Understanding new business models is vital to develop recommendations on how to adjust the corporate tax system. It can be appreciated that the OECD acknowledges this need and any reform proposals should be based on insights gained in this process.9 Our contribution at hand adds to the insights in digital business models by sketching the process of transforming raw data into knowledge and providing guidance on the application of transfer prices to allocate taxable profits across tax jurisdictions. Further, we assess the OECD’s key reform proposals and relate them to the ongoing academic debate.
Value Creation - Analyzing Business Models and Allocation of Taxable Profits From Data to Knowledge Data has become an important value driver for business. Today, ‘Big Data’ is a key asset for many digital (and traditionally non-digital) businesses and contributes to improved decision making and to value creation. Among policymakers and academics, however, it is not yet agreed upon, how to determine the value of data and how to treat it with regard to corporate income tax purposes. It is generally accepted that the value of data should be considered for taxation of corporate income. In simple terms, there are two options to take the value of data into account.10 First, if it is seen as impossible to determine the true value of data, a fundamental tax reform towards a destination-based cash flow tax or residence-based shareholder taxation seems inevitable.11 Second, a more promising and politically feasible approach would be the adjustment of prevailing principles to allocate profits according to the arm’s length principle along the value chain. To provide clear
guidance on the application of transfer prices it is necessary to understand the structural concept of how data is used to create value. The role and location of specific people functions in integrated, digital business models should be the focus for any efforts to design tax rules in response to the digital economy. While some argue that data can be compared to valuable natural resources and draw an analogy to raw oil, this comparison is flawed.12 Data is an important input factor for an IP intensive transformation of raw information to knowledge by discovering meaningful structures and patterns.13 This process, which can be split across different legal entities and functions of a globally operating company, is often referred to as ‘Data Mining’. For example, in a digital business model, such as a digital music platform, it is an important competitive advantage in the business to consumer market to understand the needs of customers and adapt the supplied services accordingly. The corporation can collect raw data from users in the market jurisdiction, pre-process and store the data in a data warehouse at a different location. Its employees can then apply self-developed and continuously revised and adapted algorithms (in the form of software applications) to the prepared data. Finally, the decision maker has to interpret and evaluate the gathered information and can use the knowledge to improve and sell services in foreign markets. The process of ‘Data Mining’ is unique for any company. Both firms that are founded in the digital sector and firms experiencing a gradual digital transformation can perform it. Central to the process of knowledge development is the involvement of specific people functions. Policymakers should refrain from any shortsighted tax legislation that intends to tax the usage of data as many firms already perform ‘Data Mining’ activities to various degrees.14
12
Similar critical considerations of this comparison can be found in Goldfein and Nguyen (2018); Marr (2018) and Olbert and Spengel (2019).
13
See Linoff and Berry (2011) and Witten et al. (2016) for an introduction to data mining techniques.
9 OECD (2019b), p. 6; OECD (2018a), pp. 24–26. 10 11
Olbert and Spengel (2019), p. 17. The reform options are discussed by Devereux and Vella (2018) and Schön (2018).
14
Ludwig et al. (2019), chapter 2.3.2. 19
