The results show that, with regards to the component of rules governing data transfers, trading partners sharing the open model exhibit positive trade correlations in digital services, whereas country-pairs sharing the limited model show negative trade correlations. The result for the conditional model is mixed, with positive and negative trade correlations found for different sectors. A differing pattern appears when looking at regulatory rules for the domestic processing of personal data: country-pairs sharing the open model as well as the limited model reveal negative trade correlations in digital services, while countries sharing the conditional model show positive trade correlations. The results are especially significant for the IT and information services sector, which covers computer services, Business Processing Outsourcing (BPO) services, as well as database and data processing services.

The study’s findings are relevant for policy makers. These findings are especially relevant for developing countries given their potential to benefit from participating in global digital services trade. Many developing countries are able to trade digital services with trade costs that are considered to be lower thanks to the very nature of the internet which reduces the burden of distance (see e.g. Lendle _et al._ , 2016). Countries that are in the process of defining their regulatory framework for personal data might therefore consider how each model correlates with trade in digital services in light of their ability to export.

The remainder of the paper is organized as follows. The next section discusses the previous literature and shows the link between this work and the work on the political economy and legal analysis of the three models for data regulation. Section 3 defines the three data models, provides a descriptive analysis of their implementation globally, and shows how this information links up with economic and development variables. Section 4 sets out the baseline gravity model and provides various robustness checks. Section 5 shows the results of the regressions, and the last section concludes by discussing the policy implications.

# **2 Literature Review**

The existing literature on the relationship between data regulation and international trade is still very limited, especially when focussing on personal data. In part, this lack may be explained by the difficulty in collecting extensive policy information on the regulatory rules in different countries. In the case of data regulations, this difficulty is further amplified by the novelty of the topic. However, in the past years, some significant efforts have been made to categorize data-related regulatory policies, upon which this papers elaborates.

Earlier undertakings to collect regulatory data policies can be found in Ferracane _et al_ . (2018), in which the authors created the Digital Trade Estimates Database and the Digital Trade Restrictiveness Index (DTRI). The database lists a wide range of policy restrictions in digital trade for 67 countries, including data-related policy measures. The analysis on data policies has been further refined with the development of the Data Restrictiveness Index presented in Ferracane _et al._ (2020), which looks at data restrictions that apply on cross-border data flows and on domestic data processing. Meanwhile, other databases have now also picked up data-related measures that affect digital trade, particularly with respect to digital services. Examples include the OECD’s Digital Services Trade Restrictiveness Index (DSTRI) as shown by Ferencz (2019) and the newly updated Services Trade Restrictiveness Index (STRI) developed by the World Bank-WTO (i.e. Borchert _et al_ ., 2019). These two 3