Urban agglomerations and innovation in developing economies
Bibliographic record
Abstract
<title>Abstract</title> Theory and evidence from developed economies suggests that innovation activities benefit from agglomeration economies in cities. However, whether the same is true of developing countries has not been investigated by large-scale cross-country analysis, despite the fact that eighteen of the world’s top twenty cities by population are in developing countries. We argue that the development path followed by developing countries creates agglomeration costs that may offset the benefits. Hence innovative activity may eventually decline as urban density increases. We build a unique cross-country database where for the first time both urban density and innovation are measured consistently across a large set of developing countries. We find that in developing countries, innovation first increases and then begins to decrease beyond a certain point. The declining part is the most prominent in largest cities. Thus, cities in developing countries follow very different patterns of agglomeration from those in developed countries. Policies based on the experience of developed countries might not adequately reduce agglomeration costs in developing countries, and therefore new policies are required to support innovation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".