Mitigating the Economic Impact of an Aging Population: Options for Bulgaria
Bibliographic record
Abstract
Bulgaria is undergoing a profound socio-economic transformation brought about by extraordinary demographic change. Between 1950 and 1990, Bulgaria’s population grew from 7.3 million to 8.8 million and then declined to 7.5 million by 2010. Low birth rates and high mortality rates combined with an emigration rate of 40 percent explain the steep decline. This radically changed Bulgaria’s age structure, resulting in the third-highest median age in the EU, surpassed only by Germany and Italy. As a result, Bulgaria is now heading for the steepest decline in working-aging population of any country. Until 2007, Bulgaria’s working-age population grew relative to the total population and constituted its largest share. The subsequent decline has meant that fewer and fewer working Bulgarians will have to support more and more children and especially people over 64. By 2050, one in three Bulgarians is projected to be older than 65 and only one in two Bulgarians will be of working-age. This report analyzes the economic impact of ageing, focusing on budgetary, social (education and health), labor, and growth effects. The objective is to quantify, to the extent possible, realistic and alternative impact scenarios depending on various policy options available to Bulgarian policymakers. The sectoral impact analyses were combined into a consistent overall, long-term projection of Bulgaria’s public finances in order to highlight key policy trade-offs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".