Europe is evolving continuously from a manufacturing to a service-oriented society. The digital revolution is also progressing. Are current accounting and tax laws armed and ready for these interrelated challenges?
In her Duet Interview with legal and data expert Dr Angelica M. Schwarz, Dr Caldarola, author of Big Data and Law, discusses accounting and tax issues in connection with data and data-driven business models.
The view concerning the market capitalization of important stock indexes illustrates our development from a classic industry to a society focussing on service, high tech and knowledge, a change which highlights data’s role as an intangible asset and strategic factor for success. The market value of equity capital in those indexes, mirroring the expectation of investors, exceeds the statutory declared book value of the equity capital by a number of magnitudes. Reasons for this discrepancy between market and book value include the use of undocumented, intangible financial assets as well as the outlook for the future by analysts. There is the assumption that the market capitalization of the US stock index Standard & Poor 500 demonstrates a clear trend towards an increasing share of intangible assets. When observing the market-to-book ratio of companies with information-based business models, one can expect that the discrepancy will keep on increasing. What is your opinion?
Dr Angelica M. Schwarz: In my view, we should be asking the question why certain intangible assets are not reflected in the balance sheets at their market values. The answer is simple: Because common accounting regulations do not provide for their inclusion. Let us consider the following example:
The International Financial Reporting Standards (IFRS) are based on the true and fair view principle. What does this mean concretely? The principle states that the annual financial statement should enable the reader or stakeholder to gain an insight into the actual position of assets and earnings of the company in questions. The financial statement should therefore mirror the transactions in a trustworthy manner, namely, to convey the actual financial situation of a company. In contrast to Swiss accounting regulations, the IFRS follows a narrow interpretation of the prudence principle. In general, prudence is defined as the exercise of caution when making judgements under conditions of uncertainty. Thus, under the prudence concept, one should follow a conservative approach in recording the amount of assets, and not underestimate liabilities. Even though the prudence principle is defined differently, both the Swiss accounting rules as well as the IFRS require further prerequisites when it comes to self-created intangibles. The reason for such reluctance is that intangible assets generated by the companies themselves have not passed a market test and are therefore deemed to be uncertain. Even though a valuation may indeed be possible (e.g., based on comparative figures from similar transactions), such assets have not been acquired on the market and, therefore, no direct fair market value (in the sense of a price) is available. Thus, individually generated intangible assets have to at least pass the research and to have reached the development phase before a capitalization is possible from an accounting perspective.
Once individually generated intangible assets have overcome this hurdle and can be reflected in the balance sheet, the question concerning under which value they should be listed remains open. The initial assessment of such resources is one of cost (e.g., production costs). Included are all attributable direct costs necessary for generating said asset. Herein lies the problem, in my opinion, because for the respective company the production costs are often lower than the actual intrinsic value of the intangible asset(s). This holds especially true if the Big Data architecture (once implemented) can be used over many years to reduce the company’s costs. I use the term “actual” deliberately. As mentioned in the beginning, does not the true and fair view principle require that the actual position of assets and earning of the company be reflected in the balance sheet? In my view, there is a certain discrepancy reflected in how the principle is adhered to.
Coming back to the question you posed at the beginning: Yes, there is a discrepancy in relation to the market-to-book ratio and the discrepancy will continue to increase if companies decide increasingly on digital business models. From an accounting perspective, digital assets generated by companies themselves (such as digital data of third parties (i.e. data subjects) collected and processed by the company itself) represent intangible assets and as long as they can “only” be priced at production cost, we will continue to observe a discrepancy between market and book value. If this is in line with the true and fair approach and if the annual financial statement can still serve as a reliable basis for capital market participants when making business decisions is another question.
Does this discrepancy also apply to the accounting of data?
This is especially true when talking about whether or not data can be listed as a business asset in the balance sheet. In my doctoral thesis I present the view that data – whether individually generated or purchased – may fulfil the conditions as provided by the IFRS in order to list them on the balance sheet. The implementation of a Big Data project can be very expensive. On the one hand, the company requires the necessary IT-infrastructure (unless a cloud infrastructure is used) and, on the other hand, the crucial know-how. Raw data needs to be transformed into a certain format so that it can be analysed. This “refinement“ process takes on a certain significance if data come in different formats from various sources and entities of a multinational and need to be reduced to a common denominator. The cost for such a Big Data architecture is, from what I have seen, likely to be far below the actual value of the data. The initial recognition according to IFRS for self-generated data is therefore cost-oriented and not benefit-oriented. The recognition of fair market value will in most cases not apply because an active and identifiable market is missing. Trade with data exists but it is questionable whether such trade can already be identified to a sufficient degree. This, of course, may change in the future and I would not exclude that at some point the trade of data similar to marketable securities.
And what about acquired data?
The initial recognition of acquired data is based on the acquisition costs. The situation here is different because the data in question have, in this particular constellation, overcome a market test and, among independent third parties, the purchase price corresponds usually to the fair market value.
In your opinion, data can fulfil the requirements for being listed on the balance sheet from an IFRS perspective. Some argue, however, that data cannot be at the disposal of a company and, thus, can hardly be substantiated. What is your view?
One should understand that the IFRS (so too the Swiss regulation) follow an economic approach when dealing with the question whether or not a certain asset is at the disposal of the company. The requirement that data must be at the disposal of the company does not necessarily postulate legal ownership. Instead, this prerequisite is to be understood as an economic power over the data, which can be determined based on criteria such as risk, opportunity, benefit, liability, etc. Furthermore, in order for data to be considered as being available for exploitation by a company, the company needs to have physical or virtual access to the data pool. However, it cannot be presumed that the data carrier itself must also be controlled at some level by the company in question; otherwise, data that is stored in an externally rented cloud could never be allocated to a data-driven corporation using this kind of a tool.
Since economic power and not necessarily legal power is the relevant aspect, the question whether legal ownership of data is even possible is of a secondary nature. Legal ownership can, however, certainly be used as an indication of control.
Data also carries risks. The legal processing of data has not been fully clarified. How should companies handle such risks when processing data and, at the same time, infringing upon data protection law? I am thinking of erasure claims in accordance with data protection laws.
Data is reported on the asset side on a balance sheet whereas risk is listed on the liability side. In my view, it is important to understand that a company collecting and analysing data can still benefit from such activities even though they may infringe upon data protection law regulations. That, of course, does not mean that I give a company carte blanche to do whatever they desire with data. What I would like to point out is that the question whether or not data can be listed on the balance sheet needs to be evaluated independently of whether and to what extent the company should make provisions in order to cover possible future liabilities. The claim for erasure, however, may be considered when evaluating whether and to what extent a value adjustment seems necessary.
Multinationals often implement their big data strategies for the whole group. How do you assess data transfer among company subsidiaries?
With regard to affiliated companies, the question arises whether and which price should be paid.
The question of “whether” can be answered more easily. Tax law requires that a transaction between affiliated companies needs to be realized at arm’s length. This means that the company must pay the price that a third party would have paid. The logic behind this is to prevent profit shifting. Otherwise, a subsidiary in a low-tax country could be tempted to make a high profit by selling its data at a price beyond market value. If the contracting party is a subsidiary in a high-tax country, it could have an interest in paying such a high price, as the company can expense the costs via the profit and loss statement which, in turn, reduces its tax base. The reality today is that data transfers among group companies exist while, however, usually no price at all is being paid. If we treat data as an asset, why should a company not compensate the data it receives from other group companies like any other asset or service as well? That might sound negative at first glance but there are also opportunities – namely where tax planning is concerned. Surprisingly, this topic has not been of great significance either for companies or for tax authorities. The question remains for how long.
If the “whether” has been settled, then the next question is how the price should be determined. Here a market-value-orientation is allowed – if not required. Different to the accounting regulations, an actual value needs to be identified. This is the point where the interplay between tax and accounting regulations becomes very interesting: A price assessed for tax reasons may have a direct influence on the accounting of a company. My view here is that any prices which are in accordance with the arm’s length principle – although they are paid among affiliated companies – should be accepted as the purchase price in an individual financial statement.
My favorite quote is:
“The art of collecting taxes is to pluck the goose without it screaming”.Maximilien de Béthune, Duc de Sully
Dr Schwarz, thank you for sharing your insights on data and accounting regulations, transfer pricing and tax.
Thank you, Dr Caldarola, and I look forward to reading your upcoming interviews with recognized experts, delving even deeper into this fascinating topic.