Inequality, SDG10 and the financial system

Non-technical summary

One of the Sustainable Development Goals, number 10, is about reducing inequality within and between countries. This paper argues that the existing structure of the (international) financial system is, for various reasons, one of the determinants of (growing) inequality. This should receive more attention.

One of the Sustainable Development Goals (SDGs) is number 10: Reduce inequality within and between countries. Like many other SDGs, it is ambitious and ambiguous. Is there an ‘optimal’ inequality, as many economists claim? What are proper indicators of inequality and should it be on income or wealth or both? Among the many questions, one seems to be lacking: what is the role of the International Financial System (IFS) in relation to inequality? Careful reading of the SDGs shows that the role of finance is confined to a couple of statements about strengthening the capacity of financial institutions and their regulation (SDGs 8 and 10), expanding financial services and assistance (SDGs 8, 11, 13, 15 and 17) and reduce illicit financial flows (SDG 16). But recent investigations provide evidence that it is the very set-up of the IFS which is contributing to the inequality in income and wealth in the world – and its increase.

Conventional wisdom among economists is that credit creation tends to reduce income inequality, as it facilitates the realization of business opportunities. This is borne out by several econometric analyses of aggregate data on finance and inequality. For instance, Beck et al. (2007) find, in an econometric analysis of incomplete sets of aggregate data for the period 1960–2005, that about one-third of changes in inequality measures can be explained by the change in Private Credit (the other two-thirds stemming from aggregate GDP growth). However, apart from the need for an update, the use of ‘total credit to the private sector’ as a proxy for financial development is questionable. Not surprisingly, other and more recent analyses show mixed findings (e.g. Kus, 2012).

Bezemer and Samarina (2016) find that, once the credit is divided into bank lending for non-financial economic sectors and bank lending to real-estate markets in the form of mortgages, the data tell a different story. They start their analysis with the hypothesis that the debt shift, that is, the shift in bank credit allocation from nonfinancial business towards household mortgages, is an important factor in explaining income inequality. It turns out that mortgage household credit correlates significantly positively with the income inequality measured as Gini-coefficient for a sample of 40 countries in the period 1990–2013, whereas business credit correlates significantly negatively. Because in this period the credit for non-financial sectors has hardly risen whereas credit for household mortgages has nearly doubled as fraction of GDP (the above-mentioned debt shift), there is clear evidence that the money creation by commercial banks has contributed to increasing inequality.

A second line of research offers a more qualitative, inference-based analysis of the processes in the IFS in the last decades (Hodsgon, 2013). Two mechanisms for the relation between credit creation and inequality are proposed. A first, positive feedback loop is: money creation by commercial banks for mortgages causes real-estate prices – mostly dwellings – to increase, which leads to high profits for investors who, in turn, reward excessively paid fund managers to spur further credit creation. A second, also positive feedback loop stems from the higher debts of households which decrease discretionary income and stimulates, given a keeping-up-with-the-Jones’ kind of imitation, credit creation. Both loops, in combination with other psychological mechanisms, tend to accumulate money in the hands of small groups of bankers, managers and CEOs. Both mechanisms are, implicitly, part of the econometric analysis of Bezemer and Samarina (2016) and are corroborated in a series of illustrative simulation experiments (van Egmond & de Vries, 2019).

Another set of statistics suggests that inequality has also increased due to yet another, third mechanism: tax evasion. Very wealthy people and large corporations have ample opportunity to escape tax payments. Apart from the moral aspect of benefitting from public services without due contribution, the mechanisms of tax evasion by wealthy individuals and the profit-shifting by multinationals is another determinant of rising income and wealth inequality, as Zucman (2015) has shown in his book Hidden Wealth of Nations. In Europe alone, tax evasion by individuals may amount to a lost tax revenue of $75 billion/year. US corporations managed to book 20% of their profits in the US in offshore tax havens and even 50% of profits made outside the US. Indeed, the Netherlands plays with the thousands of intermediaries in its jurisdiction an important role in facilitating these huge money transactions, for corporations as well as for wealthy sportspeople, artists and dictators (van Geest et al., 2013). According to Zucman, it's high time to start up a World Financial Register for more transparency and for a reform of the corporate tax regime in the US. It is a similar but more activist call for change than Shiller made in his 2012 book Finance and the Good Society (Shiller, 2012).

One general aspect in all three research strands is that basic asymmetries in access to the financial system, in terms of understanding and affordability, systematically put the poor at a disadvantage. This is true for the low-income consumer to whom intransparent loans are offered, for the millions in poor countries where elites are facilitated in siphoning off state revenues, and for the workers in a globally competitive world who are unable to maintain their share in the rise of productivity (Kunieda et al., 2014; Panico & Pinto, 2018).

Given these insights, how is it possible or defensible to formulate a lofty goal about reducing inequality without a thorough analysis and associated policy recommendations of the ways in which the existing structure of the IFS operates – and aggravates inequality? A good start would be to propose more relevant research, with an explicit focus on socio-economic feedbacks and on the power position of the banking sector, and indicators for SDG number 10, which accounts more explicitly for those dynamic and political features. Most other SDGs will benefit in turn from such an endeavour, as they are in one way or another related to the structure of the IFS.