country assembly of firms across time which would give us a collective view of the policy choices countries made in the region. The latter data sets look specifically within Malaysia, Vietnam and China and see whether those policies that are significant drivers for the cross‐country firm results are also validated for firms within the three countries separately.

# **3.1 Data Linkage**

The data linkage index builds on the methodology pioneered by Arnold _et al_ . (2011; 2015). Their approach in which the authors create a so‐called services linkage index has been widely used in the empirical field. In our case, we develop a data linkage index variable for digital innovation and use this composite indicator in our regressions. For each country, we interact the country‐specific data policy index with software use as proxy for measuring the extent to which a sector uses data in their production process. This identification strategy relies on the assumption that sectors more reliant on the use of software are more affected by data restrictions. This weighted method is a more refined way of measuring the impact of restrictive data policies rather than simply taking an unweighted approach of regressing our data policy index on any outcome variable of innovation.

In doing so, the country‐specific index of restrictive data policies we develop is multiplied with sector‐specific data‐intensity proxied by software use for each downstream industry _j_ in country _c_ . This is how the data linkage (DL) variable is set up. In this variable, data‐intensities are expressed as (D/L) which is measured by the sector’s software use over labor (see below). In equation (1), therefore, the term ϛ�� denotes the software use for each sector _j_ for which data is retrieved from the US Census ICT survey. Then in equation (1), the data‐intensities are stated as a ratio over labor, called LAB� , that is employed in each downstream sector _j._ The data for labor is retrieved from the US Bureau of Labor Statistics (BLS). As a result, we apply the following formula: Note that we put the intensity indicators in logs, in line with previous literature on factor intensities. This expression of intensities is close to the literature of comparative advantage such as Chor (2011), Nunn (2007) and Romalis (2004).<sup>2</sup> <mark>Finally, in equation (1), th</mark> e data policy index refers to a country‐ year specific variable measuring restrictive data policy (see Section 3.3), whereas the data on software refers to the US‐specific data on software use by industry for one year (see Section 3.2), which is done to avoid endogeneity issues. This may occur in the event that high data‐intensive sectors with greater digital innovation activities over time push for lower regulatory restrictions regarding data in any particular country. The use of this common sector‐specific data‐intensity for one country therefore makes the variable more exogenous.

> 2 An alternative way of measuring intensities such as the one used in Arnold _et al_ . (2011; 2015) and Bourlès _et al_ . (2013) is to create an indicator of dependency using input‐output matrixes, which we have also done in Ferracane _et al._ (2018a) and Ferracane and van der Marel (2018). We use this information as well in our paper for the country‐specific case studies. In such way, we have two approaches to data‐intensities, i.e. one based on data from surveys and one from accounting data. In our view, however, data‐intensities over labor are a more sophisticated way of measuring data‐intensities, particularly regarding innovation.

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