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Small Area Model-Based Estimators Using Big Data Sources

Author

Listed:
  • Marchetti Stefano

    (Department of Economics and Management – University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy)

  • Giusti Caterina

    (Department of Economics and Management - University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy.)

  • Pratesi Monica

    (Department of Economics and Management - University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy.)

  • Salvati Nicola

    (Department of Economics and Management - University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy)

  • Giannotti Fosca

    (KDD Lab – ISTI – National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy.)

  • Pedreschi Dino

    (Department of Computer Science – University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy)

  • Rinzivillo Salvatore

    (KDD Lab - ISTI - National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy)

  • Pappalardo Luca

    (KDD Lab - ISTI - National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy)

  • Gabrielli Lorenzo

    (KDD Lab - ISTI - National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy)

Abstract

The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.

Suggested Citation

  • Marchetti Stefano & Giusti Caterina & Pratesi Monica & Salvati Nicola & Giannotti Fosca & Pedreschi Dino & Rinzivillo Salvatore & Pappalardo Luca & Gabrielli Lorenzo, 2015. "Small Area Model-Based Estimators Using Big Data Sources," Journal of Official Statistics, Sciendo, vol. 31(2), pages 263-281, June.
    • Handle: RePEc:vrs:offsta:v:31:y:2015:i:2:p:263-281:n:7
    DOI: 10.1515/jos-2015-0017
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    References listed on IDEAS

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    1. Lynn M. R. Ybarra & Sharon L. Lohr, 2008. "Small area estimation when auxiliary information is measured with error," Biometrika, Biometrika Trust, vol. 95(4), pages 919-931.
    2. Marchetti, Stefano & Tzavidis, Nikos & Pratesi, Monica, 2012. "Non-parametric bootstrap mean squared error estimation for M-quantile estimators of small area averages, quantiles and poverty indicators," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2889-2902.
    3. Malay Ghosh & Karabi Sinha & Dalho Kim, 2006. "Empirical and Hierarchical Bayesian Estimation in Finite Population Sampling under Structural Measurement Error Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(3), pages 591-608, September.
    4. Enrico Fabrizi & Caterina Giusti & Nicola Salvati & Nikos Tzavidis, 2014. "Mapping average equivalized income using robust small area methods," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 685-701, August.
    5. Mahmoud Torabi & Gauri S. Datta & J. N. K. Rao, 2009. "Empirical Bayes Estimation of Small Area Means under a Nested Error Linear Regression Model with Measurement Errors in the Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 355-369, June.
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    Cited by:

    1. Giulio Ecchia & Francesca Gagliardi & Caterina Giannetti, 2018. "Social Investment and youth labour market participation: a EU regional analysis," Discussion Papers 2018/236, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.

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