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Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities

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  • Lorenzo Donadio
  • Rossano Schifanella
  • Claudia R Binder
  • Emanuele Massaro

Abstract

The availability of reliable socioeconomic data is critical for the design of urban policies and the implementation of location-based services; however, often, their temporal and geographical coverage remain scarce. We explore the potential for insurance customers data to predict socioeconomic indicators of Swiss municipalities. First, we define a features space by aggregating at city-level individual customer data along several behavioral and user profile dimensions. Second, we collect official statistics shared by the Swiss authorities on a wide spectrum of categories: Population, Transportation, Work, Space and Territory, Housing, and Economy. Third, we adopt two spatial regression models exploring both global and local geographical dependencies to investigate their predictability. Results show consistently a correlation between insurance customer characteristics and official socioeconomic indexes. Performance fluctuates depending on the category, with values of R2 > 0.6 for several target variables using a 5-fold cross validation. As a case study, we focus on predicting the percentage of the population using public transportation and we discuss the implications on a regional scope. We believe that this methodology can support official statistical offices and it could open up new opportunities for the characterization of socioeconomic traits at highly-granular spatial and temporal scales.

Suggested Citation

  • Lorenzo Donadio & Rossano Schifanella & Claudia R Binder & Emanuele Massaro, 2021. "Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    • Handle: RePEc:plo:pone00:0246785
    DOI: 10.1371/journal.pone.0246785
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    References listed on IDEAS

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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. -, 2014. "A World that Counts: Mobilising the Data Revolution for Sustainable Development," Coediciones, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), number 40319 edited by United Nations, May.
    3. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    4. Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher Ré & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
    5. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    6. Steve MacFeely, 2019. "The Big (data) Bang: Opportunities and Challenges for Compiling SDG Indicators," Global Policy, London School of Economics and Political Science, vol. 10(S1), pages 121-133, January.
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