The Measured Worker
Discussions of income inequality typically focus on how information technology raises the return to skilled labor, or on the rise of global trade, or perhaps on the way that politics skews power toward the rich and well-connected. But there’s another fundamental driver of income inequality: the improved measurement of worker performance. As we get better at measuring who produces what, the pay gap between those who make more and those who make less grows.
Consider journalism. In the “good old days,” no one knew how many people were reading an article like this one, or an individual columnist. Today a digital media company knows exactly how many people are reading which articles for how long, and also whether they click through to other links. The exactness and the transparency offered by information technology allow us to measure value fairly precisely.
The result is that many journalists turn out to be not so valuable at all. Their wages fall or they lose their jobs, while the superstar journalists attract more Web traffic and become their own global brands. Some even start their own media companies, as did Nate Silver at FiveThirtyEight and Ezra Klein at Vox. In this case better measurement boosts income inequality more or less permanently.
In any organization or division many colleagues do good work, but only so many would be truly difficult to replace. And those are the people who, with better measurement of economic value, receive higher salaries and bonuses.
Imagine a situation where a group of workers produces some output collectively. The tendency is to resort to equal pay scales, perhaps with some inequality built in for seniority and other highly visible characteristics, such as working overtime. Relatively equal pay structures help build group solidarity, and in the meantime the superior producers cannot easily demonstrate their worth to other potential employers because no publicly observable measurements capture that added value.
But as information about productivity improves, the better workers demand more and can get it; in fact, bosses will want to offer more to preëmpt them from leaving. Workers also stop thinking of themselves as bringing the same value to the table, and that can make inegalitarian pay structures less damaging to morale and thus more attractive.
One unfortunate possibility, or shall I say likelihood, is that some workers may not produce much of anything at all. They may be major shirkers, or perhaps they are smart and talented workers who nonetheless are poison for workplace morale. Their office scheming takes away more than their labor adds. These “zero marginal product” workers, as I have labeled them elsewhere, may have a hard time holding down a job. In the modern world it is harder for them to hide behind the labor of others.
Insofar as workers type at a computer, everything they do is logged, recorded, and measured. Surveillance of workers continues to increase, and statistical analysis of large data sets makes it increasingly easy to evaluate individual productivity, even if the employer has a fairly noisy data set about what is going on in the workplace.
This analysis, if only in crude forms, starts when workers are applying for a job. A significant percentage of bosses in America look up an employee’s credit score before making a hiring decision. Some employers are even using performance in online video games to evaluate individual talent. There are also Facebook, Twitter, LinkedIn, and numerous other social-media outlets, all of which do give us some clues about character, effort, and the quality of a person’s social connections. It’s not hard to imagine a future where an individual’s eBay and Uber ratings, among other pieces of information, are up for sale in the marketplace. The more reliable job candidates might disclose such information voluntarily. Over time schools may offer more information about their students than just GPAs and letters of recommendation, as statistical analysis improves in its ability to assess their potential.
Looking further ahead, and more speculatively, employers might request genetic information from workers. Anyone who doesn’t want to turn it over might be seen as having something to hide, and thus this information will spread even if you may feel that our society doesn’t want to tolerate genetic discrimination. Or perhaps the information can be lifted from a doorknob or from a cup of coffee during an interview visit. It’s hard to imagine that this valuable source of information will stay confidential forever, given that most databases have proved hackable.
This explanation for growing inequality has some potentially distressing features, but also some upside.
The upside, quite simply, is that measuring value tends to boost productivity, as has been the case since the very beginning of management science. We’re simply able to do it much better now, and so employers can assign the most productive workers to the most suitable tasks. Workplace incentives can also be more closely geared to the actual production of value for the enterprise.
The downsides are several. Individuals don’t in fact enjoy being evaluated all the time, especially when the results are not always stellar: for most people, one piece of negative feedback outweighs five pieces of positive feedback. To the extent that measurement raises income inequality, perhaps it makes relations among the workers tenser and less friendly. Life under a meritocracy can be a little tough, unfriendly, and discouraging, especially for those whose morale is easily damaged. Privacy in this world will be harder to come by, and perhaps “second chances” will be more difficult to find, given the permanence of electronic data. We may end up favoring “goody two-shoes” personality types who were on the straight and narrow from their earliest years and disfavor those who rebelled at young ages, even if those people might end up being more creative later on.
That said, measurement of worker value isn’t going away anytime soon. The real question is not whether we want it or not, but how to make it better rather than worse. Ideally we’d have a system where individuals can correct measurement errors in their records to prevent injustice and preserve accuracy. We’d also like a system where individuals are not tracked and segmented too early, where outsiders and immigrants receive a fair hearing, where risk taking is rewarded rather than punished, and where some degree of privacy, including privacy in the workplace, remains.
Obviously, that is a tall order.
I wonder, by the way, if MIT Technology Review will tell me how many people clicked on this article.
The author is a professor of economics at George Mason University.
The inside story of how ChatGPT was built from the people who made it
Exclusive conversations that take us behind the scenes of a cultural phenomenon.
How Rust went from a side project to the world’s most-loved programming language
For decades, coders wrote critical systems in C and C++. Now they turn to Rust.
Design thinking was supposed to fix the world. Where did it go wrong?
An approach that promised to democratize design may have done the opposite.
Sam Altman invested $180 million into a company trying to delay death
Can anti-aging breakthroughs add 10 healthy years to the human life span? The CEO of OpenAI is paying to find out.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.