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Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality

Author

Listed:
  • Giuseppe Arbia

    (Department of Statistical Science, Catholic University of the Sacred Heart, Rome 00168, Italy)

  • Giuseppe Espa

    (Department of Economics and Management, University of Trento, Trento 38122, Italy)

  • Diego Giuliani

    (Department of Economics and Management, University of Trento, Trento 38122, Italy)

Abstract

In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in a regression framework. However, in order to preserve confidentiality, spatial micro-data are often geo-masked, thus reducing their quality and dramatically distorting the inferential conclusions. In particular in this case, a measurement error is introduced in the independent variable which negatively affects the properties of the estimators. This paper studies these negative effects, discusses their consequences, and suggests possible interpretations and directions to data producers, end users, and practitioners.

Suggested Citation

  • Giuseppe Arbia & Giuseppe Espa & Diego Giuliani, 2015. "Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality," Econometrics, MDPI, vol. 3(4), pages 1-10, October.
    • Handle: RePEc:gam:jecnmx:v:3:y:2015:i:4:p:709-718:d:57989
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    References listed on IDEAS

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    1. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani, 2016. "Dirty spatial econometrics," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 177-189, January.
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    Cited by:

    1. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani & Maria Michela Dickson, 2017. "Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 326-346, July.
    2. Mahesh Karra & David Canning & Ryoko Sato, 2020. "Adding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effects," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1251-1268, November.
    3. Kassoum Ayouba & Marie-Laure Breuillé & Camille Grivault & Julie Le Gallo, 2020. "Does Airbnb Disrupt the Private Rental Market? An Empirical Analysis for French Cities," International Regional Science Review, , vol. 43(1-2), pages 76-104, January.
    4. Arnab Bhattacharjee & Ornella Maietta & Fernanda Mazzotta, 2023. "Spatial agglomeration, innovation and firm survival for Italian manufacturing firms," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(3), pages 318-345, July.
    5. Renard, Yohan, 2022. "From fees to free: User fee removal, maternal health care utilization and child health in Zambia," World Development, Elsevier, vol. 156(C).
    6. Giuseppe Arbia & Paolo Berta & Carrie B. Dolan, 2022. "Locational error in the estimation of regional discrete choice models using distance as a regressor," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(1), pages 223-238, August.
    7. Finn McGuire & Noemi Kreif & Peter C. Smith, 2021. "The effect of distance on maternal institutional delivery choice: Evidence from Malawi," Health Economics, John Wiley & Sons, Ltd., vol. 30(9), pages 2144-2167, September.

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