# B. Constructing synthetic household surveys

With a measure of non-monetary welfare and poverty for each census household in hand, we turn to drawing a synthetic household survey from each country’s census. The synthetic survey, along with the auxiliary geospatial data, are key inputs into the small area estimation procedures. To draw the synthetic survey, we utilize the actual two-stage sample conducted by the National Statistics Offices for two household budget surveys: The 2018 Tanzania Household Budget Survey, and the 2016 Sri Lanka Household income and Expenditure Survey. These surveys were merged with the census at the subarea level, which is the GN Division in Sri Lanka and the village in Tanzania. After retaining the GN Divisions and EAs present in the budget survey, we randomly select census households in each matching EA to match the number of households in each EA for each survey. Finally, we merged the sample weights from the household budget surveys for each subarea. Essentially, this procedure draws a survey that mimics as much as possible the sample drawn by the NSO for the budget surveys.

# C. Remote sensing data

The auxiliary data for the small area estimation exercise are drawn from a large candidate pool of satellite-based information, most of which is derived from publicly available layers and imagery. These include night-time lights from the Visible Infrared Imaging Remote Sensor (VIIRS), at a spatial resolution of 15 arc-seconds, precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), elevation and slope taken from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite, global forest cover change from Hansen (2013) and estimates of built-up area from the Global Human Settlement Layer (GHSL). From this last layer, we compute the percentage of total built-up area observed in 2014 that was constructed prior to 1975 or during 19751990, 1975-1990, and 2000-2014.

The Sri Lanka indicators were also supplemented by a variety of spatial “texture” features derived from a cloud-free mosaic of 2017-2018 Sentinel-2 imagery, which is collected every 5 days by Sentinel 2 sensors on board two satellites, Sentinel 2A and 2B. This imagery is made publicly available by the European Space Agency and is the highest spatial resolution (10m per pixel) optical data that are publicly available. The “texture” features were calculated using Jordan Graesser’s Sp.Feas package, an open-source Python library for processing contextual image features from satellite imagery. These contextual features have been shown to be strongly correlated with poverty and population density (Engstrom et al, 2017 and 2019a). The following contextual features were considered as candidate predictors:

- **Fourier Transform** (FT) which is used to detect high or low frequency of lines.

- **Gabor Filter** , a linear Gaussian filter used for edge detection (Mehrota et al, 1992)

- **Histogram of Oriented Gradients** (HOG), which captures the orientation and magnitude of the shades of the image. (Dalal and Triggs, 2005)

- **Lacunarity** (LAC), which describes the extent of gaps and holes in a texture. Low-lacunarity geometric objects are homogeneous because all gap sizes are the same, whereas highlacunarity objects are heterogeneous. (Myint et al, 2006)

- **Line Support Regions** (LSR), which characterize line attributes (Ünsalan and Boyer, 2004)

- **Normalized Difference Vegetation Index** (NDVI), the most widely used vegetation index, which provides information about the health and amount of vegetation

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