As discussed, we assume industry time sensitivity is technical based and relatively stable across countries. We use the “US Imports of Merchandise” data from Hummels (2007) to calculate the ratio between air transport and total international transport costs for each industry. It is the best source of customs data reporting the custom values of air transport, vessel transport and total transportation costs. We take the data from 1998 to 2001, which is prior to the first year of the Enterprise Surveys, and calculate the average to smooth out year-to-year variations. We first restrict the shipments to those exceeding one percent of total US imports in the product. The ratio between air transport and the total costs based on these large shipments is our primary measure of time sensitivity. The ratio based on the information of all shipments is used as an alternative measure. We converted the original data classified by 5-digit SITC<sup>21</sup> into the 13 industries defined in the Enterprise Surveys. The two measures are highly correlated with each other (Appendix 5). Beverage and paper products are the least time sensitive, followed by transportation equipments. Wood, food products, textiles and garments require relatively more timeliness. Electronics, chemicals, and other manufacturing products, including medical and optical goods, are the most sensitive to time. The industry classification of the surveys is highly aggregated. It makes our measures on time-sensitivity less accurate. But, the order captures the general ideas that time-sensitive products tend to be those with more extended international production networks, represented by electronics and medical and optical goods. The time sensitivity is higher when the product is more easily to spoil or if demand is less certain, illustrated by comparing food with beverage and comparing garments with paper products.

Data on other industry and country characteristics are from conventional sources. We follow Head (2003) and define a country’s remoteness as the inverse of the sum of partner countries’ GDP weighted by their distance to the country<sup>22</sup> . Data on the skill and capital intensity are from an updated version of Bartelsman and Gray (1996). Capital intensity is the ratio of the total capital stock to the total value added of an industry in the United States. Skill intensity is the ratio of non-production worker wages to total wage bills of an industry in the United States. To smooth out yearly variations, the average value of 1998-2001 is used. Information on counties’ stocks of human capital and physical capital are drawn from Antweiler and Trefler (2002). The data of 1996, the latest available year, is used. The human capital stock is measured as the logarithm of the ratio of workers who completing high school to those not and the physical capital stock is the logarithm of the capital stock per worker. Data on industry dependence on external finance is drawn from Rajan and Zingales (1999). Data on the ratio between private credit and GDP is from the World Development Indicators. We convert all the industries to the 13 industries defined in the Enterprise Surveys.

Appendix 6 reports the correlation coefficients between “time for export’ and other country characteristics, and the correlation coefficients between time sensitivity and other 21 SITC stands for Standard International Trade Classification.

where _N_ is the total number of countries, _Dkj_ is the distance between country _k_ and _j_ , and _GDPk_ is the GDP of country. A country’s internal distance is the square root of its area multiplied by 0.4.

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