<mark>simulated dynamic stochastic general equilibrium model and show that up to 100,000 jobs could disappear in the short‐run and more than 300,000 in the long‐run. Another study by Bauer</mark> _<mark>et al</mark>_ <mark>. (2013) uses a computable general equilibrium GTAP model to estimate the economic impact of the GDPR. It finds that this law could lead to losses up to 1.3 percent of the EU’s GDP as a result of a reduction of trade between the EU and the rest of the world.</mark> Goldfarb and Tucker (2012) empirically prove the adverse link between restrictive data policies and innovation and point out that stricter privacy regulations may harm innovative activities by presenting the results of previous studies undertaken with respect to two sectors, namely health services and online advertising. Both studies show that there are strong linkages between the effective sourcing and use of data and innovation based on open markets. Recent work by Goldfarb and Trefler (2018) discusses the potential theoretical implications of restrictive data policies such as data localization and strict privacy regulations on innovation and trade, albeit from the perspective of AI. The authors make clear that an expanded innovative AI industry in which data flows are an important factor would be distorted by restrictive data policies such as data localization.

This paper combines the two strands of the literature by developing a specific yet expanded data policy restrictiveness index based on Ferracane _et al._ (2018a). It then relates the index to firms’ digital innovation activities for a set of East Asian countries for which we specifically have developed the data policy index. The index covers various measures related to data activities and is much broader in scope than equivalent indexes used in the papers described above. For instance, in addition to restrictions on the cross‐border flow and domestic use of data, we now also include restrictions related to IPR for digital sectors, intermediate liability and content access for online platforms, as well as regulatory policies regarding the telecommunication market. We expect these data restrictions to be negatively correlated with the extent to which firms innovate, particularly in sectors that are more reliant on data. In assessing this hypothesis, we use the identification strategy as developed in Ferracane _et al._ (2018b).

# **3. Empirical Strategy**

This section sets out the empirical strategy. We develop a composite indicator following the works of Ferracane _et al._ (2018a) and Ferracane and van der Marel (2018). In these two works, a data linkage variable is developed that interacts their data policy index with an industry‐level measure of data‐ intensity. In our case, the composite indicator is comprised of the index that covers for data restrictions, including the ones related to IPR and telecommunication, which is interacted with variable that measures the extent to which sector are intensive in the use of software. In our view, this latter proxy crudely specifies how much each a sector employs data. Some sectors are more dependent on data than others and we expect that data‐intensive sectors are proportionately more affected by changes in restrictive data policies. To reflect this consideration, we therefore weight the data policy index with our measure of software use that signifies data‐intensity at the industry‐level.

In a second step, we present our baseline specification for the regressions in which we use different firm‐level variables that measure innovation and regress them on our composite indicator of data restrictiveness. We perform regressions using two types of firm‐level data, namely one at the aggregate cross‐country and industry level using the World Bank Enterprise Survey database, in addition to three country‐specific firm‐level data sets for a small number of East Asian countries, namely Malaysia, Vietnam and China. The results that derive from the two data sets are complementary as they provide us different insights. The World Bank data represents a cross‐ 4