multicollinearity problems because the components are highly correlated. Therefore, we construct our own International Logistics Index (ILI) based on components 1-4 using the relative weights listed. A regression of the LPI on the ILI has an R<sup>2</sup> of more than 0.99, so the international logistics index explains a large proportion of the overall index. Therefore, while the ILI is conceptually more appropriate, using it makes little practical di¤erence.<sup>7</sup> Table 1 presents summary statistics for the full LPI and for our new International Logistics Index (ILI). High income countries have measures of quality that are of the order of three standard deviations higher. Approximately 70% of developing countries have an ILI of between 2 and 3, but there is still considerable variation. Appendix Table 1 lists the values for all countries. The worst performing country in our sample is Rwanda with an ILI value of 1.90 (although there is evidence of subsequent improvement from various anecdotal and data sources). Singapore has the best international logistics with an ILI of 4.28. South Africa (3.66) and Malaysia (3.63) have the highest ILI values out of the developing countries.

Our interest is exports from developing countries. Based on the availability of logistics data and possible instruments and controls, our sample consists of 88 low- and middle-income exporters. We have 116 importers regardless of income classi...cation.<sup>8</sup> We use merchandise exports data for 2005, using the IMF Direction of Trade Statistics. We observe 7,246 positive cross-border trade ‡ows, 2,548 zeros and 826 instances of missing values. We therefore need to account for potential sample selection issues in our model and econometric methodology.

We use 2005 GDP measured in constant (2000) US Dollars from the World Development Indicators. As will become clear, each country’s GDP share is an important component of our analysis. We calculate each country’s share of world GDP by dividing its GDP by world GDP, where world GDP is the sum of the 116 countries in our sample.

The measure of bilateral distance that we use captures the internal distance in a country, accounts for the distance from a number of major cities and is constructed by CEPII.<sup>9</sup> Border data are also sourced from CEPII. Our control variables, including dummies for whether or not two countries share a common language,<sup>10</sup> a common colonizer or were once the same country, as well as a dummy for a landlocked country, are sourced from CEPII. Additional variables for identi...cation as ...xed export costs or instruments are taken from the World Bank Doing Business database. These data include the number of procedures needed to start a business, the number of days it takes to start a business and the cost of registering to start a new business in a country. We also considered data on the number of documents required to export or to import goods. Finally, we took the newly recoded data on religious similarity used by Helpman, Melitz & Rubinstein (2008) from Elhanan Helpman’s website.

> 7 The 2009 version of this paper used the full LPI. That paper also restricted the sample to developing countries that are not islands or are not classi...ed as having neighbouring countries su¢ ciently close to their major cities.

> 8 Further discussion of why we drop rich exporters is postponed until Section A.3.

> 9 The distance measure used is distw and is described at http://www.cepii.fr/anglaisgraph/bdd/distances.htm

> 10 We construct a dummy that is equal to one if two countries share either a common o¢ cial or common ethnic language.

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