Employment And Automation

It seems to me….

The test of our progress is not whether we add more to the abundance of those who have much; it is whether we provide enough for those who have little.” ~ Franklin D. Roosevelt[1].

Major traditional industries have been lost throughout history and the resulting effect on employment has always been worker displacement whether the cause was technological advancement or globalization.

There is something familiar about fears that new machines will take everyone’s jobs benefiting only a select few and upending society[2]. Such concerns sparked furious arguments two centuries ago as industrialization took hold in Britain. People at the time did not talk of an “industrial revolution” but of the “machinery question”. First posed by the economist David Ricardo in 1821, it concerned the “influence of machinery on the interests of the different classes of society”, and in particular the “opinion entertained by the laboring class, that the employment of machinery is frequently detrimental to their interests”. Thomas Carlyle, writing in 1839, railed against the “demon of mechanism” whose disruptive power was guilty of “oversetting whole multitudes of workmen”.

The Luddites protesting mechanization of textile manufacturing when automated looms were introduced in the early 19th century at the beginning of the industrial revolution is only one of numerous similar examples: entire industries were decimated due to the introduction of the automobile; agricultural employment totally changed as a result of mechanized farming equipment…. Advances in computing have created then eliminated positions for keypunch operators, in data entry, and as computer operators.

While appearing similar to what has occurred in the past, this time could be different. When loss of job categories occurred in the past, workers were able to simply transition into employment with somewhat comparable foundational skill requirements in another category. Agricultural workers sought employment in the factories. Blacksmiths and stable employees became automotive mechanics. Now, manufacturing jobs in a variety of industries are being lost and there is nothing with which to replace those jobs; those industries are gone and will never come back. Similar to how the role of horses were eventually eliminated from agricultural production by the introduction of machinery, so shall general dependence on human labor diminish in increasing segments of employment. The future belongs to the next generation of industries whether it be nanotech, biology … whatever.

Technological innovation is not a silver bullet to achieve broad-based prosperity: dramatic improvements in technology, especially software, do not translate easily into wage increases for the average worker. For most of the second half of the twentieth century the economic value generated in the U.S. – the country’s productivity – grew hand-in-hand with the number of workers. But in 2000 the two measures began to diverge. From the turn of the century, a gap opened between productivity and total employment. By 2011, that delta had widened significantly, reflecting continued economic growth but with no associated increase in job creation. Throughout advanced economies, the share of national income paid out as wages has dropped precipitously since the start of the information technology and automation revolutions began in the mid-1970s, the first time this has happened in modern history. Unlike much of the 20th century we’re now seeing a falling ratio of employment to population, something that deserves reasonable concern.

Production in this second machine age depends less on physical equipment and structures and more on the four categories of intangible assets: intellectual property, organizational capital, user-generated content, and human capital. The Nobel Prize-winning economist Wassily Leontief[3] stated in 1983 that “the role of humans as the most important factor of production is bound to diminish in the same way that the role of horses in agricultural production was first diminished and then eliminated by the introduction of tractors”.

While computerization and offshoring were not the sole causes of declining opportunities for high school graduates, both factors played an important role. Diminishing fortunes of high school educated workers had two important consequences: (1) Many people faced downward economic mobility earning less real income than their parents had earned; (2) Education moved from being one source of upward mobility (along with generally rising earnings) to the main source of upward mobility. Even as the economy has improved, jobs and wages for a large segment of workers, particularly men without college degrees doing manual labor, have not recovered. Even in the best case, automation leaves the first generation of workers it displaces at a disadvantage as they usually do not have the skills to perform the innovative and more complex tasks required by new employment opportunities.

Computerization, automation, and artificial intelligence (AI) have ratcheted up the definition of foundational skills. Jobs are being disaggregated. More highly skilled portions of any job are requiring greater skill (with higher remuneration) while the relatively routine portion will be either automated or pay minimum wages. As knowledge becomes more abstract, the average person’s earnings have become increasingly correlated with educational attainment. In 1980 the average 40-year-old male with only a bachelor’s degree had weekly earnings 26 percent higher than the average 40-year-old male whose education stopped at high school graduation; by 2009 the gap had grown to 84 percent. Technology has increased competition not only for corporations but also for individuals; there are countless additional displaced applicants for a decreasing number of less-skilled positions. This competition for employment increasingly results directly from computerization and automation.

Skills-biased technical change has increased relative demand for highly educated workers while reducing demand for less educated workers whose jobs frequently involve routine cognitive and manual tasks. Skills-biased technical changes are resulting in a worsening of economic conditions and prospects not only for families of non-college graduates but also the children of those families. As the demand for labor decreases, so do wages for the relatively less skilled.

47 percent of U.S. jobs are vulnerable to automation[4]. The proportion of threatened jobs is much greater in poorer countries: 69 percent in India, 77 percent in China, and as high as 85 percent in Ethiopia. The cheapness of labor in relation to capital affects the rate of automation. Passing laws that make it less costly to hire and fire workers is likely to slow its advance. Scale also matters: farms in many poor countries are often too small to benefit from machines that have been around for decades. Consumer preferences are a third barrier.

When considering employment categories as candidates for automation, the most probable differentiator would seem to be between human and digital labor – allowing people to focus on those tasks where they have a comparative advantage over the computer and allowing computers to do the work at which they are most suited. While generally true – computers are very good at math but not at pattern recognition; computers cannot perform many tasks which even a child can do without difficulty – recent advances are changing expectations, similar to what has become fact in other areas. Increasingly, tasks considered extremely difficult or even impossible for computers to perform are being accomplished: autonomous vehicles, advanced pattern recognition…. People who previously felt relatively secure have found that some high-level reasoning requires little computation while basic sensorimotor skills entail considerable computational resources[5].

Bank of America Merrill Lynch predicted that by 2025 the “annual creative disruption impact” from AI could amount to $14 trillion-$33 trillion, including a $9 trillion reduction in employment costs thanks to AI-enabled automation of knowledge work; cost reductions of $8 trillion in manufacturing and health care; and $2 trillion in efficiency gains from the deployment of self-driving cars and drones.

AI, which will inevitably cost (and create) jobs as it automates various tasks, is going to be a contentious issue for decades to come. There are some things at which machines are simply better than humans – but humans still have plenty going for them. AI will be hailed and vilified in equal doses; there will be AI for good as well as evil. Still, AI is going to result in the elimination of jobs[6].

While productivity and employment traditionally tracked together, they became decoupled in the 1990s with productivity since then continuing to climb but the employment to population ratio, as well as the real income of the median worker, now lower than any point since then.

Record wealth creation has resulted from capital-based technological changes that substitute physical capital for labor increasing the capital owner’s profits and reducing the share of profits going to labor. Wealth distribution in the U.S. (as well as in much of the rest of the world) has therefore changed from a normal bell curve distribution to a power law (Pareto Curve) distribution as wealth increasingly flows exponentially to a decreasing percentage of recipients essentially eliminating the middle-class. This has resulted in the combined net worth of the wealthy significantly increasing while the median U.S. household income has precipitously decreased. Economic inequality if permitted to result in corresponding political inequality enables the privileged to gain further empowerment and still greater economic advantage in a self-serving destructive spiral.

The industry most affected by automation is manufacturing. For every industrial robot per thousand workers, up to six workers lost their jobs and wages fell by as much as three-fourths of a percent[7]. Even if overall employment and wages recover, there will be losers in the process, and it’s going to take a very long time for these communities to recover. Robots are to blame for up to 670,000 lost manufacturing jobs between 1990 and 2007 and that number will rise as the installed base of industrial robots is expected to quadruple.

While tempting to blame employment decreases on globalization as many politicians attempt to do, this hypothesis is not completely supported by facts as labor markets throughout the world are experiencing similar declines as automation and digitization result in reduced lower-level educated employment demand. The overall effect of price equalization is compensated by locating manufacturing closer to sales to reduce product transit duration and to customers, engineers, designers, and adequately trained workers.

Automation, more than other factors like trade and offshoring that President Trump campaigned on, has been the more significant long-term threat to blue-collar jobs. Researchers determined that – “large and robust negative effects of robots on employment and wages” – remained strong even after controlling for imports, offshoring, and software that displaces jobs, worker demographics, and the type of industry.

Unrestrained globalization results in factor price equalization; competition will bid the factors, labor or capital, to a single common price. People who work in parts of the country most affected by imports generally have greater unemployment and reduced income for the rest of their lives. Over time, automation has had a more significant effect than globalization and would have eventually eliminated those jobs offshored anyway; only 13 percent of manufacturing job losses resulted from offshoring while the remainder were to enhanced productivity due to automation. Apparel making was hit hardest by trade while computer and electronics manufacturing were primarily affected by technological advances.

Accelerating technological change necessitates more rapid response and adjustment by displaced workers and institutions but governments have not formulated any plausible approach to respond to the possible massive social upheaval that economic dislocation resulting from computerization and automation is likely to effect. Worker displacement is a political problem for if jobs are eliminated, displaced workers no longer pay taxes. Neither companies or industrial robots pay sufficient taxes to compensate for what is lost from all the employees who are displaced.

Labor economists say there are ways to ease the transition for workers whose jobs have been eliminated by automation including retraining programs, stronger unions, more public-sector jobs, a higher minimum wage, a bigger earned-income tax credit, and free access to higher education and vocational programs for the next generation of workers. Additionally, without a substantial investment in research, there is no future.

That’s what I think, what about you?

[1] Franklin Delano Roosevelt was an American statesman and political leader who served a record four terms as the 32nd President of the United States from 1933 until his death in 1945 and emerged as a central figure in world events during the mid-20th century directing the U.S. government during most of the Great Depression and World War II.

[2] The Future of Work, The Economist, http://learnmore.economist.com/story/57ad9e19c55e9f1a609c6bb4, 9 September 2016.

[3] Wassily Wassilyevich Leontief, was an American economist of half Russian-Jewish descent notable for his research on how changes in one economic sector may affect other sectors.

[4] Frey, Carl Benedikt, and Michael Osborne. From a study published while at Oxford University, 2013.

[5] Moravec’s Paradox: the discovery by artificial intelligence and robotics researchers that contrary to traditional assumptions, high-level reasoning requires very little computation but low-level sensorimotor skills require enormous computational resources.

[6] Heath, Nick. Why AI Could Destroy More Jobs Than It Creates, And How To Save Them, TechRepublic, http://www.techrepublic.com/article/ai-is-destroying-more-jobs-than-it-creates-what-it-means-and-how-we-can-stop-it/, 1 November 2016.

[7] Acemoglu, Daron, and Pascual Restrepo. The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment, Massachusetts Institute of Technology, http://economics.mit.edu/files/11512, May 2016.

About lewbornmann

Lewis J. Bornmann has his doctorate in Computer Science. He became a volunteer for the American Red Cross following his retirement from teaching Computer Science, Mathematics, and Information Systems, at Mesa State College in Grand Junction, CO. He previously was on the staff at the University of Wisconsin-Madison campus, Stanford University, and several other universities. Dr. Bornmann has provided emergency assistance in areas devastated by hurricanes, floods, and wildfires. He has responded to emergencies on local Disaster Action Teams (DAT), assisted with Services to Armed Forces (SAF), and taught Disaster Services classes and Health & Safety classes. He and his wife, Barb, are certified operators of the American Red Cross Emergency Communications Response Vehicle (ECRV), a self-contained unit capable of providing satellite-based communications and technology-related assistance at disaster sites. He served on the governing board of a large international professional organization (ACM), was chair of a committee overseeing several hundred worldwide volunteer chapters, helped organize large international conferences, served on numerous technical committees, and presented technical papers at numerous symposiums and conferences. He has numerous Who’s Who citations for his technical and professional contributions and many years of management experience with major corporations including General Electric, Boeing, and as an independent contractor. He was a principal contributor on numerous large technology-related development projects, including having written the Systems Concepts for NASA’s largest supercomputing system at the Ames Research Center in Silicon Valley. With over 40 years of experience in scientific and commercial computer systems management and development, he worked on a wide variety of computer-related systems from small single embedded microprocessor based applications to some of the largest distributed heterogeneous supercomputing systems ever planned.
This entry was posted in Agriculture, Agriculture, AI, AI, Artificial Intelligence, Artificial Intelligence, Automation, Automation, Automation, Britain, China, China, College, Computerization, David Ricardo, Economic, Education, Education, Employment, Employment, employment, Ethiopia, Farming, Globalization, High School, Income, India, Inequality, Inequality, Infrastructure, Jobs, Jobs, Jobs, Low-Skill, Luddites, Luddites, Manufacturing, Manufacturing, Middle Class, Middle-Income, Off-Shoring, Outsourcing, Pareto Curve, Research, Robots, Skilled, skilled, Taxes, Technology, Technology, Thomas Carlyle, Unemployment, Wages, Wages, Wassily Leontief, Workers and tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , . Bookmark the permalink.

1 Response to Employment And Automation

  1. I like this post, enjoyed this one thank you for putting up. “I never let schooling interfere with my education.” by Mark Twain.

    Like

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