Demographic Origins of the Great Recession: Implications for China
Bibliographic record
Abstract
Abstract The demographic dividend, that is, the growth of the working age population aged 16 years relative to younger and older age dependents, has often been cited as a crucial component of the accelerated economic growth experienced by disparate countries and regions at different points in time. Generally less emphasized are the ramifications of this process when it occurs in reverse; that is, when the relative size of the working age population begins to shrink. Related to this is the more subtle effect of changes to the age structure of the overall working age population, which can have compounding or offsetting effects in relation to the demographic dividend noted above. This paper explores how these age‐related phenomena were instrumental to both the Great Depression and the Great Recession of 2008. We explore how the generational composition of economic actors and the aging of the baby‐boom worker may have played a role in provoking these remarkable recessionary periods. The reversal of the demographic dividend and the aging of the working age population are factors now contributing to the propagation of the global economic downturn, as witnessed in the example of Japan over the past half‐century. This paper applies the lessons of the Great Depression to offer a forward‐looking analysis of the Chinese economy. China is on the precipice of a significant demographic shift whose implications for economic growth are explored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".