Agglomeration costs limit sustainable innovation in cities in developing economies
Bibliographic record
Abstract
Theory and evidence from developed economies suggests that innovation activities benefit from agglomeration economies associated with urban economic density. However, despite the fact that eighteen of the world's top twenty cities are in developing countries, we do not know whether agglomeration affects innovation in the same way in developing countries. We propose that, while there are still agglomeration benefits, the development path followed by cities in developing countries also creates significant agglomeration costs and these act to limit innovation. We build a unique database to measure consistently both urban economic density and innovation across a large number of developing countries. Based on geospatial information, we combine data on nightlights at the city level to proxy urban density with information on innovation activity at the firm level. We find that in developing countries, as urban economic density increases, innovation first increases and then begins to decrease beyond a certain point, with the decline being most prominent in the largest cities. That is, the largest cities in developing countries are not able to act as sustainable sources of innovation. Cities in developing countries therefore display different patterns of agglomeration from those documented in the literature focused on developed countries. Our analysis explores the relationship between UN Sustainable Development Goal (SDG) 9 which fosters innovation, and SDG 11 which promotes sustainable and resilient cities. Our results suggest the importance of addressing urban agglomeration costs as a means to facilitate innovative activity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".