Redesigning the Dividend Withholding Tax & Closing the Loopholes

In October 2018 a cooperation between European journalists known as CORRECTIV uncovered the largest ever European tax fraud in the cum-ex files. Cum-ex, and related strategies known as cum-cum and cum-fake (henceforth cum-schemes) are schemes that allow investors to avoid paying the dividend-withholding tax (DWT), or to receive excessive tax reimbursements. The revenue loss in Europe is not yet known, but estimates suggest the damage exceeds ?100 billion. Despite diligent reporting by journalists, there remain many gaps in our knowledge that can best be answered by academic economists trained in econometrics and optimal taxation. TAXLOOP will collect and analyze a financial and regulatory database for at least 17 European countries that gives an overview of the current state of DWT. We will use the data to find out which countries have been affected by the fraud and how much revenue is lost. TAXLOOP will also provide policy advice on how to design the DWT to make it more robust against tax avoidance. Finally, we will take a step back and reevaluate what role the DWT should play in the overall tax system. The tools we use in our analysis are econometrics, machine learning and optimal taxation. TAXLOOP will contribute to existing knowledge by being the first to provide a European-wide analysis of the impact of the cum-schemes, and by empirically evaluating the regulations that make a country vulnerable to them. We also provide the first optimal DWT-model that will allow policy makers to better design their DWT.

In October 2018 a cooperation between European journalists known as CORRECTIV uncovered the largest ever European tax fraud in the cum-ex files. Cum-ex, and related strategies known as cum-cum and cum-fake (henceforth cum-schemes) are schemes that allow investors to avoid paying the dividend-withholding tax (DWT), or to receive excessive tax reimbursements. The revenue loss in Europe is not yet known, but estimates suggest the damage exceeds €100 billion. Despite diligent reporting by journalists, there remain many gaps in our knowledge that can best be answered by academic economists trained in econometrics and optimal taxation. TAXLOOP will collect and analyze a financial and regulatory database for at least 17 European countries that gives an overview of the current state of DWT. We will use the data to find out which countries have been affected by the fraud and how much revenue is lost. TAXLOOP will also provide policy advice on how to design the DWT to make it more robust against tax avoidance. Finally, we will take a step back and reevaluate what role the DWT should play in the overall tax system. The tools we use in our analysis are econometrics, machine learning and optimal taxation. TAXLOOP will contribute to existing knowledge by being the first to provide a European-wide analysis of the impact of the cum-schemes, and by empirically evaluating the regulations that make a country vulnerable to them. We also provide the first optimal DWT-model that will allow policy makers to better design their DWT.