as a motivating factor propelling growth, under Lagarde the IMF has been much more vocal on the negative consequences of inequality for economic development, for example in the October 2017 issue of its flagship publication Fiscal Monitor, entitled Tackling Inequality (IMF, 2017b). Other international organizations have also begun to publicly question the costs of inequality. The OECD has a Centre for Opportunity and Equality, and published an important report with a revealing subtitle, Why Less Inequality Benefits All, indicating the organization’s shift in thinking (OECD, 2015). After several years in development and negotiation (Deacon, 2013), the International Labour Organization (ILO) successfully adopted a universal Social Protection Floor in Recommendation (No. 202, 2012) and through Convention (No. 102, 1952). ‘Reduced Inequalities’ is also the tenth of the seventeen explicit SDGs adopted by the UN in 2015. With these issues in mind the next section goes on to address the evidence about inequality.
Inequality between and within nations
The two central topics for this discussion of inequality concern both inequality between nations and inequality within nations. There are various sources for data on incomes and wealth across the world,1 and the picture generated by these data sources is very predictable. With respect to wealth per head, Switzerland tops the list at $537,600, followed by Australia at $405,600 and the US at $388,600. The figure for the UK is $278,000 (Credit Suisse, 2018, p. 80). At the other end of the scale, wealth per head is below $5,000 in much of Africa and South Asia. Similar evidence emerges if the focus is on annual income rather than wealth. The term income refers to a flow of money across a specific period of time, while wealth refers to the holding of assets (including, for example, property) which may, or may not, contribute to that income. Of course, holdings of wealth affect inequalities, but it is important to be aware what sorts of data are being compared. Among the richer member states of the OECD, 2015 data on GDP (per head) range between $100,000 (Luxembourg) and $18,000 (Mexico), with the US at $56,000 and the UK at $41,000. The OECD average is $40,000. The figures for many countries are much lower, at less that $1,000 per head. Looking at the combination of between- and within-country inequality, Bourguignon summarizes that:
The gap between the standard of living of the richest 10% of the world and the poorest 10% was above 90 in 2008. In absolute values, the poorest 600 million have an average of $270 in disposable income per year, while the richest have a standard of living above $25,000. (2015, p. 22)
The best comparative measure for within-country inequality is the Gini coefficient, ‘a single-number summary index of inequality ranging from 0 to 100 per cent’, which converts a whole distribution to a single number (Atkinson, 2015, p. 17; see also there his further exploration of its use). Atkinson’s country comparison of coefficient scores shows inequality to be lowest in Sweden, Norway, Iceland and Denmark. A range of European and East Asian nations feature in the middle of his list, including Taiwan, Japan and South Korea. Higher inequality is found in the UK and the US, with scores comparable to that of Russia. The poorest OECD member country, Mexico, is also the most unequal in that group. Below Mexico in Atkinson’s list are various Latin American countries, China and South Africa. One obvious pattern, evident in Atkinson’s data, is that, as far as the richer nations are concerned, distinctions between countries have much in common with the ordering of ‘welfare regimes’ (discussed in Chapter 3). However, what is also significant are the high levels of inequality within many low-income countries, which is another factor in the recent adoption of ‘inclusive growth’ as a key element of policy discussion for international organizations. The precarious growth of a ‘middle class’ in African countries in particular (AfDB, 2011) has a direct impact on both median incomes and the shape of social policy development.
An alternative to examination of nations in terms of Gini scores is a comparison using a poverty measure. There has been a long-running debate about how to define poverty, in which absolute measures have been challenged by alternatives that take into account national contexts (Townsend, 1970, 1993; and more recently Anand et al., 2010). It is still feasible, at the extremes, to identify poverty so severe that a quantitative label can be used, and countries can be compared in that way (such as an updated version of the $1 a day standard adopted by the World Bank in 1990). However, this can be an inflexible approach that pays little attention to social and cultural variations. Moreover, it does not reflect the substantial disadvantages that are suffered by those on low incomes in many richer countries. The problem, however, about responding to this critique is that in applying a simple measure that can be deployed comparatively it has to be accepted that such a measure is open to the allegation that it is merely ‘relative’ – that to describe someone in Sweden as poor has a very different resonance to describing someone in Mozambique as poor. Comparative statistics tend to use a relative measure of poverty (a percentage of median income) which provides an indication of differences within nations. It is important, however, to move beyond this and the Gini coefficient approach which focus on the overall income distribution to consider the more revealing question of ‘whether countries can achieve low rates of poverty at the same time as having high top income shares’ (see Atkinson, 2015, p. 25).
A concern of much contemporary analysis of inequality has been with shifts over time. Again, there is a need to be aware of a combination of changes between nations as well as within them. As far as the between nation relationship is concerned, the large gap that characterizes much of the world today is a product of a divergence that began as the industrialization process ‘took off’ in Europe and other nations in the global North. This increasing divergence continued until near the end of the twentieth century (Bourguignon and Morrisson, 2002). At that point a sharp reversal became evident both in the data on the Gini coefficient and on poverty rates, but this was a product of population growth in several key countries, not a reduction in the total numbers of those in poverty (Bourguignon, 2015, pp. 26–8). Much of this change was a product of exceptional economic growth in East Asia.
A different perspective is possible on contemporary events if changes that have occurred within the industrialized nations in the twentieth and twenty-first centuries are considered. Here, two phenomena have been highlighted. One is the strong impact of the two world wars, and their immediate aftermaths, when equalization processes occurred both with respect to the distribution of gross incomes and in the impact of taxes and benefits to further that effect upon net incomes. This trend was reversed from the early 1980s (see Piketty, 2014, for a general analysis, particularly featuring developments in France, the US and the UK). For contemporary advanced economies, Atkinson (2015, pp. 66–7) specifically notes ‘the role played by the welfare state in reducing income inequality and preventing any rise in market income inequality from feeding into inequality in disposable income’ in the period after the Second World War, and the weakening of that effect after the end of the 1980s (see also Förster and Tóth, 2015).
A pertinent OECD study of differences in levels of inequality across its member nations in the 2000s indicated that tax and benefit systems have become less distributive, but noted that net incomes are distributed less unequally than market incomes. The report (OECD, 2011a, p. 36) suggested that:
Public cash transfers, as well as income taxes and social security contributions, played a major role in all OECD countries in reducing market-income inequality. Together, they were estimated to reduce inequality among the working-age population (measured by the Gini coefficient) by an average of about one-quarter across OECD countries. This redistributive effect was larger in the Nordic countries, Belgium and Germany, but well below average in Chile, Iceland, Korea, Switzerland and the United States.
This is particularly important in the context of another key point also accepted by the OECD, that when the lowest income deciles fail to gain any benefit from economic growth, ‘the social fabric frays and trust in institutions is weakened’ (2015, p. 21). At the broadest level, research on widening inequality leads to general agreement that not only does it reduce economic growth, measured for example by GDP, but that it also presents serious social
