Statistical Measurement of Illicit Financial Flows in Sustainable Development Goals: Tax Avoidance by Multinational Corporations

Illicit financial flows (IFFs) threaten countries' ability to achieve the Sustainable Development Goals (SDGs). Progressing on the IFFs target is thus crucial, as is the ability to measure achieved progress. In this paper we explore how to best statistically measure tax avoidance by multinational corporations (MNCs) as the SDGs IFFs target. Our main research question is how the best available methods for the statistical measurement of tax avoidance by MNCs reconcile with the Balance of Payments (BoP) statistics. We answer the research question using a combination of approaches, arriving at three main findings. First, we show that the three leading methods for estimating tax avoidance by MNCs are closely related to each other, theoretically as well as empirically. Second, the profit misalignment method applied to the country-by-country reporting (CBCR) data of large MNCs emerges as the most suitable method from a critical review of existing approaches and a range of available statistical data sources. Third, in their current state the BoP statistics are not suitable for estimating tax avoidance by MNCs for many countries due lacking country coverage and missing data. On the basis of our findings, we recommend piloting the use of confidential MNC-level CBCR data to estimate tax avoidance by MNCs as the SDGs IFFs target.