Abstract
The emergence and growth of cities are shaped by both geographical features and institutional factors. We are able to analyze their interplay at different levels of the urban hierarchy by exploiting a unique data set on cities in imperial China from 221 BCE to 1911 CE, a geographically diverse empire with a long history of centralized rule. Developing a stylized theoretical model, we combine econometrics with machine learning techniques. Our results suggest that the higher a city is in the urban hierarchy, the less important are geographical compared to institutional factors. At the other end of the scale, market towns without government responsibilities are most strongly shaped by geographical characteristics. We also find evidence that many cities of political importance in imperial times still enjoy a special status nowadays, underlining the modern relevance of these historical factors.
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