|Philippines|R1: Aug 2020<br>R2: Dec 2020<br>R3: May 2021|Phone survey based on<br>survey firm sample<br>frame; Self-<br>administered web<br>surveys|Survey of Well-<br>being via Instant<br>and Frequent<br>Tracking|9,448|109.6|2,980|
|---|---|---|---|---|---|---|
|PNG|R1: Jun-Jul 2020<br>R2: Dec 2020|Random digit dialing|Wealth index|3,114|8.9|2,347|
|Vietnam|R1: Jun 2020<br>R2: Jul 2020<br>R3: Sep2020|Subsample from<br>representative 2018<br>survey|Consumption<br>aggregate from<br>previous survey|6,213|97.3|2,123|

Two primary data sets from the HFPS are used in the analysis. First, an individual-level data set that includes information on pre-pandemic employment<sup>6</sup> allows examination of how workers were initially impacted, how they have coped and recovered, and who is still left behind. Due to differing sampling methods, characteristics among the sample of respondents vary across countries (Table 2).<sup>7</sup> For example, the Indonesia HFPS only asked about the employment status of the household head or breadwinner. Thus, males are overrepresented in the sample, and the average age is on the higher end among the sample of countries. In Vietnam, information from a previous household survey was used to target the household head as the respondent, while in Lao PDR and Mongolia, the targeted respondent in the first survey round was an adult member of the household who answered the phone or was available to be surveyed. In Myanmar, the Philippines, and PNG, employment data was collected for both the respondent and the household head when the respondent was not the head, but only in PNG was the pooled sample used to measure employment impacts.<sup>8</sup> Thus, in some countries, household heads are overrepresented, though at varying degrees (Table 2).<sup>9</sup> To the extent that household heads differ in terms of age, gender, type and formality of work, and other job characteristics from the average worker, the sample of respondents may not fully capture the pandemic’s impact on the labor force. To control for these differences across countries in respondent characteristics, we conduct the analysis controlling for respondent’s characteristics including relation to the household head, gender, and age. Results are generally not qualitatively different whether these controls are included, so unconditional means are reported throughout the paper unless otherwise noted. Regression results including controls are available in the appendix.

The second data set is a cross-sectional household data set that is used to analyze household-level indicators of income, coping mechanisms, food insecurity, and access to services. While it is possible to build a panel data set of households, cross-sectional data are used in order to maximize the number of

> 6 For Indonesia, Mongolia, Philippines, and Vietnam, we use an individual-level panel data set. However, building a true panel was difficult or purposely not done for Lao PDR, Myanmar, and PNG. Lao PDR and Myanmar added new respondents in each round to account for attrition and maintain representativeness, and thus restricting the sample to just panel respondents across rounds would have limited the sample size substantially. Instead, new respondents were included in the sample, which was possible due to pre-pandemic employment information that was asked to new respondents in each round. For PNG, an entirely new sample was surveyed in round 2, which had greater representation of households in the bottom 40 percent of the welfare distribution. We thus utilize retrospective information from the round 2 survey, which included questions about employment at the time of round 1 as well as pre-pandemic.

> 7 The respondents of mobile phone surveys in low- and middle-income countries typically underrepresented women, older people, the less educated, and people in rural areas (Lau et al 2019).

> 8 In an effort to maximize comparability across countries, the pooled sample of individuals were not used for Myanmar and the Philippines, which also did not have representative weights for the pooled sample. In PNG, individual-level weights were only available for the pooled sample of heads and respondents, and hence this sample was used to estimate employment impacts.

> 9 Indonesia – which targeted the household head or primary breadwinner – is at one extreme, while Philippines and Mongolia are at the other and are more similar to the actual percentage (47 percent in the original Mongolia sample survey, 34 percent in 2015 Philippines Census).

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