# **6. Conclusions**

The BRI offers Central Asia the prospect of large improvements in connectivity, linking places within the region to each other as well as improving connections to the rest of the world. The tools of international economics and economic geography provide ways of thinking about the effects of such improvements, and this paper applies these tools in a quantitative model of the region.

An exercise of this type faces many uncertainties, of which two are particularly critical. The first is that the BRI is a series of different projects, some infrastructure, others to do with logistics handling and border controls, and all combining to reshape a complete transport network. The direct impacts of these changes on travel times and wider measures of transport costs are hard to estimate. A reduction in tariffs or the construction of a particular segment of road or rail may be a fairly clearly defined policy change, but this is not so in the case of the package of measures involved in the BRI. Our work has built on the work of de Soyres et al. (2018) which, while thorough and careful in its construction, leaves many questions open due to the large degree of uncertainty about the BRI itself.

The second uncertainty is the ability of economies in the region to respond to these changes in connectivity. This is particularly important since some areas in the region will experience changes in connectivity that go well beyond the usual economics framework of marginal change. The research literature, particularly in economic geography, suggests that such non‐marginal changes may have large impacts. In the presence of economies of scale and agglomeration, places that experience an increase in activity may achieve productivity growth, and this will amplify change as clusters of economic activity develop. The present paper is innovative in so far as it captures these effects by including localization economies in the manufacturing sector. Doing this raises modeling issues concerning the best way to model localization economies, the possibilities that equilibria may become unstable (a problem we solve by only allowing limited labor mobility), and the inherent unpredictability about the exact location of clusters of activity. It means that the results we present are illustrative rather than predictive, but we think it important to explore these mechanisms which are widely believed to be present.

To place our results in context, the paper presents a series of scenarios describing different economic environments and adjustment possibilities. We start with a conservative approach in which few factors are mobile, meaning that adjustment rapidly runs into diminishing returns; direct benefits of around 1.4% of GDP (in aggregate across the region) get somewhat amplified by economic adjustment, up to around 1.9% of GDP.

We then go beyond this to increase the scale of possible response, letting more things be mobile (firms and, to some extent, workers) and adding increasing returns to scale and agglomeration economies to some productive sectors. This produces much larger quantity responses, and takes aggregate real income gains up to around 3% of GDP, with gains for some countries exceeding 5% and for some cells exceeding 10%. Gains are still larger in the combined experiment of infrastructure plus time savings at borders. Within this aggregate change, there is very substantial heterogeneity, within as well as across countries. The top performing cells in the entire region see per capita real income gains of as much as 21%, coupled with a tripling of the local population; the worst performing experience small real income declines and lose a substantial fraction of their population. Importantly, labor mobility, which without increasing returns to scale served to limit regional inequalities, can now serve to 28