Another method is **propensity score adjustments** , whereby the probability of response is modeled (typically using a probit or logit model) using the response status from the phone survey as well as observable household/individual characteristics for the entire sample (both responders and nonresponders) derived from the representative survey (Little, 1986). The probability of response for each sampled unit is then predicted using the model parameters and the inverse of the predicted response probability (or propensity) applied to the base weights. This approach could introduce extreme weight adjustments and thereby increase the variance of the weights (and estimates obtained). However, the risk of this can be reduced by forming response classes based on the predicted probability (e.g. probability deciles) and the average predicted probability within the class used as the adjustment factor. The propensity modeling approach is particularly attractive for telephone surveys drawn from representative surveys that contain a diverse set of characteristics that can be incorporated into the propensity model.

A further method that can be employed is through **calibration of nonresponse** <mark>(Lundström & Särndal, 1999; Andersson & Särndal, 2016)</mark> **<mark>.</mark>** Calibration is a weight adjustment technique utilized in many surveys whereby weights are adjusted so that the weighted sample composition matches known characteristics of the underlying population. Typically, auxiliary data from population censuses are utilized for these calibrations. However, this approach can be extended to also include sample-based estimates from a representative survey used as a frame <mark>(Andersson & Särndal, 2016)</mark> . In this case, the weights for the interviewed sample will be calibrated across all considered characteristics such that the weighed profile of characteristics obtained from the interviewed sample closely match those from the full representative survey sample. Provided the characteristics included in the calibration are closely associated with nonresponse, the calibration will serve to counteract the nonresponse bias. There are many complex calibration models that could be used (and are beyond the scope of this paper), but most are subject to an algorithm that minimizes the distance between the uncalibrated and calibrated weights and therefore might result in a lower increase in variation of the adjusted weights compared to the propensity modeling approach.

# 3. Data and Methods

## <mark>3.1 High Frequency Phone Surveys – Ethiopia, Malawi, Nigeria, & Uganda</mark>

In order to examine bias in phone surveys and effectiveness of bias adjustment methods, this study utilizes four recent high frequency phone surveys (HFPS) in Ethiopia, Malawi, Nigeria, and Uganda. These four surveys were implemented as part of a broader initiative by the World Bank to support implementation of high frequency phone surveys in several developing countries to monitor the socioeconomic impact of the COVID-19 pandemic. The baseline rounds of the Ethiopia and Nigeria HFPS were conducted in April shortly after the rapid spread of the pandemic and safety protocols and restrictions put in place. The baseline rounds of Malawi and Uganda were implemented shortly thereafter in May and June. The four HFPSs contacted households drawn from the most recent round of Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The LSMS-ISA are longitudinal, face-to-face, representative surveys conducted in partnership with National Statistics Offices in the respective countries. In Ethiopia, the fourth round of the Ethiopia Socioeconomic Survey (ESS) conducted in 2018/19 served as the frame for the HFPS. The fifth round of the Integrated Household Panel Survey (IHPS) 5