precision in multiple contexts before they can be applied. To do that, we compare estimates from these models to ground-based “truth” derived from unit-level census data in seven countries: Burkina Faso, Madagascar, Malawi, Mozambique, Sri Lanka, Tanzania, and Vietnam. While these countries were selected because of the availabiltiy of census data with either enumeration area geocoordinates or sub-area identifiers with corresponding shapefiles, they also cover a wide variety of contexts, varying in incomes as well as geography.<sup>5</sup> The official administrative boundaries available in shapefiles differ across countries. In general, we pull geospatial data for the lowest administrative level possible. For example, this is the Fokontany in Madagascar, while in Malawi it is the Enumeration Area. We refer to these levels as “subareas” throughout this paper.

Table 3 presents the number of areas and subareas for each country. Sub-areas, in particular, vary greatly across countries, from as few as 8,763 in Burkina Faso to as many as 67,239 in Mozambique.<sup>6</sup> Areas, on the other hand, show less variation, ranging from 169 (Tanzania) to 1,515 (Madagsacar). The table also shows the number of households in the unit-level census data. We have the full census for Madagascar, Mozambique, and Sri Lanka, but a sub-sample of the full census for the other countries.<sup>7</sup>

# **2.1 Outcomes**

We focus on two separate measures of welfare: an asset index and poverty rates. We calculate an asset index for all countries and estimated poverty rates for Malawi and Tanzania, both of which have a near-concurrent household survey that allows us to impute poverty into the census. Using survey data, we predict household per capita expenditures for all households in the census using assets. We then classify households as poor (or not) based on the quantile of their imputed expenditures per capita and the poverty rate in the household survey. We set a poverty rate of approximately 50 percent in Malawi and 21 percent in Tanzania, which matches the national poverty rate in the household surveys. Appendix B provides more details on the imputation procedure, as well as the calculation of the asset indices across countries.

The use of unit-record census data remains the preferred gold standard option when recent census data are available. However, census data tend to be collected infrequently in most developing countries, and small area estimates based on satellite indicators are a preferred alternative to reporting direct survey estimates when census data are old or there have been rapid changes in spatial welfare patterns. We focus on satellite and other remotely sensed data because they are widely available and predictive of spatial variation in welfare. Importantly, their wide coverage means that these indicators cover all areas of a given country, which is a prerequisite for unbiased small area estimates.

> 5For example, only four of the countries are classified as lower income, while the geography varies from the Sahel to Southeast Asia.

> 6The sub-area in Mozambique is below the admin4 level, while the sub-area in all other countries is the admin4.

> 7The size of the sub-sample varies from 10 to 20 percent.

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