they develop a measure of Generalized Transport Costs (GTC) that satisfies a number of desirable requirements, and in particular allows for the decomposition of the variations of these costs into a number of components including infrastructure, fuel price, technology and regulation. The striking conclusion is that while the deregulation of the road transport industry was the main driver of the reduction in GTC, explaining 57% of the decrease against only 8% for infrastructure, the spatial distribution of this variation in costs was mostly mediated by infrastructure developments that allowed for the targeting of specific regions.<sup>36</sup>

# _Agglomeration Patterns and Outcomes_

A related issue is the pattern of geographic industrial concentration, which I identified as an intermediate objective in the theoretical discussion. Ellison and Glaeser (1999) test an array of factors explaining industrial concentration in the US, among which a number of proxy for the quality of infrastructure services: prices of electricity and proxies for transport costs. Both of these appear to have a significant impact on firms’ location decisions. Using coagglomeration patterns to disentangle further between the different theoretical mechanisms that may drive agglomeration, Ellison, Glaeser and Kerr (2007) find support for transport costs savings secured by locating near consumers and suppliers, for labor market externalities and for knowledge spillovers.

Additionally, some authors have provided evidence of the dynamics of agglomeration, dispersion and re-agglomeration that may occur at the industrial level and of the role of pioneer firms in that process (Kim, 1995; Dumais, Ellison and Glaeser, 2002; Hanson, 1996). Dumais et al. (2002) provide the most sophisticated such analysis. In particular, they link geographic concentration and the changes thereof to firms’ life cycle, distinguishing between the effect of new firms’ birth, expansion or contraction of existing firms, plant closures, and plants switching between industries. They conclude that mobility is rife, both among concentrated and non-concentrated industries, a fact that they interpret as proof of concentration being an equilibrium outcome determined by industry characteristics. They also conclude that firms’ births tend to reduce concentration, while closures increase it.

How do these (co)agglomeration patterns relate to growth and income distribution? Overall, apart some suggestive aggregate evidence<sup>37</sup> , there is no definitive evidence on the link between agglomeration and growth. The first issue here is whether there is an excessive or suboptimal degree of agglomeration. As signaled in the theoretical discussion, models allow for both outcomes so this is ultimately an empirical matter. Related evidence is found in urban empirical analysis. Using cross-country data on city concentration, Henderson (1999) finds that there is a best degree of urban concentration (also called primacy) that maximizes productivity growth by balancing benefits (local knowledge spillovers) and costs (congestion, cost of life in megacities).<sup>38</sup> His estimations indicate that one standard deviation from the best value costs

> 36 Of course, such conclusion might not be directly transposable to a developing country context, in the sense that deregulation may fail to have an impact in a context in which the network is underdeveloped, although further research on this aspect is still due.

> 37 See for example the introduction in Baldwin and Martin (2004).

> 38 According to this paper, over-concentrated countries include Argentina, Chile, Algeria, Mexico, Peru and Thailand.

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