Small Area Model-Based Estimators Using Big Data Sources

Marchetti Stefano (), Giusti Caterina (), Pratesi Monica (), Salvati Nicola (), Giannotti Fosca (), Pedreschi Dino (), Rinzivillo Salvatore (), Pappalardo Luca () and Gabrielli Lorenzo ()
Additional contact information
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

Journal of Official Statistics, 2015, vol. 31, issue 2, 263-281

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.

Keywords: Social mining; auxiliary information; poverty measures (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:31:y:2015:i:2:p:263-281:n:7

DOI: 10.1515/jos-2015-0017

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