More specifically, each category of data policy restriction is weighted for the full index. In addition, within each category, each specific policy restriction is also weighed against each other. Yet, in most cases the policy restrictions receive equal weights within their respective category as can also be found in Table 1. For the categories, both the IPR and the CBDF also receive an equal weight of 0.25, which therefore together accounts for half of the overall index. The other half is covered by the four remaining categories in which both the DP and CA categories receive a weight of 0.15. Further, the IL and the INF categories are assigned a weight of 0.1. Note that in some occasions a new specific policy restriction is included, such as whether a country has a data protection law in place, which was not taken up in Ferracane _et al._ (2018). Annex A of <mark>Ferracane</mark> _<mark>et al</mark>_ <mark>. (2018b) p</mark> rovides further detailed information on the weights, scoring and description of the policy measures.

<mark>After applying our weighting scheme, the data policy index varies between 0</mark> (completely open) and 1 (virtually closed). The higher the index, the stricter the data policies implemented in the countries. Table 2 presents an overview of the final index for each East Asian country and shows how each category of restrictions contributes to the final index score. What becomes clear is that China is most restricted with a score of 0.91. In large part, this is caused by the high level of policy restrictiveness in the categories of IPR and CBDF. After China comes Vietnam with a score of 0.82, and then third both Thailand and Indonesia which both have a level of restrictiveness that scores 0.64. The least restricted country is Hong Kong SAR, China, with a score of 0.09 and is therefore almost virtually open. It only shows some minor restrictions related to IPR and intermediate liability. Japan is the second least restricted country with a score of 0.20. Together the set of East Asian countries allow for substantial variability in our data policy index as illustrated in Figure 2 and Figure 4.

Figure 3 shows how the full index of data policy restrictiveness has evolved over time between the years 2009 and 2019. The line is computed as the weighted average of the 15 East Asian countries covered by the index with their respective GDP used as weights. The reason for doing so is that in order to get a non‐biased trend of restrictiveness for the entire region, countries’ restrictions should be corrected for their individual developments. A small country such as Vietnam might be very restricted but compared to China or Indonesia has a much smaller economic impact in the region. Treating all countries equally would therefore give a distorted picture of the aggregate level of restrictiveness for the entire area. As one can see, there is a clear upward trend reflecting the fact that data policies in the East Asian region have become more restrictive over time.

# **3.4 Descriptive Analysis**

Before turning to the econometric assessment using firm‐level data of innovation, we first provide some descriptive analysis of our data policy index and show how it relates to existing variables of innovation that are computed at the level of country and sector.

We first do so by taking one of the firm‐level innovation variables from the World Bank Enterprise Survey and average this binary information by country and sector. Admittedly, doing so has problems as the variable is initially dichotomous and would much depend on the number of firms included in the sample. Nonetheless, it would be worthwhile to conduct this analysis in order to obtain a first impression of the potential direction that the econometric correlations may take. We undertake this analysis for all sectors, but for now focus on the computer and related services sector given that our interest lies in the responsiveness of firms in a sector that is data‐intensive. Figure 1 showed that the computer and related services sector is an intense user of software.

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