Weekend Reads

For the profit and pleasure of subscribers, here's a few longer reads on economic, political and social issues for your weekend.

Further to my piece yesterday on a debtogenic environment, a subscriber alerted me to this book by trend forecaster James Wallman called Stuffocation: living more with less. Wallman looks back at how our modern world of marketing to sell stuff was created and interviews anthropologists studying the clutter crisis, economists searching for new ways of measuring progress, and psychologists who link rampant materialism to declining well-being.

This Guardian review of the book takes a sceptical tone about futurists talking about the problems of the super rich, but there's something to this analysis about the cultural drivers for endless consumption and spending. One exception is obviously housing. That's one area where we definitely need more of the right stuff.

I stumbled across a New Yorker Radio Hour interview this week with a former maths professor and hedge fund algorithm builder, Cathy O'Neill, who has written a book of the moment called Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. . There's an awful lot of noise and hope and fear around the power of big data to understand and shape economic and social behaviour. I have yet to read it, but I've put in on my list because it's on the New York Times bestseller list and has been nominated for a National Book Award.

The New York Times Book Review wrote this about it: "O’Neil’s book offers a frightening look at how algorithms are increasingly regulating people… Her knowledge of the power and risks of mathematical models, coupled with a gift for analogy, makes her one of the most valuable observers of the continuing weaponization of big data… [She] does a masterly job explaining the pervasiveness and risks of the algorithms that regulate our lives."

There was quite a bit of discussion at this week's OECD-Commission for Financial Capability symposium in Auckland this week about the developments in 'Fin-Tech' that will make it much easier for insurers and lenders to slice and dice and price risk because of their access to huge amounts of data of how we live, what we say, how we spend and who's in our social networks. Essentially, many of the subsidies and 'mis-pricing' that currently mean that people broadly get the same price for a good or service will be removed. Poor people will find it much harder and more expensive to get the insurance and credit they need to get out of poverty.

This New York Magazine piece by Priya Rao on O'Neill's book shows that it's not just finance that is using big data to understand and change behaviour. The US justice system is a big user of data to set the length of sentences. This is particularly relevant here, given the huge push to use big data in Social Development, Education, Justice and Health. It's a central part of the Government's big Social Investment push.

"The prime example of her thesis is recidivism models, which are used across the country in sentencing convicts. “People are being labeled high risk by the models because they live in poor neighborhoods and therefore, they’re being sentenced longer,” she says. “That exacerbates that cycle. People are like, ‘Damn, there are some racist practices going on.’ What they don’t understand is that that’s never going to change because policemen do not want to examine their own practices. What they want, in fact, it is to get the scientific objectivity to cover up any kind of acts for condemning their practices.”

And let's not forget the use of big data in recruitment. Here's Rao again with a chilling passage:

"Perhaps the starkest example of how big data contributes to inequality, though, is how it affects women in the workplace. O’Neil references San Francisco–based start-up Gild, which attempts to make hiring easier by quantifying an applicant’s social capital through their engagement with influential industry contacts. Because a certain subset of talented engineers were all frequenting a Japanese manga website, that nudged up their hiring score. Few women, however, were visiting the site because of its sexual tone, which meant they didn’t get the score bump. “If you Google for high-paying jobs, the web just doesn’t think of women as successful, and that translates into every machine learning algorithm that has to do with résumés,” O’Neil says. “An engineering firm wants to hire an engineer, but in order to build an algorithm to help it, it needs to define success. It defines success with historical data as someone who has been there for two years and has been promoted at least once. The historical data says no woman has ever been here for two years and been promoted, so then the algorithm learns that women will never succeed.”

It's not often my long reads include a legal letter, but here's one from the New York Times' in-house lawyer David McCraw in response to Donald Trump's threat this week (they're a regular thing) to sue the Times over its reports on Trump's sexual harassment and assaults. Here's the Talking Points Memo report on the letter, which is a satisfying read.

"We published newsworthy information about a subject of deep public concern," McCraw wrote. "If Mr. Trump disagrees, if he believes that American citizens had no right to hear what these women had to say and that the law of this country forces us and those who would dare to criticize him to stand silent or be punished, we welcome the opportunity to have a court set him straight."

In all the debate about what's preventing a 'normal' economic recovery from the Global Financial Crisis, the issue of an ageing population is normally cited as one of the factors in a laundry list that stretches through hysteresis, secular stagnation, a savings glut, globalisation and technology change. This Washington Post Wonkblog piece instead highlights a US Federal Reserve paper that puts the blame almost entirely on the ageing population.

"The researchers — Etienne Gagnon, Benjamin Johannsen and David Lopez-Salido — created a model of the economy that shows how changes in births, deaths, aging, migration, labor markets and other trends have affected the U.S. economy since 1900. Using that model, they find that most of the decline in economic growth and interest rates since 1980 has been due intractable factors like the aging and retirement of baby boomers, lower fertility rates and longer life expectancy for Americans."

Demographics, is indeed, destiny.

That's enough for this weekend. Have a great one.