The front page of today’s print edition of the US Wall Street journal included an article exploring one of my favorite topics: productivity, and why it is flagging. The article is called, “U.S. Productivity: Missing or In Hiding?” First some facts, then some assumptions, and then a proposal.
- Increased productivity leads to growing GDP, a measure of our (monetary oriented) economy. Such growth contributes to improved living standards since we measurably get more (output) for what we do (input).
- US productivity as reported by the U.S. Government has fallen over the years. Here are selected data points from the article: “From 1948 to 1973, it grew at an annual average of 2.8%. The rate through the 1980’s slowed to half that rate, even as computers spread through the economy. From 1995 to 2004, productivity rates were close in on 3%. Then average gains fell to 2% from 2005 to 2009; since 2010, they have dipped below 1%
- Measurement definitions: Do we measure productivity correctly? Clearly a number of innovations do not directly impact monetary-measured output. The classic example is domestic housework which is not included in GDP, so all the innovations related to robotic cleaning tools and dishwashers that save us time don’t impact productivity numbers. Indirectly however that saved time might be used for more monetary-measured work, but more work (at the same productive rate) does not increase productivity. So yes, the measurement won’t be perfect but this issue has persisted all along.
- Silicon Valley effect. The article calls on some pretty powerful economists from Google and Microsoft to explore the issue. It seems Silicon Valley does not believe the data since they believe that they contribute to productivity every day. I disagree, to a point. Take email: has email made you more productive? I think it did, a long time ago, when I replaced letter writing. I now get more emails per day than I ever received as letters. Part of this is due to the fact that it’s easier to send emails than writing a letter, and so there are just more emails. But giving me a smart phone so I can handle even more communications when I wake and eat, and relax in front of the TV, does not make me more productive. I do more work, yes, but at the same productive rate. And the volume of email has grown proportionately to the allowed time available to waste on it. So email today is not a productivity tool – its productivity rate is pretty fixed. So be careful what you look at – unless you more output form your input, you don’t increase productivity – you just do more work.
- The self-service revolution. This whole idea is not saving anyone time. For sure there are great examples of where self-service has made customers happy, but there are so many more examples for where it makes customers and users unhappy, more frustrated, and so they waste more time and become less productive. Self-service just shunts work around- it does not necessarily change the productive nature of the work.
- Rate of absorption (of the innovation). It takes time for an innovation to permeate an economy. Now this sounds logical- some innovations are easier to absorb and some less so. We would therefore expect the benefits of some innovations to appear in improved productivity numbers sooner than for some others. However economists and technologists don’t have an agreed approach that explains all the seen vagaries concerning how innovations are absorbed, or not. Second it is not totally agreed as to what is an innovation (in the first place!), and the fact that there are different kinds of innovation – from product to services to practices and so on. This most recently led me to think about the kind of innovation we should look at. See my concept of “innovation platform coefficient” explained in Internet of Things will Dwarf Big Data.
I think there are at least two issues to confront, and one new metrics we should explore.
The type of innovation matters. The smart phone did not make us much more productive in terms of phone calls and browsing. We had cell phones before, very cheap ones too, and some rudimentary browsing. But smart phones operate as platforms. Over time entirely new forms of communication, trade, entertainment, and business models are emerging from that platform. Then we even have new innovations that spin off from the first wave of innovations on those platforms. Thus the initial hit on productivity is slight, but longer term it grows as new dependent innovations spin out. This is an allusion to my idea of an “innovation platform coefficient”. Some innovations have a high innovation platform coefficient, and so have a tendency to spin of lots of other subsequent innovations over time. Other innovations have a low coefficient and so don’t spin any off, and their impact on productivity might be more short term oriented.
Word processors replaced type writers and yet we struggled to see, at the time, a notable improvement in productivity. Remember the email point above? We just massively increased the number of documents produced, though the individual document production productivity improved. So the rate of improvement was lost in the total growth of output. Yet the PC that evolved from word processor continues to spin out innovations even now. Today the PC is on the wane, and now tablets are in demand. But even that won’t necessarily show productivity growth. See my blog (Mobile Gaming is Dumbing Down PC Gaming) on how game development on mobile devices is killing off innovation that would otherwise have driven productivity growth. Every dollar spent on mobile gaming is not adding to productivity, yet. We will have to wait for new business models to spin off from the large scale deployment of networked and peer-to-peer platforms that will emerge on the mobile platform, as it emerged on the PC.
ERP, a little closer to home for me, is another innovation platform. More precisely the formation of large scale packaged business application is an innovation that itself might not have driven growth, but as a platform has done so. Such systems took time to permeate the economy, and their initial positive impact of productivity has surely been eclipsed by the number of dependent innovations that has lived off its carcass, such as all the best of breed suites like CRM, SCM, PLM and so on. But those suites have a lower “spin off” rate – a lower innovation platform coefficient.
Finally we have things like big data: another short term fillip, but when conjoined with things like Master Data Management, or MDM (for trusted core data), and IOT (a new layer of organizational sensation), the longer term impact should be significant.
The second issue to confront is that complexity kills. This is not a new idea. I read a book with the same title years ago. But complexity is part of what it means to be human. Everywhere I turn we face complexity and no amount of exerted management effort seems to reduce it. I even blogged about this in 2012 when Delta Airlines ‘modernized’ its customer facing website. See Delta Airlines, what have you done to your website????? In self-service mode I only interacted with the Delta site to do three things. After the ‘improvement’, my three tasks were made more complex. What used to take 5 clicks was now 12. Every task was more work. This is just banal, but this is how our businesses operate. Somebody in marketing at Delta proposed this, and worse, someone approved it.
Right now we are seeing this again in the cloud: as more and more organizations move business applications to the cloud, the challenge of semantic consistency of the business information shared between them is becoming even harder to sustain: with one hand we simplify, only with the other we make things harder.
So we have issues with complexity – we do not seem to do a good job of always avoiding it. And the nature of what we innovate is itself complex to understand, and some things may provide more opportunities for innovation later, such that productivity growth can sometimes be masked, missed, or even bypassed. So what can we do about measuring? If only we can get the Bureau of Labor and Statistics to understand how IT drives innovation. As it stands we cannot determine this from the data. The classifications used suggest that IT is very simple stuff. Check out this blog (US Productivity grew by more than 16% in the last quarter, up from 11% in previous (newspaper headlines, April 28 2024), related to productivity, to see how the classification of ‘software’ and ‘hardware’ completely misses the point about innovation platform coefficient.
So what do we measure? GDP is still needed since we do need to measure our monetary-based economy. With that we can pay each other more money for our work and that tends to be a good thing. In GDP – A Brief but Affectionate History, author Diane Coyle explores the issue and proposes (as others have) ideas for quality of life measures (see my book review of GDP – well worth reading). I think the World Bank or some similar organization might even publish an informal figure for this already. I am not so keen for a “quality of life” metric in this context (though such an idea is welcome for other reasons) but I am interested in some measure that monitors the potential working capacity in non-financial terms.
Just look at your own working day from when you wake up and when you go to sleep. What have you spent your hard earned money on to simplify your day; what actually increased output with the same or less input? What, like email now, simply fills up what was free time to do more-of-the-same work? Perhaps in understanding how our individual capacity for work changes through the use of technology, we might get an idea for a new measure to monitor what might add to GDP – later.
So please excuse me now. I have to off and administer all those pesky emails that prevent me from talking to customers, answering their questions, and writing research. You know, the things that really matter.
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