## Source: King & Golledge (1978)

The URCA approach exploits this intuition by using travel time to locations as a proxy for cost and adopting an urban hierarchy based on city size to classify rural locations as gravitating around a specific urban center, as advocated by Partridge et al. (2007; 2008). This allows one to endogenously determine catchment areas of urban centers and do so in an exhaustive manner. Once these spatial urban–rural catchment areas (URCAs) are identified, the approach classifies the global population, allocating rural populations around differently-sized cities. Results can be mapped globally and consistently and then aggregated as administrative unit statistics to define an urban–rural continuum at any specified level of aggregation.

# _Potential applications in developing countries_

The three approaches presented above have been developed to assess, from different perspectives, the degree of interconnection between locations. The availability of two global data sets –providing estimates for FEAs (Moreno-Monroy, Schiavina, & Veneri, 2020) and URCAs (Cattaneo, Nelson, & McMenomy, 2021)– is an opportunity for development practitioners. Comparing the two approaches, one finds that FEAs tend to be more limited in their geographical extent to be able to capture sufficient economic spillovers to justify shared rural-urban governance. Researchers use FEAs if they desire a division into areas that are strongly affected by urban agglomeration economies versus those that are “rural” or weakly influenced by urban effects. The latter are excluded from the FEA, and therefore, in most situations, the approach does not provide an exhaustive partition of areas and population _._ Another feature of FEAs that affects how areas are partitioned is that a minimum threshold, usually of 50,000 people, is needed for there to be sufficient agglomeration economies. Therefore, FEAs are not focused on the rural-urban continuum _per se_ ; FEAs truncate the urban–rural continuum at the point where agglomeration economies become too weak and tend to exclude smaller urban centers.

URCAs, on the other hand, are focused on the full rural–urban continuum, linking even distant rural locations that may not have a strong social and economic integration with their urban center of reference.<sup>4</sup> This is facilitated by the use of an urban hierarchy and a travel-time gradient as opposed to a commuting time threshold.<sup>5</sup> URCAs also use 20,000 people as lower bound on population of an urban center to capture also towns without strong agglomeration economies. These differences are exemplified by Moreno-Monroy and co-authors (2020) identifying 8,790 FEAs globally that have agglomeration economies as a guiding principle, whereas Cattaneo and co-authors (2021) identify 30,709 URCAs globally that are more oriented to representing heterogeneity along the urban-rural continuum. These differences translate into FEAs representing just about half of the world population through its focus on agglomeration economies, compared to 99 percent for URCAs focusing on the whole continuum.

In summary, FEAs will be more appropriate when one needs revealed economic interdependence with sufficient levels of economic spillovers, whereas URCAs will be more useful if there is a need to be exhaustive in accounting for the whole population in a region or country. Information used in the

> 4 The rural-urban continuum approach is not included as it can be obtained as a special case of the URCA approach by assigning the administrative unit to one of its categories.

> 5 Using travel-time gradients also greatly simplifies the need for commuting data that are often unavailable, especially in developing countries.

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