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

Author

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  • 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 nationale des ponts et chaussées - 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 nationale des ponts et chaussées - 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 However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered.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and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents.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," Sciences Po Economics Publications (main) halshs-03673240, HAL.
    • Handle: RePEc:hal:spmain:halshs-03673240
    DOI: 10.1016/j.regsciurbeco.2021.103711
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03673240v1
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    2. Ahlfeldt, Gabriel M. & Barr, Jason, 2022. "Viewing urban spatial history from tall buildings," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    3. 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.
    4. Erokhin, Dmitry & Zagler, Martin, 2024. "Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms," Economic Modelling, Elsevier, vol. 139(C).
    5. Bosker, Maarten, 2022. "City origins," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    6. Stephan Heblich & Dávid Nagy & Alex Trew & ​Yanos Zylberberg, 2023. "The Death and Life of Great British Cities," Working Papers 1398, Barcelona School of Economics.
    7. Imryoung Jeong & Hyunjoo Yang, 2021. "Using maps to predict economic activity," Papers 2112.13850, arXiv.org, revised Apr 2022.
    8. 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).
    9. 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).
    10. Dávid Nagy & 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. Hanlon, W.Walker & Heblich, Stephan, 2022. "History and urban economics," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    16. 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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    Keywords

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    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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