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Caleb Myers
Caleb Myers

Measuring Inequality Fixed



A radical new way of measuring inequality, based on the work of the distinguished economist and philosopher Amartya Sen, has been developed as part of a collaboration between academics at LSE and the School of Oriental and African Studies and practitioners at Oxfam.




Measuring inequality



The MIF, along with accompanying toolkits, provide all the resources necessary to measure, analyse and take action on multidimensional inequality. The resources are free to use and the MIF can be easily adapted to suit different countries and parts of the world.


Recent research has shown the usefulness of nighttime light (NTL) data as a proxy for growth and economic activity. This paper explores the potential of using luminosity at night, recorded by satellite imagery, to construct measures of inequality. We develop a new methodology to construct a Gini index for each country using the nighttime light per capita over millions of small pixels. To assess the usefulness of our procedure, we check the correlation of our measure with the common factor extracted from the analysis of several Gini indices calculated using traditional data sources. Finally, we show two specific applications of our methodology: the calculation of within and between inequality across regions and ethnic groups.


Over the last decade, the world has seen increasingly bitter political debates around growing levels of inequality. According to a growing body of research, effectively addressing this trend requires a more rigorous toolkit to measure the varieties of inequality societies face and the different consequences these types of inequality may have.


At a recent Policy Research Talk, World Bank Senior Advisor Francisco Ferreira delivered a wide-ranging overview of the theory and empirical evidence on inequality of opportunity, taking his audience on a journey starting with the philosophical foundations laid down by John Rawls and Amartya Sen and ending at the cutting-edge of machine learning.


Zooming out to a broader view, Ferreira also compiled the work of multiple researchers to compare the extent of inequality of opportunity across 51 different countries around the world. This broader view produced a larger range of estimates of the minimum contribution of circumstances to income inequality, ranging from 3 percent in Norway to 40 percent in Mali.


While this first wave of research helped lay important groundwork, it has limitations, said Ferreira. The fact that it only identifies the minimum contribution of circumstance to inequality means there is still much uncertainty about just how significant inequality of opportunity really is.


According to Ferreira, these advances in measuring inequality of opportunity have finally reached a stage where they can make a difference in concrete policy issues. Two examples include a better understanding of the impact of Mexico's Oportunidades program on children's future opportunities and the impact of child care reform in Norway.


Another area with real-world consequences is machine learning. The role of algorithms in decision making is rapidly spreading to everything from online ad delivery to who is granted parole, and researchers are already using inequality of opportunity measurement to find ways to ensure machine learning tools produce non-discriminatory outcomes.


But perhaps even more importantly, measuring inequality of opportunity is helping break a logjam in a longstanding debate about whether inequality has a negative or positive impact on growth. In a study of differences in economic growth between U.S. states, researchers found a negative relationship between inequality of opportunity and growth and a positive relationship between inequality of effort and growth.


Stefanie Stantcheva, economist at Harvard University, founded the Social Economics Lab to study inequality, our feelings about it, and how policies influence it. She says when we estimate how much money our colleagues make or how much taxes impact us, we are often very far off from the truth. Her research also shows that our misconceptions are often linked to political beliefs. She argues that we need to be more aware of the realities of inequality if we want to create better economic opportunities.


STEFANIE STANTCHEVA: Yeah. So when we look at inequality and trying to devise better policy solutions, we cannot just study what the effects of policies are, which is of course very important, but we also need to understand how people think about them. In a sense, when people decide what policies they want to support, what policies they want to have and accept, there are a lot of perceptions, a lot of concerns that go into that, and understanding those is really critical in order to actually be able to implement good policies.


STEFANIE STANTCHEVA: I think the core results are probably quite generalizable. So Denmark is certainly a more equal country, and it has some different policies in place than the US, but this basic pattern, we have reasons to think that it would also hold in the US, and it would probably have similar strong implications. Your position among others actually really shapes your views on fairness and on what you want to do about inequality.


When it comes to tackling the challenges of inequality, are we asking the right questions? Or, for that matter, measuring the right indicators? Angus Deaton, winner of the Nobel Prize in economics in 2015, says no, and it is masking a public health crisis.


Inequality in the US is higher than in most developed countries. Many people attribute the higher inequality to policies favouring the rich. Worsening inequality in the US can be explained by a range of factors, including tax policies that favour the rich, education policies that dampen the opportunities for intergenerational mobility (see Section 19.2 of The Economy), the skill-biased technological change that raises the incomes of workers with skills complementary to ICT and reduces that of workers with skills substitutable by ICT, and the decline of labour unions.


Share of income going to the top 1%: This measure looks at the high end of the income distribution (the right tail). Larger values indicate that the very rich have a larger share of the income, and that there is therefore more inequality between the very rich and the rest of society. However, this is a narrower measure of inequality than the Gini coefficient because it only tells us about how the very rich are doing.


There are large disparities in health inequality across countries. For example, availability in the Russian Federation is 100%, whereas in China it is about 15%. The availability of medicines within a country can differ depending on whether an outlet belongs to the public or the private sector. In some countries, such as Brazil, private sector availability of medicines is far higher than that in the public sector. The reverse is true for other countries such as Sao Tome and Principe. Note that a higher availability of medicines in the private sector does not necessarily mean greater access for the entire population, since the private sector is only open to individuals with the ability to pay. This disparity means that richer individuals can access a wider range of medical treatments.


A variety of databases provide data on inequality from a wide range of developed and developing countries. However, the data is hard to compare, as survey coverage is still relatively limited and data collection across countries is not harmonised (UNDESA, 2013).


Decile Dispersion Ratios are the simplest measurement of inequality. They sort the population from poorest to richest and shows the percentage of expenditure (or income) attributable to each fifth (quintile) or tenth (decile) of the population (Haughton & Khandker, 2009). They are defined as the expenditure (or income) of the richest decile divided by that of the poorest decile. They are popular but considered a crude measure of inequality, albeit easy to understand (Haughton & Khandker, 2009).


The most widely used measure of inequality is the Gini coefficient, which ranges from 0 (perfect equality) to 1 (perfect inequality, one individual has everything), but is typically in the range of 0.3 to 0.5 for per capita expenditures (Haughton & Khandker, 2009). It is derived from the Lorenz curve, which sorts the population from poorest to richest, and shows the cumulative proportion of the population on the horizontal axis and the cumulative proportion of expenditure (or income) on the vertical axis. The benefits of the Gini coefficient are described as: mean independence (if all incomes were doubled, the measure would not change), population size independence (if the population were to change, the measure of inequality should not change, all else equal), symmetry (if any two people swap incomes, there should be no change in the measure of inequality), and Pigou-Dalton Transfer sensitivity (the transfer of income from rich to poor reduces measured inequality; Haughton & Khandker, 2009); it is also the most commonly used measure. A problem, however, is that it cannot easily be broken down to show the sources of inequality, and it is very sensitive to changes in the middle distribution where there is often less change than at the extremes (Haughton & Khandker, 2009; Cobham & Sumner, 2013). Nor is it clear about its underlying normative assumptions about inequality (Cobham & Sumner, 2013).


The data.europa.eu portal also offers datasets on the Gini coefficient, which measures the concentration of earnings in a given population. A value of 1 indicates perfect equality in the distribution of income (everybody earns the same) while a value of 0 indicates maximum inequality (all the existing income goes to a single person).


A dataset produced by the Belgian Federal Planning Bureau shows how the Gini coefficient has changed over time in Belgium since 2004, and allows for comparison to the EU-27 since 2010. For more recent years, regional breakdowns are available, which reveal disparities in income inequality between the Brussels Region and the Flemish and Walloon Regions. 041b061a72


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