Assessing the Impact of Demographic Composition on Productivity
Bibliographic record
Abstract
We examine how demographic factors influence potential output, focusing on how the age distribution of the working-age population and the old-age dependency ratio affect aggregate productivity. Following Feyrer (2007), we emphasize that the contribution to aggregate productivity varies by age group, with middle-aged individuals (aged 40 to 49) being the most productive. Our analysis shows that changes in demographic composition could explain some of the productivity trends observed in China and the United States over the past few decades. This demonstrates why it is important to incorporate the impact of demographic composition when estimating potential output. In particular, demographic factors are expected to narrow the differential in trend labour productivity (TLP) growth between China and the United States by nearly 1 percentage point between 2024 and 2030. On average, TLP growth in China could be reduced by 0.8 percentage points, while that in the United States could rise by 0.1 percentage point. Moreover, demographic factors in Canada portray a similar story to that of the United States. After averaging about 1 percentage point per year from 2010 to 2019, demographic headwinds are expected to dissipate fully through the 2020s, which could signal an upside risk to Canadian TLP growth.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".