# 1. Introduction

The outbreak of the COVID-19 virus has had far-ranging impacts across the globe. In addition to the serious health effects, COVID-19 has caused widespread economic disruption. The epidemic has resulted in closure of schools and markets, disruption in public services, banning of social gatherings and restriction of mobility and transportation of goods and people. Given these wide-ranging impacts of the pandemic, governments, policy makers and researchers are in dire need of just-in-time and reliable data both to monitor the health and socio-economic impact of the epidemic on livelihoods and well-being. However, amid a pandemic it is difficult to deploy enumerators to the field to collect information from households and communities through face-to-face (F2F) interviews given the risk of transmission to both interviewer and respondent. As a solution to this challenge and to address the urgent demand for rapid and informative data, high frequency phone surveys (HFPS) of households are being implemented in many developing countries. The improvement of phone penetration in these counties in recent years, as well as the existence of recently conducted representative household surveys that contain re-contact information for some or all household members, has created opportunities to conduct phone surveys.

While phone surveys have distinct advantages that are particularly relevant during the pandemic, they also suffer from drawbacks. One of the most substantial weaknesses of phone surveys is that they are often subject to several sources of bias that can compromise the representativeness of the sample and the external validity of the estimates obtained from the survey (Himelein et al, 2020). This study investigates the magnitude and source of bias in recent telephone surveys as well as exploring the effectiveness of techniques to reduce it. Using telephone surveys in Ethiopia, Malawi, Nigeria and Uganda that were sampled from recently completed representative F2F surveys, this study will first explore the sources of bias introduced at each stage of the phone survey selection and interview process, by comparing the profile of characteristics of the F2F and the phone survey samples. After examining the source and extent of bias, it will then assess the effectiveness of reweighting adjustment methods to correct for these biases. These adjustment methods are especially powerful when using samples drawn from existing representative face-to-face surveys, like the Living Standards Measurement Study (LSMS) surveys, which contain a wealth of detailed information on all sampled units. While similar adjustments can be performed under other sampling approaches, such as sampling from a list of numbers from a telecom provider or random digit dialing, the scope of the adjustments is more limited in those approaches since they often do not have detailed information on sampled units that is available in a representative F2F survey. This could present one advantage of sampling from representative F2F surveys over other sampling approaches.

The remainder of this paper will proceed as follows. In Section 2, the benefits and limitations of phone surveys will be briefly reviewed including the different sources of bias and methods available to reduce the bias. In Section 3, data and methods will be discussed. Section 4 presents the results and Section 5 concludes.

# 2. Phone Surveys

Even though F2F surveys have been the main mode of data collection in developing countries for many years, there is also a growing list of successfully implemented phone surveys (see Tomlinson, 2