Taxing Data-Driven Business: Towards Data Point Pricing

Abstract

Datafied business models avoid traditional taxation in many respects since data, being among the important value drivers of datafied business, are neither adequately priced nor accounted for in the firm’s accounts. From a tax perspective, ignoring the value of data is inconsistent with the data economy paradigm, where it has been claimed that “data is the new oil”. The stringent legislative response to datafied business models the authors propose herein is to assign a financial value (a “price”) to each data point collected, herein referred to as “data point pricing”. If the raw material (data) is thus priced, its use and transfer can be traced by applying traditional accounting methods. Certainly, data point pricing is no panacea; the inherently political question of who holds taxation rights in a cross-border context remains. Yet, data point pricing would make the locus of an important part of value creation transparent and facilitate the application of traditional tax assessment and transfer pricing methods to data-driven business models. As well as bringing about taxable measures, data point pricing yields beneficial side effects in the fields of antitrust law, financial regulation, data protection, anti-money laundering and criminal enforcement. Data point pricing thus has value even where taxation rights are allocated by way of a multilateral arrangement on the basis of the OECD statement of 1 July 2021.