  Ending poverty in all its forms everywhere is the number one Sustainable
Development Goal of the UN 2030 Agenda. To monitor the progress towards such an
ambitious target, reliable, up-to-date and fine-grained measurements of
socioeconomic indicators are necessary. When it comes to socioeconomic
development, novel digital traces can provide a complementary data source to
overcome the limits of traditional data collection methods, which are often not
regularly updated and lack adequate spatial resolution. In this study, we
collect publicly available and anonymous advertising audience estimates from
Facebook to predict socioeconomic conditions of urban residents, at a fine
spatial granularity, in four large urban areas: Atlanta (USA), Bogot\'a
(Colombia), Santiago (Chile), and Casablanca (Morocco). We find that behavioral
attributes inferred from the Facebook marketing platform can accurately map the
socioeconomic status of residential areas within cities, and that predictive
performance is comparable in both high and low-resource settings. We also show
that training a model on attributes of adult Facebook users, aged more than 25,
leads to a more accurate mapping of socioeconomic conditions in all cities. Our
work provides additional evidence of the value of social advertising media data
to measure human development.
