Does Technology Aid the Workforce?

I always thought that engineer’s aided the workforce by developing new products that put people to work. After all, the invention of the transistor put millions to work and new technology aided the growth of the computer, automotive, and aircraft industries.

In the 21st century the situation may be reversed with too much technology causing a decrease in jobs. At least that’s the opinion of Carl Benedikt Frey and Michael A. Osborne of the University of Oxford in the U.K. These are people with the appropriate credentials. Frey is with the “Programme on the impacts of Future Technology,” and Osborne is in the Department of Engineering Science at Oxford. They highlighted only one aspect of increased technology: computerization, and it is a major consideration.

A September 2013 paper by Frey and Osborne asks the question: “The Future Of Employment: How Susceptible Are Jobs To Computerization? To answer the question they reviewed papers from dozens of sources that covered the subject. And, they employed a methodology to categorize occupations according to their susceptibility to computerization. Then, they implemented the methodology to estimate the probability of computerization for 702 detailed occupations, and examined expected impacts of future computerization on the US labor market.

Motivation for the paper came from John Maynard Keynes’s frequently cited prediction of widespread technological unemployment “due to our discovery of means of economizing the use of labor outrunning the pace at which we can find new uses for labor.” John Maynard Keynes was a British economist whose ideas have fundamentally affected the theory and practice of modern macroeconomics, and transformed the economic policies of governments. His ideas are the basis for the school of thought known as Keynesian economics, and its various offshoots.

Frey and Osborne noted that “over the past decades, computers have substituted for a number of jobs, including the functions of bookkeepers, cashiers and telephone operators.” And, they pointed out that “recently the poor performance of labor markets across advanced economies has intensified the debate about technological unemployment.” Although they said “there is ongoing disagreement about the driving forces behind the persistently high unemployment rates, a number of scholars have pointed to computer controlled equipment as a possible explanation for recent jobless growth.”

“The impact of computerization on the labor market is well-chronicled in the literature with the decline of employment in occupations mainly consisting of tasks following well-defined procedures that can easily be performed by sophisticated algorithms. For example, studies emphasize that the ongoing decline in manufacturing employment and the disappearance of other routine jobs is causing the current low rates of employment. Besides the computerization of routine manufacturing tasks, studies have documented a structural shift in the labor market, with workers reallocating their labor supply from middle-income manufacturing to low-income service occupations. “Arguably, this is because the manual tasks of service occupations are less susceptible to computerization, as they require a higher degree of flexibility and physical adaptability. At the same time, with falling prices of computing, problem-solving skills are becoming relatively productive, explaining the substantial employment growth in occupations involving cognitive tasks where skilled labor has a comparative advantage, as well as the persistent increase in returns to education.

“According to Brynjolfsson and McAfee (2011), the pace of technological innovation is still increasing, with more sophisticated software technologies disrupting labor markets by making workers redundant. What is striking about the examples in their book is that computerization is no longer confined to routine manufacturing tasks. The autonomous driverless cars, developed by Google, provide one example of how manual tasks in transport and logistics may soon be automated. In the section “In Domain After Domain, Computers Race Ahead”, they emphasize how fast moving these developments have been. Less than 10 years ago, in the chapter “Why People Still Matter”, Levy and Murnane (2004) pointed at the difficulties of replicating human perception, asserting that driving in traffic is insusceptible to automation: “But executing a left turn against oncoming traffic involves so many factors that it is hard to imagine discovering the set of rules that can replicate a driver’s behavior.” Six years later, in October 2010, Google announced that it had modified several Toyota Priuses to be fully autonomous (Fig. 1).

Fig. 1. The Google driverless car involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car."

To the authors’ knowledge, no study has yet quantified what recent technological progress is likely to mean for the future of employment. “This present study intends to bridge this gap in the literature. Although there are indeed existing useful frameworks for examining the impact of computers on the occupational employment composition, they seem inadequate in explaining the impact of technological trends going beyond the computerization of routine tasks.”

Current literature distinguishes between cognitive and manual tasks on the one hand, and routine and non-routine tasks on the other. “While the computer substitution for both cognitive and manual routine tasks is evident, non-routine tasks involve everything from legal writing, truck driving and medical diagnoses, to persuading and selling. In the present study, we will argue that legal writing and truck driving will soon be automated, while persuading, for instance, will not. Fig.2 shows the ENON personal assistance robot.

Fig. 2. Enon was created to be a personal assistant. It is self-guiding and has limited speech recognition and synthesis. It can also carry things.

“Drawing upon recent developments in Engineering Sciences, and in particular advances in the fields of Data Mining, Machine Vision, Computational Statistics and other sub-fields of Artificial Intelligence, we derive additional dimensions required to understand the susceptibility of jobs to computerization. Needless to say, a number of factors are driving decisions to automate and we cannot capture these in full. Rather we aim, from a technological capabilities point of view, to determine which problems engineers need to solve for specific occupations to be automated. By highlighting these problems, their difficulty and to which occupations they relate, we categorize jobs according to their susceptibility to computerization. The characteristics of these problems were matched to different occupational characteristics, allowing us to examine the future direction of technological change in terms of its impact on the occupational composition of the labor market, but also the number of jobs at risk should these technologies materialize.”

“While computerization has been historically confined to routine tasks involving explicit rule-based activities , algorithms for big data are now rapidly entering domains reliant upon pattern recognition and can readily substitute for labor in a wide range of non-routine cognitive tasks. In addition, advanced robots are gaining enhanced senses and dexterity, allowing them to perform a broader scope of manual tasks. This is likely to change the nature of work across industries and occupations.”

In this paper, we ask the question: how susceptible are current jobs to these technological developments? To assess this, we implemented a novel methodology to estimate the probability of computerization for 702 detailed occupations. Based on these estimates, we examine expected impacts of future computerization on labor market outcomes, with the primary objective of analyzing the number of jobs at risk and the relationship between an occupation’s probability of computerization, wages and educational attainment.”

“We distinguish between high, medium and low risk occupations, depending on their probability of computerization. We make no attempt to estimate the number of jobs that will actually be automated, and focus on potential job automatability over some unspecified number of years. According to our estimates around 47 percent of total US employment is in the high risk category. We refer to these as jobs at risk – i.e., jobs we expect could be automated relatively soon, perhaps over the next decade or two.”

“Our model predicts that most workers in transportation and logistics occupations, together with the bulk of office and administrative support workers, and labor in production occupations, are at risk. These findings are consistent with recent technological developments documented in the literature. More surprisingly, we find that a substantial share of employment in service occupations, where most US job growth has occurred over the past decades are highly susceptible to computerization. Additional support for this finding is provided by the recent growth in the market for service robots and the gradually diminishment of the comparative advantage of human labor in tasks involving mobility and dexterity.”

“Finally, we provide evidence that wages and educational attainment exhibit a strong negative relationship with the probability of computerization. We note that this finding implies a discontinuity between the nineteenth, twentieth and the twenty-first century, in the impact of capital deepening on the relative demand for skilled labor. While nineteenth century manufacturing technologies largely substituted for skilled labor through the simplification of tasks, the Computer Revolution of the twentieth century caused a hollowing-out of  middle-income jobs . Our model predicts a truncation in the current trend towards labor market polarization, with computerization being principally confined to low-skill and low-wage occupations. Our findings thus imply that as technology races ahead, low-skill workers will reallocate to tasks that are non-susceptible to computerization – i.e., tasks requiring creative and social intelligence. For workers to win the race, however, they will have to acquire creative and social skills.”

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