interacted with intl as part of the C ∗intlo term. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A4.

A similar conclusion is reached in Table 6 when adding the OECD Digital STRI as a control variable instead of the DTRI. This indicator considers the sub-index that includes restrictions related to Infrastructure and connectivity. This area of the Digital STRI covers restrictions on cross-border data flows, as presented in Ferencz (2019), amongst various others. Note that this index does not cover any measures related to domestic data processing. In this case too, the results of the variables of interest remain unchanged. One notable change however is that the positive significance of the coefficient of the open model for the cross-border flows of data in column 2 is reinforced, as it is also the case of the negative significance of the limited model regarding the domestic processing of data, also in column 2. The outcome on the OECD Digital STRI variable has varying coefficient signs, which is probably due to the fact that it measures exports (see footnote 24).

# **5.2 Performing Panel Series**

Thus far this paper has dealt with a cross-section analysis. Even though informative, a one-year assessment doesn’t allow for any inferences over time, nor does it say anything about a causal relationship between the data models that countries opt for and trade in digital services. For instance, countries change their regulatory regime for data over time, which likely has an influence on trade patterns between them. Among the 116 countries analysed, a substantial number has changed their regulatory framework across the two components of cross-border data transfers and domestic data processing. Given that the default option is the open model, Table A4 shows the list of switching countries that since 2000 have changed their governing framework to either the conditional model or the limited model.

Although it’s hard to resolve any endogeneity concern that may arise due to the fact that countries with stronger bilateral trade ties may therefore choose a certain data model, a panel data series would be a first step in solving a potential bias due to reverse causality. In doing so, equation (3) replicates the baseline equation from the previous section to which now a time dimension is added: D o 1GRAVo 2OP o 3CTo 4LTo 5C ∗intlo o o o (3) oo oo oo oo oo oo DD = exp [θθ + θθ + θθ + θθ + θθ + δδ + γγ ] ∗εε In equation (3), all variables stay the same for D o and the two vectors of GRAVo and C ∗intlo but now vary over time, as all the dummy variables as part of the vectors denoting the oo oo three models (i.e. OP o , CTo and LTo ). Note that the equation applies exporter-year and DD oo importer year fixed effects, denoted by o and o . Finally, o is the residual term. Dyadic fixed oo oo oo effects are not applied in the panel setting as that appears to be too demanding for the available oo δδ γγ εε dataset. As previously said, note that not all countries explicitly state when their regulations are implemented, and if a country does not explicitly allow for a framework at all, the default option of the open data model has been assigned to the country. The time period for regressions is 2005-2015 given that TiVA does not provide any trade data before nor after this time span.

Before turning to the results, some basic regressions are first performed without the three data models due to reasons for measuring the trade impact by their proper coefficient sizes, as explained 32