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COVID-19 and the Future of US Fertility: What Can We Learn from Google?

Author

Listed:
  • Wilde, Joshua

    (Max Planck Institute for Demographic Research)

  • Chen, Wei

    (Fordham University)

  • Lohmann, Sophie

    (Max Planck Institute for Demographic Research)

Abstract

We use data from Google Trends to predict the effect of the COVID-19 pandemic on future births in the United States. First, we show that periods of above-normal search volume for Google keywords relating to conception and pregnancy in US states are associated with higher numbers of births in the following months. Excess searches for unemployment keywords have the opposite effect. Second, by employing simple statistical learning techniques, we demonstrate that including information on keyword search volumes in prediction models significantly improves forecast accuracy over a number of cross-validation criteria. Third, we use data on Google searches during the COVID-19 pandemic to predict changes in aggregate fertility rates in the United States at the state level through February 2021. Our analysis suggests that between November 2020 and February 2021, monthly US births will drop sharply by approximately 15%. For context, this would be a 50% larger decline than that following the Great Recession of 2008-2009, and similar in magnitude to the declines following the Spanish Flu pandemic of 1918-1919 and the Great Depression. Finally, we find heterogeneous effects of the COVID-19 pandemic across different types of mothers. Women with less than a college education, as well as Black or African American women, are predicted to have larger declines in fertility due to COVID-19. This finding is consistent with elevated caseloads of COVID-19 in low-income and minority neighborhoods, as well as with evidence suggesting larger economic impacts of the crisis among such households.

Suggested Citation

  • Wilde, Joshua & Chen, Wei & Lohmann, Sophie, 2020. "COVID-19 and the Future of US Fertility: What Can We Learn from Google?," IZA Discussion Papers 13776, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp13776
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    References listed on IDEAS

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    Cited by:

    1. Doepke, Matthias & Hannusch, Anne & Kindermann, Fabian & Tertilt, Michèle, 2022. "The Economics of Fertility: A New Era," IZA Discussion Papers 15224, Institute of Labor Economics (IZA).
    2. Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
    3. Arpino, Bruno & LUPPI, FRANCESCA & Rosina, Alessandro, 2021. "Changes in fertility plans during the COVID-19 pandemic in Italy: the role of occupation and income vulnerability," SocArXiv 4sjvm, Center for Open Science.
    4. Jurić Tado, 2022. "Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany," Comparative Southeast European Studies, De Gruyter, vol. 70(1), pages 125-152, March.
    5. Lawrence M Berger & Giulia Ferrari & Marion Leturcq & Lidia Panico & Anne Solaz, 2021. "COVID-19 lockdowns and demographically-relevant Google Trends: A cross-national analysis," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-28, March.
    6. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    7. Jurić, Tado, 2021. "Google Trends as a Method to Predict New COVID-19 Cases and Socio-Psychological Consequences of the Pandemic," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7(forthcomi).
    8. Anna Kurowska & Anna Matysiak & Beata Osiewalska, 2023. "Working from Home During Covid-19 Pandemic and Changes to Fertility Intentions Among Parents," European Journal of Population, Springer;European Association for Population Studies, vol. 39(1), pages 1-31, December.
    9. Giray Gozgor & Mehmet Huseyin Bilgin & Peter Rangazas, 2021. "Economic Uncertainty and Fertility," Journal of Human Capital, University of Chicago Press, vol. 15(3), pages 373-399.
    10. Jurić, Tado, 2022. "Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 70(1), pages 125-152.
    11. Emery, Tom & Koops, Judith C., 2021. "The Impact of COVID-19 on Fertility behaviour and Intentions in the Republic of Moldova," SocArXiv fcqd9, Center for Open Science.
    12. Luca Maria Pesando & Alejandra Abufhele, 2022. "Declining Quantity and Quality of Births in Chile Amidst the COVID-19 Pandemic," Working Papers 20220081, New York University Abu Dhabi, Department of Social Science, revised Nov 2022.

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    More about this item

    Keywords

    prediction; fertility; google; COVID-19; statistical learning;
    All these keywords.

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • I10 - Health, Education, and Welfare - - Health - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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