# **4.2 Country‐Specific Results**

This section presents the regression findings for the country‐specific cases of Malaysia, Vietnam and China. Further details of the survey questions and variables covered in the regressions, as well as some summary statistics for each of the specific country data sets for Malaysia, Vietnam and China, are provided in Annexes B, C and D, respectively.

# **4.2.1 Malaysia**

For Malaysia, we have data on the extent to which each firm has purchased, used and produced intangible capital such as patent, goodwill, work in progress (including imports of both new and used intangible assets), and to the amount of R&D spending for every firm. Used assets are the purchases of assets previously used in Malaysia including those reconditioned or modified before acquisition in the country. Purchased assets are newly bought assets and finally, produced assets are assets produced by the establishment in Malaysia for its own use. Data only span the years after 2008, because of its use of the MSIC 2008 classification which neatly corresponds to ISIC Rev. 4. However, this leaves us with only two years for the analysis, namely 2010 and 2015, which is demanding if we apply year fixed effects. All variables are transformed in a binary mode so that positive values of greater than 0 will be assigned a 1 and 0 otherwise.

Before turning to the regression results, Figure 7 provides a descriptive examination of the main variables used for the empirical specification. The graph plots the IO coefficients of ICT‐services inputs for Malaysia on the horizontal axis against a composite indicator of all the four firm‐level innovation variables from our Malaysian data set, which we call innovation score, and which is summarized into the INNO term. This Innovation Score is then computed as ∑INNO�� ��N� where _N_ is the total number of questions. The innovation score is averaged by sector and year. In this graph, the fitted values line is plotted on the basis of excluding the sectors Coke & Petroleum and Other manuf. & Repair as they appear to be extreme outliers. (Note that the two sectors are also excluded from the regressions.) An upwards sloping correlation is visible in the sense that more ICT‐services intensive sectors have a higher value on our innovation score. The regressions will show whether the index of data policy restrictiveness in Malaysia, as shown in Figure 8, has any role to play.

Results are reported in Table 5. In there, the regression coefficients for R&D expenditures in column 1 gives significant results, which is somehow counterintuitive. This unexpected result could be seen in light of a reaction by firms to perform more R&D as a consequence of the restricted access to foreign markets for their innovation activities that otherwise is essential for digital innovation. In column 3, the coefficient result gives a negative and significant outcome. It indicates that firms in data‐intense sectors (proxied by their share of ICT‐services inputs) faced with higher levels of data policy restrictions is associated with a lower use of firms’ intangible assets as part of their production. Both variables of purchased and produced intangible assets in columns 2 and 3 respectively provide negative coefficient signs but are statistically unimportant.

# **4.2.2 Vietnam**

In the case of Vietnam, we have a different set of variables although the first variable to use overlaps with the Malaysian data: both report whether the firm performs the size of R&D activities. The second innovation variable measures more precisely whether the firm’s R&D activities are targeted 13