income per capita in the region. This is associated with large increases in the value of trade, rising from 38% of GDP to 43% of GDP within the region and between the region and the rest of the world. Some cells and countries expand manufacturing production relative to the primary sector, and others do the reverse; the former is more likely for places with a relatively high initial share of employment in manufacturing, and for which the connectivity improvements brought about by the BRI are principally affecting export routes and are intra‐regional, rather than links to the rest of the world. With a more elastic supply response – in particular localization economies and labor mobility – the aggregate gains are likely to be much larger, rising to 2.7% of initial GDP, nearly double the direct cost savings.

In both these cases, the effects are spatially uneven, with different cells in a country having widely different outcomes. In the absence of localization economies, labor mobility tends to equalize differences in GDP per capita; workers move to regions with more positive outcomes, this increase in labor supply tending to reduce wages. However, the presence of localization economies generally reverses this; as workers move into a cell so scale effects tend to raise productivity, creating a virtuous circle of growth. There are then large changes in economic geography, with some places experiencing large‐scale manufacturing growth, per capita income increases of around 12%, and local population doubling. Such population increases seem large, until they are set against historical change in rapidly developing economies.

The results, particularly those concerning the exact places where development takes place, are sensitive to the description of the direct effects of the BRI; for example, western regions of China benefit much more in the combined infrastructure plus border improvements experiment than in the experiment with transport infrastructure alone. This dependence of results on the experiment is of course as it should be, but does alert us to the fact that the direct effects – which parts of the transport network have the largest improvement – are not known with any precision; a fortiori, the results from modeling their full economic impacts are indicative, not predictive.

This paper supplements other recent work written on the BRI. Lall and Lebrand (2019) also provide analysis of the spatially differentiated effects of the initiative, using similar data. They build an economic geography model, a development of work by Fajgelbaum and Redding (2014) in Argentina, which allows them to find the potential winners in terms of population and wages. They find that gains are concentrated in locations near to border points, and in urban centers. Higher levels of labor mobility help lower any potential impacts on spatial inequalities. Our paper complements this work, looking at the cells and countries as part of one wider trading system rather than as individual countries each facing an independent shock, and also providing a richer modeling of the supply side response of the economy to shocks. We draw heavily on de Soyres et al. (2018), who estimate the transport costs within and across the region, and how these may be affected by the Belt Road Initiative, including through improving rail infrastructure and reducing border times. From this they construct a detailed database of transport times pre‐ and post‐BRI for cities across the region, and beyond. They use the data in a quantitative trade model assessing how the BRI may increase international trade flows. We use their analysis to construct our transport network and the experiments that we simulate.

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