The third and fourth group of assessors were anonymous online workers mobilized through Amazon’s Mechanical Turk (MTurk). This is a crowdsourcing marketplace that facilitates the virtual breakdown and distribution of manual time-consuming tasks among thousands of anonymous workers and has become increasingly popular as a tool for research.<sup>3</sup> While MTurk workers often have a college degree or above, they are unlikely to be urban experts (Ross et al. 2010).

To ensure quality, we required participants in the third and fourth groups of assessors to have MTurk “master” accreditation, which reflects substantial experience and low rates of rejection. MTurk allows to restrict the nationality of participants in crowdsourced tasks. Our third group of assessors was thus made exclusively of MTurk workers from India, and the fourth one exclusively from the US. In all, the third group had 72 members and the fourth one 207.

# _Two assessment protocols_

All four groups of assessors followed a tightly structured protocol for the classification of places. However, before being exposed to this structured approach, assessors in the second, third and fourth groups were also asked to provide an impromptu judgment. Without any guidance they had to decide by themselves whether the Google images shared with them corresponded to urban or rural places. As a result, each of the places in the sample was subject to seven rounds of classification: three of them impromptu and four following a structured protocol.

Under the structured protocol, assessors were requested to rely on a decision tree to classify the places allocated to them. The decision tree had three nodes, each involving the interpretation of Google images along one dimension. The first node referred to the distribution of land cover types. The second node was about the characteristics of the buildings and the relationship between them. And the third one concerned the presence of transportation networks and the availability of amenities such as schools, universities, hospitals or cultural sites (appendix 2).

The decision tree was built on the economic geography and urban economics literatures. Indeed, models of land use predict that the density of construction declines as one moves away from a city center to more rural areas (Alonso 1964; Mills 1967; Muth 1969; Brueckner 1987; Duranton and Puga 2015). It has also been shown that large infrastructure investments facilitating access to markets spur the concentration of economic activities (Duranton and Puga 2004; Fujita et al. 2001; Krugman 1991; Scotchmer 2002). Finally, services and amenities are crucial in explaining where firms prefer to operate, and where households prefer to live (Ahlfeldt et al. 2015; Combes et al. 2010; Straszheim 1987).

While the structured protocol was the same across all four groups, the diversity of the assessors required slight adjustments to its implementation in each case. With the first group, the sample of cities, towns, and villages was randomly split into three subsets, each allocated to one of the three analysts. A randomly selected subset of 10 places was reallocated from the original research analyst to a different one to assess the robustness of the classifications. When one research analyst could not reach an unambiguous conclusion on a specific place, another analyst was called in to consult and make a joint decision.

With the second group we used an open-source data collection tool, Collect Earth, to be able to work remotely. Collect Earth enables data gathering and image analysis through Google Earth.<sup>4</sup> For each

> 3 Information on Amazon’s Mechanical Turk is available through https://www.mturk.com/.

> 4 Information on Collect Earth is available through http://www.openforis.org/tools/collect-earth.html.

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