# Economic geography and the scaling of urban and regional income in India

**Authors:** Anand Sahasranaman, Luis M. A. Bettencourt  
**arXiv:** [1902.09872](https://arxiv.org/abs/1902.09872)  
**Date:** 2019-02-26  
**Summary page:** https://www.summarizepaper.com/en/paper/1902.09872v1/

## Key points

- The paper titled "Economic geography and the scaling of urban and regional income in India" explores the relationship between economic income (Gross Domestic Product, GDP) and population size in Indian districts and cities.
- Scaling analysis is used to identify network effects in socioeconomic organization.
- Almost linear scaling of GDP with population for districts is found, which is different from urban functional units in other national contexts.
- Strong distinct geographic patterns of economic behavior are discovered that are connected to the literature on regional economic development for a diverse subcontinental nation like India.
- A new dataset of GDP based on districts for large cities using a set of assumptions reveals superlinear scaling of income with city size, as expected from theory while displaying similar underlying patterns of economic geography observed for district economic performance.
- The analysis is limited by the absence of higher-fidelity direct city-level economic data.
- The authors discuss the need for standardized and consistent estimates of the size and change in urban economies in India while pointing out several proxies that can be explored to develop such indicators.

## Summary

In their paper titled "Economic geography and the scaling of urban and regional income in India," Anand Sahasranaman and Luis M. A. Bettencourt explore the relationship between economic income (Gross Domestic Product, GDP) and population size in Indian districts and cities. Using scaling analysis as a method to identify network effects in socioeconomic organization, the authors find almost linear scaling of GDP with population for districts, which is different from urban functional units in other national contexts. The authors also use deviations from scaling to examine the behavior of these regional units and discover strong distinct geographic patterns of economic behavior that they connect to the literature on regional economic development for a diverse subcontinental nation like India. Due to the lack of economic data for Urban Agglomerations in India, the authors create a new dataset of GDP based on districts for large cities using a set of assumptions. This reveals superlinear scaling of income with city size, as expected from theory while displaying similar underlying patterns of economic geography observed for district economic performance. However, this analysis is severely limited by the absence of higher-fidelity direct city-level economic data. The authors discuss the need for standardized and consistent estimates of the size and change in urban economies in India while pointing out several proxies that can be explored to develop such indicators. Overall, this study provides valuable insights into understanding how socioeconomic organization affects cities' growth patterns and highlights areas where more research is needed to improve our understanding further.