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A network-based method to harmonize data classifications

Author

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  • Dario Diodato

Abstract

A frequent problem in research is the harmonization of data to a common classification, whether that is in terms of ? to name a few examples ? industries, commodities, occupations, or geograph- ical areas. Statistical offices often provide concordance tables, to match data through time or with different classifications, but these concordance tables alone are often not sufficient to define a clear methodology on how the matching should be performed. In fact, the concordance tables have, in numerous occasions, a many-to-many mapping of classifications. The issue is exacerbated when two or more concordance tables are concatenated. In this Jupyter notebook, I discuss a network- based abstraction of this problem and propose, as a general solution, a method that identifies the network components (or the network communities) to make data converge to a new classification. The method simplifies the issue and reduces greatly conversion errors.

Suggested Citation

  • Dario Diodato, 2018. "A network-based method to harmonize data classifications," Papers in Evolutionary Economic Geography (PEEG) 1843, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Dec 2018.
    • Handle: RePEc:egu:wpaper:1843
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    File URL: http://econ.geo.uu.nl/peeg/peeg1843.pdf
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    References listed on IDEAS

    as
    1. Diodato, Dario & Neffke, Frank & O’Clery, Neave, 2018. "Why do industries coagglomerate? How Marshallian externalities differ by industry and have evolved over time," Journal of Urban Economics, Elsevier, vol. 106(C), pages 1-26.
    2. Glenn Ellison & Edward L. Glaeser & William R. Kerr, 2010. "What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns," American Economic Review, American Economic Association, vol. 100(3), pages 1195-1213, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Daniel Straulino & Mattie Landman & Neave O'Clery, 2020. "A bi-directional approach to comparing the modular structure of networks," Papers 2010.06568, arXiv.org.
    2. Diodato, Dario & Hausmann, Ricardo & Neffke, Frank, 2023. "The impact of return migration on employment and wages in Mexican cities," Journal of Urban Economics, Elsevier, vol. 135(C).
    3. Dario Diodato & Ricardo Hausmann & Frank Neffke, 2020. "The impact of return migration from the U.S. on employment and wages in Mexican cities," Papers in Evolutionary Economic Geography (PEEG) 2012, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Mar 2020.
    4. Lukaszuk, Piotr & Torun, David, 2022. "Harmonizing the Harmonized System," Economics Working Paper Series 2212, University of St. Gallen, School of Economics and Political Science.
    5. Mattie Landman & Sanna Ojanperä & Stephen Kinsella & Neave O’Clery, 2023. "The role of relatedness and strategic linkages between domestic and MNE sectors in regional branching and resilience," The Journal of Technology Transfer, Springer, vol. 48(2), pages 515-559, April.
    6. Ana Grisanti & Douglas Barrios & Eric S. M. Protzer & Jorge Tapia & Nikita Taniparti & Ricardo Hausmann & Rushabh Sanghvi & Semiray Kasoolu & Tim O'Brien, 2021. "Western Australia – Research Findings and Policy Recommendations," CID Working Papers 395, Center for International Development at Harvard University.

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    More about this item

    Keywords

    classification; concordance; harmonization; network; Python; Jupyter;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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