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.
Highlights
With the onset of the coronavirus disease 2019 (COVID-19) pandemic, governments, academic institutions, and international organizations scrambled to measure and monitor the pandemic’s impacts on livelihoods and tailor policy responses
The longitudinal survey data informing our analysis originate from (i) the national high-frequency phone survey (HFPS) that was implemented on a monthly basis in Ethiopia, Malawi, Nigeria and Uganda during the COVID-19 pandemic, and (ii) the pre-COVID-19 F2F household survey that served as a sampling frame for each high-frequency phone surveys on COVID-19 (HFPS)
To assess the effectiveness of the bias reduction techniques for the individual-level phone survey data analysis, we focus on the individual-level variables that are captured in the preCOVID-19 F2F survey and that are related to gender, age, marital status, relationship with the household head, education, and employment, which are the individuallevel variables included in the logit regression as part of creating the recalibrated weight w2
Summary
With the onset of the coronavirus disease 2019 (COVID-19) pandemic, governments, academic institutions, and international organizations scrambled to measure and monitor the pandemic’s impacts on livelihoods and tailor policy responses. Selected phone survey respondents are most often household heads or their spouses, and on average, are older, better educated and more likely to own a non-farm enterprise vis-a-vis the general adult population To account for these differences and improve the representativeness of individual-level phone survey data, we recalibrate the household-level phone survey sampling weights based on propensity score adjustments that are derived from a cross-country comparable model of an adult individual’s likelihood of Representativeness of individual-level data in COVID-19 phone surveys being interviewed in a phone survey household as a function of a rich set of individual- and household-level attributes [23] and assess to what extent the recalibrated weights can address selection biases.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
