On the potential future effects of population structure on financial stability
Bibliographic record
Abstract
In several advanced economies, the post World War II baby-boom was followed by a sustained drop in fertility rates to sub-replacement levels. In parallel, adult life expectancies have seen an unprecedented increase. The aim of this study is to better understand the potential future effects of these demographic dynamics on financial stability. Two major channels of influence are investigated. The first channel is through the housing market and the second is related to debt sustainability. We present a probabilistic numerical model that simulates and projects future population dynamics. This model is used to project potential future growth of two major actors of the supply and demand in the housing market; old age market leavers and first time buyers. We also use this model to project actual income and consumption trends as well as their potential quantile distributions. The models parameters are estimated and calibrated to Canada, Germany, Spain, France, Italy, Japan, the United Kingdom and the United States. We run Monte Carlo simulations and sensitivity analysis to convey uncertainty. Our results show an overall continuous and long lasting increase in old age generated supply. Potential first time buyers demand varies from country to country. However, in all countries in focus, it is increasingly sensitive to migration. Projections of actual income and consumption trends leads to decreasing support ratios. We discuss how these potential imbalances could impact the housing market, debt and to a larger extent financial stability.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".