at an innovation that is new for the market or world in which case the value of this variable takes a 1. If the innovation is only new to the firm, this observation receives a score of 0. The next two variables measure whether the firm has any national or international patents which is also provided in the Vietnamese data set. Finally, the last variable that is included measures whether the firm undertakes a research collaboration in any format. All variables cover the years 2010‐2013, but due to our lagged structure only three years can be included.

Figure 9 first provides an overview of the extent to which the IO coefficients and the Innovation Score for the five firm‐level variables are correlated. The Innovation Score for Vietnam is computed in similar way as for Malaysia and the IO coefficients are from the Vietnamese IO tables. An upward sloping fitted values line is plotted indicating that, on the whole, a positive association exists between the two variables. (Note however that for similar reasons as described above the Coke & Petroleum sector is excluded as well as Paper & Printing sector when plotting the fitted values. The two sectors are also excluded in our regressions.) The sector of Chemicals & Pharmaceuticals has a much higher Innovation Score and also a high ICT‐services inputs as part of its total domestic input use. On the other hand, a sector like Food & Beverages reports much lower levels on both indicators. Figure 10 provides an overview of the Vietnamese developments of the data policy index.

Results of the regressions as correlations are reported in Table 6. In almost all columns the results are statistically insignificant with positive coefficient results. The only variable that is negative and significant at the 5 percent level is whether firms target innovations that is new to the market or world in column 2. Interestingly, however, is the fact that also in this case the R&D variable in column 1 is positive as in the case of Malaysia. When applying a 1‐year lag this result becomes significant at the 10 percent level, which is also the case for the research collaboration variable in column 5.

# **4.2.3 China**

For China, we have different data which are not survey‐based. Data on innovation for China are generally extremely hard to obtain. We are therefore forced to use data from the Thomson Reuters data base that records information of private and public companies whose headquarters are in China. Only two variables are recorded that seem relevant for our research purpose which are the net intangible assets and R&D expenditure (both in USD). Years span a longer time period, which therefore covers our entire duration of the data policy index, namely from 2009‐2019.<sup>11</sup> Figure 11 provides an overview of the developments of our restrictiveness index for China. As one can see, little variation can be detected for the country given that the level of restrictiveness is extremely high throughout the entire period.

Figure 10 shows how the variable of R&D expenditures when divided by the number of employees for each firm is correlated with our ICT‐services input coefficient. (Note again that the Coke & Petroleum sector is excluded.) As one can see, the correlation is positive and tight and shows that sectors intensive in the use of ICT services as part of their overall input structure have higher per capita firm‐level expenditures on R&D. For the regressions, because we perform an LPM we 11 The Thomson Reuters data report data by fiscal year which may not entirely overlap with the calendar year of our restrictiveness index. Moreover, the database reports data calling each year “Fiscal Year 0”, Fiscal Year ‐ 1”, etc. In order to assign a calendar year value for each fiscal year, we assume that the first reporting year of the Thomson Reuters data base refers to 2019, which is Fiscal Year 0. Usually the end of fiscal years falls in the middle of the calendar year although this may vary by firm or country.

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