Work

Billionaires and shaping the norm of taxing high-net-worth individuals : a discourse analysis of the United Nations negotiations to develop a framework convention on international tax cooperation

Year: 2026
Type: dissertation
Abstract: As the world faces immense wealth inequality at both a country and individual level, the United Nations is holding negotiations to develop a Framework Convention on International Tax Cooperation. One of the mandates for these negotiations is specific to taxing high-net-worth-individuals (HNWIs). Due to limited data on individual wealth, I will focus on billionaires, a specific class of HNWIs that control immense wealth and power yet are generally understudied in political science. This research aims to determine how states with different income levels, number of billionaires per capita, and number of billionaire politicians are shaping the discourse on international tax cooperation to tax HNWIs. I conduct inductive discourse analysis on the first four negotiating sessions and states’ inputs for these meetings to develop a coding scheme of states’ efforts to either strengthen or weaken the norm of taxing HNWIs using NVivo to support the qualitative text analysis. Through this work, I find that, in general, higher-income states are successfully shaping the discourse to hollow out the norm of taxing HNWIs. In contrast, lower- income states are advocating for a stronger norm regarding taxing HNWIs. States with more billionaires per capita and billionaire politicians are mainly higher-income, while states with fewer billionaires per capita and billionaire politicians are predominantly lower-income, making it difficult to draw conclusions on the influence of these individuals. While the negotiations are only halfway over, the norm of taxing HNWIs has already been significantly diluted. Without a significant shift in dialogue, the systemic inequalities in the international financial system are likely to be entrenched in the final Framework Convention.
Language: English
Subfield: Accounting
Open Access status: green
OpenAlex ID: W7208771471
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