where Y is GDP, _dist_ is the bilateral distance between country _i_ and country _j_ , _bord_ is a dummy for whether the two countries share a border, _isl_ is a dummy for being an island, _llock_ is a dummy for whether the country is landlocked, and _open_ measures the degree of openness.

We estimate foreign market access here at the city level rather than at the country level, by replacing the distance variable _dist_ by the variable “international distance,” What we refer to here as national supplier access is the same thing as the supplier access variable in the Redding and Venables model. In addition, we measure supplier access at the local or city level as well, by modifying equation (13) to a) replace own country GDP, _Yii_ , by own local GDP as defined above, and b) replace bilateral distance by our “international distance”; and c) setting domestic distance, _distii_ to unity.

To obtain the three access variables listed in Table 1, we needed the list of trading partners of our countries of interest and estimates of the ρ -parameters of equations (12) and (13). Redding and Venables (2000) obtained the parameters from the estimation of a gravity equation on aggregate data on a cross-section of 101 countries. We too have used their estimates here.<sup>13</sup> We identified trading partners of the countries of interest based on the COMTRADE Database.<sup>14</sup> Country GDP figures were obtained from World Bank’s World Development Indicators. Domestic distances were calculated using the ^ formula _Tii_ 1 −σ = _distii_ δ 1 , where _distii_ = .033( _area_ / π )1 / 2 , and “area” refers to the area of the territory of the country in question. We used the Sachs-Warner measure of trade openness all cases. Our source for city distance and population variables is the Geographical Information Systems (GIS) database.

We show the contrast between African and non-African economies with respect to the three access variables in Figures 1 through 3. In each figure the access index is scaled to India’s. Figure 1 shows that the average firm of any of the African countries in our list has far less foreign market access than its Indian counterpart. By contrast the average firm in everyone of the non-African group has a significantly higher foreign market access than its Indian counterpart. As a rule, the average firm in Africa also has a far smaller national supplier access than its counterpart in any of the non-African economies (Figure 2). This situation is somewhat mitigated if we account for the fact that industrial firms in Africa tend to be more concentrated spatially, which is what we do when use the 13 We thank Steve Redding for kindly providing the estimates. The equation estimated in Redding and Venables (2000) is log _xij_ = ρ 0 + ρ 1 log _Yi_ + ρ 2 log _Y j_ + ρ 3 log _distij_ + ρ 4 log _bord ij_ + ρ 5 _isli_ + ρ 6 _isl j_ + ρ 7 _llocki_ + ρ 8 _llock j_ + ρ 9 _openi_ + ρ 10 _open j_ + _uij_ where,<sup>_x_</sup> _ij_<sup>is aggregate exports from country i to partner j;</sup><sup>_u_is a stochastic disturbance term, and other variables as</sup> described above in equations (13) and (14). 14 Because trade statistics were incomplete for Africa, we had to use the “mirrored” (reported by partners) data for African countries. We limited this exercise to the top ten trading partners of each country since these accounted for more than ninety percent of annual trade volumes in each case.

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