# **I. Introduction and background**

Phone interviewing opens new possibilities for empirical research in social sciences. It greatly reduces the costs of conducting surveys because all communication occurs without the face-to-face interaction customary in traditional surveys. With the outbreak of the COVID-19 pandemic limiting face-to-face interviews, phone surveys are more prevalent among academic institutions, survey companies, and individual researchers for individual-level and household-level data collection. To track households’ living conditions on a timely basis during a pandemic, the World Bank launched the COVID-19 High-Frequency Phone Surveys (HFPS), which have been carried out in around 80 countries since March 2020. These surveys allow policy makers to monitor a wide variety of socioeconomic indicators in a timely and frequent manner.

Phone surveys have shortcomings. First, it is difficult to monitor poverty using the COVID-19 HFPS. The COVID-19 HFPS does not collect consumption or income data, which are necessary for measuring poverty and inequality under the traditional poverty monitoring approach. It is time-consuming, costly, and complex to collect such data. An interviewer must ask households a number of complex questions regarding their recent consumption, expenditures, and detailed income components. Such an interview requires at least 30 minutes and up to two hours, even by well-trained and qualified interviewers. It is also challenging to administer the interview in developing countries where telephone connections are not always stable enough to complete such a long interview.

_A solution to Challenge 1: SWIFT as a rapid poverty monitoring tool_ In March 2020, the World Bank launched a pilot for the use of a rapid poverty monitoring tool, Survey of Wellbeing via Instant, Frequent Tracking (SWIFT), to estimate monetary poverty using the COVID-19 HFPS. SWIFT adds 10 to 15 simple questions to the COVID-19 HFPS questionnaire, and these additional questions take 3 to 5 minutes to ask. Based on households’ responses to these questions, the SWIFT method can be adopted to impute poverty and inequality rates among the population of interest.

SWIFT was chosen among many poverty projection methodologies because it has been thoroughly tested and reviewed by experts inside and outside the World Bank. The results are encouraging – the difference between the true and SWIFT-based poverty projections is 1.2 percentage points on average and is within +/- 2 percentage points, except for one case (see more details in Appendix 5 or Yoshida et al., 2022a). The pilot was conducted in 20 countries around the world, and the results of seven countries are summarized in Yoshida et al. (2022b).

Second, the sample of the COVID-19 HFPS is unlikely to be nationally representative. In many developing countries, the coverage of mobile phones or land lines is limited. Poor households that do not own a phone are excluded from phone interviews. In addition, phone interviews usually face a much higher rate of nonresponse than in-person interviews. Such nonresponses are usually concentrated among urban residents who are too busy to participate in the interviews. Hence, the sample collected in a phone survey is often far from nationally representative and can result in bias in the projections of poverty.

_A solution to Challenge 2: Reweighting – sampling weight adjustments to address sampling biases_ Many approaches for adjusting sampling weights, or “reweighting,” have been developed to correct for various types of sampling bias. This paper classifies them into two major categories: (1) propensity score weighting (PSW) and (2) “non-PSW adjustments.” Propensity score weighting is a method for adjusting sampling weights based on propensity scores and was developed from the propensity score matching proposed by Rubin and Rosenbaum (1983 and 1984). In the context of causal inferences, propensity score matching makes the control and treatment groups comparable, minimizing bias in estimating treatment effects. Unless the samples of the control and treatment groups are selected randomly (which is usually not the case with observational data), baseline characteristics may exhibit 2