Agglomeration, Inequality and Economic Growth (WP)
Bibliographic record
Abstract
The impact of income inequality on economic growth is dependent on several factors, including the time horizon considered, the initial level of income and its initial distribution. Yet, as growth and inequality are also uneven across space, it is also pertinent to consider the effects of the geographical agglomeration of economic activity. Moreover, it would also seem pertinent to consider not just the levels of inequality and agglomeration, but also the changes they undergo -i.e., their within-country evolution- and how these two processes interact with each other. By applying different econometric specifications and by introducing different measures of agglomeration at country level -specifically, urbanization and urban concentration rates-, this study analyzes how inequality and agglomeration -both their levels and their evolution- influence economic growth in function of the country’s level of development and its initial income distribution. Our results suggest, in line with previous studies, that while high inequality levels are a limiting factor for long-run growth, increasing inequality and increasing agglomeration have the potential to enhance growth in low-income countries where income distribution remains relatively equal, but can result in congestion diseconomies in high-income countries, especially if income distribution becomes particularly unequal.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".