# **3 Data Description**

This section describes the data used in the analysis and the methodology adopted to construct the variables of interest.

## **3.1 RoO Index at the Intermediate Level**

We start the analysis by discussing the construction of the main variable of interest, which summarizes the difficulty in sourcing an intermediate product according to the RoOs faced by the related final good. We follow Conconi et al. (2018) in constructing a data set that allows us to map input-output linkages to the EU-RoO (which are defined according to the six digits of the 1992 Harmonized System). These input-output linkages are based on the US IO1997 table and are converted into the six digits of the 1992 Harmonized System of classification, HS6 (see Conconi et al., 2018). Despite focusing on the EU context, we use the US IO tables for two reasons. Firstly, the match allows us to keep the same level of disaggregation of the EU-RoO Index, that is HS6. Secondly, it enables us to follow the rationale in Rajan and Zingales (1998), who construct a measure of the extent of an industry’s dependence on external funds in the United States, and apply this measure to all other countries. As pointed out by the authors, the assumption is that “there is a technological reason why some industries depend more on external finance than others” and these technological reasons are the same in the United States as in other countries. Additionally, the authors use US data on capital markets since, among other things, these markets are among “the most advanced in the world, and large publicly traded firms typically face the least frictions in accessing finance. Thus the amount of external finance used by large firms in the United States is likely to be a relatively pure measure of their demand for external finance.” In the same spirit, we exploit the US IO tables to better identify a production process that is less subject to distortions (such as product market restrictions), and therefore can be considered among the most efficient, allowing the identification of a production process driven by technological reasons.

Furthermore, we exploit the index of restrictiveness of EU-RoO developed by Cadot et al. (2005). This RoO index applies to final goods classified at the HS6 level, and varies from 1 to 7, with 7 representing the most restrictive EU-RoO. This index is based on change in tariff classification (CTC) and almost always accompanied by other criteria to be met to obtain origin. As a matter of comparison, less than 17% of EU-RoO are uniquely based on CTC, against 89% of NAFTA RoO. Additionally, EU-RoO are largely based on value added requirements (VA). In fact, 26% of tariff lines are based on CTC and VA requirements, 13% of which uniquely rely on VA criteria.<sup>5</sup> This seems to suggest that the EU-RoO are more heterogeneous than the NAFTA RoO. To account for the heterogeneous level of complexity embodied in the EU-RoO, the synthetic RoO index constructed by Cadot et al. (2005) seems more appropriate than counting the number of final goods products that use a specific intermediate.

To translate the EU-RoO for final goods in terms of intermediates, we combine the previous

> 5For further details see Table 2 in Cadot et al. (2005).

8