Equation (2) also includes fixed effects by country ( δ� ), sector ( θ� ) and time ( γ� ), respectively. Note that despite the fact that our dependent variable is given at the firm‐level, we cannot include firm‐ level fixed effects because of the repeated cross‐sectional nature of the Enterprise Survey data set and so following developments of the same firm over time is not possible. Finally, the ε���� is the error term, which for the LPM regressions are clustered by sector country. For our Probit regression, we are unable to cluster, but the data are grouped by sector.

Our second approach is using country‐specific firm‐level data. We have firm‐level data sets from Malaysia, Vietnam and China. Obviously, the three data sets differ in variable coverage which means that the innovation indicators are not consistent across each other despite all three data sets report companies’ balance sheets information. Data are available for the manufacturing sector only.

In the regression specification presented below in equation (3), the innovation variables are again summarized in a vector called INNO in which the dependent innovation variables are dummies as well. Hence, INNO���� ∈�0,1� in equation (3). The empirical setup is largely similar compared to equation (2) with only some minor differences. One is that our data usage indicator as defined in equation (1) needs to be adjusted as we do not observe software use and labor in any of the countries. Data on these two variables are hard to find for any of the East Asian countries. Second is that the regression equation is specified for one country only so that policy changes over time are the focus (as opposed to policy differences across countries in the cross‐section analysis). In order to analyze this latter aspect in more detail, our DL measure will now be lagged with 2 years when possible and 1 year otherwise.

In all, the baseline regression equation for the three countries looks as follows: (3) where INNO is comprised of the innovation variables recorded in each country‐specific data set for Malaysia, Vietnam and China. The DL term is exactly similar to the one in equation (2) where the data policy index is interacted with the data/software intensity. In our three country cases however, given the lack of data on this variable in the region, we are left with measuring the sheer proportion of data usage as part of total input use instead. We use national input‐output (IO) matrices to compute the proportion of ICT‐services in total input use for each sector in the three countries. The national IO tables are taken from the World Bank and are reported at 2‐digit ISIC Rev. 4 level. IO tables are available for each country and therefore represent a consistent source. For each regression, we take input coefficients at the domestic level (i.e. excluding imports) and for a year that falls at the beginning or in the middle of the time period of analysis.

Further, the terms μ� , θ� and γ� are the firm, sector and year fixed effects, respectively. Note that it follows naturally that due to the fact we have three country‐specific regressions, we are unable to include any country fixed effects. Finally, the ε��� is the error term, which now for the LPM regressions are clustered by sector. Of note, due to technical constraints we cannot run the Probit model and therefore perform LPM for all three countries.<sup>9</sup> In addition, for China we also perform OLS on several occasions as the type of data allows us to do so.

> 9 More specifically, when running a Probit model while performing the regressions the combinations of groups and observations result in a numeric overflow in the two country cases of Vietnam and Malaysia. This

11