population, thereby ensuring a more sustainable and resilient future. Originality/value The study makes a significant contribution to the extant body of knowledge on the subject through the use of advanced models, thus providing a novel perspective on the dynamics of the aforementioned subjects.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".