Economics Innovation Technology

Are We Running Out of Innovation?

I come across a lot of interesting writing in my work, but I have to note that one of the most thought-provoking scholarly pieces I have read recently is the working paper published earlier this year by Nicholas Bloom, Charles I. Jones, Michael Webb (all Stanford) and John Van Reenen (MIT). Their paper is entitled, Are Ideas Getting Harder to Find?, and both the question they pose and the conclusion thy reach are worth consideration by all executives and certainly ones working in innovation.

The author’s basic proposition is that economic growth comes from smart (or perhaps lucky) people creating new and valuable ideas. Of course, we could counter that across history growth has come from other sources (exploitation of natural resources or monopolies, etc.), but let’s leave that aside for a moment. This source of long-term economic growth, they note, is “is the product of two terms: the effective number of researchers and the research productivity of these people (“idea TFP”).” In other words, Economic Growth (EG) = Idea TFP x Number of People Having Ideas.


The authors point out that most people assume that TFP is constant across a given number of researchers, regardless of their number. This makes sense to us: we assume two MIT PhDs will have twice as many good ideas as one. However, note the authors, their analysis reached a very different conclusion: “in many different contexts and at various levels of disaggregation, research effort is rising substantially, while research productivity is declining sharply.”

As the authors note:

The bulk of the evidence presented in this paper concerns the extent to which a constant level of research effort can generate constant exponential growth within a relatively narrow category, such as a firm or a seed type or Moore’s Law or a health condition. We provide consistent evidence that the historical answer to this question is no: idea TFP is declining at a substantial rate in virtually every place we look.

To back up their conclusions, the authors list several cases but two stand out. The first is the oft-noted Moore’s Law on the evolution of semiconductor productivity. The output of this Law is constantly cited; however, the input to this steady stream of progress is not.


Semiconductor TFP Over Time (Source: Authors)


“The striking fact,” note the authors, “is that [semiconductor] research effort has risen by a factor of 78 since 1971.” They go on to highlight the implications of this phenomenon:

This massive increase occurs while the growth rate of chip density is more or less stable: 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. Assuming a constant growth rate for Moore’s Law, the implication is that idea TFP has fallen by this same factor of 78, an average rate of 10.1 percent per year. If the null hypothesis of constant idea TFP were correct, the growth rate underlying Moore’s Law should have increased by a factor of 78 as well. Instead, it was remarkably stable. Put differently, because of declining idea TFP, it is around 78 times harder today to generate the exponential growth behind Moore’s Law than it was in 1971.

Their conclusions about cancer research are much the same. Using, the reduction of mortality as their indicator for TFP in this field, they note a similar pattern.

Cancer TFP Over Time (Source: Authors)


From the period 1975-2010, the ultimate impact of cancer research (measured here as “years of life saved”) decreased, which means that it took much more research in 2010 to effect the same reduction in mortality as in 1975.

Across all of the sectors analyzed the authors reached the same conclusion: TFP is declining at a substantial rate everywhere they looked. In fact, they concluded that TFP declines at “an average rate of 5.3 percent per year, meaning that it takes around 13 years for idea TFP to fall by half.” In other words, “the economy has to double its research efforts every 13 years just to maintain the same overall rate of economic growth.”

The authors seemed somewhat surprised by the consistency of this observed phenomenon:

We document qualitatively similar results essentially no matter where we look in the U.S. economy. We consider detailed microeconomic evidence on idea production functions, focusing on places where we can get the best measures of both the output of ideas and the inputs used to produce them. In addition to Moore’s Law, our case studies include agricultural productivity (corn, soybeans, cotton, and wheat) and medical innovations. Idea TFP for seed yields declines at about 5% per year. We find a similar rate of decline when studying the mortality improvements associated with all cancers and with breast cancer. Finally, we examine firm-level data from Compustat to provide another perspective on idea TFP. While the data quality from this sample is not as good as for our industry case studies, the latter suffer from possibly not being representative. We find substantial heterogeneity across firms, but idea TFP is declining in more than 85% of the firms in our sample. Averaging across firms, idea TFP declines at a rate of 12% per year.

At this point, it’s natural to wonder what might explains these observations. Retuning to their analysis of Moore’s Law inputs, the authors decide that it’s the initial success of a given research field that seems to cause, at least in part, its own future of decreasing TFP:

Idea TFP for semiconductors falls so rapidly, not because that sector has the sharpest diminishing returns — the opposite is true. It is instead because research in that sector is growing more rapidly than in any other part of the economy, pushing idea TFP down.

In other words:

Because it gets harder to find new ideas as research progresses, a sustained and massive expansion of research likewe see in semiconductors (for example, because of the “general purpose technology” nature of information technology), may lead to a substantial downward trend in idea TFP.

So the more good ideas a research population has, the more it attracts other researchers. The more smart people work on a problem, the less productive any individual researcher becomes over time. To their credit, the authors note that other investigations have reached complimentary conclusions:

Others have also provided evidence suggesting that ideas may be getting harder to find over time. Griliches (1994) provides a summary of the earlier literature exploring the decline in patents per dollar of research spending. Gordon (2016) reports extensive new historical evidence from throughout the 19th and 20th centuries. Cowen (2011) synthesizes earlier work to explicitly make the case. (Ben) Jones (2009) documents a rise in the age at which inventors first patent and a general increase in the size of research teams, arguing that over time more and more learning is required just to get to the point where researchers are capable of pushing the frontier forward. We see our evidence as complementary to these earlier studies.

The phenomenon this paper outlines is, of course, familiar to any hedge fund manager or VC, who can all commiserate with having to invest larger sums of money across [at best, quality-constant] deal flows that are not increasing at the same rate as investor deposits. What is interesting is that this phenomenon of diminishing returns might also apply to an intellectual/innovation, as well as financial, investment portfolio.

What, then, does this line of research imply for innovators whose job is to find and nurture good ideas? I see four possible implications of this, admittedly very early, research:

  1. It’s important to understand the TFP of the sector in which you are investing time and/or money. As we see from the analysis, it’s one thing to invest in a sector where TFP is still high and quite another to invest in one just trying to keep TFP constant.
  2. Research investment yields decline as investment dollars grow, so understanding where a serious TFP yield curve inflection happens is critical to maximizing returns on innovation investments.
  3. Innovation diversification is important, since research funding allocated across various sectors might yield higher overall returns than funds concentrated in one, particularly, attractive sector.
  4. “Hot” research fields seem to have the highest TFP risk, so invest carefully in the sectors that are attracting the highest levels of overall research efforts.

Though still preliminary, the analyses presented in this paper are worth a close read by anyone working or investing in innovation. The authors have expanded a interesting and relevant line of inquiry, and one hopes real insights can be drawn from this line of research while its own TFP yield stays positive.

One comment

  1. Carlos, could this be related to the phenomena Thomas Kuhn identified years ago: as research progresses (basic or applied) along a current, or established line, it more resembles retooling an existing paradigm, eventually becoming exhausted, versus radical jumps in thinking, where foundational assumptions are re-thought? This would lie at the heart of real innovation it seems to me.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: