Income inequality is on the rise in many countries around the world, according to the United Nations. What’s more, disparities in global income were exacerbated by the COVID-19 pandemic, with some countries facing greater economic losses than others.
Policymakers are increasingly focusing on finding ways to reduce inequality to create a more just and equal society for all. In making decisions on how to best intervene, policymakers commonly rely on the Gini coefficient, a statistical measure of resource distribution, including wealth and income levels, within a population. The Gini coefficient measures perfect equality as zero and maximum inequality as one, with higher numbers indicating a greater concentration of resources in the hands of a few.
This measure has long dominated our understanding of what inequality means, largely because this metric is used by governments around the world, is released by statistics bureaus in multiple countries, and is commonly discussed in news media and policy discussions alike.
In our paper, recently published in Nature Human Behaviour, we argue that researchers and policymakers rely too heavily on the Gini coefficient—and that by broadening our understanding of how we measure inequality, we can both uncover its impact and intervene to more effectively correct it.
In the plainest terms, inequality refers to the degree to which some have more than others. To help compare different societies and measure changes in inequality over time, researchers often use the Gini coefficient to capture the concentration of income in geographic locations. And in many cases, the Gini coefficient is a useful metric that accommodates such comparisons.
However, the Gini coefficient may not always reflect how resources are actually distributed. To illustrate why it’s important to consider other measurements, imagine what it’s like to buy trousers. You could go to the store and ask for trousers in size medium if that’s the size you usually wear. And in many cases, these trousers may fit decently—since medium conveyed the most pertinent information you wanted to get across. However, sometimes the catch-all size medium isn’t enough to get the size and fit just right.
Because people come in different shapes and sizes, many retailers offer trousers that accommodate two different dimensions: waist and length sizes. After all, two people who may wear a size medium may actually require trousers that differ in how long and wide they are, and in selecting clothes that take into account these two additional measurements, they may end up with trousers that fit them better. For retailers, the goal is to devise measures that capture the variability in people’s preferences well enough, while minimizing the number of measurements as much as possible to curtail the costs required to produce and stock different kinds of trousers.
And so it is with inequality measures as well: In our analyses of data from the United States, we show that the Gini coefficient—while often a decent measure—is not the best way to capture the information contained in US income distributions.
Instead, after comparing 17 different measures, we find that a measure comprised of two separate variables—called the Ortega parameters—reflect the best-fitting model for our dataset of over 3,000 income distributions at the US county level. The Ortega parameters jointly contain more information than the Gini alone can capture. Indeed, each one of the Ortega parameters focuses on different aspects of the income distribution; the first captures the extent to which income is distributed between low-income earners and medium-to-high-income earners, while the second captures how super-high-income earners compare to the rest of the population. Aggregating both Ortega parameters provides the Gini coefficient.
This nuance allows us to identify different kinds of inequalities in society. For instance, when measured by the Gini coefficient, two areas show very high inequality: both Teton County, Wyoming—which is home to Jackson Hole, a popular resort town, and where the Federal Reserve held its annual Economic Policy Symposium last week—and Monroe County, Alabama—which is home to Monroeville, the setting of Harper Lee’s To Kill a Mockingbird.
But when using the two Ortega parameters, we gain a deeper understanding of where the disparity in incomes comes from: The inequality in Teton County is driven primarily by the presence of a handful of super-rich individuals, whereas the inequality in Monroe County is driven by a more widespread difference between low-income earners and medium-to-high-income earners. We created interactive maps to show where different US counties fall both in terms of overall inequality as measured by the Gini coefficient, and where this inequality is concentrated through the more specific Ortega parameters, which is available here.
Not only do these measures of inequality capture more information about the income distribution, but using these measures may also offer novel insights about how inequality affects important societal outcomes. We collected 100 policy-relevant measures—including education, obesity, and other indicators—and compared how they relate to inequality when measured through the Gini coefficient, as prior research has done, or the Ortega parameters, as we propose here. In the vast majority of the 100 cases we inspected, we find that the Ortega parameters reveal more information about the relationship to societal outcomes than the Gini coefficient.
For instance, consider the connection between inequality and obesity. In studying our data, we find no significant correlation between inequality and obesity when using the Gini coefficient. Both Ortega parameters, however, show significant correlations in opposite directions: In areas where inequality is driven by the difference between low-income and medium-to-high-income earners, we find higher obesity. Meanwhile, in areas where inequality is driven by the difference between super-high earners and the rest of the population, we find less obesity.
We also studied the correlation between inequality and educational outcomes. Our data shows that the relationship between the Gini coefficient and the share of the population holding a bachelor’s degree is not statistically significant, but both Ortega parameters again show statistically significant associations. More specifically, where inequality is driven by the difference between low-income and medium-to-high-income earners, we find a lower share of bachelor’s degrees in the population, whereas areas where inequality is driven by the difference between super-high-earners and the rest of the population are associated with a greater share of bachelor’s degrees.
Using the two Ortega parameters, as these two examples illustrate, we can pinpoint what’s driving inequality in relation to both obesity and education. These parameters may be used in other ways to drive a large number of other policy-relevant outcomes including initiatives related to health, crime, and social mobility. The Ortega parameters reveal more detailed results that have important implications for researchers and policymakers alike.
Across academic, policy, and public spheres, inequality has received growing attention in recent years, with many calling for change to the status quo. Indeed, a recent survey suggests that a majority of Americans think there is too much economic inequality. At the same time, public support for measures to address inequality are more of a mixed bag.
Our research highlights that one way to understand the diverging beliefs about inequality and preferences for redistribution may be to focus on the specific kind of inequality respondents were dissatisfied with the most. If we devised policy to address inequality based solely on the Gini, we would treat places like Teton and Monroe County the same. But that may not be the right thing to do.
For example, reducing inequality can be achieved by both reducing the difference between low-income and medium-to-high-income earners, such as by raising the minimum wage; and inequality can also be reduced by closing the gap between super-high earners and the rest of the population, such as by raising income taxes for top-earners. Our findings suggest that moving beyond the overall concentration of inequality as reflected in the Gini coefficient may be fruitful in both pinpointing different kinds of inequality and in determining how to make meaningful change to remedy inequalities.
One limitation of our research is that our results are restricted to our US dataset, which may call into question whether the two Ortega parameters are also the best measures in other datasets, including in other countries. The problem is that most datasets that are currently available to researchers do not contain sufficient information to conduct the kinds of analyses we have demonstrated here. So if we want to move beyond the US in studying inequalities, similar high-quality data from other countries needs to be made publicly available in order to follow the approach we suggest here and identify which measures are most suitable to capture inequality.
Ultimately, we hope our work encourages statistics bureaus throughout the world to publish more detailed inequality data for public access. After all, the stakes are high, and doing so can only help efforts to better understand and address inequality around the world.
Jon M. Jachimowicz is an assistant professor in the Organizational Behavior Unit at Harvard Business School. Kristin Blesch is a doctoral student at the University of Bremen. Oliver P. Hauser is an associate professor of economics at the University of Exeter Business School.
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