TisEM - Jochem de Bresser

Arthur van der Linden

  • TiSEM - Arthur van der Linden

    Arthur van der Linden

    Lecturer Fiscal Institute Tilburg

    “It is refreshing and challenging to connect different disciplines, such as data sciences, which ‘float’ on mathematics and statistics, and taxation.”

Arthur van der Linden works as a Business Analyst at the Dutch Tax and Customs Administration and is affiliated with the Fiscal Institute Tilburg as a course coordinator and lecturer of the course Introduction to Tax Technology. Arthur van der Linden (Department of Tax Law) was named the winner of the sixth edition of the Stevens Prize on January 22.

What is the main goal of your research?

My aim is to approach taxation not solely from a legal angle, but from a mathematical and statistical perspective, too. The focus of that multidimensional approach is on how income tax and allowances interact. Tax facilities are based on calculation rules that determine how much someone has to pay in taxes and/or how much they are to receive in allowances. These calculation rules I have converted into computer code, and it is on that code that the income tax and allowances micro-simulation model runs. The model can help determine, for each tax year and for each income and family situation, the effects or potential effects of the relevant tax facilities.

There appears to be this widespread and persistent idea that the tax system, because of its sheer complexity, defies modeling. I don’t subscribe to that view. The law impacts on each of us. And it should therefore be possible, using the relevant facts and circumstances, to determine what that impact is. That is the paradox of the micro-simulation model: everyone is special, yet a tax model can capture all. Its ability to factor in a variety of facts and circumstances sets the model apart from existing statistical models. To paraphrase our head of state in his address on the occasion of opening the 2019 parliamentary year: "not a single life conforms to the median of a statistical model."

How does your research contribute to understanding and solving societal problems? 

The current income tax and allowances system attracts societal as well as political attention, and I hope my findings can help improve or possibly even renew it. For example, using the micro-simulation model I can very clearly show the effects of income tax on and between certain demographics. What effects does the law currently have on the financial capacity of the citizens concerned? I also evaluate proposed policy measures, for example by clarifying and reflecting on the effects. And I try to offer suggestions for practicable improvement and simplification of the income tax and allowances system.

I am also keen to pick up on current events, and one way of doing that is to evaluate proposed policy measures. Consider, for example, the proposal in 2020 of the Borstlap Committee on Regulating Work to tax employees and entrepreneurs more equally, because entrepreneurs pay less tax than employees do on the same amount of earned income. But how much less are entrepreneurs currently paying in taxes? It was gratifying to be able to show in one figure or one key table what the exact tax burden differential is. And that allows non-tax specialists to engage in more deeply understanding the whys and wherefores of this societal and political debate.

What motivates you?

It is refreshing and challenging to connect different disciplines, such as data sciences, which ‘float’ on mathematics and statistics, and taxation. If as a result findings can be presented that may be relevant to an improved tax system, that is deeply gratifying.

Another aspiration I have is to more closely connect the disciplines of data sciences and taxation. Fundamentally, these two disciplines are very similar: there are facts and circumstances (“input variables”) that must be assessed on the basis of a regulatory framework (“the formula”) to determine the legal effect (“the result”). At the same time data scientists and tax specialists often follow completely different lines of reasoning: 98% accuracy can be truly extraordinary from a data sciences perspective, but decidedly underwhelming from a taxation perspective.

Who inspires you?

Anyone and everyone, potentially. Each one of us has a unique perspective and unique qualities. I seek to learn from all of these perspectives and qualities. That is why many people inspire me, and I would short-change many people if I were to mention only a few here.