Ignoring the politics, a front page article in today’s US print edition of the Wall Street Journal says it all: Labor Force Poses Limits to 3% Growth. The challenge of course is how to increase economic growth such that increased profits and resulting taxes help pay for potentially crippling costs in the future as the US (in fact western and developed nations) ages. And to make matters worse, productivity growth has slowed and remains sub-par compared to recent times.
The extensive article highlights a simple truth: the size of the labor pool and the rate at which it produces things (its output per hour or productivity) are what dictate economic growth limits. And the US demographics (indeed, again western and developed nations) are against us. The baby boomers are retiring; retirement age is not increasing; immigration is a political hot potato; labor participation rate is relatively low; and recently we even saw a decline in life expectancy. So all told it’s a bad bunch. The key then, short term, is how to increase productivity. That maybe the only chance we have until (and if) structural demographic forces change the direction of population growth.
Productivity is a difficult thing to understand. We are told and we know that innovation is all around us with examples like Uber, Amazon and smart phones. Yet the data that describes the productivity of our economies do not affirm increasing effectiveness. In fact, numerous reports tell us that productivity growth is slowing again. How can this be?
I have explored this issue at length. But I noted two Opinion pieces recently in the US printed edition of the Financial Times that offers two unrelated ideas that sound interesting. One is more political and the other more economic.
In ‘Post-crisis shortage of affordable homes undermines US industrial productivity‘, John Dizard of the FT suggests that affordable housing for the middle class was a driver of growth in years long past. When funds were flowing and construction of such assets active, workers would move to where the jobs were. In other words labor mobility was high and it helped align resources with demand. Wave after wave of labor would relocate, and then attract higher wages (increasing consumer spend) and increased investment (so driving productivity).
Today lenders and investors are driven by profitable high-end luxury apartments and condominiums gathered together in major commercial locations, not funding less profitable (apparently) mass-market housing for the middle class near productive centers. Labor mobility remains muted. And we can couple here more related data: home ownership is at a near all-time low, yet house prices have recovered in many areas to pre-crisis levels. Why? Because the investor class is snapping up all the properties and renting it out. So even first time buyers are being squeezed. All told a worrying underplaying story.
But now the good news. In ‘Technology is the tool to spur a healthcare revolution‘ John Thornhill of the FT identifies how big data, IoT and AI all offer varying opportunities for innovation, disruption and so productivity improvement using healthcare as the example. But here is the question:
Which innovations will yield what range of productivity improvements over what time scale?
We can add to this ‘which?’ question another about how: How is productivity improved and how does that productivity inducing innovation assimilate across an economy? It turns out we know less about these questions than we think.
The smart phone is a great example:
- At once it replaced a line-locked service with a computer
- It offers specific capabilities that were not as accessible before such as a browser or other specific apps looking at data elsewhere
- It offers a network-based platform in which other, later innovations can be deployed
The first aspect (wave 1) was the immediate improvement of the innovation over established technology: we were already mobile but not at a cost-effective level, and having access to the internet meant we could reconstitute how some work could be done. I could schedule meetings and read books far more easily. But at the end of the day, the way in which books are read and meetings scheduled has not changed.
The second aspect (wave 2) offered unique one-off jumps in productivity and flexibility. New apps such as Amazon or Maps meant I could do things I could not do before. This was good stuff and was the focus for much of the innovation taking place in smart phones for most of their lives.
But it’s the third aspect (wave 3) that is now in focus. The smart phone is a platform: a network or mesh of people connect just waiting to do stuff. So now new business models are popping up in the ready made network: Uber, Facebook, and more. This is the hardest of innovations to understand since the connection to it and its propensity and ability to yield productivity are not well understood. But when the smart phone was conceived we did not in general call out the longer term impacts possible with the innovation.
For example, is Uber more productive than say your favorite Color Cab Company? Yes. at some levels it is able to operate with a very different cost basis. But there are still cars and deprecation, insurance and repair costs still exist- the costs in the economic model have just moved around. The nature of transportation services has not really changed. So overall productivity has not changed that much.
Maybe self-driving cards will change the economics of innovation and productivity? Maybe. By taking out of the equation a major cost element, the economic model does indeed change. So this kind of innovation does offer some chance of improved productivity.
This 3-wave model is something I have been talking about for some time – as a means to classify innovations. Classifying or calling the likely impact of an innovation is useful, but to classify it as a kind of innovation such that some are identified over others as offering long term, slower burning capabilities for subsequent productive growth, seems interesting to me – and useful.
Bottom line: we, as leaders, need to get smart about picking and backing the right kind of innovation in order to drive the right kind of productivity. We need to be prudent when looking at innovation to ascertain its likely impact short, medium and long term – since not all innovations behave or support the same kinds of dependent benefits and future innovations.
Read Complimentary Relevant Research
Predicts 2017: Artificial Intelligence
Artificial intelligence is changing the way in which organizations innovate and communicate their processes, products and services. Practical...
View Relevant Webinars
The BI & Analytics Challenge for T&SPs: Major Disruptions on the Way
From artificial intelligence (AI) to machine learning to smart data discovery, the BI market is once again going through a major transformation...
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.