Data and methodology

# **<u>High Frequency Phone Surveys and Global Monitoring Database</u>**

This section describes the data sources and the methodology used to examine the impact of Covid-19 across the welfare distribution in 14 countries in Sub-Saharan Africa. We rely on two different databases for our analysis: High Frequency Phone Surveys (HFPS) implemented since the pandemic began and the Global Monitoring Database (GMD), a database of household surveys implemented prior to the pandemic, compiled and managed by the World Bank. We rely on survey-to-survey imputation, which entails developing a model of household consumption in the GMD for each country, and using the model to impute the welfare status of households in each wave of that country’s HFPS.

Since Spring 2020, the World Bank has supported national statistical offices in developing countries to design and implement phone surveys to examine various aspects of Covid-19 on lives and livelihoods.<sup>2</sup> The rapid pace at which the Covid-19 pandemic has affected countries has made access to timely knowledge on the impacts of Covid-19 critical to effectively design, target and evaluate policy interventions. However, implementing face-to-face interviews was severely restricted in the context of widespread government-imposed social distancing practices. Phone surveys filled this gap by eliciting information from individuals and households rapidly and at low cost. In all 14 of the countries we consider, phone surveys were implemented as follow-up surveys from previous nationally representative surveys. These previous surveys were used to reweight the phone survey to make it more representative of the national population of households. However, the phone surveys disproportionately interview household heads, meaning that individual level characteristics such as the ability to work at usual were not representative of the underlying population of workers. Nonetheless, phone surveys can be helpful in tracking the responses to and impacts of the pandemic, particularly if they are combined with traditional household surveys. Table 1 lists the countries and waves of HFPS that were used for this analysis.

**Table 1: Sample size of HFPS waves and the corresponding multi-topic survey from GMD**

|Country<br>|||Hig <br>|h Frequency<br>|Phone Su<br>|rvey<br>|||||G<br>s<br>|MD<br>urvey<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Wave|1|2|3|4|5|6|7|8|9|10|11|Survey|
|Burkina Faso|1,968|2,210|2,125|2,104||2,071|2,032|2,011|1,998|||2018 EHCVM|
|Ethiopia|3,249|3,107|3,058|2,878|2,770|2,753|2,536|2,222|2,077|2,178|1,982|2015 HICES|
|Gabon|1,656|||||||||||2017 EGEP|
|Ghana|3,265|||||||||||2016 GLSS-VII|
|Gambia|1,436|1,385|1,335|1,332|1,297|||||||2018 EHCVM|
|Mali|1,766|1,935|1,901|1,901|1,766|1,884|1,760|1,694||||2018 EHCVM|
|Mozambique|1,097|978|960|841|901|||||||2014 IOF|
|Malawi|1,729|1,646|1,624|1,616|1,589|1,591|3,112|3,098|3,090|||2016 IHS-IV|
|Niger|1,264|||||||||||2018 EHCVM|
|Nigeria|1,950|1,820|1,790|1,789|1,773|1,762|1,726|1,723|1,706|1,699|1,680|2018 LSS|
|Senegal|1,220|||||||||||2018 EHCVM|

> 2 https://www.worldbank.org/en/topic/poverty/brief/high-frequency-monitoring-surveys

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