Elbers et al. (2008) assess the validity of the approach using data from Brazil and find that small-area estimates based on the ELL procedure are close to direct calculations of sub-national poverty in this setting. ELL has been adopted to obtain small area estimates of poverty and inequality in over 60 countries worldwide.<sup>1</sup> Poverty maps are also increasingly used as a source of data in a variety of empirical applications, see e.g. Araujo et al. (2008), Baird et al. (2013), Bazzi (2017), Crost et al. (2014), Demombynes and Ozler (2005), Elbers et al. (2005), and Maloney and Caicedo (2015).

An important limitation of the ELL approach, however, is precisely its reliance on the population census. For all the strengths of a population census, a notable disadvantage is its limited availability. Population censuses are generally conducted only once every 10 years, and it often takes two years or longer to process the data. These factors combine to imply that ELL-based poverty maps are rarely up-to-date (i.e. are often already outdated at the time of release). Furthermore, even when current population census data exist and are ready for analysis, getting access to such data is often not trivial. As a result, areas that may be most in need of a timely poverty map may lack the data necessary to implement the ELL approach. An alternative approach that can produce a poverty map in the absence of population census data will thus have obvious appeal.

The objective of this study is to evaluate whether an accurate map of poverty can be obtained using predictors derived from publicly available remote sensing data instead of a population census. A requirement in such a case would be that the household expenditure survey is also geo-referenced, i.e. that the enumeration areas (or primary sampling units) can be located on a map. While this would have been a demanding requirement a decade ago, geo-referencing of household surveys is now increasingly becoming standard practice. To preserve the anonymity of sampled households, the geographic coordinates of the primary sampling units in public use datasets are not exact, i.e. some random offsets are applied. Our secondary objective is to investigate whether these random offsets undermine this alternative approach of obtaining a poverty map. The primary sampling unit, such as a village or a neighborhood in a city, will be the unit of observation (rather than the household). Correspondingly, the level of poverty in the primary sampling units will now serve as the dependent variable. Examples of possible predictors of local poverty rates include local Night-Time-Lights (NTL), road network, urban footprint, vegetation, elevation, surface temperature, rainfall.

The use of remote sensing data in the economics literature has expanded dramatically in recent years, as these data have become richer and more accessible, see e.g.

> 1See e.g. Alderman et al. (2002) and and Bedi et al. (2007). Recently, the approach has also been extended to the small area estimation of child malnutrition (Fujii, 2010). For an overview of the literature on small area estimation we refer the reader to Elbers and van der Weide (2014).

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