# 1. Introduction

Identifying the poor in developing countries is crucial to inform development policies and programs, particularly those related to social assistance. However, governments and the development community are severely constrained by the high cost of collecting household surveys, censuses, or social registries that are typically used to inform targeting decisions. For example, between 2002 and 2011, 57 countries had conducted zero or one nationally representative household budget survey, preventing them from producing timely poverty estimates, and typically four years pass between nationally representative surveys on consumption or asset wealth in most African countries.<sup>1</sup> Even when household data is collected, data collected by household surveys are typically too small to provide reliable estimates of welfare for small geographic areas such as villages. Estimating poverty at the small area level requires alternative sources of data, traditionally census data, and utilizing this type of auxiliary data for small area estimation can provide resources to the poor more efficiently.<sup>2</sup> Due to this lack of timely and adequate information on measures of well-being indicators in small areas, satellite imagery and other types of non-traditional data have great potential to fill these data gaps and complement traditional household surveys to provide more timely and accurate estimates for local areas.<sup>3</sup> This paper investigates the benefits of combining traditional data with publicly available remote sensing indicators to predict welfare across approximately 4,500 villages in 10 districts in Malawi. The 10 districts correspond to the ones selected for phase one of the Unified Beneficiary Registry (UBR), which was conducted in 2017, collecting information on living standards to determine the eligibility of households for social programs.

We evaluate four alternative village targeting methods which are compared to a benchmark welfare measure derived from an extract of the 2018 household census: (1) PMT scores calculated in the 2017 UBR administrative data, (2) the Meta Relative Wealth Index,<sup>4</sup> (3) predictions derived from a village-level model estimated using a 2016 household survey and publicly available geospatial indicators, and (4) a two-step procedure that utilizes a hypothetical partial registry of 450 randomly selected villages – 10 percent of the villages in the population -- in addition to the 2016 household survey and publicly available geospatial indicators. This hypothetical partial registry would collect selected proxy welfare indicators such as asset and demographic information from _all_ households in the selected villages. The first step entails predicting per capita consumption from the household survey into the partial registry data, which we simulate by sampling from the census extract. The second step uses the partial registry predictions to train a model using publicly available geospatial data to generate estimates for the remaining 90 percent of non-registry villages.

> 1 Serajuddin et al. 2015, Yeh et al., 2020.

> 2 Van Der Weide et al. (2022). 3 Burke, 2021, World Bank, 2021. 4 The Meta relative wealth index is described in Chi et al (2021).

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