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Promises and Pitfalls of Using Digital Traces for Demographic Research

  • Published:
Demography

Abstract

The digital traces that we leave online are increasingly fruitful sources of data for social scientists, including those interested in demographic research. The collection and use of digital data also presents numerous statistical, computational, and ethical challenges, motivating the development of new research approaches to address these burgeoning issues. In this article, we argue that researchers with formal training in demography—those who have a history of developing innovative approaches to using challenging data—are well positioned to contribute to this area of work. We discuss the benefits and challenges of using digital trace data for social and demographic research, and we review examples of current demographic literature that creatively use digital trace data to study processes related to fertility, mortality, and migration. Focusing on Facebook data for advertisers—a novel “digital census” that has largely been untapped by demographers—we provide illustrative and empirical examples of how demographic researchers can manage issues such as bias and representation when using digital trace data. We conclude by offering our perspective on the road ahead regarding demography and its role in the data revolution.

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Notes

  1. For a review of these data, see Ruggles (2014) in a past issue of Demography.

  2. See https://developer.twitter.com/en/docs/tweets/filter-realtime/overview for an overview of Twitter’s public streaming APIs.

  3. See Facebook’s Data Policy: https://www.facebook.com/policy.php.

  4. See Twitter’s Privacy Policy for more information: https://twitter.com/en/privacy.

  5. See https://www.opalproject.org/about-opal for more information.

  6. See http://iussp.org/en/panel/big-data-and-population-processes for more information on IUSSP workshop events.

  7. Find more at http://www.facebook.com/business/.

  8. See https://developers.facebook.com/docs/marketing-api/audiences-api.

  9. See https://www.linkedin.com/ad/accounts.

  10. See Preston et al. (2001:28–30) for details on conducting an age decomposition analysis.

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Acknowledgments

This work is supported by Grants DMS-1737673 and SES-1559778 from the National Science Foundation and K01 HD078452 from the National Institute of Child Health and Human Development (NICHD). This material is based upon work supported by, or in part by, the U.S. Army Research Laboratory and the U. S. Army Research Office under contract/grant number W911NF-12-1-0379, and by the Washington Research Foundation. We also appreciate the support of the Earl and Edna Stice lectureship in the Social Sciences; the University of Washington Information School, Center for Statistics and the Social Sciences; eScience Institute; and Sociology Department for supporting speakers at the frontier of data science in demographic research.

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Authors and Affiliations

  1. Department of Global Health, Boston University, Boston, MA, USA

    Nina Cesare

  2. Department of Sociology, Washington University, St. Louis, MO, USA

    Hedwig Lee & Emma Spiro

  3. Department of Sociology, University of Washington, Seattle, WA, USA

    Tyler McCormick

  4. Department of Statistics, University of Washington, Seattle, WA, USA

    Tyler McCormick

  5. Information School, University of Washington, Seattle, WA, USA

    Emma Spiro

  6. Max Planck Institute for Demographic Research, Rostock, Germany

    Emilio Zagheni

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Correspondence to Nina Cesare.

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Cesare, N., Lee, H., McCormick, T. et al. Promises and Pitfalls of Using Digital Traces for Demographic Research. Demography 55, 1979–1999 (2018). https://doi.org/10.1007/s13524-018-0715-2

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