As a proxy for the spatial dispersion of economic activity beyond simple shares attributed to major cities, we compute Pareto exponents ( _α_ ) (Gabaix, 2009; Soo, 2005). The third column in Table 2 shows that while county-level GDP and population show relatively even distributions ( _α_ close to one), measures of economic activity derived from the VAT data exhibit much higher inequality. The Pareto exponents for employment, value added, and trade flows are 57-76% lower than for overall economic activity, indicating higher spatial concentration.

Is this spatial concentration driven by firm locations and number of firm-to-firm relationships (i.e. the extensive margin of trade) or trade volumes (i.e. the intensive margin of trade)? In Appendix A.5, we show that the spatial concentration of both trade out- and inflows is primarily driven by firm locations and the number of firm-to-firm relationships. The number of transactions and average trade volumes per transaction only play a minor role in explaining spatial variation in trade patterns. This insight later informs our choice to focus on a model that allows us to predict the structure of the production network along the extensive margins.

To assess whether the observed spatial concentration and urban bias are idiosyncratic features of VAT data or reflect patterns innate to formal activity, we also compute the spatial concentration for a range of other indicators for economic activity. These range from the least formal to increasingly formalized economic activities and are included in Table 2. We observe a clear pattern: activities that capture very little formal activity such as the number of micro-, small-, and medium-size enterprises (MSMEs) and employment in MSMEs exhibit relatively little spatial concentration ( _α_ = 0.86 and 0.78, respectively). As we focus on more and more formalized activity, e.g. licensed businesses from micro to medium sized, all the way to firms in the census of industrial production, the degree of concentration increases. This suggests that the spatial concentration observed in the VAT data represents a feature of the formal sector more broadly.

Unlike formal sector trade, we find that other observable types of domestic flows, like migration flows also do not exhibit a similar spatial concentration (see Table 2).<sup>16</sup> For instance, while Nairobi is the destination for 29% of all long-term inter-county migration, it accounts for 69% of total outlinks and 65% of total inlinks in the formal sector firm network.

A potential concern is that the observed spatial concentration in urban centres is mainly driven by firm headquarter locations being more likely to be based in Nairobi or Mombasa. While this concern is mitigated by the fact that we observe a similar spatial concentration in other measures of formal economic activity, we also use micro-data from the 2010 Census of Industrial Production to address the question about the role of multi-establishment firms more explicitly. In Appendix A.6, we compare the spatial concentration of sales and firm locations with and

> 16We compute long-term migration flows using data on birthplace and current residence from the 2019 population census.

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