broader public attitudes is limited (Munoz, 2021, p. 1). Using a similar, convenience sample approach a UNICEF U-report poll found less than a third of participants were willing to be vaccinated (UNICEF, 2021).

# **4. Methodology**

This section provides details about the data collection methodology and empirical analysis we conducted for the phone survey and online randomized survey experiment.

# <u>4.1 Phone survey data collection and analysis</u>

To collect information about levels of vaccine hesitancy and what factors could be contributing to it, a phone survey covering 2,533 households was in the field from May 26 to June 6, 2021. The most recent data available for mobile phone penetration estimates usage at 32 percent of the population (World Bank, 2021). The phone survey used geographic quotas based on the most recent tower that mobile phones had been connected through to ensure there was coverage across all provinces and districts in PNG. To address concerns that the population with access to mobile phones may be disproportionally wealthier, efforts were made to oversample phone users that do not send text messages and/or rely on others to transfer them credit to pay for their phone use. These attributes have been shown to be far more common among poorer phone users in PNG (Himelein and McPherson, 2021).

The responses provided by participants in the phone survey were weighted to match the characteristics of the 2016–18 Demographic and Health Survey (DHS) in PNG (National Statistical Office of PNG and ICF, 2019). The DHS is the most recent nationally representative data set including a measure of welfare and therefore this survey was used as the base for the reweighting. The mobile phone survey weights were designed to follow the distribution of the sample by province and urban/rural location. The weights were calibrated to the DHS distribution. This addressed issues with oversampling related to the number of calls but did not adjust for differences in the distribution of the wealth index or for differences in demographic variables. To adjust for differences in the distribution of the wealth index, the DHS and mobile phone survey data were appended, and a logit model was run with the mobile phone survey equal to one and the DHS equal to zero. The dependent variables were the wealth index, square of the wealth index, and the cube of the wealth index. The inverse of the prediction was then taken and collapsed into deciles, and these deciles were multiplied with the calibrated weights. These weights were then winsorized<sup>4</sup> at the top 0.5 percentile to address any outliers, adjusted to account for households with multiple mobile phones (and therefore higher probabilities of selection), and calibrated again using the region and urban/rural location. This further adjusted the weights for differences in the distributions between the phone survey and the DHS, but only adjusted for differences in the demographics to the extent that they were correlated with wealth. As a final step, the weights were raked to match the mean values from the DHS on the following characteristics: gender of the respondent, age group of the respondent, education of the respondent, household size, province, and urban/rural location. The resulting weights were based on the household level as the DHS is a representative household survey. It is important to recognize that the low rates of phone ownership means that reweighting of responses is unlikely to be able to fully take into account unobserved characteristics of the population without access to a phone.

> 4 Winsorizing involves the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers.

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