Urban economics in a historical perspective: Recovering data with machine learning

Pierre-Philippe Combes, Laurent Gobillon and Yanos Zylberberg

PSE Working Papers from HAL

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 However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered.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and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents.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.

Keywords: Urban economics; History; Machine learning (search for similar items in EconPapers)
Date: 2021-05
New Economics Papers: this item is included in nep-big and nep-his
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03231786v1
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Urban economics in a historical perspective: Recovering data with machine learning (2022)
Working Paper: Urban Economics in a Historical Perspective: Recovering Data with Machine Learning (2022)
Working Paper: Urban Economics in a Historical Perspective: Recovering Data with Machine Learning (2022)
Working Paper: Urban Economics in a Historical Perspective: Recovering Data with Machine Learning (2022)
Working Paper: Urban economics in a historical perspective: Recovering data with machine learning (2021)
Working Paper: Urban Economics in a Historical Perspective: Recovering Data with Machine Learning (2021)
Working Paper: Urban economics in a historical perspective: Recovering data with machine learning (2020)
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