The Impact of the Delayed Retirement Pilot Policy in Jiangsu on the Employment Rate of the Middle-aged and Youth Population
Bibliographic record
Abstract
China’s rapidly aging population has placed an unprecedent strain on the labor market and pension system. This paper empirically investigates the causal effects of a delayed retirement policy implemented in early 2022 on the labor market for youth and middle-age worker. Using county-level panel data of Jiangsu province in China from 2018 to 2024 for Jiangsu province, the treatment group, and Zhejiang province, the control group, we discover obvious policy trade-offs. We find that within three years of implementation, middle-aged employment rate in Jiangsu province increased, while youth employment rate decreased. The positive effects on the middle-age were stronger in cities with higher aging rates and heavy industry experience. Whereas for youth, unemployment effects were concentrated in sectors of low-turnover. In addition to this result, were discovered that firm’s retention of middle-aged workers was reinforced rather than being reduced, causing a set of trade-offs. These results provide policy implications to contribute to the stability and development of the entire labour market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".