Understanding the Gini Index

How can we use this index to better understand wealth inequality in the United States

The Gini index or coefficient is a measure seldom considered by the average American when evaluating the economy. It represents the degree to which income or wealth is distributed within a population. A Gini Coefficient of 0 implies total equality i.e., everyone earns the exact same salary or posses the exact same amount of wealth. While somewhat confusing in isolation, I hope to provide a somewhat better understanding by visualizing it and comparing it to other economic indicators. I hope to generate some visualizations utilizing this index to better understand how it varies across the country, what it means, and how we can consider this measure of inequality in determining economic well-being across income ranges.

Income inequality is a longstanding and worsening issue within the United States. The last 40 years have been characterized by a increase in the share of income and wealth accumulated by the nation’s highest earners. More recently, Americans have witnessed a growth in the number of billionaires and watched as their wealth accumulates while the working class ultimately suffers in the midst of a global pandemic. There is a possibility that worsening economic outcomes for lower-income workers are suffering while the wealthiest earn more. My goal is not to evaluate causation, but to start a data-driven conversation about wealth and income inequality.

Income inequality is usually considered on a national level. While it is most certainly a national issue, I wanted to dig through some data to see if there were any trends or traits on a state-by-state and county-by-county level. I aim to look at wealth inequality, income, poverty rates, and taxation to gain further insight into the kinds of factors that go into the widespread inequality that faces Americans today.

Policy surrounding wealth inequality is extremely complex, so it will be hard to generate any concise answers or solutions. I do hope, however, to show how many factors should be addressed when thinking about policy and that expanding how we think about wealth is critical to making good decisions that successfully address the problems that face many Americans. I also want to emphasize that further research and understanding by both experts and voters is integral to remedying this issue.

Data for this project was downloaded from the U.S. Census Bureau and scraped from World Population Review.

The Gini Coefficient

To begin, here is a map of the Gini coefficient for each state in the United States.

Utah and Alaska have a notably low coefficient, while New York has a notably high one. This means that income is more distributed in the former states and concentrated at the top for the latter.

Some Helpful Maps

By State

Below are some maps that provide information or relevant metrics.

By County

Comparing the Gini Index to Other Economic Measures

For the next stage of this analysis. I wanted to see how economic indicators relate to income inequality for each state.

Poverty Rate

There is a weak positive correlation between poverty rate and the Gini coefficient; however, states with low poverty rates have Gini coefficients on the lower end, while states with high poverty rates have relatively high Gini coefficients. This would imply that high poverty rates are more often associated with states with worse poverty. This is not surprising, but it is worth considering that remedying poverty and distributing income may be linked and could be tackled together.

Unemployment

There is a similar pattern here with unemployment as there was with poverty. Typically high unemployment is associated with a greater level of inequality. Alaska is a surprising outlier, especially considering a high median income and low poverty rate.

Median Household Income

There is not a very strong correlation between median household income and Gini coefficient, however we are seeing that states with a lower median income have consistently higher Gini coefficients, while states with higher median incomes have more variable Gini coefficients. We are also starting to see a few states that tend to be on the outskirts of the data.

Income Tax

There is a similar somewhat confusing mix of data in this comparison as well, however, the same states are showing up on the outskirts. It appears as though states with high income taxes are not necessarily more equal by income.

What Does This Mean?

States that are overall more economically healthy by typical economic measures such as poverty rate and unemployment are often more equal by share of income. This association between the overall economic health of individuals within an economy and income equality is something to consider in terms of how we think about implementing policies that impact each income bracket differently.

The Outliers

As noted, some states stood out in both the maps and the comparisons. Utah and Alaska have very low Gini coefficients. It is unclear how they achieve this, but their poverty rates are low and median income is high. Unemployment is high in Alaska, but interestingly enough, the rest of the economic indicators are positive. The reason for why these states seem to have more equal distribution of income with a strong median income and low poverty rates is certainly worth researching further.

The Tax Impact

Taxes do not seem to successfully indicate much at all. California, for example has a high median income but a less impressive poverty and unemployment rate. The high taxes in California are not conducive to a more equitable share of income or benefit for the lowest earners.New York is similar in that it has a high income tax and Gini index, but average economic indicators. Also evident by having one of the main financial centers of the world, it is clear that there is a big difference between its highest earners and its lowest. These states, other similar ones, and their tax structures are also worth researching further.

Looking Ahead

There is much more to be explored, visualized, and put into a story in order to understand inequality in the United States. More analysis on a location by location basis may be valuable in getting a sense of what inequality looks like in each part of the United States. It may also be useful to look at statistics on other variables such as type of employment, family size, race, residence, and community type. It is also worthwhile to find data on the Gini Index for wealth as opposed to income. Since many wealthy people do not necessarily accumulate most of their money from work income, it would be valuable to see if parts of the country are more or less equal in this sense. With more exploration, in depth data, and analysis, we might be closer to implementing better policy that truly helps all Americans.