Bibliographic record
Abstract
The existing body of literature underscores the crucial role of technology, driven by both innovation and imitation, in fostering economic growth. Human capital emerges as a key factor influencing technology adoption and innovation. We consider a R&D-based growth model to analyze how improvement in schooling quality impacts technical progress (via the twin channels of imitation and innovation) and therefore, long- run economic growth of an economy by working through the influence of fertility rates and education decisions at household level. The results indicate that improvement in schooling quality triggers a child quantity-quality trade-off at the household level when quality of schooling exceeds an endogenously determined threshold. At household level, parents invest more in the education of their children and have lesser number of children. This micro-level trade-off has two opposing effects on aggregate human capital accumulation at the macroeconomy wide level. A higher investment in education of a child stimulates the accumulation of human capital, which fosters technical progress, but the simultaneous decline in fertility rate reduces total factor productivity growth by contracting the stock of human capital. The former effect prevails over the latter only when quality of schooling is higher than the threshold and therefore, economic growth is driven by rate of aggregate human capital accumulation under both innovation and imitation regimes. However, when the quality of schooling is lower than this threshold, parents do not invest in education and focus on maximizing fertility. Therefore, the economy grows at the rate of population growth at the macro level under the two regimes. Also, it is advantageous for an economy to innovate upon the local technology frontier instead of imitating from the world technology frontier if the rate of human capital accumulation is higher than the growth rate of world technology frontier in the presence of constant or diminishing returns to R&D sector.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".