The politics of inequality; who is measuring what and why?

Deborah Hardoon Inequality, Methodology, Real Geek

Our latest real geek instalment explores different measurements of inequality and how our understanding of the data they produce is crucial to the issue as a whole.

There is no one ‘right’ way to measure anything. That’s what measurement is; one way to quantify out of many.  There can of course be a wrong way and plenty of statisticians work on identifying measures that are just plain wrong.

The paper we presented at the Development Studies Association conference is not just about the diversity of ways to measure economic inequality and what this says about the economy. It also discusses the rationale for using and communicating specific measures over others, particularly in the development context. It asks: who is measuring what and why?

Inequality of opportunity and inequalities based on forms of discrimination such as gender have long been an issue of concern in international development. Inequality of economic outcomes, however, has not traditionally been a priority, notwithstanding a concern for poverty reduction in absolute terms. This is due to an unwavering acceptance of the need for growth to fuel development and a belief in meritocracy.

Inequality of economic outcomes has not traditionally been a priority

Globally, the extreme poverty line is fixed at $1.90 (USD) a day, which frames thinking around meeting an absolute level of basic needs and doesn’t consider the unequal spread of incomes. Despite this, it is increasingly evident that economic inequality matters for poverty reduction and development.

Economic inequality can’t by captured by a single metric and each of the different measurements reveals a different story. It can be measured by analysing wage differences and the wealth distribution, as well as incomes – each reveal a very different story. Even if we stick with ‘income’ there are different meanings, for instance, depending on whether you take into account taxes and transfers.

 Inequality of economic outcomes has not traditionally been a priority 

After choosing what type of income inequality to look at, there are always multiple ways to cut your data. You can calculate the Gini coefficient, which looks at the whole distribution, use the Palma (or similar) ratio relating different income groups or the share held by the top 1%. Different methods place very different emphasises on the distribution.

Let’s look at some income examples where different measures tell different stories.

Brazil, a relative success story for reducing inequality

Over the past decade, the incomes of those living in poverty have been growing faster than those of the rich, and poverty and inequality, by most relative measures, have been falling.

The red bar on the chart below shows the real incomes of the bottom 40% of the working population. Although low, it almost doubles in size between 2004 and 2014 as this group becomes better off. The blue bar represents the highest income earners.

Their incomes have also been increasing but because the percent growth rate is lower than that of the poor, inequality is said to be falling. However, look at the green bar; the difference in the difference in absolute Brazilian Real terms continues to increase as the highest decile pulls away from the bottom. The gap between the rich and the poor is still growing.

This data from the World Bank is based on consumption data from surveys conducted every few years. The last survey in 2012 calculated a Gini coefficient of 42. In contrast, the data collected by the Ugandan Bureau of statistics estimated the Gini at 39.5 in 2012/13, equivalent to the World Bank’s 1996 data – an important difference in inequality terms. Here the choice of underlying data (as opposed to how we cut it) can generate contrasting stories.

Now let’s look at some examples of organisations that use specific measures and their reasoning.

Sustainable Development Goal (SDG) 10 is to reduce inequality within countries. The indicator proposed to measure this is the income growth of the bottom 40% compared to the average. When the incomes of the bottom 40% grow faster than the average, relative inequality falls.

Last year the World Bank found, that the incomes of the bottom 40% need to grow at least 2% faster than average incomes to eliminate poverty by 2030. But this indicator doesn’t include any measure of what is going on at the top of the distribution, nor the absolute gap between the poorest and average (or highest) and also ignores starting levels of inequality.

Oxfam has published multiple analyses of wealth inequality, including the 62 statistic. The paper is based on wealth distribution data from Credit Suisse and Forbes’ Rich List.  We chose this particular type of inequality because of the importance of wealth in the poverty story.

Wealth accumulation at the top of the distribution creates power and influence whilst lack of wealth reduces people’s ability to react to shocks. In addition, wealth is more unequally distributed than income.

As with any metric, this one also has limitations. Firstly, estimates on wealth are just that – data is patchy, wealth is hard to assess as it comes in diverse forms and we tend to underestimate it at the top. Secondly, credit markets, net wealth and incomes are not always correlated – think of the Harvard graduate with a high salary who is still paying off student debt (on the global scale, the absolute exception).

Descriptive measures of inequality are clearly deeply entangled with political and economic narratives. Why, for instance, are we so enslaved to the Gini index as opposed to more intuitive measures? Do we care to know about the absolute distance that separates us from the top? How can data and methods reduce the margin of error of inequality estimates?

There is nothing false about any facet of measuring inequality. Rather, recognising what is behind indicators in terms of data and the intentions of the communicator is essential to understanding both the economics of inequality and the underlying politics.

Related reading:

Graphics Credit: Oxfam, using data from the World Bank, Ugandan bureau of statistics and Brazilian bureau of statistics.


Laila Barhoum