President Trump’s New Economy Challenge (Part 13 of 20). The Network Technology Revolution (NTR) is defined by Jobenomics as the “perfect storm” of next-generation network and digital technologies that will (1) transform economies, (2) revamp existing institutions, businesses, labor forces and governments, (3) institute new and different ideas, beliefs, behaviors and cultures, and (4) change the very nature of human endeavor and work. While the NTR can create tens of millions of American jobs, it can also obsolete tens of millions of American jobs via automation that has already being accomplished to a much greater degree than most people realize. Unfortunately, this major economic and labor force issue has not been part of election season rhetoric from either political party, or clearly articulated by the Trump Administration.
The NTR is not today’s version of the 1980/90s Information Technology Revolution (ITR) 2.0. While both the ITR and NTR incorporate revolutionary technology, the NTR portends to be significantly more intrusive than its earlier and more benign ITR cousin. ITR tools were designed to assist mankind’s productivity via rule-based computation of routine-tasks. NTR agents are designed not only to augment, but also replace human endeavor via automation of non-routine tasks. As stated earlier, the NTR represents a perfect storm of technologies that emulates human form, attributes and intelligence. Not only does the NTR have the ability to create tens of millions of net new American jobs, it has the ability to eliminate tens of millions of American jobs via automation.
As skilled labor becomes less available or too costly, employers are turning to automation in order to augment, displace or replace the traditional workforce. While automation has been replacing routine manual labor tasks for decades, as evidenced by factory floor robotics, emerging NTR technologies, systems, processes and services are replacing non-routine cognitive tasks, skills, jobs and occupations at greater and greater rates.
By 2025, automated algorithms and smart machines could take on tasks equivalent to 140 million knowledge workers, equating to a global economic impact/savings of up to $6.7 trillion annually. Knowledge work automation is possible by only three of the three dozen NTR technologies: increased computer processing speeds and memory, machine learning and enhanced machine/human interfaces (such as speech recognition and other forms of biometric readers).
U.S. Occupations Subject To Computerization
According to a 2013 Oxford University study on computer automation “about 47% of total U.S. employment is at risk over the next two decades.” If Oxford’s estimates are correct, out of the 151 million U.S. workers, 71 million jobs could be at risk. It is incumbent on policy-makers, decision-leaders and NTR CEOs to plan now to mitigate this risk to the greatest degree possible.
The Oxford University study regarding the effects of computer automation on the American labor force is the first major effort to quantify what recent technological advances may mean for future employment and the labor force. Oxford analyzed 702 occupations from the U.S. Department of Labor. This Jobenomics chart above, derived from Oxford data, shows the probability of computerization of 100 occupations arranged from 0% (not computerizable) to 100% (fully computerizable).
A job is considered to be “exposed to automation” or “automatable” if the tasks it entails allows the work to be performed by a computer, even if a job is not actually automated. For example, technology has progressed to the point where secretarial and cashier jobs can be automated, but corporations and retail stores still employ approximately 6 million administrative assistants and cashiers in the United States.
The NTR’s impact will be felt across all industries that will become less labor intensive as NTR technologies, systems, processes and services are assimilated, which is happening at greater rates causing large swaths of the U.S. labor force to become less competitive against their mechanical and digital counterparts. A McKinsey Global Institute (MGI) report that showed the 44% of U.S. firms that reduced headcount during the Great Recession did so via automation. In the future, contingent workers will likely provide machines with the wherewithal to replace a substantial percentage of the human labor force with cheaper and more efficient mechanical forms of labor.
The Oxford study also acknowledges that political and sociological forces will likely restrict many of these jobs from actually being computerized. Historical objections to automation of factory floor manual labor eventually gave way to free-market forces. At the dawn of the Industrial Revolution (England 1811-16), Luddites tried to organize and destroy factory automation to preserve standard jobs. Today’s Luddites maybe able to slow down the rate of transformation but the economics of automation will eventually defeat techno-pessimists who are resistant to disruptive technologies and change.
In cooperation with Citi Global Perspectives & Solutions, Oxford University conducted two subsequent studies in 2015 and 2016 that addressed computer automation in greater detail.
The February 2015 Oxford/Citi study reaffirmed the 2013 study probability that 47% of the US labor force is at a high risk of automation. It also assigned the probability that 33% of the U.S. workforce is at a low risk of automation (namely the jobs that are highly creative and require social and cultural skills) and the remaining 20% at a medium risk of automation.
According to a 2015 study, “the dominant narrative now characterizing how global labor markets are responding to technological change is one of job polarization: the fact that employment growth has been most robust at the highest and lowest ends of the skills spectrum. The middle skill jobs, in contrast, contain the highest concentration of routine tasks and are thus relatively easy to automate.”
According to a Federal Reserve Bank of St. Louis analysis, the U.S. labor force is undergoing “job polarization” with declining middle-skill cognitive and manual routine jobs compared to increasing higher-skill cognitive and manual nonroutine jobs as shown. The Fed believes that the most likely drivers of job polarization are automation and offshoring, as both of these forces lower the demand for middle-skill occupations relative to high-skill occupations. Jobenomics includes the rising contingent workforce as a major factor as standard full-time work is replaced by temporary part-time and task-oriented work.
U.S. Employment by Type of Work
Source: Federal Reserve Bank of St. Louis, Census Bureau Current Population Survey
According to a report published by the U.S. Federal Reserve Bank of Kansas City, job polarization is a primary cause for the vanishing American middle-class. Per the Fed’s report, “Over the past three decades, the share of middle-skill jobs in the United States has fallen sharply. Middle-skill jobs are those in which workers primarily perform routine tasks that are procedural and repetitive. The decline in the employment share of middle skill jobs has been associated with a number of sweeping changes affecting the economy, including advancement of technology, outsourcing of jobs overseas, and contractions that have occurred in manufacturing. As the share of middle-skill jobs has shrunk, the share of high-skill jobs has grown, and that trend has drawn considerable attention. Less well known is the fact that the share of low-skill jobs has also risen. This employment phenomenon where job opportunities have shifted away from middle-skill jobs toward high- and low-skill jobs is called ‘job polarization’.”
From a Jobenomics perspective, low-skill jobs are the easiest to automate, whereas medium-skilled jobs are the easiest to bifurcate into task-oriented work that can be performed by a combination of humans and machines. While the NTR is creating new positions for high-skilled workers, it is causing increased competition for medium and low-skilled workers who are increasingly being replaced by artificially intelligent algorithms and machines. Increased competition causes workers to accept lower wage jobs or forcing medium and low-skill workers into the contingent workforce or out of the labor force entirely. As discussed in detail in the Jobenomics Unemployment Analysis, the number of able-bodied adults that voluntarily have departed the U.S. labor force has grown from 68 million to 95 million citizens over the last seventeen years, and the number of people working part-time or in other “non-employee” contingent jobs is now 40% of the employed workforce.
The major reason for concern regarding computer automation and other NTR-related technologies is that these advancements benefit the few rather than the many. While NTR has produced remarkable achievements like the iPhone, Google, eBay, Facebook, Skype and a myriad of other advancements in genome and autonomous systems, median wages have stagnated in about half of all OECD countries since 2000. Unlike 19th Century Industrial Revolution innovations that created gains for both producers and workers, the NTR has benefited mainly the producers and is displacing workers via the revolution in network technology. In other words, while the digital age has been a blessing to consumers, it is changing the world of work in ways that may make a growing share of workers worse off.
The January 2016 Oxford/Citi study takes a deeper dive into the effects of automation not only in the United States but the rest of the world. Building on the Oxford’s original work showing 47% of the U.S. workforce at risk, recent data from the World Bank suggests the risks are higher for other countries. Equivalent figures for India are 69% and 77% for China. As compared to the developed world, emerging and developing economies have a much higher rate of low-skilled workers that are more susceptible to automation.
As labor-intensive industries succumb to more automated-intensive industries, middle-income countries like China and India will face a major dilemma inasmuch as more automation will be required to compete internationally. The major downside to these countries is the likelihood that they may have to reverse labor force gains that recently raised hundreds of millions of Asians out of poverty. In addition, many emerging economies with large low and medium-skilled populations are especially vulnerable to the so-called “middle income trap”, where a country gets stuck at a level of development out of poverty without the wherewithal to elevate to levels of more advanced economies.
China created its economic miracle via labor-intensive industries that required low and medium-skilled labor. Over the last two decades, China lifted 700 million people out of poverty largely by state-controlled labor-intensive industries in urban areas. Today, China is considered a middle-income country with a per capita income of $7,600, compared to $54,600 for the United States. Over the last five decades only a few countries (Japan, Israel, South Korea and Singapore) have been able to escape the middle-income trap and evolve to the high-income club. NTR automation is likely to make the jump even harder since it advantages smaller high-skilled nations and disadvantages larger low-skilled nations.
In terms of manufacturing, computer automation incentivizes companies to move facilities closer to consumers, which could reduce the offshoring trend. 22% of the study respondents believe that North America has most to gain from automation, while 24% believe China has the most to lose.
Within the United States, there is a wide disparity between metropolitan areas in regard to automation. Cities like, Boston, Washington DC, Raleigh, New York, San Francisco are considered low risk, while, Fresno, Las Vegas, Greensboro, Harrisburg and Los Angeles are considered higher risk cities. Generally speaking, diversified, rich, highly-educated cities are least exposed. The cities that are most exposed are older single industry centers replete with poorer and lower skilled workers. Cities with a high concentration in information, communication and network-centric industries are the best prepared to embrace the upsides of NTR automation and the up-skilling that these industries produce for their labor forces. The most promising industries for job creation are in information technology, automotive, robotics, 3D printing, health and medical, which collectively will generate over 50% of all new American jobs. The bulk of these jobs will be in small businesses and micro-businesses, which is the sweet spot for non-core contingency businesses like independent contractor, consultants and high-skill contract labor.
76% of the 2016 Oxford study respondents consider themselves as “techno-optimists” compared to 21% who see themselves as “techno-pessimists.” From a Jobenomics perspective, this is an extremely important statistic. Too often, pundits overstate the extent of machine substitution and ignore the positive aspects of human/machine partnership in terms of increased productivity, earning potential and skilled labor demand.
The introduction of machines to the labor force has not historically hurt the labor force. The machine-smashing Luddites certainly did not foresee the massive labor force expansion caused by the industrial revolution in the 1800s. Agricultural machines displaced tens of millions of farmers and farmhands but created the food services industry. Mass-produced automobiles displaced skilled artisans but led to an explosion in transportation and commerce related industries. Power tools displaced construction workers but made residential and commercial buildings more affordable and the creation of vastly more construction jobs. The Information Technology Revolution (ITR) of the late 20th Century created the information age and the billions of new jobs.
On the other hand, a high percentage of economists believe that while automation has not historically reduced employment, the disruptive power of the NTR makes future artificially intelligent systems vastly superior to their simpleton automated forerunners. Highly intelligent machines and software are likely to displace many more humans than the new jobs they create.
According to the Federal Reserve, a recent poll on the impact of technology on employment and earnings of leading academic economists conducted by the Chicago Initiative on Global Markets, 43% of the respondents agreed with the statement that “information technology and automation are a central reason why median wages have been stagnant in the US over the past decade, despite rising productivity,” whereas, only 28% disagreed or strongly disagreed with the statement.
The 2016 Oxford/Citi study calculates that “between 2002 and 2012, 33 legacy jobs were lost for every new digital job that was created.” The 2015 Oxford/Citi study cited three primary reasons why the NTR is likely to be different from previous technology revolutions: (1) the pace of change has accelerated; (2) the scope of technological change is increasing; and (3) unlike innovation in the past, the benefits of technological change are not being widely shared — real median wages have fallen behind growth in productivity and inequality has increased.
With a proper U.S. national strategy, that currently does not exist, the NTR can replace jobs lost to automation via the creation of new small business and career paths. Jobenomics agrees with the 2016 Oxford/Citi report recommendations on the top four policy responses to the risks of automaton impacting labor and wealth distribution are (1) invest in education, (2) encourage entrepreneurship, (3) fund active labor market policies that help people find jobs, and (4) fund research that enables innovation and enhances employment.
Jobenomics agrees with Oxford/Citi with the following caveats. Rather than investing in education, invest instead in skills training and certification as opposed to degree based education. While degree-based programs are absolutely necessary for many citizens, it is not an affordable or timely path for many at the bottom of America’s economic pyramid or entrepreneurs who are focused on a particular innovative opportunity. Jobenomics also asserts that the focus ought to be on business creation as the primary means to create occupations that will satisfy next-generation business opportunities, align the workforce with new labor market realities with emphasis on the growing contingent workforce and developing new industries in the emerging energy and network technology revolutions.
As history has demonstrated, technological innovation initially has a destructive effect as automated systems replace labor, but as new industries are established, employment expands along with wage growth. Some believe that the NTR may be different from an industry standpoint. Jobenomics does not concur. A proper national strategy, led by visionary and patriotic corporate leaders, entrepreneurial contingent workforce professionals and government strategic planners, could transform the U.S. labor force and economy for generations to come. To be successful, this strategy would have to maximize productivity and prosperity of both the standard and contingent workforce, as well as achieving a proper balance between the existing traditional economy and the emerging digital economy.
The business world has already started the replacement process. With the advent of computers and personal digital assistants, most businesses have mostly eliminated the secretarial workforce. Today, semantic (thinking) websites know our shopping and buying habits and modern e-commerce is rapidly upending traditional brick-and-mortar retailing. Intelligence agents are now entering the scene. Got a question, need a direction or need a solution? Just ask Apple’s Siri, Amazon’s Echo or IBM’s Watson for the answer.
When artificial intelligence approaches human intelligence, humans will be compelled to turn more decision-making to intelligence agents. Hypothetically, machines will eventually mature from general-intelligence to the level of human-intelligence at the point of technical “singularity” when machines become as cognitive as humans. Many experts believe that intelligence agents will achieve singularity as early as mid-century. However, in several critical domains, such as the worldwide financial system, singularity will occur much sooner.
Automation will slowly supplant cognitive work task by task giving rise to “centaurs” (a combination of human operators, and intelligent agents and smart machines). Smart machines (that communicate with humans) and intelligence agents (that learn human behavior) are entering the cognitive workforce at a greater and greater rate. Today, these automated machines/agents need human support to perform most tasks. However, they can perform enough complex tasks to reduce the need for full-time human labor, thereby giving rise to centaurs where contingent human workers will provide input as needed or warranted.
In conclusion, via the perfect storm of NTR technologies, systems, processes and service, automation of the U.S. labor force is likely to move from first-gear into overdrive within the first term of President Trump’s presidency. From a Jobenomics perspective, it is imperative that the incoming Trump Administration quickly establishes plans and policies to guide the automation engine in ways to maximize business and job creation in the new economy and mitigate the destruction of American businesses and jobs.
Stay tuned for the next installment in the President Trump’s New Economy Challenge series entitled, “Lawyer at Labor’s Helm” scheduled for release on 20 March 2017.
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About Jobenomics: Jobenomics deals with economics of business and job creation. The non-partisan Jobenomics National Grassroots Movement’s goal is to facilitate an environment that will create 20 million net new middle-class U.S. jobs within a decade. The Movement has a following of an estimated 20 million people. The Jobenomics website contains numerous books and material on how to mass-produce small business and jobs. Monthly website traffic exceeds one-half million hits, which is indicative of the high level of public interest regarding economic, business, labor force and workfare solutions. For more information, see Jobenomics Overview and the Author’s Biography.
