How Network Data Science Can Help Solve Global Inequality

The debate over global economic inequality ” including income inequality ” is a data-based debate. Economists such as Branko Milanovic and Christoph Lakner argue the data shows things are getting better because more people are now above the extreme poverty line than ever before. The other side of the argument says that, although incomes have increased, the wealthy have jumped far ahead, exacerbating inequality. To provide some context, consider the following data points on global income inequality:

• In 1975, developed economies had 10 times more wealth than undeveloped economies.
• After the year 2000, incomes in developing countries started to increase for the lower class, decreasing inequality in those countries, while poor people’s incomes stagnated in developed countries, increasing inequality in those countries.
• Overall, 71 percent of adults worldwide have less than $10,000 to their name.
• The top 1 percent own 40 percent of the wealth.

Regardless of whether the poor have inched up in total median income worldwide, the fact still remains that the wealthy have outpaced them to an extraordinary degree. In 2017, the Panama Papers revealed the wealthy may be holding far more money in secret offshore tax havens than anyone realized, meaning wealth inequality could be much higher than previously estimated. There’s a ton of data to extrapolate and analyze, but here are some the findings thus far:

• The top 0.01 percent have 30 percent more wealth than they report on their tax returns.
• The wealthy are stashing 10 percent of the world’s GDP in tax havens, which comes out to about $7.56 trillion.

It’s easy to be frustrated or nonplussed by these findings. For one, the top 1 percent oftentimes earn their money by using data and technology to their advantage and making smart moves. It’s hard to see a problem with wealth accumulation in a Darwinian light, where survival of the fittest means people naturally do their best to gain economic advantage. If no one is stopping them from accumulating and hoarding wealth, that’s because the system is set up so that they can do it. Why blame them for doing what the system allows and evolution has enabled them to do?

In a different light, plenty of rich people are also philanthropists, and without money they wouldn’t be able to establish NGOs that make a big difference in the world.

Offshore tax havens are there because banks and foreign countries facilitate them. While we can make incremental progress with litigation to shut them down, it’s hard to see a way forward through this complex issue.

While legal loopholes make it tough to force bad actors to repatriate funds, people who do care about the plight of the impoverished can look toward network science data to help make a difference.

A Helpful Data Experiment  

It turns out that if people were to change their shopping habits it would promote economic equality. A team of network scientists headed by Northeastern University’s Albert Laszlo-Barabasi collected anonymous data on 150,000 people from Barcelona and Madrid. They studied a total of 95,000 business transactions. They noted economic status of all the people in the study, with an emphasis on charting the affluence or lack thereof in neighborhoods. Then, they conducted an experiment with the data.

They ran the numbers through predictive analytics software that had already been fed data on how networks of people interact, and modeled what would happen if people began to reroute their shopping trips to poor neighborhoods. The results were surprising: the neural network determined that if people were to alter just 5 percent of their shopping trips, it would create economic equality over time. Another way of putting it: shop in a poor neighborhood 5 times out of 100, and encourage your friends and neighbors to do the same, and you can help impoverished people gain wealth.

A rebuttal to this experiment is the problem of scarcity in less economically advantaged areas. In rural communities in America, where 16 percent of people are below the poverty line, 16.5 million people have limited access to supermarkets. This is very similar to the problem of food deserts in urban areas. Basically, supermarket chains build locations in wealthier areas because they’re the locus of economic activity. So how can wealthier people reroute their shopping trips to poor neighborhoods when there are very few supermarkets in these areas to begin with?

The answer is simple: shop at the small stores you can find in poor areas. These stores are all part of a network. Chapter 2 of Barabasi’s Network Science book discusses Metcalfe’s Law, which provides an important foundation for the valuation of networks. The idea behind Metcalfe’s law is that the more individuals use a network, the more valuable it becomes. Indeed, the more of your friends use email, the more valuable the service is to you, says Barabasi.

This makes perfect sense when it comes to economic inequality. The more people shop at stores in a poor neighborhood, the more money those stores have to distribute throughout the neighborhood. Then, up and coming entrepreneurs have more funds to start bigger businesses in the area. Supermarkets will be enticed by the data to build in the neighborhood. Over time, traffic, development, and jobs generate wealth.

This is like teaching someone to fish instead of giving them a handout. In the absence of wealth redistribution from governments, and in the presence of billionaires who hoard wealth, consumers must play the redistributive role through our purchases.