Technology and Inequality

Every time there is a big lottery drawing, I wonder why as a society we don’t make a few hundred millionaires, rather than award the big prize to a small group of people. While China and the market meltdown will dominate conversations this week at Davos, it is a fair bet global inequality and the work of economists like Thomas Piketty will get plenty of play. It is a Presidential election year in the US, so we will be hearing plenty about wealth polarization. So, yes economic equality has been on my mind recently, especially as I continue research for my next book on automation and trends like the gig economy have on the future of work .

What’s interesting is much of the research on automation tends to focus on job losses that come with every wave of technology. But few seem to focus on the new categories of jobs and the wealth that technology creates. In our lifetimes, we have seen the Chinese create what is now the largest middle class in the world: “In 2000, the country’s wealth was similar to that of the U.S. circa 1939, the report found. By 2015, it had expanded to the level of the U.S. in 1972, effectively accomplishing a 33-year leap in less than half the time.” Much of that has come from manufacturing, electronic and other technology. India ‘s software sector has been a catalyst for a vibrant middle class. An Amazon exec tells Fortune “The size of opportunity is so large it will be measured in trillions, not billions—trillions of dollars, that is, not rupees,” Japan, S. Korea, Singapore have done similarly with biotech and other technology.  Ditto with many E. European countries.

Of course, in many of these countries, and in Africa and elsewhere, there are truly destitute folks. So, much more needs to be done and can be done by applying STEM rigor to their infrastructure, agriculture and other sectors. The problem, is we have a tendency to play Robin Hood to redistribute wealth, or in the case of automation, try to slow down its pace.

As Paul Graham, the co-founder of Y-Combinator (which has created quite a few millionaires with the startups it has funded) writes in his passionate essay “let's attack poverty, and if necessary damage wealth in the process. That's much more likely to work than attacking wealth in the hope that you will thereby fix poverty.”

And as we focus on “bottom of pyramid” poverty, I was struck by comments by Brazil’s ex-President Lula in a Foreign Affairs article on his Bolsa Familia initiative aimed at its very poor (which after a decade the magazine says is surprisingly successful)

“With all due respect to experts and academics, they know very little about the poor. They know a lot about statistics, but that’s different, sabe?”

Graham makes a similar point

“Except in the degenerate case, economic inequality can't be described by a ratio or even a curve. In the general case it consists of multiple ways people become poor, and multiple ways people become rich. Which means to understand economic inequality in a country, you have to go find individual people who are poor or rich and figure out why.”

Taking Graham’s point further,  the impact automation technologies (machine learning, robotics, wearables, 3D printing, digital publishing) varies dramatically by industries ( I am looking at agriculture, accounting, advertising, outsourcing, shop floors, and many other sectors)  and countries. I have to keep reminding myself to ignore politicians and economists who opportunistically or smugly talk as if they truly understand the impact technology has on work and on economies.