Impact Based Taxation

Taxation of firms is a complicated matter that often lacks transparency and stirs controversy. We aim to address the inconsistencies and lack of transparency in taxation policies, monopolization, market centralization, and biases against startup firms in economic systems. Digital firms get taxed disproportionately less than their physical counterparts: a prime example of this is Amazon. Relative to its size, Amazon pays about 1% of the tax that Walmart pays. We introduce a radically different approach to taxation by taxing a firm based on its direct and indirect impact on the economy. Such apparent inconsistencies are partially due to the fact that tax is based on the firm’s balance sheet, which allows for intricate tax optimization schemes. However, firms do not act in isolation, but instead their actions affect other firms and the economy as a whole. With the rise of big data, it has become possible to track firm interactions beyond legally required self-reported information, and we demonstrate how such knowledge may be turned into self-consistent taxation schemes that lead to more balanced growth. This work illustrates methods for effective network interventions and the design of strategies to affect the evolution of socioeconomic networks.