Working Paper: CEPR ID: DP15308
Authors: Pierre-Philippe Combes; Laurent Gobillon; Yanos Zylberberg
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 However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered.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and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents.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
JEL Codes: R11; R12; R14; N90; C45; C81
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| historical agricultural productivity (N52) | urban development (R58) |
| agricultural productivity (Q11) | urban growth (R11) |
| trade costs (F19) | agricultural productivity and urban growth (O49) |
| agricultural productivity (high trade costs) (Q17) | urban growth (R11) |
| agricultural productivity (low trade costs) (Q17) | urban growth (R11) |
| historical agricultural practices (N50) | urbanization (R11) |