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Locational error in the estimation of regional discrete choice models using distance as a regressor

Author

Listed:
  • Giuseppe Arbia

    (Catholic University of Sacred Heart)

  • Paolo Berta

    (University of Milan-Bicocca)

  • Carrie B. Dolan

    (William and Mary)

Abstract

In many microeconometric studies distance from a relevant point of interest (such as a hospital) is often used as a predictor in a regression framework. Confidentiality rules, often, require to geo-mask spatial micro-data, reducing the quality of such relevant information and distorting inference on models’ parameters. This paper extends previous literature, extending the classical results on the measurement error in a linear regression model to the case of hospital choice, showing that in a discrete choice model the higher is the distortion produced by the geo-masking, the higher will be the downward bias in absolute value toward zero of the coefficient associated to the distance in the models. Monte Carlo simulations allow us to provide evidence of theoretical hypothesis. Results can be used by the data producers to choose the optimal value of the parameters of geo-masking preserving confidentiality, not destroying the statistical information.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:anresc:v:69:y:2022:i:1:d:10.1007_s00168-022-01116-y
    DOI: 10.1007/s00168-022-01116-y
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    References listed on IDEAS

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    1. Daniel P. Kessler & Mark B. McClellan, 2000. "Is Hospital Competition Socially Wasteful?," The Quarterly Journal of Economics, Oxford University Press, vol. 115(2), pages 577-615.
    2. Giovanni Perucca & Massimiliano Piacenza & Gilberto Turati, 2019. "Spatial inequality in access to healthcare: evidence from an Italian Alpine region," Regional Studies, Taylor & Francis Journals, vol. 53(4), pages 478-489, April.
    3. Paolo Berta & Gianmaria Martini & Francesco Moscone & Giorgio Vittadini, 2016. "The association between asymmetric information, hospital competition and quality of healthcare: evidence from Italy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 907-926, October.
    4. 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.
    5. Rob Deardon & Babak Habibzadeh & Hau Yi Chung, 2012. "Spatial measurement error in infectious disease models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1139-1150, November.
    6. Daniel Runfola & Geeta Batra & Anupam Anand & Audrey Way & Seth Goodman, 2020. "Exploring the Socioeconomic Co-benefits of Global Environment Facility Projects in Uganda Using a Quasi-Experimental Geospatial Interpolation (QGI) Approach," Sustainability, MDPI, vol. 12(8), pages 1-13, April.
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    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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