More Than Economics: How to Measure Inequality

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We can all understand the concept of inequality, but how do you measure it? 

All measurement starts with a definition. In the case of inequality, you're essentially asking how one group is doing relative to another. Of course, "how one is doing" can mean any number of things. For example, you can measure income, health, lifespan, or any number of political and social factors. 

Taken together, these different approaches can give you a broad picture of how things are going in a particular country. 

The Gini coefficient 
The Gini coefficient is, according to the World Bank, the most widely used of the inequality measures. It measures the distribution of income in a population on a scale of 0 to 1 -- 0 being perfectly equal distribution and 1 being perfectly unequal distribution. You can also use it to measure the distribution of wealth and consumption -- often with weirdly various results.

For example, if you had a population of 10 people and each person got $5 in income, your Gini coefficient would be 0. On the other hand, if you had a population of 10 people and one person got $50, the Gini coefficient would be 1. 

In practice, income is usually spread out a bit more, but the meaning of the number is always the same: The closer you get to 1, the more unequal the distribution of income. In the U.S., the Gini coefficient is 48, while Finland comes in at an impressive 26.9. 

But Haiti has a Gini coefficient of 26.6, virtually the same as Finland's. 

How can that be? Like most metrics, the Gini coefficient doesn't tell you the whole story: Finland is wealthy and has a very strong redistributive tax system, which makes it more equal, whereas Haiti is simply very poor.

Life expectancy
Because the Gini coefficient is limited, another way to look at how people are doing is to examine health outcomes. These can give you a bit more insight into how people are doing on a tangible level. 

One simple measurement is life expectancy. This tells you how long someone can expect to live based on his or her current age and trends in death rates for a population. Generally speaking, it's presented as the number of years a newborn can expect to live. For example, the life expectancy in the U.S. is 79.8 years, meaning a 2014 baby is expected to live that long, taking into account both male and female life expectancy. Women live longer on average.  

Life expectancy gives you a useful picture of general health. For populations at high risk of disease, life expectancy declines, while for those with access to regular medical care and nutrition, it goes up. 

Again, looking at Finland, life expectancy is 79.34, while in Haiti it's just 63. Thus, the measure provides a lot more insight than just income inequality measures alone -- people in Norway can obviously expect much better health.

Infant mortality 
One of the factors that influence overall life expectancy is infant mortality. In fact, this is one of the reasons life expectancy shot so high in the 20th century, in addition to improvements in medical care. 

Infant mortality tells you a lot about how effective a society is at caring for mothers and their infants, so it can give you an unfiltered insight into how a given country is doing. 

More deaths among infants means a lower average lifespan, and the U.S. has a much higher infant mortality rate than Europe. A study comparing the U.S. with Finland and Austria found that differences in record-keeping can explain about 40% of the American infant mortality gap, but there's still a large number of deaths unaccounted for. Most of the difference is driven by mortality between 1 and 12 months of life, and it persists even for babies with normal birth weights.

What's behind it? The researchers found that "infants born to white, college educated, married women in the U.S. have mortality rates that are essentially indistinguishable from a similar advantaged demographic in Austria and Finland." 

In other words, it's an advantage issue -- while infants from all socioeconomic classes in Finland and Austria have basically the same infant mortality rates, "lower education groups, unmarried, and African-American women [have] much higher infant mortality rates."  

Thus, infant mortality is a metric that can not only give you a picture of overall health, but it can also provide you with clues about underlying inequalities that might not always be so easy to quantify. 

Legal and political equality 
Finally, there are also the more vague but still important issues of equality, fairness, and liberty to take into account. These could include a just legal system, access to education, or the chance to participate in civic matters. You could consider everything from the right and ability to vote (or not vote, if that's your choice) in a fair election to the knowledge that you won't need to bribe someone to get a fishing permit. 

But how can you measure something so vague? There's the rub. You could look at something like voter turnout, but that won't tell you whether voters have the opportunity to voice their individual views -- or if they're being coerced into voting for someone in particular. You could look at the ability to protest, but it won't tell you whether protesters are being heard. 

That's why measures of political and civic participation are necessarily a little bit messy. However, they could present a good jumping-off point to understanding the more nuanced aspects of equality.

Understanding the limits of metrics
In the end, there are a lot of metrics out there, but it's important to remember that they're metrics for a reason: Within a given number, there's often a lot of nuance, and statistics are useful only to the extent that you recognize their limitations. The Gini coefficient might tell you that Finland is more equal than the U.S., but it's not going to tell you whether Haitians are as wealthy, healthy, or politically active as Finns. 

That's why it's useful to supplement the economic measures with the health and political ones -- very rarely can you get the full picture of a place in one or two pieces of data. It's how you put it all together that really provides the story. 

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