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Urban Economics in a Historical Perspective: Recovering Data with Machine Learning

Author

Listed:
  • Pierre-Philippe Combes

    (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, CEPR - Center for Economic Policy Research)

  • Laurent Gobillon

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CEPR - Center for Economic Policy Research, IZA - Forschungsinstitut zur Zukunft der Arbeit - Institute of Labor Economics)

  • Yanos Zylberberg

    (University of Bristol [Bristol], CESifo - CESifo - Munich)

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 However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered.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and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents.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

  • Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
    • Handle: RePEc:hal:journl:halshs-03673240
    DOI: 10.1016/j.regsciurbeco.2021.103711
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03673240
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    3. Latika Chaudhary & Fenske, James, 2020. "Did railways affect literacy? Evidence from India," CAGE Online Working Paper Series 529, Competitive Advantage in the Global Economy (CAGE).
    4. 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).
    5. Nagy, Dávid Krisztián, 2022. "Quantitative economic geography meets history: Questions, answers and challenges," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    6. 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.
    7. Hanlon, W.Walker & Heblich, Stephan, 2022. "History and urban economics," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    8. 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.
    9. Ahlfeldt, Gabriel M. & Barr, Jason, 2022. "Viewing urban spatial history from tall buildings," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    10. Bosker, Maarten, 2022. "City origins," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    11. Imryoung Jeong & Hyunjoo Yang, 2021. "Using maps to predict economic activity," Papers 2112.13850, arXiv.org, revised Apr 2022.
    12. Dávid Krisztián Nagy, 2021. "Quantitative Economic Geography Meets History: Questions, Answers and Challenges," Working Papers 1249, Barcelona School of Economics.
    13. 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).
    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.

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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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