Skip to Content

IT and Productivity

Information technology can continue to boost productivity, as long as businesses use it to innovate.
February 28, 2011

If companies learn to make strategic decisions based on rigorous data-driven experimentation and analytics, not only will they see larger profits but they could drive the next great boom in productivity—which is in turn key to national wealth and standard of living, says Erik Brynjolfsson, a professor at MIT’s Sloan School of Management and director of the MIT Center for Digital Business.

It’s not simple to tease out the relationship between IT investment and labor productivity, or economic output per hour of labor worked (see “U.S. Labor Productivity Growth”). Between 1973 and 1995, productivity grew by only 1.4 percent annually. But it jumped much more from 1996 to 2003, and economists recognized that IT investment had taken time to pay off.

The new productivity jump was due partly to surging investments in IT starting in the late 1980s (see “Dollar Value of Total U.S. Corporate IT Stock” and “Rising Share of Infotech Investments by Companies”). Reduced productivity between 2004 and 2006 can be attributed in part to a drop in IT investment from 2001 through 2003, after the dot-com bubble burst, Brynjolfsson argues.

Evidence that greater IT-driven efficiencies lie ahead can be found in analyses of profitability data (see “Profit Gap Widens”). Brynjolfsson notes something remarkable about profit trends in industries that intensively use IT, including Internet companies and consumer-electronics firms, during the early 2000s, when productivity growth was high. He finds a widening gap between profits at the most profitable of these firms (the leaders) and the least profitable (the laggards).

This occurred, Brynjolfsson says, because the profit leaders had learned to exploit the inherent potential of IT far more effectively than had the laggards. The gap remained modest among companies in other industries because they had less IT to exploit. Brynjolfsson predicts that as businesses learn to use IT and analytics to innovate and drive change—in processes, product development, incentives, and just about everything else they do—a new era of sustained growth and higher living standards will follow.

Charts by Mark McKie

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.