Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/236423
Authors: 
Combes, Pierre-Philippe
Gobillon, Laurent
Zylberberg, Yanos
Year of Publication: 
2021
Series/Report no.: 
IZA Discussion Papers No. 14392
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
A recent literature has used a historical perspective to better understand fundamental questions of urban economics. However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered. In this paper, we describe how and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents. We first discuss how important questions of urban economics rely on the analysis of historical data sources and the challenges associated with transcription and harmonisation of such data. We then explain how machine learning approaches may address some of these challenges and we discuss possible applications.
Subjects: 
machine learning
history
urban economics
JEL: 
R11
R12
R14
N90
C45
C81
Document Type: 
Working Paper

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