# 1. Data: High Frequency Phone Surveys (HFPS) in the EAP region

Starting from May 2020, the High Frequency Phone Surveys (HFPS) were conducted in 11 middle-income countries in the EAP in order to monitor the socioeconomic impacts of the pandemic. The surveys covered a wide range of topics, including employment, income, food insecurity, access to health and education services, and coping mechanisms, among others. World Bank teams working in each country led the design and collection of the data. The first rounds of surveys were administered in May-June 2020 and subsequent rounds continued into 2021. This paper utilizes data from seven of the 11 countries in which these data were collected (Indonesia, Lao PDR, Mongolia, Myanmar, Philippines, PNG, and Vietnam) and spans the period from May 2020 to May 2021. We include at least two survey rounds for each country to allow examination of the temporal aspects of the pandemic’s impacts. In the EAP, the HFPS relied on one of two sampling methods: Four countries drew from a sample frame of a recent representative household survey, while three used random digit dialing from a roster of phone lines (Table 1). Sampling weights were constructed for all surveys to ensure unbiased estimates from the sample. As the high-frequency surveys were phone-based, the samples are representative of households who have access to a phone. This may limit representativeness in areas and subpopulations for which phone penetration is low.

One major contribution of this paper relative to other studies that have used the HFPS is the use of household-level welfare data to estimate the distributional impacts of the pandemic. For countries that drew their sample from a previous pre-pandemic household survey (Mongolia and Vietnam), consumption data from these existing surveys could be used to identify each household’s welfare status before the pandemic. For the other five countries that employed random digit dialing methods or did not have prepandemic consumption information, welfare was imputed using a basic set of welfare predictors collected in the HFPS or from recent surveys. As shown in Table 1, various methods employing survey-to-survey multiple imputation such as proxy means testing or Survey of Well-being via Instant and Frequent Tracking (Yoshida, et al., 2015) were used, relying on recent household surveys with household expenditure data.

Table 1. Overview of HFPS data used

|Country|Surveytiming|Samplingmethod|Construction of<br>welfare<br>indicator|Household<br>sample size<br>in Round 1|2020<br>Population<br>size<br>(million)|2020 GDP<br>per capita<br>(2010 USD)|
|---|---|---|---|---|---|---|
|Indonesia|R1: May 2020<br>R2: May-Jun 2020<br>R3: Jul 2020<br>R4: Nov 2020<br>R5: Mar 2021|Subsample of the 2018<br>Urban Perception, 2018<br>Rural poverty, and the<br>2020 Digital economy<br>household surveys|Proxy means<br>test|4,338|273.5|4,312|
|Lao PDR|R1: Jun-Jul 2020<br>R2: Feb-Mar 2021|Random digit dialing|Proxy means<br>test|2,500|7.3|1,836|
|Mongolia|R1: May 2020<br>R2: Sep 2020<br>R3: Dec 2020<br>R4: Apr 2021|Subsample of 2018<br>Household Socio-<br>Economic Survey|Consumption<br>aggregate from<br>previous survey|1,334|3.3|4,054|
|Myanmar|R1: May 2020<br>R2: Jun 2020<br>R3: Aug 2020<br>R4: Oct 2020|Random digit dialing<br>from an existing list<br>owned by contracted<br>firm for the survey|Survey of Well-<br>being via Instant<br>and Frequent<br>Tracking|1,500|54.4|1,478|

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