|Sierra Leone|6,571|4,885|||||2018 SLIHS|
|---|---|---|---|---|---|---|---|
|Uganda|2,226|2,199|2,145|2,135|2,122|2,100|2016 UNHS|
|Zimbabwe|1,747|1,639|1,451||||2017 PICES|

A secondary source of data is the Global Monitoring Database (GMD), which is a collection of globally harmonized household survey data compiled by the World Bank. The GMD includes most recent household surveys used by national statistical agencies around the world, and subsequently, by the World Bank, to compute the official poverty statistics. In addition to the welfare aggregate, surveys in GMD include variables on demographics, assets, labor, education, and other topics that have been harmonized to a common set of specifications, making the GMD well-suited for cross-country analysis.

This analysis covers 14 countries in SSA (Table 1). Although more countries are included in both the GMD and HFPS, we were limited by three factors. First, the analysis could only be conducted in countries where the HFPS was conducted and which also has a survey available in GMD that was conducted prior to the pandemic. Second, we limited our analysis only to surveys in GMD that were conducted in recent years, using a cutoff of 2014. Third, we limited our analysis to countries that both had a minimum set of variables that could be used to model household per capita consumption.

# **<u>Survey-to-survey imputation</u>**

Survey-to-survey imputation (S2S) predicts household per capita consumption in a household survey that collects information only on selected covariates of consumption by using information from another survey that collected both the per capita consumption and its covariates. This literature builds largely on the poverty mapping literature (Elbers, Lanjouw, and Lanjouw, 2003) and has been widely implemented (Dang 2020, Newhouse et al. 2014, Stifel and Christiaensen 2007). This literature is in contrast to the multiple imputation literature inspired by Rubin (1987), which largely focuses on filling missing data within a survey rather than using information from one survey to make predictions in another one. There are generally two types of S2S studies in the literature: one that conducts the imputation over time and another that conducts the imputation within the same period but across surveys. The first type includes studies such as those by Newhouse and Vyas (2022) and Stifel and Christiaensen (2007). Studies that impute across surveys within the same time frame include Beltram et al. (2020), Dang (2019), Lucchetti et al. (2018), Dang et al. (2018), and Cuesta and Ibarra (2017). Dang (2020) and Dang et al. (2019) provide guidance on the appropriate methodology and caveats to using S2S.

Implementation of S2S in this study first involved identifying a set of variables that are available both in HFPS and GMD. This yielded the following variables: household size, urban/rural location, subnational administrative area, share of children, and share of elderly residents in the household. This list of variables was selected by carefully comparing the data dictionaries of HFPS and GMD. Both the HFPS and the GMD are both designed to be nationally representative, except for Mozambique, where the HFPS only covers urban areas. A major difference between the HFPS and the GMD is that while the GMD sample covers both households that own phones and those that do not, the HFPS (by design) only covers households that own a mobile phone. Since households that own mobile phones are likely to be a non-random sample of the population, we select a comparable sub-sample of mobile phone owners in the GMD for the purposes of S2S. Figure 1 compares these samples for the 14 countries in the sample. Appendix Figure 1 presents these differences for individual countries in our analysis sample.

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