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

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  • Gobillon, Laurent
  • Combes, Pierre-Philippe
  • Zylberberg, Yanos

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 However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered.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and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents.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

Suggested Citation

  • Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:15308
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    5. Stephan Heblich & David Krisztián Nagy & Alex Trew & Yanos Zylberberg, 2023. "The death and life of great British cities," Economics Working Papers 1867, Department of Economics and Business, Universitat Pompeu Fabra.
    6. Imryoung Jeong & Hyunjoo Yang, 2021. "Using maps to predict economic activity," Papers 2112.13850, arXiv.org, revised Apr 2022.
    7. Dahl, Christian M. & Johansen, Torben S.D. & Sørensen, Emil N. & Wittrock, Simon, 2023. "HANA: A handwritten name database for offline handwritten text recognition," Explorations in Economic History, Elsevier, vol. 87(C).
    8. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
    9. Chaudhary, Latika & Fenske, James, 2020. "Did railways affect literacy? Evidence from India," The Warwick Economics Research Paper Series (TWERPS) 1320, University of Warwick, Department of Economics.
    10. Dávid Krisztián Nagy, 2021. "Quantitative Economic Geography Meets History: Questions, Answers and Challenges," Working Papers 1249, Barcelona School of Economics.
    11. Nagy, Dávid Krisztián, 2022. "Quantitative economic geography meets history: Questions, answers and challenges," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    12. Albers, Thilo N.H. & Kappner, Kalle, 2023. "Perks and pitfalls of city directories as a micro-geographic data source," Explorations in Economic History, Elsevier, vol. 87(C).
    13. Hengran Bian & Yi Liu, 2023. "A Deep Graph Learning-Enhanced Assessment Method for Industry-Sustainability Coupling Degree in Smart Cities," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    14. Albers, Thilo N. H. & Kappner, Kalle, 2022. "Perks and Pitfalls of City Directories as a Micro-Geographic Data Source," Rationality and Competition Discussion Paper Series 315, CRC TRR 190 Rationality and Competition.
    15. David Krisztián Nagy, 2020. "Quantitative economic geography meets history: Questions, answers and challenges," Economics Working Papers 1774, Department of Economics and Business, Universitat Pompeu Fabra, revised Mar 2021.

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    More about this item

    Keywords

    Urban economics; History; Machine learning;
    All these keywords.

    JEL classification:

    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • N90 - Economic History - - Regional and Urban History - - - General, International, or Comparative
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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