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Why is Productivity Slowing Down?

By Craig Roth | January 09, 2017 | 0 Comments

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If you feel less productive than you used to be, you are not alone. A WSJ article from Jan 5 2017 (“The Fed’s Point Man on Productivity“) states that “Total factor productivity grew an average 1.8% a year from the end of 1995 through 2004, but growth has slowed since then to an average 0.5% annually.”

There seems to be some tsk-tsking going on various academic and media circles for technology failing to continue driving productivity growth.

His research found that the information technology boom of the 1990s helped businesses become more efficient until about 2003. But that boost began fading by 2004, and now the benefits of tech innovation flow more to leisure activities, such as social media and smartphone apps.

More fun, perhaps, but not much increase in economic output per labor hour, or productivity—the key to rising living standards …

[John] Fernald, a senior research adviser at the Federal Reserve Bank of San Francisco, has played a key role in convincing many top Fed officials that the productivity slowdown began well before the 2008 financial crisis.

This means productivity growth is unlikely to bounce back after the effects of the crisis dissipate, and it implies U.S. economic growth is unlikely to pick up much either.

I have another explanation: that further productivity benefits from enterprise technology require deeper levels of organizational commitment. Once you’ve enabled customers to check their bank balance on a website or mobile app, the next features get tougher and tougher.

I’ve covered technologies aimed at nonroutine work for well over a decade now. That includes products like spreadsheets, co-authoring, Microsoft SharePoint, EFSS, ECM, and wikis. They are very different technologies, but one aspect in common is that their adoption tends to take off quickly and produce a few quick wins. Then, right as expectations are rising, they level off.

The reason is that these technologies are first used against the low-hanging fruit that they can address. You can do some lunch-and-learn sessions or a posting in the company newsletter to shake the tree and find a few more bits of low-hanging fruit, but it just stalls again.

Getting to further stages of usage and value requires more work, risk, and time. Steps can include ethnography, surveys, or focus groups to find out how people work and where their collaboration hassles are; top-down analysis of key business processes and the nonroutine work that supports them; process engineering; culture change. And this requires actual ownership and responsibility of the technology and the adoption process. And it may fail, which can make it hard to procure funding for such an effort.

Mr. Fernald touches on this when he says “to really change the productivity numbers, those things have to translate into how businesses throughout the economy operate.”

If the Fed wants to gain more insight into this phenomenon they should develop a set of adoption curves for different technologies and determine the “absorbtion inhibitors” that can be identified in organizations, industries, and countries to predict the degree to which a productivity enhancing technology will be absorbed and ride further along the adoption curve.

I’d work on producing those curves, but I’ve heard that spending time on social media (like this blog) just isn’t as productive as it used to be …

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