Hidden Inequality: How Much Difference Would Adjustment for Illicit Financial Flows Make to National Income Distributions?

Abstract

A recent innovation in measuring inequality is the incorporation of adjustments to top incomes using data from tax authorities, revealing higher inequality. The thesis of this paper is that the incorporation of estimates of income from illicit financial flows (IFF), reflecting untaxed capital, may be as significant to national inequality – but with greater variation across countries. We propose a method of adjusting national inequality data for illicit flows, and present preliminary results. These estimates suggest that untaxed illicit flows could be as important as (taxed) top incomes to estimates of inequality – highlighting the importance of improving estimates of underlying illicit flows.

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“…Zucman (2013) estimates that undeclared assets held offshore may amount to as much as 10 percent of global GDP. Forthcoming analysis (Cobham et al, 2015b) using IFF (rather than stock) estimates suggests that the resulting adjustment to national Gini coefficients for undeclared incomes may be of the same order as that found by allowing for declared incomes via administrative tax data.…”

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“…These illicit flows can also be linked to other SDGs, such as SDG-10 (reduced inequalities) or SDG-1 (no poverty). Financial crimes are empirical phenomena through which illegal wealth is accumulated, inequality increases and political turmoil grows (Bak, 2020; Cobham et al , 2017; Gilsinan et al , 2020; Hendriyetty and Grewal, 2017; Kukutschka et al , 2019; Neu et al , 2013).…”

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“…The concept of hidden inequality emerges from the economic theory on inequality (Cobham et al., , ; Schutz, ). This is the component of inequality which is not captured by current measures and datasets and it results in being the main driver of the multiplicity of messages on inequality trends and rankings.…”

“…Even if there seems to be a consensus that within-country income inequality has risen substantially, notably in most wealthy economies 2 (Alvaredo et al, 2017;Atkinson, 2015;Bourguignon, 2015;Milanovic, 2016;Piketty, 2014), data gaps, lack of data harmonization and differences in the definition of inequality indexes may lead to different (and possibly biased) conclusions (Ferreira and Lustig, 2015), ultimately preventing the formation of a shared understanding on the different dimensions of inequality and a coherent approach to tackling them. 3 The concept of hidden inequality emerges from the economic theory on inequality (Cobham et al, 2016a(Cobham et al, , 2016bSchutz, 1951). This is the component of inequality which is not captured by current measures and datasets and it results in being the main driver of the multiplicity of messages on inequality trends and rankings.…”