therefore provides us further insights on which specific part of innovation that firms perform data restrictions have an impact.

We start with the cross‐country approach. Equation (1) is used in our baseline regression which is specified in equation (2) below. Equation (2) measures the correlation over time between the data linkage index as described above and several variables of innovation (see below) measured at the level of the firm. Hence, we regress our variables of firm‐level innovation that is recorded for each firm _f_ ̧ for country _c,_ in sector _j,_ at year _t,_ on the data linkage (DL) index which itself is specified at country‐sector‐year level. As a result, the baseline specification for our regressions as correlations takes the following form: (2) In equation (2), the vector INNO consists of four firm‐level innovation variables across our selected group of East Asian countries. These variables are: (1) whether the firm has introduced a new product / services over the last 3 years; (2) whether the firm has introduced a new process over the last 3 years; (3) whether the firm uses technology that is licensed from a foreign company; and finally (4) whether the firm has spent on new R&D (excl. market research) in the last 3 years. The four variables are respectively indicated by h1, h5, e6 and h8, which is consistent with the labeling of the World Bank Enterprise Survey database from where the data are sourced. Note that these firm‐ level data are cross‐sections for each year between 2009 and 2018 with intervals and as such do not record data for the same firm each year. Tables A1, A2 and A3 and Figure A1 in Annex A provide an overview of the cumulative firm distribution of the four innovation variables and gives summary statistics by country and sector.

Note that our dependent variables are formulated for which responses are only allowed in a binary way. The Enterprise Survey database reports these answers with a simple Yes or No. We have transformed the variables in a dummy so that effectively it becomes a non‐linear estimation in the sense that INNO���� ∈�0,1� . We are therefore compelled to perform a Probit model. However, we first perform an LPM model with fixed effects before moving into a Probit regression, as the former provides us additional information about the direction in which the Probit results are most probably going to when regressing.<sup>8</sup> Moreover, for our three country cases only LPM regressions can be performed and thus for reasons of consistency we report both types of result. We estimate our Probit model with a conditional (fixed‐effects) logistic regression, because of the inclusion of our various dimensions of fixed effects.

As described above, our DL variable is defined at country‐sector‐year level following equation (1) and therefore varies over all three dimensions. Although we have data for our data policy index up until 2019, we can only include up to 2018 as the Enterprise Survey data do not go any further.

provided above and because it effectively tests for two different kinds of variations. Moreover, the country‐ specific data source reports different types of innovation variables which are used as our dependent variable. 8 There are however problems with the LPM. One of the main issues is that the LPM does not estimate the structural parameters of a non‐linear model (Horace and Oaxaca, 2006). If the Conditional Expectation Function (CEF) is linear (which means that conditional mean of a random variable is its expected value), then even an LPM regression gives the CEF. Instead, if the CEF is non‐linear the otherwise standard approach of using Probit approximates the CEF, in which case the LPM does not give any meaningful marginal effects. However, given that we do not know whether the model is truly non‐linear both LPM and Probit are useful.

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