which are typically used in cross-national studies, fail to capture the role of roads in connecting cities, and are subject to a great deal of error.<sup>26</sup> A recent World Bank project on infrastructure in Africa has improved the state of georeferenced roads data for the continent so that they can be used to assign infrastructure to specific cities and routes between cities (World Bank, 2010).<sup>27</sup> The resulting dataset combines information on road location and surface assigned to specific (and recent) years from each country’s Transport Ministry or equivalent, or a consultant specific to the project.<sup>28</sup> It contains information on over a million kilometers of roads in 39 countries.<sup>29</sup> For over 90 percent of this length, a measure of the surface type is recorded. The comprehensiveness of the coverage varies by country, but only in that some countries contain more minor roads. Intercity roads are available for all countries. Figure 8 shows these roads data for Tanzania. Roads go through all the populated cities shown. Most roads are unpaved, and most paved roads are found along a few major corridors.

The shortest path along the road network was calculated between the centroid of each city-light and three destinations: (1) its country’s primate city, defined as the light with the largest associated population in 1992, (2) the nearest city in the same country with a 1992 population of at least one hundred thousand, and (3) the nearest city in the same country with a 1992 population in the top quintile of the population distribution for that country. Plausible primate city routes were found for 287 out of 299 cities in the 15-country sample.<sup>30</sup> Figure 9 shows all roads and primate routes for Tanzania. Descriptive statistics are in Table 2.

> 26Countries such as Canada, Australia, and Botswana have low road density relative to their economic peers. But their road systems are not particularly inadequate. In each case, a contiguous region containing half or more of total land area has very low population density, so that the marginal benefit of an additional road there is very low. The chief problem with the percentage of roads paved is that the denominator is affected by the coverage of national roads data systems, which can vary substantially. The World Development Indicators (WDI) reports both of these measures. But of the 120 annual changes in road density available for the 255 country-years in the present sample, 66 are zero, while another 8 are, implausibly, over 10 percent in absolute value. When data are missing for a period of one or more years for a given country, the annual growth rates implied by the values before and after the data gap are even more implausible. Similar statements can be made about the percent paved data. These large changes are likely due to reclassification rather than actual road construction. 27The most comprehensive previous spatial database, Vector Map Level 0 (VMAP0, formerly known as Digital Chart of the World, DCW), is a declassified US military product combining data of unknown quality from 4 decades, with little metadata. In some countries, there are clear gaps in coverage. Most strikingly, the most densely populated areas of Bangladesh, surrounding the capital Dhaka, have essentially no roads.

> 28I am unaware of any systematic time-varying data on road surface in the countries under study. Burgess et al. (2011) use time-varying data for Kenya.

> 29Of the 43 countries in Sub-Saharan Africa, only Djibouti, Equatorial Guinea, Guinea-Bissau, and Somalia do not have data.

> 30The remaining 12 cities include 4 on islands or in exclaves. See Appendix for more details.

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