Representativeness of Individual-Level Data in COVID-19 Phone Surveys : Findings from Sub-Saharan Africa
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. This paper assesses, and attempts to mitigate, selection biases in individual-lev...
| Main Authors: | , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2021
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/131501620912712969/Representativeness-of-Individual-Level-Data-in-COVID-19-Phone-Surveys-Findings-from-Sub-Saharan-Africa http://hdl.handle.net/10986/35609 |
| Summary: | 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. This paper assesses, and attempts to mitigate,
selection biases in individual-level analyses based on phone
survey data. The research uses data from (i) national phone
surveys that have been implemented in Ethiopia, Malawi,
Nigeria, and Uganda during the pandemic, and (ii) the
pre-COVID-19 national face-to-face surveys that served as
the sampling frames for the phone surveys. The availability
of pre-COVID-19 face-to-face survey data permits comparisons
of phone survey respondents with the general adult
population. Phone survey respondents are more likely to be
household heads or their spouses and non-farm enterprise
owners, and on average, are older and better educated
vis-à-vis the general adult population. To improve the
representativeness of individual-level phone survey data,
the household-level phone survey sampling weights are
calibrated 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. Reweighting improves the representativeness of
the estimates for the phone survey respondents, moving them
closer to those of the general adult population. This holds
for women and men and a range of demographic, education, and
labor market outcomes. However, reweighting increases the
variance of the estimates and fails to overcome selection
biases. Obtaining reliable data on men and women through
phone surveys requires random selection of adult
interviewees within sampled households. |
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