To increase realism, this basic model formulation is often augmented with a variety of additional explanatory variables. A mainstay of empirical trade analysis, the gravity model expresses bilateral trade between two regions as a function of their respective economic masses (e.g., gross domestic product – GDP) and distance (Baldwin & Taglioni, Citation2006). As the name suggests, gravity-RAS techniques seek to reconstruct the bilateral flows in an unobserved trade network using a gravity model in conjunction with the RAS algorithm. Earlier examples of these methods can be found in Riddington et al. Recently, renewed consideration has been given to a family of approaches some refer to as ‘gravity-RAS’ (Cai, Citation2021 Distefano et al., Citation2020 Fournier Gabela, Citation2020 Wilson, Citation2016).
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As a result, how to overcome the lack of interregional trade data remains a lively area of investigation. Even so, no consensus has emerged over a standard set of methods. Over decades, researchers’ efforts to get around this obstacle have given rise to a large and varied body of literature (Miller & Blair, Citation2009). The fact that in most applied contexts such data do not exist has been a long-standing problem for regional analysts. Modelling the differential impacts of economic policy on the regions of a country often requires data on how those regions interact with each other through trade.
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