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Promises and Pitfalls of Using Digital Traces for Demographic Research

Author

Listed:
  • Nina Cesare

    (Boston University)

  • Hedwig Lee

    (Washington University)

  • Tyler McCormick

    (University of Washington
    University of Washington)

  • Emma Spiro

    (Washington University
    University of Washington)

  • Emilio Zagheni

    (Max Planck Institute for Demographic Research)

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.

Suggested Citation

  • Nina Cesare & Hedwig Lee & Tyler McCormick & Emma Spiro & Emilio Zagheni, 2018. "Promises and Pitfalls of Using Digital Traces for Demographic Research," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1979-1999, October.
    • Handle: RePEc:spr:demogr:v:55:y:2018:i:5:d:10.1007_s13524-018-0715-2
    DOI: 10.1007/s13524-018-0715-2
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    References listed on IDEAS

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    1. Nick Couldry & Alison Powell, 2014. "Big data from the bottom up," LSE Research Online Documents on Economics 57941, London School of Economics and Political Science, LSE Library.
    2. Sevcíková, Hana & Raftery, Adrian E. & Waddell, Paul A., 2007. "Assessing uncertainty in urban simulations using Bayesian melding," Transportation Research Part B: Methodological, Elsevier, vol. 41(6), pages 652-669, July.
    3. John Palmer & Thomas Espenshade & Frederic Bartumeus & Chang Chung & Necati Ozgencil & Kathleen Li, 2013. "New Approaches to Human Mobility: Using Mobile Phones for Demographic Research," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 1105-1128, June.
    4. Emilio Zagheni & Ingmar Weber & Krishna Gummadi, 2017. "Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants," Population and Development Review, The Population Council, Inc., vol. 43(4), pages 721-734, December.
    5. Leontine Alkema & Adrian E. Raftery & Patrick Gerland & Samuel J. Clark & Francois Pelletier, 2012. "Estimating trends in the total fertility rate with uncertainty using imperfect data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(15), pages 331-362.
    6. Mark Graham & Scott Hale & Monica Stephens, 2012. "Featured Graphic: Digital Divide: The Geography of Internet Access," Environment and Planning A, , vol. 44(5), pages 1009-1010, May.
    7. Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.
    8. Steven Ruggles, 2014. "Big Microdata for Population Research," Demography, Springer;Population Association of America (PAA), vol. 51(1), pages 287-297, February.
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