and transport improvements will only affect particular areas. Stimulus to economic activity may be spatially concentrated so its effects – in aggregate and on regional inequalities – need to be modeled at quite a fine level of geographical detail.

Second, the model distinguishes between three sectors of activity in each economic geographical cell; (henceforth we refer to geographical units as cells). Two of these sectors have limited spatial mobility. The primary sector (accounting for around 25% of employment in the region) may have quite an elastic supply response, but is spatially anchored because of its dependence on land and natural resource inputs. Non‐tradable services (accounting for around 39% of employment) are relatively immobile because they serve local demand. The third sector is a composite of manufacturing and tradable services, a sector that is relatively mobile, vulnerable to import competition, and with a potential to expand to serve export markets. We devote considerable attention to the implications of different supply responses by this sector. We look first at an ‘Armington’ modeling of the sector, where the number of ‘varieties’ (or firms) produced in a cell is held constant and constrains the supply response. We compare this with two other cases. One is monopolistic competition, giving entry and exit of firms (and varieties) in response to changes; this captures relatively ‘footloose’ activities and, since there are input‐output linkages in the model, creates some (albeit weak) incentives for cluster formation. To this we also add localization economies, i.e. external economies of scale that are sector and location specific.<sup>2</sup> This captures the idea that some places may be able to gain productivity advantage by developing clusters of activity. In this last case the potential gains from the project are greatest, but it is difficult to predict which places will develop these clusters, so the results presented in this paper should be regarded as indicative of possibilities, rather than predictive of actual change.

The third distinctive feature of the model is to pay attention to the mobility of labor and the possibility of land‐use change that enables cities to grow. Our starting case assumes that labor is immobile, tied to its initial cell and country. We then relax this, allowing labor to move between cells within countries (but not internationally) in response to changes in real wages. This additional source of economic flexibility generally magnifies the impact of the BRI on particular cells and is important in facilitating cluster formation. The largest benefits are derived (for some places, if not all) when labor is relatively mobile and when cities and other manufacturing sectors are enabled to grow without driving urban rents up too sharply.

The main experiment that we analyze is the change in connectivity due to infrastructure improvements from the Belt and Road Initiative. We consider a reduction in transport times across the region through the building and expansion of railway lines, using World Bank estimates from De Soyres et al. (2018). We also, in a penultimate section, add to this the effects of a package of other measures to reduce border crossing times.

The main conclusions we derive from the modeling are as follows. First, with limited supply response (monopolistic competition) the full economic gains are about 36% larger than the direct transport cost savings created by building the BRI infrastructure, amounting to a 1.9% increase in average real

> simulating changes to links outside the country. This paper combines a region‐wide approach with internal country detail.

> 2  ‘Localization economies’ is the term given to agglomeration economies which are both location and sector specific, and are also sometimes referred to as Marshallian economies.

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