First of all, as summarized in Henderson, Shalizi and Venables (2001), transport costs in many developing region of the world are far from negligible. For example, the costs measured by the cif/fob ratio can rise to 30 to 40% for remote landlocked countries. The impact on trade is also very important. Using distance as a proxy for transport costs, these authors report that doubling costs reduces trade volume by 80% and that the median landlocked country has 60% trade less than the median coastal country, a finding partly due to the fact that land transport is approximately seven times more costly than sea transport.

Using a gravity model of trade with either transport costs or cif/fob ratios, Limao & Venables (2001) show that, on top of distance, infrastructure matters strongly. Indeed, in such a model distance alone explains approximately 10% of transport costs, while including infrastructure variables increases the pseudo-R<sup>2</sup> to 50%. More precisely, improving a country’s infrastructure stock from the median to the top 25<sup>th</sup> percentile would reduce the cif/fob ratio from 1.28 to 1.11, a change equivalent to the country getting 2,358km closer to all its trading partners. Conversely, deterioration from the median to the 75<sup>th</sup> percentile would increase the cif/fob ratio from 1.28 to 1.40, an increase equivalent to getting 2,016km further away from all trading partners. Similarly, Bougheas et al. (1999) estimate a gravity model of trade applied to EU countries, using both the stock of public capital and the length of the motorway network, and show that infrastructure is a significant determinant of trade volumes.

Similar evidence is found for African countries in Limao & Venables (2001), who estimate that intra-African transport costs are higher (136%) and trade volumes are lower (6%) than predicted by a standard gravity model, much of this corresponding to poor infrastructure (e.g. 59% of the total for costs) and the very high cost of distance. Moreover, an interesting element in that paper is the recognition of non-linear effects in the case where transit countries with very poor infrastructure virtually “kill” trade.

There is however an issue with the quality of transport costs proxies, which seriously limits the inferences that one can draw regarding the impact of specific infrastructure development policies.<sup>35</sup> First of all, cif/fob measures appear to do a relatively bad job at explaining the link between distance and transport costs. Moreover, by construction, they are limited to inter-country trade and do not allow for intra-country data, a serious problem when the objective is to assess the impact of regional infrastructure policy for example. As for distance, the main problem is that usual measures do not allow for the decomposition of the sources of variations in transport costs resulting from changes in the environment such as new investment in infrastructure, technological or regulatory changes. Indeed, Combes and Lafourcade (2005), after developing a more sophisticated measure, which I detail below, show that while simple time or distance measures do relatively well in a cross-section setting, they very imperfectly capture variations in transport costs in a time-series perspective, a particularly preoccupying feature when it comes to policy evaluations.

Using GIS data on the French road sector, as well as distance and time aspects of traffic conditions, transport technology and market structure of the transport industry,

> 35 See Combes and Lafourcade (2005) for a more detailed discussion of the criteria that good transport cost measures should satisfy.

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