database only, as the main results are valid when considering trading times across countries from the regional database.

A difficulty in this exercise is that the sensitivity of trade flows to shipment time strongly depends on the characteristics of the product considered. In other words, the “value of time” is not equal across industries. Among many sector‐specific characteristics governing the impact of time on trade, one can cite the rate of depreciation, storability, seasonality, demand uncertainty or substitutability with domestic products. All those traits influence the elasticity of shipment time to trade flows and it is important to take into account this heterogeneity across sectors.

In the rest of the section, we first discuss the methodology used to transform shipment times into trade costs, then we present the results for the global and regional databases.

# **a. From shipment times to trade costs**

To transform the shipment times from our network analysis into trade costs, we use the seminal work of Hummels and Schaur (2013). This paper provides estimates for the ad‐valorem value of a shipment day for many sectors, at both four‐digit and two‐digit levels of disaggregation in the Harmonized System (HS). Recognizing the fact that air cargo is many times more expensive than maritime transport but arrives in destination markets much faster, Hummels and Schaur (2013) use U.S. imports data and detailed information on the premium paid for air shipping and in time lags for ocean transit to identify consumers’ valuation of time. To our knowledge, these results constitute the most comprehensive and detailed estimates of value of time at the sectoral level, providing rich heterogeneity and overcoming several endogeneity issues faced by previous work.

An obvious limitation of these estimates is that they are based on information from the United States, which might create at least two types of potential biases when using the values for a worldwide analysis. First, there may be a selection issue since the United States does not have positive imports in every sector, which means that we must rely on averages for some sectors. Second, the value of time may be to some extent country‐specific. Consumers in different countries could be characterized by different valuation of delivery time, because of different taste or different ability to store goods. Below, we discuss extensively in this section how we deal with the first problem. The second problem cannot be easily solved due to lack of data. It is a caveat to bear in mind, although the direction of the bias is difficult to assess ex ante.

In order to construct new data for the ad‐valorem “value of time” in GTAP, we must aggregate the estimates at HS‐levels from Hummels and Schaur (2013) into GTAP sectors. To perform such aggregation, it must be noted that GTAP sectors are aggregates of many HS categories which are represented with different weights for different country‐pairs. As a result, a given GTAP sector should not necessarily be associated with the same “value of time” when we consider different origin and destination countries. To make things clear, let us describe the following simplistic example. A GTAP sector named GTAP_1 is made of three HS categories HS_1 HS_2 and HS_3. The associated “value of time” raw estimates for the HS categories are τ<sup>HS1</sup> , τ<sup>HS2</sup> and τ<sup>HS3</sup> respectively.

Looking at the trade flows between two countries (Country 1 and Country 2) for the GTAP sector GTAP_1, we observe that the composition of GTAP_1 is not the same when country 1 is exporting or importing: 20