At the same time, the rapid proliferation of publicly available “big data” from satellite and crowdsourcing applications has made geographically comprehensive data freely available, while survey enumeration area geocoordinates or administrative shapefiles required to link surveys to geospatial data are also becoming increasingly available. These developments have sparked a burgeoning literature – ably reviewed in Burke (2021), McBride et al. (2021), and World Bank (2021) – on the use of satellite imagery for economic measurement. Most early work has focused on the ability of geospatial data to predict agricultural yields and crops (Erciulescu et al., 2019, Lobell et al., 2020), household asset wealth and poverty (Jean et al., 2016, Yeh et al., 2020 Steele et al., 2017, Engstrom et al., 2021), and population (Wardrop et al.., 2020, Engstrom et al., 2020).

This is the first paper to our knowledge that explores whether combining survey data with geospatial indicators can be used for the purposes of improving estimates of labor market outcomes, such as female and male labor force participation and unemployment, through small area estimation. Demonstrating that this methodology works is important, as many developing countries do not have the requisite data traditionally used for small area estimation, such as up-todate censuses or detailed administrative data. We show that the incorporation of geospatial data – which is available across the globe – is a feasible alternative when preferred sources of auxiliary data are not available.

This paper also contributes to the ongoing discussion over the proper methods for combining survey and geospatial data for the purpose of prediction. Some analysts take an explicitly Bayesian approach (Steele et al., 2017, Pokhriyal and Jacques, 2017, Erciulescu et al., 2019), while others employ an empirical best predictor model (Battese Harter and Fuller, 1988 Masaki et al., 2020).

We undertake this exercise in the context of Mexico, which has publicly available data that makes our approach feasible. We focus on three separate levels of aggregation: the state, the municipality, and the AGEB (Área Geoestadistica Básica, i.e. the Basic Geo-Statistical Area). Importantly, the census data has AGEB-level identifiers for all urban AGEBs in the country. This allows us to match geospatial data to AGEBs. In addition, we simulate a random sample from the census before implementing our preferred small area estimation approach. The use of census data allows us to compare the resulting estimates to the “truth,” derived from the census data itself. This type of data is rarely available in developing countries and, as such, Mexico is the perfect context in which to apply our approach.

It is important to note that our results on unemployment and labor force participation differ from official rates in two key ways. First, we include only urban AGEBs due to data limitations. Second, we are using census data, not a labor force survey, so the instruments differ. For example, the age range for which employment questions are asked differs across the instruments. However, despite these differences in the instruments, the underlying concepts of labor force participation and unemployment in the census and labor force survey are similar. There is therefore no reason to believe that the findings on the benefits of incorporating geospatial data would not apply to the official definitions as well.

This paper implements a sub-area model, which is essentially a unit-level model in which the unit is taken to be the AGEB. In particular, we specify a weighted empirical best predictor model, with conditional random effects specified at the municipality level in our main results. Municipal 3