The results presented above have not considered the possibility that other country characteristics, rather than export time and remoteness, affect time-sensitive industries more. We check this possibility by allowing time sensitivity to interact with endowments and ratios of private credit to GDP. Table 3 summarizes the results. The alternative interactions between time sensitivity and capital endowment, skill endowment and availability of credit do not matter for firms’ export performance in most specifications. The only exception is when interacting time sensitivity with human capital endowment. In all cases, however, the estimated coefficient for time interaction remains negative and statistically significant in both probability equation and intensity equation. The magnitude of the estimates is also consistent with the baseline results.

As a next step, we test the robustness of our results to alternative measures on export time and time sensitivity. We first consider using the “time for export” indicator reported in the same year to replace the value of 2005. This way, we measures export time more accurately but lose the observations by half. To make comparisons, we first re-estimate (4) and (5) restricting to the subsample of 2005-2008 (Columns 1, 2, 7 and 8 of Table 4). The estimated coefficients on time interaction are consistent with our baseline results. We then proceed with using the “time for export” reported in the same year. The overall results are in line with baseline results regardless we control for additional industry-country interactions or not (Columns 3, 4, 9 and 10 of Table 4). The estimated coefficients on time interaction remain negative and statistically significant for both equations. The magnitude stays in the same range as baseline results as well. We also test the robustness of the measure on industry time sensitivity by using the shares of air transport to total transportation cost based on data of all shipments, _ts2_ (Columns 5, 6, 11 and 12 of Table 4). Again, time interaction is shown to be significantly and negatively correlated with the probabilities and the intensities of exporting. The magnitude remains in the range of baseline estimates. The results stay when controlling for other industry-country interactions.

In table 5, we also check the robustness of our results by restricting the number of surveys. In the final sample, some surveys only include a small number of observations, for instance, 8 for Georgia, 12 for Niger, and 35 for Laos. The firms kept might be the most successful ones –more than half of the observations are exporters for all three countries. It may bias our results. We check out results against this possibility by excluding country surveys in which the number of the selected observations is less than 50 (Column 1, 2, 5 and 6 of Table 5). As noted, for twelve countries, we include observations from two surveys. Some firms might be included in both surveys which we do not have sufficient information to track. The duplication may bias our results if these firms tend to be more successful or come from certain sectors. As another check, we only include the latest survey for each country in addition to excluding small surveys (Column 3, 4, 7 and 8 of Table 5). Overall, our results are not biased by the way we select surveys.

# **Endogeneity**

The relationship identified could run the other way around. The required time for exports to happen could depend on the relative export performance of time-sensitive industries due to the 15