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Representativeness of individual-level data in COVID-19 phone surveys: Findings from Sub-Saharan Africa

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  • Joshua Brubaker
  • Talip Kilic
  • Philip Wollburg

Abstract

The COVID-19 pandemic has created urgent demand for timely data, leading to a surge in mobile phone surveys for tracking the impacts of and responses to the pandemic. Using data from national phone surveys implemented in Ethiopia, Malawi, Nigeria and Uganda during the pandemic and the pre-COVID-19 national face-to-face surveys that served as the sampling frames for the phone surveys, this paper documents selection the biases in individual-level analyses based on phone survey data. In most cases, individual-level data are available only for phone survey respondents, who we find are more likely to be household heads or their spouses and non-farm enterprise owners, and on average, are older and better educated vis-a-vis the general adult population. These differences are the result of uneven access to mobile phones in the population and the way that phone survey respondents are selected. To improve the representativeness of individual-level analysis using phone survey data, we recalibrate the phone survey sampling weights based on propensity score adjustments that are derived from a model of an individual’s likelihood of being interviewed as a function of individual- and household-level attributes. We find that reweighting improves the representativeness of the estimates for phone survey respondents, moving them closer to those of the general adult population. This holds for both women and men and for a range of demographic, education, and labor market outcomes. However, reweighting increases the variance of the estimates and, in most cases, fails to overcome selection biases. This indicates limitations to deriving representative individual-level estimates from phone survey data. Obtaining reliable data on men and women through future phone surveys will require random selection of adult interviewees within sampled households.

Suggested Citation

  • Joshua Brubaker & Talip Kilic & Philip Wollburg, 2021. "Representativeness of individual-level data in COVID-19 phone surveys: Findings from Sub-Saharan Africa," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-27, November.
    • Handle: RePEc:plo:pone00:0258877
    DOI: 10.1371/journal.pone.0258877
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    3. Kugler, Maurice & Viollaz, Mariana & Duque, Daniel & Gaddis, Isis & Newhouse, David & Palacios-Lopez, Amparo & Weber, Michael, 2023. "How did the COVID-19 crisis affect different types of workers in the developing world?," World Development, Elsevier, vol. 170(C).
    4. Calogero Carletto, 2021. "Better data, higher impact: improving agricultural data systems for societal change [Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity r," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(4), pages 719-740.
    5. Kanyanda,Shelton Sofiel Elisa & Markhof,Yannick Valentin & Wollburg,Philip Randolph & Zezza,Alberto, 2021. "Acceptance of COVID-19 Vaccines in Sub-Saharan Africa : Evidence from Six National Phone Surveys," Policy Research Working Paper Series 9739, The World Bank.
    6. Gourlay, Sydney & Kilic, Talip & Martuscelli, Antonio & Wollburg, Philip & Zezza, Alberto, 2021. "Viewpoint: High-frequency phone surveys on COVID-19: Good practices, open questions," Food Policy, Elsevier, vol. 105(C).
    7. Martin Paul Jr. Tabe-Ojong & Emmanuel Nshakira-Rukundo & Bisrat Haile Gebrekidan, 2023. "COVID-19 and food insecurity in Africa: A review of the emerging empirical evidence," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(3), pages 853-878.
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    9. Zezza,Alberto & Mcgee,Kevin Robert & Wollburg,Philip Randolph & Assefa,Thomas Woldu & Gourlay,Sydney, 2022. "From Necessity to Opportunity : Lessons for Integrating Phone and In-Person Data Collectionfor Agricultural Statistics in a Post-Pandemic World," Policy Research Working Paper Series 10168, The World Bank.

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