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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T04:51:06Z","timestamp":1697518266342},"reference-count":95,"publisher":"Oxford University Press (OUP)","issue":"651","license":[{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n <jats:p>The emergence of cities in specific locations depends on both geographical features (such as elevation and proximity to rivers) and institutional factors (such as centrality within an administrative region). In this paper, we analyse the importance of these factors at different levels of the urban hierarchy. To do so, we exploit a unique data set on the locations of cities of different status in imperial China from 221 BCE to 1911 CE, a geographically diverse empire with a long history of centralised rule. Developing a stylised 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 local geographical features compared to institutional factors. At the lower 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.<\/jats:p>","DOI":"10.1093\/ej\/ueac063","type":"journal-article","created":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T13:39:52Z","timestamp":1662817192000},"page":"1067-1105","source":"Crossref","is-referenced-by-count":1,"title":["The Emperor\u2019s Geography\u2014City Locations, Nature and Institutional Optimisation"],"prefix":"10.1093","volume":"133","author":[{"given":"Christian","family":"D\u00fcben","sequence":"first","affiliation":[{"name":"Hamburg University, Helmut Schmidt University & Hamburg Institute of International Economics , Germany"}]},{"given":"Melanie","family":"Krause","sequence":"additional","affiliation":[{"name":"Leipzig University , Germany"}]}],"member":"286","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"2023101607504192800_bib1","volume-title":"The Narrow Corridor: States, Societies, and the Fate of Liberty","author":"Acemoglu","year":"2019"},{"issue":"1","key":"2023101607504192800_bib2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.2307\/2118515","article-title":"Trade and circuses: Explaining urban giants","volume":"110","author":"Ades","year":"1995","journal-title":"Quarterly Journal of Economics"},{"key":"2023101607504192800_bib3","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.regsciurbeco.2005.01.003","article-title":"The size distribution of Chinese cities","volume":"35","author":"Anderson","year":"2005","journal-title":"Regional Science and Urban Economics"},{"key":"2023101607504192800_bib4","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1146\/annurev-economics-080217-053433","article-title":"Machine learning methods that economists should know about","volume":"11","author":"Athey","year":"2019","journal-title":"Annual Review of Economics"},{"issue":"1","key":"2023101607504192800_bib9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/jae.659","article-title":"Computation and analysis of multiple structural change models","volume":"18","author":"Bai","year":"2003","journal-title":"Journal of Applied Econometrics"},{"issue":"2","key":"2023101607504192800_bib10","doi-asserted-by":"crossref","first-page":"677","DOI":"10.3982\/ECTA13448","article-title":"Elite recruitment and political stability: The impact of the abolition
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