Bibliographic record
Abstract
This thesis investigates the influence of aging on the labor market, specifically focusing on its impact on unemployment and the prices of goods.The analysis is grounded in a collection of empirical observations from different countries, revealing a noteworthy trend: retired individuals not only exhibit reduced consumption expenditures compared to working individuals, but also tend to purchase the same goods at lower prices.To understand this phenomenon, two different models are created: one is a conventional mathematical search-matching model, and the other is an agent-based model.These models offer insights into the underlying mechanism that enables retired seniors to access more affordable goods, as they possess increased time for thorough searches in the product market.We find two interesting and opposing effects: Initially, the growing population of retirees amplifies net demand, thereby stimulating employment opportunities.Simultaneously, however, the retirees have higher price sensitivity, compelling firms to maintain competitive pricing.This, in turn, erodes corporate market power, diminishes profit margins, and deters incentives for employment generation.Paradoxically, this increased demand results in lower profits for firms and higher unemployment rates.Finally, we show that our models exhibits similar qualitative trends as data from the World Bank's World Development Indicators, and offers a way of reconciling predictions on the economic impact of aging populations.i
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".