Modern-day inventors — even those in the league of Steve Jobs — will have a tough time measuring up to the productivity of the Thomas Edisons of the past.
That’s because big ideas are getting harder and harder to find, and innovations have become increasingly massive and costly endeavors, according to new research from economists at the Stanford Institute for Economic Policy Research.
As a result, tremendous continual increases in research and development will be needed to sustain even today’s low rate of economic growth.
It’s become the norm that we think about innovation as an input with a high tech output. But it’s much more than this, it’s related to new ideas and a new way of doing things; it’s definitely not all based on complexity or new technology. In fact, innovation can be a new way of doing things within an established traditional paradigm. Put another way, you can have innovation without the constraint of technology.
We are creating an excuse landscape around our businesses. Too often we fall in to the trap of saying we don’t have access to a certain technology, therefore we’re out of the innovation game. It’s not true, innovation can come in many guises and can spring out of the most constrained, unlikely places.
In addition to the excuse landscape there a number of common fears and barriers that put the brakes on innovation within our organizations. The fear of failure, a risk averse senior management, a fear of sharing ideas or a mismatch between company vision and company leadership.
There can be a misalignment between strategic direction, new ideas and management execution practices. There is a real need for companies to self-reflect on their own behaviors and look at where they are encouraging or creating barriers to innovation.
The key question for business is, can we systematically continue to deliver the core business whilst creating an environment that fosters innovation? this is the tricky balance at the heart of businesses seeking more innovative practices. Leaders need to find a way to continually deliver systematic and methodical results for their business whilst generating new ideas.
There are real challenges here for business leaders as the Bi-Polar Challenge. Businesses have to focus on the key drivers of scale; efficiency, repetition, process, and hierarchy while also getting comfortable with wasted efforts associated with searching for new ideas and taking risks. Embedding innovation can be seen as a process of self-destruction with self-undermining results, meaning a company needs to commit time and resources to self-reflection, playing with new ideas and taking risks. It’s here where business can become unstuck, but it’s also where businesses can learn a lot from the research and those who’ve successfully juggled the demands of scale, stability, and innovation.
The first step towards success is to create a dedicated innovation function with a framework that fosters new ideas and innovation. There is not a one size fits all framework here, rather a group of principals from which business can build their own framework that suits their business model and culture. One such principal is the need to build a network that transfers and shares information openly within an organization. Networks within businesses are pivotal to the sharing of ideas but are also crucial when it comes to picking the good ideas and investing in them. Ideas are like signals and signals can get weakened or strengthened within a network.
There’s a powerful message here for senior leaders as the success of these networks largely comes down to how they are managed. If senior managers are effective at identifying ideas and exploring them then innovation will start to blossom, but if the opposite is true and managers either don’t have the time, inclination or ability to support the network then the innovation project will quickly wither on the vine. So a couple of starting questions for any senior leader looking to innovate should be how healthy are your information networks and how good are you at picking up the signals.
Big data and the Internet of Things is set to transform industrial processes, but it’s an open question which companies will ride that wave. Across the globe, industry is about to change. Big data and the Internet of Things are going to transform industrial processes over the coming decade in the same way that the consumer world was transformed by the internet over the past decade. In this process, there will be leaders and then there will be followers.
There are already concerns that Europe will miss the boat on the industrial internet, in the same way it missed the boat for the consumer internet, because of imperfect regulation on this continent. The industrial internet is set to transform sectors such as manufacturing, logistics, energy, health care and transport. But if Europe doesn’t strike the right regulatory balance, those innovations will be developed not here but in North America and Asia.
One of the biggest regulatory concerns in Europe is about the portability of data, and the barriers that exist in this still fractured digital market.
When we are talking about the Internet of Things, we are talking about machines that will be able to communicate with each other and perform some activities through this communication. In this process, when we cross the borders of a country, we expect all the digital services we were receiving in one country, we should receive in the other. Europe should be able to guarantee the continued provision of services, without any dependence on location.
European legislators are hearing this complaint a lot from companies. Ensuring the free flow of data in the EU is crucial if we want to keep pace with Asia and North America. Data localization rules in different member states inhibit growth and are a barrier to the much-needed investment in infrastructure and innovative products and services.
Data protection is another thorny issue of EU legislation that could hold back innovation in this field. EU’s increasing data privacy legislation runs the risk of stymieing growth. We don’t buy the premise that legislation drives companies to innovate. We see legislation doing the opposite. The first thing we need to do is legislate less. We need a less complex general data protection regulation, one that ensures more certainty about data flows. That data needs to come freely within the EU and into the EU, so that companies aren’t told where they can keep their data centers.
Data security is a real concern. Data portability is an important issue, but so is data privacy. The question is, how can we harmonize these tools so that we have the confidence to feel safe.
When it comes to digitalization, there is much fear among the public that it will lead to a loss of jobs – especially in sensitive industrial areas. Worker protection is another legislative area in the EU that companies are concerned could hold back innovation and actually end up killing jobs.
The risk of losing jobs is there, and it is serious, because robots can replace people. But it depends how we are defining a job. Technology brings new opportunities for employment, and in the first preliminary steps in this era we have already seen a lot of new forms for getting income.
Some job losses may be inevitable, but can be replaced by new opportunities. digitalization is an opportunity rather than a threat. We have gone through changes before – look at the rise of information technology in the 1980s and 1990s. These have been for the better in the long run in terms of job creation. Maybe some low-skilled jobs will become replaceable, but there will be different jobs in their place.
The government’s role then is to make sure that workers are appropriately skilled up to be ready to slot into those new jobs. One of the key things we need to do legislatively at member state level is make sure there’s proper training at schools, so that everyone who enters the work market is skilled up.
European legislators will continue to grapple with the thorny issues of data portability, data privacy and worker protection. But it is important that they get it right quickly. Because if the legislative playing field in Europe is not prepared for the coming industrial internet revolution, Europe could miss the boat once again.
Nicholas Bloom, a SIEPR senior fellow and co-author of the forthcoming paper, contends that so many game-changing inventions have appeared since World War II that it’s become increasingly difficult to come up with the next big idea.
“The thought now of somebody inventing something as revolutionary as the locomotive on their own is inconceivable,” Bloom said.
“It’s certainly true if you go back one- or two hundred years, like when Edison invented the light bulb. It’s a massive piece of technology and one guy basically invented it. But while we think of Steve Jobs and the iPhone, it was a team of dozens of people who created the iPhone.”
To better understand the nation’s sluggish economic growth, Bloom and his three co-authors — SIEPR senior fellow Chad Jones, Stanford PhD candidate Michael Webb, and MIT professor John Van Reenen — examined research productivity at an aggregate national level as well as within three swaths of industry: technology, medical research and agriculture. For another measure, they also analyzed research efforts at publicly traded firms.
Their paper follows a common economic concept that economic growth comes from people creating ideas. In other words, when you have more researchers producing more ideas, you get more economic growth.
But Bloom and his team find a not-so-rosy imbalance. While research efforts are rising substantially, research productivity — or the ideas being produced per researcher — is declining sharply.
So the reason the U.S. economy has even grown at all is because steep increases in research and development have more than offset the decline in research productivity, the study found.
Specifically, the number of Americans engaged in R&D has jumped by more than twentyfold since 1930 while their collective productivity has dropped by a factor of 41.
“It’s getting harder and harder to make new ideas, and the economy is more or less compensating for that,” Bloom said. “The only way we’ve been able to roughly maintain growth is to throw more and more scientists at it.”
The paper spelled it out bluntly in numbers: “The economy has to double its research efforts every 13 years just to maintain the same overall rate of economic growth.”
Bloom initiated this research a year ago, inspired to dig deeper after speaking on a panel at the SIEPR Economic Summit that discussed “Is the Productivity Slowdown for Real?” He admits the paper — and its somewhat pessimistic analysis — has dampened his previous, more optimistic stance.
“I’ve changed my mind,” Bloom said. “Pretty much all mainstream economists have become rather depressed about productivity growth.”
At the 2016 SIEPR Summit, Bloom was more positive about the nation’s productivity, saying its declining rate was only a temporary effect of the financial crisis of 2008. He even caricatured ways of looking at U.S. productivity levels, and contended the up-and-down swings between 1950 and 2010 do not necessarily signal a long-running trend of slow productivity growth.
A year ago, Bloom recalled, “I thought we were recovering from a huge global recession and we’re about to turn around.”
Now, his perspective takes into account new insights that research productivity — one of the underlying components of economic growth — has been clearly dropping for decades.
“This paper says productivity growth is slowing down because ideas are getting harder to find,” Bloom said.
While the study builds on the earlier work of Jones and others on R&D, the new paper also weaves a tight connection between empirical data on what’s happening in the real world and growth models.
The robust finding of declining idea productivity has implications for future economic research, the paper concluded. The standard assumption in growth models has historically been a constant rate of productivity, and “we believe the empirical work we’ve presented speaks clearly against this assumption,” it states.
Moore’s Law and beyond
Everywhere they looked, the researchers said they found clear evidence of how exponential investments in R&D have masked the decline in productivity. The tech industry’s signature guidepost, Moore’s Law, which marked its 52nd year in April, is a prime example.
Introduced in 1965 by Gordon Moore, the co-founder of computer chip giant Intel, the theorem postulates that the density of transistors on an integrated circuit would double roughly every two years, doubling computing power.
Moore’s Law has certainly played out — the computing power on a chip today is remarkable compared to even a decade ago — but the study found that the research effort behind the chip innovations rose by a factor of 78 since 1971.
Put another way, the number of researchers required today to maintain the innovative pace is more than 75 times larger than the number that was required in the early 1970s.
“The constant exponential growth implied by Moore’s Law has been achieved only by a staggering increase in the amount of resources devoted to pushing the frontier forward,” the paper stated.
Other industries also exhibited falloffs in idea productivity.
For instance, to measure productivity in agriculture, the study’s co-authors used crop yields of corn, soybeans, wheat and cotton and compared them against research expenditures directed at improving yields, including cross-breeding, bioengineering, crop protection and maintenance.
The average yields across all four crops roughly doubled between 1960 and 2015. But to achieve those gains, the amount of research expended during that period rose “tremendously” — anywhere from a threefold to a more-than-25-fold increase, depending on the crop and specific research measure.
On average, research productivity in agriculture fell by about 4 to 6 percent per year, the study found.
A similar pattern of greater-input-but-less-output followed in medical research. The study’s authors analyzed R&D spending on new, federally approved drugs against life expectancy rates as a gauge of productivity. They also examined decreases in mortality rates of cancer patients against medical research publications and clinical trials.
The empirical findings on breast and heart cancer suggest that at least in some areas, “it may get easier to find new ideas at first before getting harder,” the paper stated.
Turning its focus to publicly traded companies, the study found a fraction of firms where research productivity — as measured by growth in sales, market capitalization, employment and revenue-per-worker productivity — grew decade-over-decade since 1980. But overall, more than 85 percent of the firms showed steady, rapid declines in productivity while their spending in R&D rose.
The analysis found research productivity for firms fell, on average, about 10 percent per year, and it would take 15 times more researchers today than it did 30 years ago to produce the same rate of economic growth.