# 2.1 Description of the data sets

The primary sources of information are the following: (1) the Unified Beneficiary Registry (UBR), collected in 2017; (2) a 20 percent extract of the 2018 census provided by the National Statistical Office of Malawi; (3) the Integrated Household Survey (HIS) collected in 2016; and (4) publicly available remote sensing indicators.

## Unified Beneficiary Registry (UBR)

Malawi’s Unified Beneficiary Registry contains information on the households’ socio-economic characteristics to determine their eligibility for social programs.<sup>11</sup> For the analysis, we use the data set collected during the first phase of the UBR. These data were collected in 2017 in 10 districts: Lilongwe, Ntchisi, Kasungu, Rumphi, Chiradzulu, Nkhota-Kota, Blantyre, Karonga, Ntcheu, and Dowa. During this phase, half of the households in these districts were registered based on Malawi’s average poverty rate. The UBR data set contains apporximately 595,000 households, spread across 14,986 villages in the 10 districts.

The UBR is a crucial data set for this analysis for two reasons. First, it contains the PMT scores for the poorest 50 percent of households, which allows us to evaluate the PMT scores as a targeting mechanism. Secondly, these data include the geocoordinates of sample households, which allows us to merge the satellite data with the census data.

## Census data

The analysis uses a 20 percent extract of the 2018 census data for 10 districts provided by the National Statistics Office of Malawi. The study utilizes data from the 4,500 villages that were matched, by name, with the UBR data. The census extract includes 235,600 households in 26,150 villages in the ten UBR districts. The census extract also serves two main purposes. First, it is used, along with the parameters of the census model estimated in the household survey, to generate the benchmark welfare used for evaluation. Second, it provides a randomly selected sample of villages that is used to simulate a partial social registry, as explained in the methodology section below.

## Survey data

The analysis uses the fourth Integrated Household Survey (IHS) of 2016, which is made publicly available through the World Bank’s Living Standard Measurement Survey (LSMS) program. The survey includes a cross-sectional sample of 12,447 households surveyed in 779 enumeration areas (EAs). Thus, there are roughly 16 sample households per enumeration area. It is considered to be representative at the district level. Because it is an LSMS survey, jittered enumeration area coordinates are also publicly available.<sup>12</sup> The IHS is used in the analysis for two primary purposes. The first is to estimate a model that predicts per capita consumption as a function of household variables common to the survey and the census extract, as a basis for constructing the benchmark measure of welfare in the census. Besides serving as a benchmark for evaluation, this predicted welfare measure is also used to simulate a partial registry in a subsample of villages. The second

> 11 See Lindert et al., 2018, for more information on the UBR.

> 12 Van der Weide et al (2022) finds that the jittering reduces the correlation between census and geospatial-based estimates for traditional authorities in Malawi by a modest amount.

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