“General purpose technologies (GPTs) are engines for growth,…” wrote Erik Brynjolfsson, Daniel Rock, and Chad Syverson in The Productivity J-Curve, a working paper recently published by the National Bureau of Economic Research (NBER). “These are the defining technologies of their times and can radically change the economic environment. They have great potential from the outset, but realizing that potential requires larger intangible and often unmeasured investments and a fundamental rethinking of the organization of production itself.”
As we’ve learned over the past two centuries, there’s generally been a significant time lag between the broad acceptance of a major new transformative technology and its ensuing impact on companies, governments and other institutions. Even after reaching a tipping point of market acceptance, it takes considerable time, - often decades, - for these new technologies and business models to be widely embraced across economies and societies, - and for their full benefits to be realized.
In her 2002 influential book, Technological Revolutions and Financial Capital, economic historian Carlota Perez wrote that since the advent of the Industrial Revolution, we’ve had a major technological revolution every 60 years or so. First was the age of machines and factories in the latter part of the 18th century. This was followed by the age of steam, coal and iron in the early to mid 19th century; electricity and steel around the 1870s-1880s; and automobiles, oil and mass production in the early decades of the 20th century. Then came the computer and communications revolution in the latter part of the 20th century, ushering the transition from the industrial economy of the past two centuries to our present digital economy.
According to Perez, the economic transformations accompanying these technologies are composed of two distinct periods, each lasting roughly 20 to 30 years. First comes the installation period when the new technologies emerge into the marketplace, entrepreneurs launch many new startups, and venture capitalists encourage experimentation with new business models. This is then followed by the deployment period, when the now well accepted technologies and business models become the norm, leading to long-term economic and productivity growth.
The NBER paper also identifies two phases, investment and harvesting, and explains their evolution in the life cycle of a historically transformative technology. Since these technologies are general purpose in nature, they require massive complementary investments, such as business process redesign, co-invention of new products and business models, and the re-skilling of the workforce. Moreover, the more transformative the technologies, the longer it takes for them to reach the harvesting phase when they are widely embraced by companies and industries across the economy.
The decades-long time lags between the investment and harvesting periods has led to a kind of productivity paradox that’s puzzled economists seeking to reconcile exciting technological breakthroughs with slow near- and mid-term productivity growth.
For example, US labor productivity grew at only 1.5% between 1973 and 1995. This period of slow productivity coincided with the rapid growth in the use of IT in business, giving rise to the Solow productivity paradox, a reference to Nobel Prize MIT economist Robert Solow's 1987 quip: “You can see the computer age everywhere but in the productivity statistics.” But, starting in the mid 1990s, US labor productivity surged to over 2.5%, as fast growing Internet technologies and business process re-engineering helped to spread productivity-enhancing innovations across the economy.
Similarly, productivity growth did not increase until 40 years after the introduction of electric power in the early 1880s, because It took until the 1920s for companies to figure out how to restructure their factories to take advantage of electric power with new manufacturing innovations like the assembly line. And, while James Watt’s steam engine ushered the Industrial Revolution in the 1780s, its impact on the British economy was imperceptible until the 1830s because productivity growth was restricted to a few industries.
The authors called this phenomenon the Productivity J-Curve, because like the letter ‘J’, GPT productivity dips initially in its investment phase while later rising in the harvesting phase. The paper includes a model that explains these J-curve dynamics, and applies the model to help understand the Solow paradox of recent decades, as well as to analyze whether recent advances in AI, machine learning and related technologies indicate the emergence of AI as a 21st century GPT.
Their model takes into account both tangible and intangible inputs in evaluating total productivity growth, that is, the difference between the growth rates of all the inputs and all the outputs in a production process. Tangible capital inputs like physical equipment, infrastructure, and labor expenses are relatively easy to measure. However, along with such tangible inputs, a company spends capital on intangible inputs like innovative offerings, competitive business strategies, streamlined processes, talent development, and the creation of entirely new asset classes. The extensive intangible investments required to embrace a GPT and transform an organization are often forgotten, because they’re hard to quantify and their benefits accrue over a number of years.
“Suppose a company wants to become more ‘data-driven’ and reorganize its production processes to take advantage of new machine learning prediction technologies. This firm might want, for example, to change its labor mix to build more software and to teach its customers to order products online instead of in person. While the company develops online product ordering applications and business processes for that purpose, it will not be able to use those investment resources to produce more final goods inventory. At the same time, though, the capital assets the firm is building - institutional software knowledge in the company, hiring practices, organization building, and customer retraining to use digital systems - are left unmeasured on the balance sheet.”
“On the margin, the (present-discounted and risk-adjusted) value of these unmeasured assets equals the costs incurred to produce them. But during the period in which that output is foregone, the firm’s (traditionally measured) productivity will suffer because it will seem as though the company produces proportionately less output relative to its inputs. Later, when those hidden intangible investments start to generate a yield, it will seem as though the measured capital stock and employed workers have become much more productive. Therefore, in early investment periods productivity is understated, whereas the opposite is true later when investment levels taper off.” Eventually, input and output growth rates reach a steady state and the productivity measurement problems disappear.
Is there a way of measuring the value and productivity impact of a company’s intangible investments? The paper proposes a method based on the idea that hidden intangibles are still captured by the markets, and their investment value can be estimated using forward-looking measures derived from stock market valuations. It uses these methods to estimate the impact of intangible capital investments in R&D, software, and computer hardware by comparing a firm’s observable investments to its market valuation.
R&D investments are large, but since it’s a mature asset type that has persisted over the long term, its J-curve dynamics are at steady-state levels. In contrast, heavy capital investments in software and computer hardware are a more recent phenomenon, so J-curve dynamics are still present. This is particularly the case with software. “Software investment has been and continues to be growing faster than overall capital investment, and its level is sufficiently large to suggest that part of the productivity slowdown might be explained by a compositional shift of investment toward digital assets.”
“The Productivity J-curve explains why a productivity paradox can be both a recurrent and expected phenomenon when important new technologies are diffusing throughout the economy,” write Brynjolfsson, Rock and Syverson in conclusion. “Adjusting productive processes to take advantage of new types of capital requires the kind of investments the statistics miss. In future, after making appropriate adjustments accounting for the Productivity J-curve, we can see new technologies everywhere including the productivity statistics.”
Comments