Demographic and Economic Dependency Ratios – Present and Perspectives
Bibliographic record
Abstract
In the present research article, we outline the distinction between the demographic dependency ratio and the economic dependency ratio and present its evolution in Romania within the European Union, but not restrictive to the EU27. The evolution of demographic dependency ratio changed dramatically in Romania in the last 15 years comparing to the UE27. On the other hand, the evolution of economic dependency ratios is much more relevant because it also reflects the problems the economy is facing and should be brought to the fore in the political debates and to decision makers. In the paper we present the factors that are leading to the increase of the economic dependency ratio and we conclude with the solutions which a state has to adopt in order to prevent excessive public debt and structural gaps due to long term rise in economic dependency ratio. Moreover, policy-makers must face up the painful inter-temporal transfer choices that have to be done. Our concern about Eastern-European Countries is strengthened by the global results reached by OECD through Minilink Model Study, IMF Study of G7 and QUEST II Model that suggest the fall of the living standards over the next 50 years due to economic dependency ratio. For Romania we considered two main solution to this problem: increasing birth rate (long term solution) and lowering the unemployment rate through investment and a high rate of EU funds absorption (medium term solution).
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".