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  • [Economics/Productivity/IT]

    Putting the Great Stagnation Behind Us

    By Global Trends Editor Group

    For the American economy, the quarter century after World War II represented the “golden age” of the Mass Production Techno-Economic Revolution. Real GDP growth averaged nearly 4 percent annually, driven by labor productivity growth of nearly 3 percent.

    Then, almost exactly 50 years ago in 1973, came a sudden and shocking slowdown. This involved a slower pace of productivity and economic growth, which has continued for so long that it seems inescapable. We call this period in history “the Great Stagnation.”

    To appreciate the implications of this downshift, consider a question posed by Jason Furman when he was the top economist in the Obama White House.

    “… what if productivity growth from 1973 to 2013 had continued at its pace from the previous 25 years? In this scenario, incomes would have been 58 percent higher in 2013. If these gains were distributed proportionately in 2013, the median household would have had an additional $30,000 in income. [And] Had income inequality and labor force participation not worsened markedly, middle-class incomes would be nearly twice as high.”

    And as economist James Pethakoukis of the American Enterprise Institute reminds us:

    - From 1973 through 1981, productivity growth averaged just 1.1 percent. And,

    - Since then, productivity growth has averaged around 2.1 percent despite a Dot-Com spurt from 1997-to-2005.

    Given the expected collapse in labor-force growth due to Baby Boomer retirements, falling fertility rates and immigration restrictions, productivity growth will be indispensable if we are going to achieve future economic growth.

    As a chart in the printable issue clearly shows, real GDP growth in the 1950s and 1960s averaged 4% a year despite the workforce growing more slowly than in the 1980s.

    The key to this was strong Multi-Factor Productivity growth enabled by technological and managerial innovation. That means better technology and better business models were deployed to address high-value opportunities.
     
    This is perfectly consistent with the Theory of Techno-Economic Revolutions originally formulated by Professor Carlotta Perez, which has been discussed extensively in Trends.

    According to that framework, our enormous economic progress since the beginning of the original Industrial Revolution 250 years ago, has been characterized by repeated techno-economic revolutions.

    Each of these five revolutions has been dominated by a transformative, general-purpose technology. The current revolution which began in the early 1970s has been dominated by “digital information processing,” characterized first by integrated circuits, then by the Internet, and now by artificial intelligence.

    Each techno-economic revolution reliably follows a clear-cut pattern influenced by human behavior and maturation of the transformational technologies.

    The fifty years since 1973 encompassed the speculative “installation phase” of the Digital Techno-economic Revolution, as well as its inevitable “crash,” leading to “institutional re-composition.”

    The Mass Production Techno-economic revolution beginning in 1908, underwent a similar speculative boom which ended in a “crash.” Like the early 2000s, the 20 years that followed the 1929 crash was a time of financial angst as well as business and technology innovation which prepared the economy for the next “golden age.”

    From a human suffering perspective, the combination of the Dot-Com crash, the Great Recession and the current geopolitical strife have added up to “a minor inconvenience” when compared to the Great Depression and World War II. 

    However, they’ve served the same purpose in terms of resetting our system for the “Golden Age” to come.

    Notably, just as many feared a return to the Great Depression when the world emerged from World War II, many today fear that our best days are behind us. Pundits and financial advisors, typically fear that the Great Stagnation we’ve faced since the Dot-Com crash will continue or lead to further decline.
     
    Fortunately, many factors indicate that a new “Golden Age,” analogous to the 1949-to-1973 period, is just ahead for the United States.

    Consider the facts.

    As during the late 1940s, disrupted labor markets, production bottlenecks, a shortage of housing and persistent foreign threats are forcing innovation, migration and increased capital investment in North America.

    Fortunately, North America’s abundant human, financial and intellectual capital, combined with its unique mix of natural resources, business climate, and geography, has positioned it for strong growth in these turbulent times.

    The key to maximizing this opportunity is to efficiently integrate innovations in technology and business models, which exploit the Digital Techno-Economic Revolution’s underlying economics.

    To appreciate this, keep in mind that the growth surge of the 1950s and 1960s occurred during the deployment phase of the Mass Production Techno-Economic Revolution.

    In that context, innovation delivered huge productivity benefits because it focused on meeting the world’s demand for inexpensive standardized products ranging from automobiles and televisions to McDonalds’ hamburgers and Heinz Ketchup. Mass markets, mass media, mass production and global logistics leveraged the “learning curve” and “economies of scale” to make almost everything better, faster and cheaper. Plastics, antibiotics, vaccines, fertilizers and appliances dramatically changed people’s lives for the better.
     
    Foundational capabilities provided by automobiles, highways, mass retail, television and ubiquitous electricity had emerged in the 1920s, 1930s and 1940s, creating a foundation for the mass production Golden Age.

    Similarly, personal computers, broadband Internet, cloud computing, smartphones, the gig economy and rudimentary artificial intelligence have emerged over the past 30 years, setting the stage for the digital Golden Age. In both cases, the foundational capabilities not only facilitate the creation of new solutions but help shape consumer acceptance of those solutions.

    At the same time, just as explained in prior issues, technologies are becoming so powerful and inexpensive that they are creating formerly unimaginable possibilities. For instance, by 2030 the total ownership cost of AI accelerator hardware is expected to drop by a factor of 238,000 versus 2014.

    Inevitably, better price-performance will lead to new, innovative solutions, which change our lives and lead to the wholesale reallocation of resources.

    As we explain in trend #2, the biggest potential impact of AI-based deep learning technologies on scientific progress and economic growth, will come from making researchers orders-of-magnitude more productive.

    According to new research, “Since its emergence around 2010, deep learning has rapidly become the most important technique in Artificial Intelligence, producing an array of scientific firsts in areas as diverse as protein folding, drug discovery, integrated chip design, and weather prediction.

    As more scientists and engineers adopt deep learning, it is important to consider what effect widespread deployment would have on scientific progress and, ultimately, economic growth.

    [The researchers] find that deep learning’s idea production function depends notably more on capital. This greater dependence implies that more capital will be deployed per scientist in AI-augmented R&D, boosting scientists’ productivity.

    Specifically, our point estimates, when analyzed in the context of a standard growth model of the U.S. economy, suggest that AI-augmented areas of R&D would increase the rate of productivity growth by between 1.7-and 2-fold compared to the historical average rate observed over the past 70 years.”

    That sort of increase in U.S. productivity growth would be highly significant and, by itself, would enable the U.S. economy to grow as fast in the future as in the past.

    The researchers go on to say, “With the widespread adoption of deep learning raising ideas production in the economy to this level of capital intensity, we would expect the productivity growth rate to rise to between 2.1% and 2.4%.

    To put that in context, this would amount to an increase of between 1.7-and 2-fold relative to the 1.2% average U.S. productivity growth from 1948 to 2021, and a 2.6-to-3-fold increase versus the post-2000 growth rate of 0.8% (reported by the Federal Reserve in 2022).”

    Another leading indicator of the coming Golden Age is the recent favorable trend in entrepreneurship. As University of Maryland economist John Haltiwanger recently told CNBC, “Even with the volatility we’re still [seeing a business formation rate] 30 percent higher in 2022 than in 2019.

    People are being optimistic about the future and that’s a good sign.” According to the Census Bureau, there were almost 433,000 new business applications in October 2022; that’s up from 413,000 as recently as June 2022 and 313,000 in December 2019, before the Covid pandemic began.

    Furthermore, during the pandemic, monthly new business applications soared, topping out at 552,000 in July 2020, declining to 350,000 by Christmas, and rebounding to 500,000 by mid-2021.
     
    This upturn is also good news in a longer-term context. It’s a good sign for an economy if it can shift, or “reallocate,” capital and workers to their most productive use. A key mechanism for this process is the creation of new businesses.

    Until the pandemic, the business formation trend had been going in the wrong direction since the late 1970s.

    Three charts from the Economic Innovation Group appear in the printable issue. These indicate a broader slowdown in key measures of American entrepreneurial dynamism in the years prior to the pandemic.

    These disappointing metrics include the share of total employment in startups, the job reallocation rate, and the interstate migration rate.

    So, for those who understand that “creative destruction” is a necessary factor driving employment, productivity, and living standards, these trend were very worrisome. Fortunately, the most recent data on business formation and related metrics is good news.

    According to a recent research study titled, “ Surging Business Formation in the Pandemic: Causes and Consequences ” by University of Maryland economist John Haltiwanger and Ryan Decker of the Federal Reserve, “Applications for new businesses surged during the COVID-19 pandemic.

    We find evidence that surging applications is associated with increased creation of employer businesses and related job and worker flows. Applications rose most in industries rooted in pandemic-era changes to work and lifestyles, with significant cross-industry restructuring.

    Surging applications were quickly followed by increased births of employer establishments with notable associated job creation, and establishment entry is positively correlated with business applications across industry and geography. 

    We also observe a strong increase in job reallocation across firm age groups and a tight spatial correlation between applications and excess job separations (a proxy for quits). Within major cities, applications, net establishment births, and excess job separations exhibit a “donut pattern” with less growth in city centers than in the surrounding areas.

    Our findings strongly suggest that the pandemic surge in business applications was followed by true employer business creation with significant labor market implications.”

    So, what does this upturn imply?

    First, given the volatile nature of startups, it will take several years to see how this story plays out.

    Second, that multiyear analysis will indicate if the forces “dampening the pace of business entry and business dynamism more generally” in recent decades, have faded or are just being overwhelmed by new pro-dynamism forces. And,

    Third, it remains to be seen if this startup surge “is associated with a burst of innovation, with startups being an important component of the experimentation leading to that innovation.

    If the answer is yes, the implications for economic growth are very positive. As Haltiwanger and Decker point out, “hints of this possibility may be seen in the industry composition of surging applications and establishment openings, with high-productivity industries like non-store retail, software publishing, computer systems design, and data processing apparently seeing especially elevated entry.”

    As you can see, there are many reasons for optimism despite the turmoil all around us. However, just as many experts expected the post-World-War-II economy to return to the depression-era “normal” that had persisted for so long, many today expect the “great stagnation” to continue into the future. Others see the ongoing “phase-change” in technology and conclude that a golden age resembling the 1950s and 1960s lies around the corner.

    Just as we saw 70 years ago, no one can be sure until it happens. So, managers, investors and policymakers will need to carefully monitor emerging threats and opportunities. They also need to expect turbulence. Those who understand the possibilities but remain flexible, will have a huge advantage.

    Given this trend, we offer the following forecasts for your consideration.

    First, an economic Golden Age resembling the 1950s and 1960s will soon begin.

    The exact timing is hard to assess because so many factors are in play. However, it’s fair to say that AI price-performance has reached the point where explosive deployment across myriad applications is attractive.

    The impact of new devices, materials, processes and business models will be felt across the entire economy to an extent we have not seen in 50+ years.

    Second, while most new businesses fail, some of the new startups will be gamechangers.

    For more than a decade, a large share of Millennials have said they want to be entrepreneurs. Now they are finally at the stage of life when they have the skills needed to “give it a try.” With tech firms laying off programmers and engineers for the first time in two decades, the talent is available to launch a wave of new firms. A few will become the “unicorns of the 2020s.”

    Third, the expected recession will not stifle the emergence of successful firms in the 2020s any more than it did in the 1970s.

    Remember that Apple, Microsoft and many other tech giants emerged in the 1970s, despite multiple recessions. That’s because economic stresses create new opportunities and free up critical assets. And,

    Fourth, the secular bull market which began in 2013 will continue into the mid-2030s.

    That market anticipated the deflationary surge in innovation and productivity associated with cloud computing and AI. Despite interruption by cyclical bear markets like what we saw in 2022, the long-term bull market will continue as productivity surges.

    However, productivity growth is likely to continue longer than strong stock market gains. During the Mass Production Revolution, productivity growth persisted throughout the “maturity stage” which ended in 1972. However, the Dow peaked in 1966 marking the end of “the Golden Age.”

    Resource List
    1. Peterson Institute for International Economics. July 9, 2015. Jason Furman. Productivity Growth in the Advanced Economies: The Past, the Present, and Lessons for the Future.

    2. Faster, Please!. January 2, 2023. James Pethokoukis. ( Un)Happy 50th anniversary of the Great Stagnation! But there’s hope!

    3. Economic Innovation Group. October 2022. The Case for Economic Dynamism: And Why It Matters for the American Worker.

    4. Arxiv. December 15, 2022. Tamay Besiroglu, Nicholas Emery-Xu, and Neil Thompson. Economic impacts of AI-augmented R&D.

    5. National Bureau of Economic Research. June 2021. John C. Haltiwanger. Entrepreneurship During the COVID-19 Pandemic: Evidence from the Business Formation Statistics.

    6. FEDS Notes. May 06, 2022. Ryan A. Decker and John Haltiwanger. Business entry and exit in the COVID-19 pandemic: A preliminary look at official data.