In my experience, the hallmark of a good article is that, after struggling through a few close reads, I eventually (at times somewhat begrudgingly) conclude I learned something new and valuable. The hallmark of a great article, on the other hand, is when I reach the same conclusion but after a single, almost effortless feeling, read. The difference is a precision and clarity in writing, structure, and organization that only the confidence instilled from a deep understanding of a subject affords. Yet at the same time a small part thinks to myself – “it seems so obvious, why didn’t I think of it?” But of course, to paraphrase a famous movie line, “if I really had come up with the idea, then I would have written it.” But, as I eventually admit to myself, I didn’t.
Such was my experience reading When Data Comes Home: Next Steps in International Taxation’s Information Revolution (“When Data Comes Home”) by Shu-Yi Oei and Diane Ring. Oei and Ring are frequent co-authors, writing on subjects ranging from taxation of the sharing economy like Uber and AirBnB, to the role of large scale financial information leaks like the Panama Papers, to the impact of the Tax Cuts and Jobs Act on reshaping the workplace environment. I mention this only to emphasize what emerges as the particular strength of Oei and Ring’s collaborations – they combine backgrounds and methodologies and apply them to areas of common interest to uncover patterns or trends that otherwise might remain hidden. When Data Comes Home represents another successful example.
The article begins with a survey of recent developments in information sharing in the international tax regime. Importantly it rejects a myopic focus on multinational efforts such as the OECD Harmful Tax Competition project and the Base Erosion and Profit Shifting (BEPS) projects and instead also incorporates unilateral efforts such as the US Foreign Accounts Tax Compliance Act (FATCA), bilateral agreements such as Tax Information Exchange Agreements (TIEAs), and even data leaks such as the Panama Papers. While the article refers to these as Historical Events, in actuality the nominal survey of the past cleverly foreshadows the conceptual taxonomy to come, which the article refers to as Intersecting Forces. Allowing the reader to discover how the Intersecting Forces seem to emerge inexorably from the Historical Events proves both effective and compelling.
The article would make a valuable contribution if it stopped there, but it moves on to take the important but challenging step of incorporating state-level interests and strategic interactions into the substantive taxonomy developed earlier. While I admit to potential hyperbole, I believe this is the first article I have read (including my own) to pull off this move successfully. The article deftly avoids two of the most common traps when doing so, neither resigning itself to a slippery slope of the inevitable failure of any international tax regime overwhelmed by the insatiable demands of each country’s domestic politics nor proposing another one-size fits all normative solution if countries agree. While it may sound trivial at first, these common traps matter because an issue is one of international tax only if there are two or more sovereign states with potentially valid but competing claims to tax an item of income or taxpayer. Absent two or more states, an issue nominally or appearing to be one of international tax ultimately collapses into one of domestic tax.
That said, no matter how persuasive the article, one might be skeptical of their ultimate conclusion that data produced by international tax reporting rules and agreements will reshape domestic law. In particular, I am not fully convinced that the revolution they describe is a function of reforms in the international tax regime instead of a symptom of introducing Big Data into law more generally. For example, many proponents of big data claimed it could help root out implicit bias from hiring decisions by replacing human managers, many of whom may not identify or acknowledge their implicit bias, with algorithms that mine data which they claim would only include factors relevant to job performance. While this is appealing on its face, unfortunately the problem is that it turns out Big Data also incorporates any implicit or systemic bias within the system generating the data. Much like TwitterBots that quickly become racist in response to the data they receive on Twitter, neutral algorithms can spit out biased results if the society in which they are built is biased.
If indeed the effect identified in the article turns out to be caused by the rise of Big Data and not the other way around, I suspect the problems of Big Data in other areas may soon follow as well. By drawing a bright line between “domestic politics” and “global information sharing” I question whether the article runs the risk of missing whether and to what extent one could be influencing or biasing the other or whether a confounding factor exists. In fairness, however, this ultimately is an empirical question implicated by but not directly within the scope of this article.
Even if my empirical concern proves correct, if the primary reason it came to mind in the first place was the taxonomy of the article itself, that only further proves the importance of the contribution. Any article that can survive the scrutiny of its own analysis in this manner is robust enough to survive any scrutiny I could apply to it. In this respect, When Data Comes Home is a success.
Editor’s Note: Jotwell’s Contributing Editors choose what articles they review. Shu-Yi Oei had no role in the editing of this review.