Why Better Statistics are Needed
by Natasha Stotesbury and Danny Dorling

A growing body of evidence points to high and rising inequality as one of our current decade’s most important global issues in light of the far-reaching implications increasingly associated with it. Recent papers detail how inequality is a source of extensive negative externalities: obstructing economic growth, hampering poverty reduction and generating large social and environmental costs (OECD, 2015; Dabla-Norris et al., 2015; Holland et al., 2009). It is thus unsurprising that President Obama described the trend towards widening inequality as the “defining challenge of our time” and, in September 2015, the United Nations enshrined inequality reduction in the list of Sustainable Development Goals.

Efforts to tackle inequality suffer from a lack of understanding certain statistical issues. This article highlights two weaknesses typically marrying studies of income inequality: ambiguous indicators and inadequate data. The aim is to underscore both the limitations of current inequality analysis and to highlight the significant relationship suggested between inequality and various environmental, educational and health-related outcomes nevertheless. We hope that this will, in turn, propel further, more rigorous, statistical research to overcome the present weaknesses, leading to a better understanding of the possible implications of income inequality.

We make the case for a clear and analytically useful indicator of income inequality – termed the ‘1st-to-10th ratio’. Calculating this ratio across twenty-five of the wealthiest economies shows that substantial geographical variations in inequality levels arise even between affluent economies. Secondly, despite enduring data limitations, coupling this measure of inequality with data on social outcomes gives rise to intriguing associations. These are in line with previous research claiming that equality improves the quality of life for almost everyone in a given population (Wilkinson, 2005; Wilkinson and Pickett, 2010; Pickett and Wilkinson, 2015).

A plethora of indicators have been developed to measure income inequality (Longford, 2014). Many suffer from significant drawbacks. For example, the Gini coefficient attaches more weight to income transfers affecting middle-income groups and hence is relatively insensitive to changes at the extremes of income distributions (Atkinson, 1970). This matters: not only is the Gini implicitly including a normative judgment, but it turns out that focusing on the tails of income distributions is crucial for understanding current inequality trends. In short, changes in the income share of the best-off and worst-off appear to be the most important potential driver of both recent changes in affluent nations’ inequality levels and of the close connection with particular outcomes. Inequalities within the population as a whole may matter less because unusually the middle 50% of earners tends to consistently capture approximately 50% of gross national income (Cobham et al., 2015).

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