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International Conference on Social Informatics

SocInfo 2017: Social Informatics pp 14–23Cite as

Towards Real-Time Prediction of Unemployment and Profession

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10540))

Abstract

At a societal level unemployment is an important indicator of the performance of an economy and risks in financial markets. This study provides the first confirmation that individual employment status can be predicted from standard mobile phone network logs externally validated with household survey data. Individual welfare and households’ vulnerability to shocks are intimately connected to employment status and professions of household breadwinners. By deriving a broad set of novel mobile phone network indicators reflecting users’ financial, social and mobility patterns we show how machine learning models can be used to predict 18 categories of profession in a South-Asian developing country. The model predicts individual unemployment status with 70.4% accuracy. We further show how unemployment can be aggregated from individual level and mapped geographically at cell tower resolution, providing a promising approach to map labor market economic indicators, and the distribution of economic productivity and vulnerability between censuses, especially in heterogeneous urban areas. The method also provides a promising approach to support data collection on vulnerable populations, which are frequently under-represented in official surveys.

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Author information

Authors and Affiliations

  1. Telenor Group Research, Fornebu, Norway

    Pål Sundsøy, Johannes Bjelland & Bjørn-Atle Reme

  2. MIT Institute for Data, Systems and Society, Cambridge, USA

    Eaman Jahani

  3. Flowminder Foundation, Stockholm, Sweden

    Erik Wetter & Linus Bengtsson

  4. Stockholm School of Economics, Stockholm, Sweden

    Erik Wetter

  5. Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden

    Linus Bengtsson

Authors
  1. Pål Sundsøy
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  2. Johannes Bjelland
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  3. Bjørn-Atle Reme
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  4. Eaman Jahani
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  5. Erik Wetter
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  6. Linus Bengtsson
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Corresponding author

Correspondence to Pål Sundsøy .

Editor information

Editors and Affiliations

  1. Indiana University, Bloomington, Indiana, USA

    Giovanni Luca Ciampaglia

  2. University of Washington, Seattle, Washington, USA

    Afra Mashhadi

  3. University of Oxford, Oxford, United Kingdom

    Taha Yasseri

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© 2017 Springer International Publishing AG

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Sundsøy, P., Bjelland, J., Reme, BA., Jahani, E., Wetter, E., Bengtsson, L. (2017). Towards Real-Time Prediction of Unemployment and Profession. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10540. Springer, Cham. https://doi.org/10.1007/978-3-319-67256-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-67256-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67255-7

  • Online ISBN: 978-3-319-67256-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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