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On the Unemployment That Technology May Generate

As the fourth industrial revolution brings fears of unemployment, what can history teach us about technology’s impact on labor markets?

technology unemployment essay

Jack Welch, the veteran former CEO of General Electric, once commented that you don’t know if you have a great company until it’s gone through a near-death experience. What is called the “fourth industrial revolution” is in full swing and its potential impact on the labor market reminds us of Welch’s maxim. Once again, however, history comes to the rescue by furnishing us with helpful examples. Let’s look at what “technological unemployment” is, what drives it, what lessons history can teach us, and then draw a conclusion.

In his 1930 essay “Economic Possibilities for our Grandchildren”, Keynes introduced the concept of “technological unemployment” (although he didn’t call it that). The British economist saw technological disruption as having the virtue of generating new jobs, the “vice” of destroying other jobs, and also the potential to boost productivity which would mean we worked less (he predicted a fifteen-hour working week, something he clearly got wrong). In his essay Keynes ventured that although in the medium term the creation of new jobs would offset the destruction of others, in between there might be some friction in which job destruction was temporarily higher than job creation and consequently some groups of workers would be hit by unemployment.

When we look at the emergence of the fourth industrial revolution as technological disruption, there are several areas that once again raise the alarm bells that Keynes sounded. In particular, the impact of artificial intelligence, whether in its new formulation as generative AI, based on large language models, in its ChatGPT-style expressions or in its other variants involving task automation, the influence of robotization and the advent of autonomous vehicles (especially trucks) will have far-reaching consequences for the employment market.

Jobs will be destroyed gradually rather than suddenly.

This “alarm” is not just a possibility – it is now a reality. For example, the United States shed six million manufacturing jobs between 2000 and 2020. Contrary to claims, the “culprit” has not been China’s accession to the World Trade Organization (only one of the six million is associated with Chinese trade) but rather manufacturing process automation.

Academic studies unpacking the impact of automation, especially of the first factor (artificial intelligence), on the labor market try to break down the tasks in occupations so as to identify the ones which can be automated. We all perform tasks in our daily lives which could be automated. The fact that a task will be carried out by a machine in the future should not be a bad thing in itself. Tomorrow it will free up our time, time which we can spend on other, more creative pursuits. However, studies warn that occupations where more than half of their tasks could be automated are likely to die out. Albeit with significant differences in methods, they generally conclude that one in four jobs might “disappear.” Often the headlines are “scary.” Nevertheless, jobs will be destroyed gradually rather than suddenly and new ones will also slowly but surely emerge, although this creative destruction also results in groups that will find it more difficult to match the new skills in demand.

The concern prompted by this state of affairs makes it important once again to draw on history. With the first industrial revolution came the Luddites, groups of workers who in response to the threat posed to craftsmanship by process mechanization turned to terrorism against factories. This was so widespread that in 1810 the British Parliament introduced the death penalty for attacks on production facilities and the government had to commit large numbers of Redcoats to counter the Luddites at a time when it was also fighting Napoleon in Spain. Yet in spite of the threat of factories, unemployment went back to normal. The advent of the tractor in the early 20 th century triggered a similar scare in economies where farming accounted for almost half the workforce (Europe and the US). It did destroy many agricultural jobs as productivity rose, but many new jobs were also created, first in industry and later in the service sector. The computer revolution in the 1960s engendered a similar process.

What lessons does the history of these three episodes hold for us? Firstly, the fears were relatively unfounded. Unemployment rates in the OECD today are the lowest on record. Secondly, technological revolutions led to educational revolutions: mass primary schooling was introduced during the first industrial revolution (children were no longer needed in craft workshops), mass secondary schooling with the agricultural revolution (young people were no longer needed on farms) and mass access to university for young people with the computer revolution. In my view, this time round the fourth industrial revolution will bring with it another educational revolution: lifelong learning. The challenges and opportunities shaped by technological developments will compel us to approach education as an ongoing process so that we can cope with the sweeping changes which are set to unfold in the labor market.

As Mark Twain once said: “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”

This article was originally published in Spanish in El Confidencial .

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Technological unemployment

Technological unemployment occurs when developments in technology and working practices cause some workers to lose their jobs.

Technological unemployment is considered to be part of a wider concept known as structural unemployment .

Example of technological unemployment

When labour-saving machines are introduced into the productive process, a firm can get rid of workers and produce the same amount of goods than before. Therefore some workers can lose their job.

Overall Impact on Unemployment

Technological change doesn’t have to increase overall unemployment, even though some types of workers may temporarily lose their jobs.

For example, in 1800, the majority of British workers were employed in agriculture. Labour-saving technology meant that food could be produced with fewer workers and so some agricultural labourers lost their jobs as farms used more machines.

However, as jobs were lost in agriculture, new jobs were created in producing machines.

Similarly, advances in computers and robots meant that firms could produce manufactured goods with fewer workers. The increased productivity in manufactured goods meant that the relative cost fell, giving more opportunities for people to work in the service sector.

Why does technological change not cause unemployment?

Let us suppose, technological change means we can produce food with fewer workers. Therefore, it is cheaper to produce food and the price of food should fall.

This means that people can spend a smaller percentage of their income on buying food. Therefore, people have more money to spend on other goods and services.

This increased demand for manufactured goods causes higher demand, and therefore there will be a higher demand for workers.

Technological innovation merely changes the types of jobs that occur in the economy. If labour productivity increases, we can enjoy a greater range of goods and services.

In 1920, there were 1.2 million coal miners in the UK. Technological change was a factor in the number employed fall to less than 5,000 in 2012. (decline of UK Coal)

Why Technological change can increase unemployment

If labour markets are flexible, then technological change will not cause unemployment. However, if there are labour market inflexibilities, then it can cause unemployment – at least, for a certain time period.

For example, due to technological change, coal miners may lose their jobs. However, due to occupational and geographical immobilities, they may be unable to take new jobs in the service sector. (e.g. a miner may not have skills to work in computers; he may find it hard to relocate).

In this case, technological change can cause a temporary increase in unemployment – which will last until the coal miners develop greater skills and ability to move.

Evidence of technological unemployment in the US?

technologocical-unemployment-labour-prod

Since 2000, productivity growth has become detached from employment growth. During the early 2000s employment grew at a slower rate than productivity. Since the end of the great depression, employment growth has picked up (though in a flexible labour market – many new jobs are low paid). But, this might indicate the gains in productivity from automation are leading to lower job growth (though there could be other factors too)

us-corporate-profit

In this period from 2000, there was a sharp jump in corporate profits, which suggests companies are gaining higher profit from increased productivity.

Labour share of GDP

labour-share-us

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technology unemployment essay

Mounting a Response to Technological Unemployment

Of all the great economic anxieties, there’s something particularly disquieting about the potential of artificial intelligence and other forms of technology replacing human labor. Some estimates indicate that as many as 47 percent of current jobs could be replaced by technology. 1 Even the most conservative estimates find nearly one in ten jobs at high risk of automation, which would still constitute a seismic change in the economy. While many have disputed that there is evidence of the rise of job-stealing robots, one does not have to look further than the rise of Amazon and the fall of traditional retail to see the potential for technology-related job los s. 2 The uncertainty related to rapid advances in technology adds to the anxiety—take the 4.1 million Americans employed in driving occupations who could be quickly displaced by developments in autonomous vehicles. 3 What matters is the pace of these changes—if they evolve over time, workers and communities have the chance to adapt to a new economy; but if they happen rapidly, large numbers of workers can get displaced without the skills needed to qualify for other available jobs.

Designing a policy response for such uncertainty is challenging. This report will focus on the trade adjustment assistance (TAA) program as a ready-made model that could be expanded to the challenge of technology-related job loss. While certain improvements are needed, TAA has the ingredients of a comprehensive response to job loss, including retraining, extended income support, case management, health care protection, wage insurance, and relocation assistance. It also provides a model for the delivery of additional assistance to workers who experience a shock from the economy, and a way for the “winners” in the economy to ensure that fewer workers are left behind by technological change.

The report will start with a review of the research to date on technology-related job loss, and the growing consensus on the types of tasks that are vulnerable to displacement. It will reflect on the research done on what we know about previous spells of major displacement and their impact on workers, especially the trade-related job losses addressed by trade adjustment assistance. It will then review the key elements of a policy response to this risk, focusing on the possibility of expanding trade adjustment assistance, proposing a pilot program that will first designate occupations as being vulnerable to technology-related job losses, and then open up applications by states to operate an economic adjustment program for workers in occupations experiencing significant job losses. Lastly, we will conclude with ideas for improving the underlying TAA program.

Technology and Unemployment

Will advances in technology lead to widespread unemployment? This is one of the most passionate public economic debates of our time, one set off by rapid advances in computer technology. While there is significant disagreement about whether technology would decrease levels of employment in the United States, there is substantial consensus about the types of tasks that may be at risk of being replaced by automated technologies.

Rapid Advances in Computing Technology

The rapid advances in the capacity of computing technology are raising the specter of “technological unemployment:” permanent job loss caused by labor-saving technology. In 1965, Gordon Moore, the co-founder of Intel, predicted that the per-dollar computing speed of microchips and other processors would double every year—a prediction so crucial that it’s become known as Moore’s Law. As chronicled by MIT’s Erik Brynjolfsson and Andrew McAfee in their book, The Second Machine Age , this prediction of exponential growth has largely come true. 4 For example, in 1996, the government invested $55 million in ASCII RED, the first supercomputer to reach one teraflop of processing speed; nine years later, the Sony Playstation reached this same processing speed at a cost of $100 per unit. The increase in processing speed is the underlying resource making possible previously unimaginable advances in artificial intelligence (AI) and automation.

The process of automation, with robots or computer programs doing the work once completed by humans, has been happening for decades, from factories where robotic arms work in assembly lines to office software that completes routine clerical work. The speed of computing alone is making these processes more effective. The computing power in a smart phone, alongside databases and handheld software, has empowered automated retail to rapidly outpace traditional retailers and wholesalers, leading to rapid transformations in the economy.

Moreover, automated technologies, like industrial robots, have generally been limited to tasks that could be programmed in advance and were repetitive. Tasks that seem relatively simple, like repairing a windshield or picking and packaging the right size and style of box in a warehouse, have been beyond the practical reach of computers because there are too many permutations to program in advance.

Artificial intelligence has the potential to tackle this next frontier of automation. AI allows computer-driven processes to learn how to do more complicated tasks by analyzing data and learning from trial and error. The increased processing speed has allowed computers to learn these tasks so quickly and efficiently that they now mirror aspects of human intelligence. A common example has been game-playing, wherein AI programs can rapidly analyze different moves and countermoves and troves of data, using that processing advantage to best even the highest-level human players. Well-known examples include Alphabet Inc.’s AlphaGo , which became the first computer to beat a professional Go player, and IBM Watson , who bested Jeopardy! all-time champion Ken Jennings.

The increased availability of data is creating new frontiers for artificial intelligence. Not only can computers analyze data more quickly: now there is much, much more data available to analyze. This combination has the potential to have automation become a much more powerful component in the economy, by automating tasks previously thought to be too complex for machines. Take driving, which was a task identified by experts as recently as 2004 as being unlikely to be automated. 5 The laser “eye” of the Google autonomous car can collect and analyze 1.3 million points of data per second, allowing it to create a 3D model of its surroundings that the car’s algorithms can react to and drive through. 6 This capacity will be enhanced as more cars on the road collect this data and share it with nearby vehicles—a capacity that already exists in smartphones and enables route-enhancing apps like Waze. In terms of robotics, this technology will enhance the ability of robots to learn how to do tasks by sensing their environment, calculating different scenarios, and engaging in rapid trial-and-error calculations. The advances will enable computerized processes to more closely resemble human decision-making, hence the term “artificial intelligence.” Assuming that Moore’s Law holds, these advances in AI will continue to propel the capabilities of automated technology into previously, and even currently, unimaginable territory.

Technology’s Effect on Overall Employment and Wages Is Unclear

Employment changes due to technology are nothing new in the economy. Rapid advances in technology have decreased agricultural employment from 40 percent in 1900 to less than 2 percent today, even though agricultural output has increased. 7 The underlying economic assumption is that AI and other “job-killing” technology improvements will only gain market share if they are more effective and productive than current labor. 8 The increase in productivity will increase income and spending power for individuals and companies, creating demand for new types of good and services. For example, the conventional wisdom was that the growth of ATM machines would largely replace bank tellers . 9 But in fact, they’ve allowed banks to increase the number of branches, each with fewer tellers focused more on customer service.

The key question is whether the rapid development of artificial intelligence will replace jobs faster than new uses for human talent are invented. This is a question that has been asked as far back as John Maynard Keynes, who warned of the possibility of technological unemployment, namely “unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.” 10 And at least part of that question is whether the rate of technological change will come faster than the ability of workers to adapt and, if necessary, retrain for new jobs. In recent years, leading thinkers as diverse as Andrew Stern , former president of the Service Employees International Union, 11 to Elon Musk of Tesla 12 have predicted that technology will replace so many jobs that a universal basic income will be needed to support the population. The World Economic Forum predicts that a technology-driven ‘fourth industrial revolution” will only add one new job for every three jobs eliminated. 13

Other analysts are more optimistic. McKinsey predicts that increases in productivity (0.8- to 1.4-percent increase in annual productivity growth) will maximize the current world workforce rather than replace it. David Autor, a professor of economics at MIT, concludes that the “complementarities between automation and human labor will [on the whole] increase productivity, raise earnings and augment demand for labor.” 14

Most analysts agree that the largest effects of automation on employment will be in coming years, through further technological advances. The most concrete evidence to date comes from the introduction of industrial robots in manufacturing. From 1990 to 2005, the quality-adjusted cost of industrial robots dropped down by one-fifth , leading to a trebling of use of robots in relevant industrial sectors. 15 A widely-cited econometric analysis by Daren Acemoglu and Pascual Restrepo, two economists at Boston University, estimates that each industrial robot reduces net employment by six workers, not just in the factories but in entire communities impacted by spillover effects. 16 To date, on a national scale these effects are small, amounting to approximately 40,000 jobs lost—for context we should note that, in recent years, trade policy changes and the import shock from China have contributed to at least four times as many job losses. 17 Indeed, the problem is that overall productivity growth in manufacturing has been growing too slowly due to limits in automation technology, as well as the slow pace of small- and medium-sized manufacturers to adopt high-productivity strategies, such as Toyota-style lean project management. 18 One analysis finds that occupational churn in the United States has decreased to historic lows, indicating that overall technological-related job change has slowed down in recent years. 19

The other primary concern is whether technology is contributing to increased inequality due to labor market polarization, with growth in the high- and low-paid ends of the workforce but slowing in the middle. Automated technologies (by robots and computers) are most adept at replacing tasks with well-codified procedures . 20 The first wave of automation in factories and the rise in personal computers led to decreases in routine middle-class employment. Examples include well-paid blue collar jobs, like machine operators in factories, that required reliability and attention to detail, but were repetitive enough in nature that they could be. Similarly, secretarial and sales occupations required orderly record-keeping, production of documents, and scheduling that was able to be programmed into word processing, spreadsheets, and database applications. During this period there were larger increases in both non-routine jobs that were low-paid as well as those that were high-paid. 21 Janitors and house cleaners are good examples of low-paid non-routine applications. Generally, the physical environment facing such cleaners is varied, as are the techniques need to clean (although in recent years technologies like iRobot’s Roomba have showed potential for automation). Professional jobs like management also embody a great degree of variability and would be described as anything but routine. In short, the rise of automated technologies helped to hollow out the U.S. middle class. The evidence of the causative role of technology on inequality is not conclusive, however. Another factor has been trade liberalization, which also contributed to the loss of blue-collar middle-class jobs over this period, and since 2000 the trend of growth in non-routine occupations at the top appears to have moderated. 22

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Task-based analysis and identifying at-risk jobs.

While there is great debate about the net employment impacts of artificial intelligence, automation, and related technologies, there is a growing research consensus on the types of work that could be impacted over the next several decades. Increasingly, research is using rich data on occupational duties contained in the U.S. Department of Labor O*NET database , which breaks down each occupation in the economy by the types of tasks for which they’re responsible. 23 The database collects detailed information on 974 occupations using the standard occupational classification, and is updated periodically through a data collection program with 277 descriptors for each occupation. 24

As described above, David Autor and his colleagues have analyzed this data and provide a broad typology of the types of jobs that are at greatest risk of being replaced by technology, including those that have already been impacted. Figure 1 further elaborates on the task-based analysis pioneered by Autor, and his colleagues. They divide occupational tasks by two major dimensions: whether the tasks are cognitive or manual, and whether the work required is routine or non-routine. As computing power increases, there will be more automated processes (including but not limited to robots) that are able to perform routine tasks more efficiently than humans; this applies to both cognitive and manual tasks. Non-routine, harder-to-automate work includes cognitive tasks that require problem solving, intuition, persuasion, and creativity, which can be divided into analytic work done by engineers and scientists or more interpersonal cognitive work like health care. As discussed, technology is also challenged to replicate non-routine manual occupations like repair jobs that require the ability to adapt to a variety of situations, complex visual and language recognition, and frequent interactions with other people. Their analysis finds that non-routine cognitive tasks occupied an increasing percentage of the economy from 1960 to 2000 at the expense of routine tasks, with trends flattening from 2000 to 2009.

Carl Benedikt Frey and Michael A. Osborne, two researchers at the University of Oxford, also used an O*NET task analysis to look further at non-routine occupational tasks at risk of replacement due to further advances in machine learning and mobile robotics. 26 Examples of non-routine cognitive jobs now being accomplished by technology include fraud detection, certain types of health care diagnosis, and monitoring of physical infrastructure, such as water pipes. Their main finding is that nearly half of U.S. workers (47 percent) are in occupations that could be replaced by technology in the coming decades, including a much larger share of low-wage service occupations than have been at risk in past waves of advances in computer technology.

Most importantly, for the purposes of thinking about how to prepare for possible worker displacement, Frey’s research (conducted in concert with robotics experts) identified a set of skills within jobs (as defined by O*NET) that serve as barriers to the computerization of tasks. These include unique human advantages in perception and manipulation (applying to non-routine manual tasks), such as the abilities of fingers to manipulate and assemble very small objects, and human advantages (over robots) in doing manual work in cramped worked spaces. They also codify aspects of human intelligence that AI has a harder time mimicking, including the creative ability to generate new ideas and solutions—a signature trait of many fields, such as business, engineering, health care, and education, and especially the fine arts and the broad area of social intelligence that requires gauging people’s reactions, negotiating, persuading, and assisting others.

While Frey and Osborne’s earlier research accelerated the debate about the impact of artificial intelligence and automation, further research by others has sharpened our understanding of the potential impact of the acceleration of technology. The McKinsey Global Institute has published an important series of reports using a similar task-level analysis. They first group tasks and then rank them by automatability. Collecting data, processing data, and predictable physical tasks score as most “automatable,” while managing people, applying expertise, interfacing with stakeholders, and unpredictable physical activities score as least automatable.

The institute finds that 50 percent of all working hours in the United States could be replaced if companies were able to “adapt currently demonstrated technology” in a cost-effective way to those tasks. While the conclusions are similar to those of Frey and Osborne, McKinsey ’s finer-grained research has pointed out that most occupations have a mix of tasks, including ones that can as well as ones that can’t be outsourced. 27 Figure 2 illustrates a critical take-away from the research. Mckinsey finds that less than five percent of occupations can be fully automated, while most U.S. jobs (60 percent) have 30 percent of tasks that could be automated. The future of these jobs that have at least 30-percent-automatable tasks is unclear: as technology takes on more tasks, jobs could be downskilled, if not eliminated. But with the right training, many of these workers could retain employment in jobs where technology complements their skills. Intelligent analyses of test results could, for example, transform radiology technicians from a highly-skilled to a medium- to low-skill job . 28 Another challenge is how technology is facilitating the fragmentation of occupational roles, with firms able to contract out for smaller tasks rather than hire workers with benefits. 29 Applying the methods developed by McKinsey, the Center for an Urban Future in New York concluded that only 7,000 jobs in New York City are fully automatable, but that one in three New Yorkers are in jobs that have 30 percent more of their tasks at risk of automation. 30

The World Economic Forum’s ongoing research has contributed to our increasing understanding that while technology will lead to large employment losses, the right reskilling approaches should allow workers to transition following technology-related job loss. The Forum’s initial work found that for every job gained through improvements in technology, three jobs would be lost, and that women would suffer disproportionately (for whom only one job would be gained for every five lost). 33 However, a subsequent analysis comparing the skills of jobs that the Forum predicts will shrink to those that will remain viable finds that 96 percent of workers could find a good fit in a new job, often by reskilling into a entirely new field of occupations. 34

Several other analyses have used similar task-based analysis. Mark Muro and colleagues at the Brookings Institution have analyzed the tasks of U.S. occupations and found that the proportion of tasks involving computers and technology has increased by 57 percent over the past fifteen years. 35 Similar to Autor, they find that the share of jobs with high and medium digital skills have increased, like software developers and financial managers (high digital skills) and auto mechanics and registered nurses (medium digital skills). And those with low digital skills, like security guards and construction laborers, have decreased. This too indicates that jobs are being changed by the demands of technology alongside a changing occupational mix, with automotive mechanic serving as a prime example of a job that now requires significantly more use of computer diagnostics than it did just a few years ago. Brookings’ work is a good reminder that when analysts are talking about the impact of automation and artificial intelligence changing the nature of work, they are not simply referring to a robot literally sitting in the seat once occupied by a worker. Rather, analysts are identifying the broad set of computer-assisted technologies that are changing how tasks are completed.

A more recent analysis by Bain forecasts net employment losses of 30–40 million workers in the United States, with large impacts on low- and middle-income workers that will deepen income inequality. 36 That net figure accounts for the 18 percent of employment losses that will be mitigated by income gains from increased productivity. As displayed in Figure 3, Bain forecasts that automation will heavily impact service-sector industries like retail, hotels, restaurants, and transportation, with employment losses after 2020 that could exceed major employment losses in agriculture, manufacturing, and construction during previous economic transformations. Bain notes that, in part, accelerating advances in automation and artificial intelligence are needed to maintain economic growth as the U.S. workforce decreases. A major portion of this growth is the development of “cobots,” robots that work in coordination with humans to get tasks done more productively, which may in fact make the United States more competitive with low-cost-labor nations like Mexico in manufacturing.

Research Findings Point Way to a Policy Response

In sum, the findings of the research done to date are promising for those developing public policy responses to the issue of technology-related job loss. There are now increasingly replicable methods for identifying tasks that are the most automatable, and which jobs have the highest concentration of such work. There’s a good understanding of which types of tasks are less likely to be impacted by technology due to constraints, such as the inability of artificial intelligence to replicate human creativity and certain physical requirements. There’s less clear, but still solid, understanding of how automation, artificial intelligence, and other computer technologies put jobs at risk. Also unclear is the pace of technological change, and to what extent workers will lose employment altogether as opposed to their jobs merely changing. In others words, it’s unclear whether there will be net employment loss, changes in the types of jobs in the economy but without major employment losses, or little change in occupational employment due to technology.

If net employment loss ends up being the result, there will need to be both policies to permanently boost the incomes of Americans and provide retraining. If there is less net employment loss, but significant changes in occupational mix and occupational demands, retraining and temporary adjustment policies will be more effective. Any policy response designed in the next several years needs to be geared towards addressing this substantial uncertainty, and with an awareness of the significant consequences of permanent job loss and the challenges of getting rehired at good wages.

Impacts of Worker Displacement

Americans fear the prospect of widespread technology-related job losses because of the dismal experience of Americans who permanently lose their jobs. However, many observers, including leading progressive thinkers, 37 have argued that productivity-enhancing technology grows the economy and thus will increase net employment.

Even if this is true economy-wide, there is overwhelming evidence that those individuals who are directly impacted by permanent job losses experience lasting damage to their career. Since the early 1980s, the Bureau of Labor Statistics has conducted the Displaced Worker Survey, which seeks to understand the impact on workers whose positions have been eliminated. Experienced workers who lose their jobs face a double disadvantage: they lose out on extra pay they’ve earned as a result of demonstrated loyalty and company-specific knowledge; and they are more likely to have skills made obsolete by technology and economic changes.

  • High rates of unemployment: Figure 4 displays data from the most recent Labor Department survey (2016), which reveals that 35 percent of those who had been laid off in the past three years had not been reemployed. 38 In January 2016, 15.9 percent of these workers were unemployed at a time when the national unemployment rate was 4.9 percent. Another study found that among those laid-off workers who exhausted unemployment benefits without finding a job during the last recession, 38 percent still remained without a job four to six years late r. 39
  • Reemployment pay cuts: Those who do find new jobs often endure big pay cuts: workers laid off during the last recession had an average earnings decline of 17.5 percent, greater than during the prior two recessions. 40 More than two-thirds of those unemployment insurance (UI) exhaustees who found employment had to switch their industry or occupation to do so. 41 Displaced workers face a very difficult road in getting back to their pre-layoff wage, with wage scarring as long as 20 years post-displacement . 42 Men lose an average of 1.4 years of post-displacement earnings if mass layoffs occur when the national unemployment rate is low (below 6 percent) and 2.8 years if it is high (above 8 percent). 43
  • Health impacts: The health impacts of permanent job loss are severe. Not only do long-term unemployed workers face stress, more than half put off needed health care . 44 The mortality effects of displacement are severe, with an increase in death rates of 10 to 15 percent many years after a layoff . 45 Research has found a consistent link between permanent job loss and suicide, contributing to this increase in mortality. 46
  • Older workers are less likely to be employed, and face greater earnings losses than do younger workers. 47 For example in 2011, the average period of unemployment for older job seekers (over fifty-five) was 52.2 weeks compared to 37.5 weeks for younger workers. 48 To give a sense of the relative difficulties, displaced men ages fifty to sixty-one are 39 percent less likely to be reemployed than otherwise similar men ages twenty-five to thirty-four. 49 Older workers cite job hiring as the most likely site of age discrimination, and the EEOC received more than 20,000 complaints of age discrimination in 2016. 50

The damage on workers is greatest when large numbers of workers lose jobs in a short period of time. Over their working lifetime, those who are displaced when the national unemployment rate is 8 percent lose 2.8 years of their pre-displacement earnings, compared to 1.4 years among those laid off when the unemployment rate is low (6 percent or less.) 51 This is one reason that the promise that liberalized trade will bring greater living standards and new jobs has fallen flat. For example, the liberalization of trade with China in 2000 rapidly exposed U.S. workers to import competition and job loss in manufacturing. Among those who lost their jobs because of the rapid increase in trade with China, 10 percent end up on federal disability insurance and permanently out of the labor force . High levels of trade exposure decrease the overall employment levels of entire communities, not just those who lost their jobs. 52

Policy Responses to Technology and Unemployment

Workers and communities need help cushioning the significant impacts of displacement described above. As major advances in technology threaten further waves of worker displacement, it’s time to think about more aggressive responses. Technologists and other leaders concerned about large job losses have proposed a universal basic income to provide minimum living standards and increase the leverage of displaced workers. As an alternative to a universal basic income, proponents of a federal jobs guarantee would have the government hire displaced workers and other long-term unemployed into beneficial community work, such as tutoring.

Workers and communities need help cushioning the significant impacts of displacement described above. As major advances in technology threaten further waves of worker displacement, it’s time to think about responses that could scale to meet potentially massive disruption. The United States spends only 0.1 percent of its GDP on active labor market policies to support transitioning workers, compared to 0.6 percent among other OECD nations. 53 Even if one accepts only the lowest, most conservative estimates of impending technology-related job losses, the current level of spending would still be insufficient. A more robust response would have a number of critical elements:

  • Funding for retraining: Retraining grants should not just focus on short-term training, but include the availability of longer-term training programs that often are needed to switch into a new occupation. 54 Importantly, training grants should include both short-term training and longer programs, like those at community colleges that can lead to a degree or a recognized post-secondary credential.
  • Income Support: Workers permanently dislocated from their jobs face an extended period of time wherein they will face no or lower wages. Ideally, wage replacement programs that allow workers to complete full-time training will result in reemployment at a good wage. 55 If that’s not viable, wage replacement should provide a bridge to retirement, make up for lost wages upon reemployment at a new job, or supplement earnings for workers working part-time while retraining in a classroom, in an on-the-job training program, or through an apprenticeship.
  • Work-based learning: Reemployment approaches should facilitate easy access to work-based learning, including but not limited to apprenticeships, accommodating those dislocated workers who are more likely to learn by doing than while in a classroom.
  • Reemployment services: Workers should receive personalized reemployment services that guide them into new positions, and even new occupations, using their existing skills. This should include help with relocation expenses if necessary, but many experienced workers who have not had to look for a job for many years can benefit from job search assistance that targets their own community. 56
  • Community economic development: Reemployment services only work if there are jobs available. Communities suffering from an overall general decline in employment need strategic federal economic development assistance.
  • Early warning: The government should play a proactive role in monitoring occupations that could be at risk through adaptations of currently available technologies and be prepared to seamlessly deliver services to a wide group of workers as soon as significant dislocations begin. 57 When appropriate, government should incentivize companies to retrain workers in ways that adapt them to new automated technologies and that maximize those technologies’ potential, rather than simply replacing the workers. 58

There are a variety of approaches to expand assistance to workers threatened by dislocation towards the vision outlined above. These include the creation of a universal program for all dislocated workers, the expansion of the Just Transition framework, and the expansion of Trade Adjustment Assistance (TAA) to technology-impacted workers. This paper goes into depth about the possibility and merits of expanding TAA, providing more details to an idea that has been briefly mentioned in the literature but not fleshed out. Before doing so, we will highlight the two other leading approaches, both of which also merit close attention from policymakers and researchers.

Universal Dislocated Worker Benefit: One approach is to create a stronger, universal program for displaced workers, regardless of why an individual has lost their job. Mark Muro of the Brookings Institution makes a compelling case for a universal adjustment benefit that would include access to job search counseling, training grants, relocation grants, and wage insurance, and which would apply to workers permanently losing their jobs due to trade, technology, or other reasons. 59 In its fiscal-year 2014 budget proposal, the Obama administration proposed consolidating different U.S. programs , including dislocated workers benefits provided by the Workforce Innovation and Opportunity Act (WIOA) 60 and Trade Adjustment Assistance into a combined universal dislocated worker program that would provide each worker with an $8,000-per-person training voucher and a seventy-eight-week extension of unemployment benefits so workers can complete that training. This fiscal-year 2014 proposal would have increased annual spending on dislocated workers by an average of $1.8 billion per year, essentially doubling current spending. 61 The advantage of these universal benefit proposals is that they would be far simpler to access and use than is our current patchwork of programs, and could adapt to a variety of threats to employment (including trade, globalization, climate change, and technology).

Just Transition: Just Transition, another framework gaining momentum in the labor movement , envisions a collaborative process among community, labor, and government and has been a key part of proposals to help industrialized nations move towards a clean economy. 62 In the example of the decommissioning of the Diablo Canyon nuclear plant in Southern California , an agreement negotiated extended pre-layoff notice to workers, retraining and redeployment provisions, and a “just transition” fund that made up for property tax dollars lost by the local community. 63 The focus of Just Transition on redeveloping communities is particularly welcome: retraining strategies can only work in local economies that are able to develop other employment opportunities. Applying the Just Transition process to technology-related job loss would require companies engaging in significant job losses to negotiate transition packages with their employers and community level readjustment efforts like the Base Realignment & Adjustment Commission to redevelop communities with high levels of job loss. 64

Protection of health benefits: Dislocated workers need continued access to health insurance and should have the option of maintaining access to their employer-based coverage through COBRA.

Expanding Trade Adjustment Assistance: Another option suggested by a number of experts, such as Robert Atkinson of the Information Technology Innovation Foundation, is to expand the existing Trade Adjustment Assistance program to cover technology-related job loss. 65 The main contribution of this report is to flesh out this option, which could provide a path for a relatively simple pathway to the policy vision outlined above. Congress has been more generous with funding TAA’s more targeted approach of support, allocating around five times more per participant to retraining than the WIOA Dislocated Worker program. TAA already has many of the elements listed above, unlike the more limited WIOA Dislocated Worker program. While the latter could be reformed into a generous universal program for all dislocated workers, including those who lose their job through technology, the historic stinginess of our aid to dislocated workers speaks to the political challenge to that approach. Extending TAA to include technology-related job loss provides a ready-made avenue for providing more generous reemployment assistance to the next major historical threat to employment in the United States.

The Case for Expanding Trade Adjustment Assistance

Our most generous program for dislocated workers.

Since the 1960s, Congress liberalized trade with the goal of boosting the economy , knowing that certain groups of workers would be negatively impacted. TAA was meant to cushion these negative impacts, helping redeploy human capital to a changing economy all while bolstering public support for trade. 66 Created in 1962, Trade Adjustment provides federal support both for tuition for retraining, extended income support so workers can provide for themselves and their families while they retrain, and an increasing array of reemployment options. Significant expansions to the program were made in the Trade Act of 2002 and the American Recovery Reinvestment Act in 2009, but then narrowed in 2011. 67 The program was most recently reauthorized in 2015 through 2021. The current benefits provided by TAA include:

  • Trade readjustment (TRA) benefits: TRA benefits provided extended income support beyond what is provided by unemployment insurance. TAA qualifies workers for 104 additional weeks of payments (at the same level) beyond what UI provides . Workers can only receive the full TRA allotment if they are in a retraining program, but can get a waiver of training in limited circumstances for the first twenty-six weeks of assistance. 68
  • Retraining: TAA pays for a wide variety of training programs, including post-secondary education, classroom training, apprenticeship, and customized training, as well as remedial education like language classes for workers for whom English is not their first language. The average per-participant spending on training in TAA is $11,000 . That’s far greater than the average short-term training provided by the WIOA dislocated worker services, which is just $2,861 per participant. 69
  • Continued health care benefits: TAA recipients can maintain their employer-based health insurance through the health care tax credit (HCTC), which covers 72.5 percent of a family’s premiums . Like the credits provided by Affordable Care Act, the HCTC is paid each month directly to insurance companies. 70
  • Wage insurance: TAA provides wage insurance, known as Reemployment Trade Adjustment Assistance (RTAA). RTAA recipients receive up to $10,000 over a two-year period. RTAA payments are equal to half of the difference between a TAA recipient’s pre-layoff salary and their new job. Only workers earning $50,000 or less in their new jobs are eligible for RTAA. 71
  • Relocation and job search allowances: TAA recipients can receive up to 90 percent of the expenses of relocating outside of their community in order to secure a good-paying job, up to a maximum of $1,250.
  • Case management and reemployment services: All TAA recipients are eligible for job counseling and case management, including assessments, development of an individualized employment plan, career counseling, and referrals to supportive services like child care.

Eligibility for TAA benefits is limited to workers employed at a firm that is trade-impacted. Each group of workers must petition for eligibility: petitions can be filed by the company, a union, or any group of three workers on behalf of a firm or subdivision of that firm. To prove that trade is a primary cause of their job loss, they must demonstrate one of the following:

  • An increase in competitive imports and a decrease in sales of the petitioning company in a narrowly defined similar good or service;
  • A shift in production to a foreign country, including moving of production overseas;
  • The U.S. International Trade Commission has found that the firm was a victim of unfair trade; or
  • That they have been laid off from a firm that supplies a TAA-certified firm.

The Department of Labor investigates petitions and makes determinations on them. A typical petition is reviewed and decided within fifty days of receipt. 72 Workers have twenty-six weeks after the petition is certified or after the date of the “adverse impact” (layoff or plant closing) to begin services.

TAA has evolved since its initial passage in 1962 to include a comprehensive set of services recommended by experts and based on international experiences. A lack of income support is one of the main reasons unemployed workers cannot complete training. 73 The basic twenty-six weeks of unemployment benefits are not enough time for most workers to find, enroll, and complete a meaningful training course. TAA allows for a wide variety of training options, spanning classroom training to apprenticeship —and it is one of the only retraining programs that would provide long enough retraining for a dislocated worker to claim a post-secondary credential. Unemployment rates remain far lower for workers with college degrees than for those without. 74

Starting with 2002 reform legislation, TAA has been expanded to include services beyond retraining. Most dislocated workers take a pay cut when they are re-employed, and wage insurance compensates them for part of that earnings loss. While wage insurance is not a silver bullet for the major challenges of long-term unemployment, this option is particularly relevant for certain workers who may be less motivated to pursue extended retraining programs. Older workers are one such population. RTAA has slowly increased as part of the TAA program, with 12 percent of participants receiving benefits. In addition, many commentators have noted that globalization has increased geographic inequality, and that workers should have the option of relocating to a new community that has greater employment options; TAA now offers such help. The combination of services that TAA provides more than earn its reputation as a Cadillac program. 75

Understanding the Results of TAA

In recent years, TAA has gotten an undeserved reputation as an ineffective program. 76 TAA does not have an easy job: its recipients are older than the average workforce , adding to reemployment challenges, and moreover they are concentrated in trade-impacted communities that have experienced overall declines in employment and income. 77 Despite these barriers, in 2015, 73 percent of TAA participants found employment in the quarter after completing the program, and 92 percent of those retain that employment six months after acquiring it. 78 That’s better than the WIOA dislocated-worker program, which posted scores of 65 and 84 percent on the same measures over the same time period. 79 As shown in figure 5, this trend has held in recent years, with TAA consistently outperforming WIOA.

The negative reputation is largely the result of an evaluation of TAA funded by the U.S. Department of Labor (DOL) that found that those receiving assistance from the program earned about $3,000 less during a four-year follow up period than did those who were not in the program. On its face this finding is not surprising. TAA recipients can be out of work for up to two years in order to attend full-time retraining programs while receiving benefits, making a four-year assessment a problematic framework. The authors of the assessment recognize this, emphasizing that a longer follow-up period would be needed to find out if incremental gains in monthly earnings made up for the time spent away from the labor market in a retraining program. 80

The authors of this well-designed evaluation dug deeper than this headline, providing data that reveals some of the strengths of TAA—chiefly that individuals who completed TAA training and found a job in the field in which they’d trained had earnings gains likely to dwarf short-term earnings losses. For example, TAA recipients who completed their training and found a job in their fields received a $5,000-to-$6,000-per-year earnings boost. 81 The problem was that only 37 percent of those who completed training were able to find employment in the occupations they trained for. 82 This is not simply a failing of the program or its model, but rather reflects the fact that TAA recipients are laid off in communities with few good jobs, regardless of what retraining can be accomplished. As detailed in Amy Goldstein’s book, Janesville: An American Story ,  community colleges seeking to engage TAA participants faced a labor market where even occupations termed “in-demand” faced major downturns in hiring as decreases in manufacturing reverberated through local economies. 83

The current TAA program serves a population facing significant barriers to being reemployed . As stated above, during the recession, as few as half of those permanently separated from a job in the past three years were employed when surveyed. 84 The dates of the DOL’s TAA evaluation also count against the significance of its findings, in that it compared workers certified for TAA between November 2005 and October 2006. 85 Thus, workers who opted for extended retraining entered the labor market just as the recession began in December 2007, while the comparison group was looking for work while the labor market was strong. (Moreover, the TAA program adopted a number of critical reforms in 2009, which were not available to workers studied under the evaluation).

A more apt comparison is among TAA recipients and those not on TAA who exhausted their regular unemployment benefits and were forced to look for work in a declining labor market. And the DOL evaluation does find that TAA participants compared favorably to those who exhausted their unemployment insurance without finding work but were not in TAA. In the last four quarters of the follow-up period, TAA participants were 11.3 percentage points more likely to be employed than were a comparison group of unemployed workers who had exhausted their benefits and were not eligible to to benefit from TAA. Indeed, those who were in TAA would only have to earn $757 more per year for the rest of their career (after the four-year period in which they earned less) to be better off than UI exhaustees over their lifetime. 86 A longer-term evaluation would likely find that those who completed TAA and moved on to employment did better than those who just exhausted UI with no option to extend benefits for additional training. In short, it’s time for a closer look at the results of TAA, and to put to bed the idea that it’s an “ineffective” program.

Improving TAA

Historically, the greatest TAA-related concern among those researching active reemployment measures is that the generous benefits provided by TAA have been difficult to access. A Demos analysis found that 1.5 million jobs were lost to trade from 2002 to 2007, but only 1 million of those jobs were certified for TAA. 87 Structurally, the group petitioning process is laborious and limits the numbers of workers who become certified for TAA, and even if certified, many don’t collect. For decades experts and Congressional leaders have explored alternative means for certification, such as certifying entire industries and not just individual workplaces. 88

Another major concern is the limited wage replacement provided by TRA benefits. TRA benefits were reduced from 70 percent of prior earnings to UI levels (typically capped at 50 percent) in 1982, and were only payable (with limited exceptions) to workers in retraining programs. This modification substantially reduced the share of jobless workers receiving TAA benefits. 89 It also made it more difficult for many workers to complete training. Struggling to pay bills on a TRA check that is at most half of their prior wages, many TAA recipients drop out of training before being able to complete their program, or fail to sign up for training at all. International examples like Denmark’s flexicurity program pay up to 90 percent in wage replacement for workers in retraining. 90

As Congress considers the reauthorization of TAA in 2021, it will have the opportunity to improve the program in multiple ways.The first set of reforms should involve bringing the program back to the standard set by the American Recovery and Reinvestment Act. TAA legislation passed in 2011 peeled back several important reforms made in the Recovery Act, reforms that need to be restored:

  • Benefit length: One key issue is allowing workers enough time to complete meaningful training and education. In particular, TAA recipients should be allowed an additional twenty-six weeks of TRA benefits for workers who need remedial education, before entering occupational or classroom training. TAA should return to its 2009 status, offering jobless workers time to complete a full two-year program after their regular unemployment benefits, by offering a total of 130 weeks (a net of 104 weeks after the twenty-six weeks of UI). Current rules limit the last thirteen weeks to students demonstrating progress, which could dissuade enrollment in longer-term programs.
  • Wage insurance: Wage insurance is not a silver bullet to the problem of job loss, and should not weaken the focus of TAA on retraining for good-paying careers. 91 That being said, wage insurance is covering a slowly increasing percentage of TAA benefits (now 12 percent of what TAA participants receive) and constitutes a useful benefit for workers that go back to work with a pay cut. Wage insurance should be restored to the $12,000 max over two years (an equivalent of $500 per month) and the cap should be increased from $50,000 to $55,000.
  • Training waivers: The 2011 legislation eliminated three reasons that workers could waive training and still receive extended TRA benefits: when they had a definite date to be recalled to work; when they had marketable skills and did not need retraining; and when they were within two years of retirement. Restoring this last provision is of high priority. Part of TAA’s role is to compensate workers and communities for the collateral damage of free trade, and creating a reliable bridge to retirement is a cost-effective way to do so.

Beyond returning to its 2009 assistance levels, there are several other ideas that might strengthen the TAA program. These include several ideas that have been proposed in previous reauthorization debates, as well as other, more speculative changes:

  • Easing certification: Simplifying the certification process would make it easier for more bona-fide trade-impacted workers to receive benefits. The U.S. Government Accountability Office (GAO) found that a prior congressional proposal to certify an entire industry as TAA-eligible if firms in that industry had three TAA certifications in the past 180 days could as much as double the number of workers covered. 92
  • Link automatic investigations to WARN Notices: Under the Work Adjustment and Retraining Notification (WARN) Act of 1988, any firm that lays off more than fifty workers must give at least sixty days notice to the workers and the government. WARN should be modified to require firms to notify the government when the layoff is a result of a shift of production overseas or as a result of import competition for a similar article or service. This WARN notice should trigger an automatic investigation by the state rapid-response unit to initiate a TAA petition for approval by the U.S. DOL. In this model, states would have a responsibility of including TAA screening and application support in their rapid response requirements, interviewing workers, unions, and employers, and filling out the required details for the petition. Federal rapid-response funding should be increased accordingly.
  • Improving notification: States should be required to notify all workers potentially eligible for UI. To facilitate this, companies should be required to submit a list of employees and suppliers at certified facilities. State DOLs should use UI and business records to proactively reach out to all workers, and Congress should provide resources to state UI agencies to complete a data match and do proactive outreach to impacted workers.
  • Further promotion of on-the-job training and apprenticeships: The Trump administration’s fiscal-year 2019 budget proposes to refocus TAA “on apprenticeship and on-the-job training [OJT], earn-as-you-learn strategies that ensure that participants are training for relevant occupations.” 93 However, TAA already removed some of the policy barriers to OJT and apprenticeships in 2002 reforms, specifically allowing customized training for employers and removing a requirement that employers must retain OJT participants for six months after the training is over. 94 However, those in OJT and apprenticeships lose access to TRA benefits because they are working—instead, workers could be incentivized to choose OJT or apprenticeships by allowing them to collect a partial TRA check. 95 In addition, TAA could operate on a sliding scale up to 75 percent of the cost of OJT, as WIOA does (TAA currently pays 50 percent). 96
  • Prioritize sectoral partnerships: WIOA now requires states to develop sectoral partnerships to bring together businesses and the training/education community to develop training that is related to the current and future needs of sectors. TAA is now a mandatory partner of state WIOA planning, and the Department of Labor should closely monitor the synergy of TAA training and the increased use of sectoral partnerships in local labor market areas.
  • Paths for workers not interested in retraining: In practice, workers enroll in TAA primarily if they are interested in training. In particular, WIOA and Wagner–Peyser staff should do a better job of enrolling anyone TAA-eligible among their clients for the purposes of receiving relocation assistance or wage insurance, if the client and program agree that such would be the best path forward. One approach would be to require an automated data match between TAA certification and UI, so that UI claimants attending reemployment services appointments (RESEAs) would be automatically notified about wage insurance.
  • Buy into social security: Retraining and reemployment become more challenging for workers approaching retirement age. In addition to waiving retraining, TAA could be used to buy into social security benefits. TAA-certified workers could be allowed to collect social security early, such as at age fifty-eight. The funds from TAA (equivalent to 104 weeks of income support) could be added to the total lifetime actuarial value of social security benefits, meaning that this expansion would come at no cost to social security. For the TAA-eligible worker, they may receive a smaller social security than if they had retired at age 62. TAA would limit the reduction by replacing $36,600 of the lost actuarial value of social security.
  • Permanent reauthorization: The parameters of TAA have frequently changed, with significant rule changes in 2002, 2009, 2011, 2014 and 2015. This has caused significant confusion for workers and their advocates concerning the benefits they are eligible for, and hase caused frequent backlogs at U.S. DOL. Worker advocates have held up petitions to wait for a more favorable policy environment. With globalization a feature of the U.S. economy, there’s a case for a permanent authorization of TAA, like unemployment insurance or a longer, ten-year authorization done for the Children’s Health Insurance program.

Expanding TAA to Include Technology: The Extra T in TAA

What follows is an outline of how TAA certification might be expanded to better reach workers threatened by technological unemployment, resulting in what could be called a Trade and Technology Adjustment Assistance (TTAA) program. This would be a single program in terms of benefits provided but with added qualification rules for technology on top of the current rules for trade. The idea would be to utilize the existing structure of the TAA program to respond to the challenges of job loss due to technology, with a new certification scheme developed to reach those workers impacted by technological job loss. Critical elements of TAA are well-suited to the challenge of technological unemployment. Special benefits are accrued to workers who suffer from job displacement for a specific reasons, in this case technology. An application, followed by an investigation, certifies eligibility for a group of workers in a certain occupation. Lastly, TAA has the mix of robust services needed for the potentially serious levels of technological unemployment, including extended unemployment benefits for workers who need retraining, wage insurance for those who take lower-paying jobs, protections of health insurance and pension coverage, relocation assistance, job counseling, and case management. Figure 6 provides a schematic for how the Trade Adjustment Assistance program could be adapted for technology. A natural way for this expansion to occur would be as a temporary expansion of eligibility during the next reauthorization (hopefully with a period of at least five years of implementation), with a process-related evaluation that would inform the contours of a permanent program.

Pre-Certification of Occupations

The group certification process of TAA has consistently raised concerns about its ability to be timely and responsive to job loss. A certification process that would offer a more streamlined experience for accessing TTAA benefits would significantly ameliorate, if not resolve, this issue.

The first part of the proposal takes advantage of the increasing convergence among researchers about the types of occupations that are at risk due to artificial intelligence, automation, and information technology. We don’t know which jobs will be replaced by technology, but we do know with some certainty about the kinds of jobs that could be replaced. This allows us to prepare for a more robust set of benefits for those workers whose jobs are made suddenly obsolete by technology. While predictions of the pace of change have moderated over the past year, rapid advances in technology (e.g. self-driving cars) could quickly lead to workers losing their jobs before Congress or individual states could mount a response. To address this major challenge, we propose pre-certifying a list of occupations that could see a quick increase in unemployment. This would allow for a rapid response of benefits and services to workers struggling to get back to work, frequently in occupations that are quite different than their prior employment.

To perform these pre-certifications, an independent commission would create a list of occupations at risk of being replaced by technology, based on an analysis of the current federal O*Net definition of the tasks within each of the federally defined occupations. The commission would establish a risk score for each occupation, based on its task distribution and limitations to technological adoption, such as the amount of creativity, interpersonal interaction, and finger dexterity required. Those meeting a minimum risk score would be deemed pre-certified for assistance through TAA due to technology. State rapid response agencies could use this data to carefully monitor employment levels in these occupations, as well as technological development that could impact employment. Owing to its dual function, this independent commission would be staffed by the National Institute for Standards and Technology (of the U.S. Department of Commerce) and the U.S. Department of Labor.

As emphasized in the research summarized above, technological capacities and the digital nature of occupations are constantly changing. To respond, the commission would update its criteria on an annual basis as technology changes, and apply those criteria to the most up-to-date O*Net definitions. It is anticipated that the risk profile of occupations will change from year to year. Like bank tellers, certain jobs may become more complementary to emerging technologies and be less at risk than currently anticipated. This will show up in the analysis of changing tasks within occupations.

Petitioning for Technology and Trade Adjustment Assistance

Learning from the experience of TAA, a process where entire occupations of workers could be certified as proposed eligible to receive benefits would be most effective. Such a process would dovetail well with the industry-wide certification for the existing TAA program discussed above. Unlike TAA certifications, which are filed at the factory level, TTAA petitions would be filed at the occupational level. Given the importance of state involvement and learning in the early years of the TTAA program, these TTAA petitions should be made for workers across a particular state. State agencies would thus be a likely initiator of a TTAA petition, as they are in a position to be aware of multiple announced layoffs and most readily prepared to deliver rapid response and dislocated-worker services. In other words, state DOLs would be empowered to petition for these enhanced adjustment services when workers in their state are being impacted. However, other eligible petitioners would include local governments, 97 companies, unions, or other groups of workers. 98 But these petitions, as well, if granted, would certify all workers in the impacted occupation in that state.

Each petition would need to include evidence of job loss within the occupation over the past three years and evidence of the introduction of new technologies within that economic sector during that three-year time period. 99 Evidence for technology-related job loss would include one of the following:

  • Examples of layoffs directly attributable to the introduction of a specific piece of technology into a workplace—for example, a company that lays off a long-haul truck driver because it has switched to a self-driving fleet. The application could include data on capital expenditures on technology, data on the penetration of technologies (such as International Federation of Robotics data on industrial robot usage), and secondary sources (such as interviews) about the technological nature of the employment changes.
  • An increase in technology-related substitutes among new companies that diminishes employment in an existing occupation. For example, if large numbers of grocery-store clerks are laid off during the time period that use of online grocery delivery service increases, the clerks could be eligible for TTAA. The introduction of technology could be demonstrated by increasing sales among such technology-related substitutes in the year before the impacted layoffs.

In addition, the program would need to set a minimum level of occupation decline, such as 8 percent over a three-year period. To put this in perspective, even over the three-year period of economic growth from 2013–2016, 10 percent of occupations decreased by 14 percent or more. A targeted program would thus need to require both a decrease in occupational size and the link to technology. Figure 7 displays a probability density function for three-year occupational job loss. While the average occupation increased by 14 percent over this period, 10 percent declined by 14 percent or more.

chart

This straightforward expansion would make the same mix of services available to both trade- or technology-certified workers. As stated above, TTAA workers would be eligible for up to two years of paid retraining with income support so they could pay their bills while they train for a new career. The current menu of standard options for TAA recipients outside of training would be available likewise for those on TTAA, including wage insurance and relocation allowances. In addition, TTAA recipients would have access to subsidized COBRA to continue their employer-based insurance.

Pilot Program

TTAA could be developed as a five-year pilot program in the next TAA reauthorization, with positive results leading to further expansion. In two scenarios, McKinsey predicts that the large increase in impacts of automation on job loss will occur either from 2025 to 2030 or from 2030 to 2035, depending on the pace of change. 100 The purpose of a TTAA program would be to establish a workable model that can be scaled alongside demand. We can anticipate job losses even before 2025, especially among those applications that have more than 70 percent of their tasks replaceable by automation, with number potentially rapidly increasing after 2025.

Given this, a pilot program approach would be particularly viable now. In the appendix, details for a cost estimate are provided. It focuses on the 9 percent of jobs most at risk for automation and McKinsey’s estimate that technology adoption could reach 20 percent of tasks by 2025. The take-up rate is based on the percentage of workers who would exhaust unemployment benefits and be likely to apply for a program like TTAA rather than find a job on their own. That produces an estimate of 103,000 program participants from 2020 to 2025. The per-participant training cost in TAA is currently $11,986, and the per participants payment of TRA benefits is $8,555 per participant. Wage insurance would account for $25 million per year. This would require a Congressional commitment of $1.8 billion dollars over five years, including 5 percent of administrative costs. 101 This allotment would allow for $10 million annually (5 percent) of funds to go to state and federal administration of the program, including the establishment of the annual list of occupations. The pilot program would allow multiple applications per state to the federal government until program funds are exhausted, based on an estimated cost per participant in each petitioned occupation.

Important Development Considerations

Finding the political and funding nexus.

Throughout the TAA program’s long history , its relationship with trade liberalization has been a crucial factor. As Congress acted to loosen regulations of international trade, there was bipartisan support for a program to support those who would lose more from the changing trade rules than they would gain. An expansion of TAA to technology-related employment does not have as easy a policy nexus, as public policy is not as clearly responsible for such job displacement. However, many forms of artificial intelligence and automation, like autonomous vehicles, may need Congressional or regulatory permission to expand. These may provide political opportunities for enacting a program like TTAA in exchange for government approval of the technology. Moreover, like TAA, many consumers will benefit from the lower prices, convenience, and efficiency brought forward by automation and artificial intelligence. A political argument can be made to balance out such broadly felt gains with targeted help for those Americans for whom employment loss is a consequence of that convenience.

TAA has been funded out of general revenue, a nod to the idea of taxing the broadly shared welfare gains promised (but not necessarily delivered) by trade agreements. Congress could fund a TAA program with general revenue under the same kind of logic. More specifically, a value-added tax (VAT) would get at the idea of taxing the purchase of cheaper technologically produced goods to fund TTAA and other valuable social programs, recognizing the twin roles of trade and technology in the declining cost of goods. For example, the percent of U.S. consumer spending on food has declined from 20 percent of income to 8 percent of income from 1960 to 2000. 102 Finding a scalable funding source, like a VAT, may be needed in the decades to come, as there is a real possibility that millions of Americans might need retraining through a program like TTAA.

Over the years, several union leaders have proposed using import duties as a direct source of funding for improvements to TAA. 103 A similar policy nexus and funding source could go towards the TTAA expansion . A “ robot tax ” has already been proposed as a possible way to pay for a universal basic income, and could be a way to pay for a program like TTAA. 104 A more targeted example would be a vehicle-miles tax on self-driving cars, part of which could be directed to retraining programs. 105 The nature of internet companies is that there are fewer winners, with each able to gain larger profits. For example , Instagram built an internet-age photo company worth a billion dollars with only a few hundred employees, while Kodak needed thousands of employees for photo production using twentieth-century technology. 106 In this type of economy, a tax on these big winners may be the best way to foster shared prosperity, and technology leaders like Bill Gates have recognized as much. 107

How to certify occupational loss?

A seemingly minor but important detail is how to base a program on a worker’s occupation rather than on industry. Unemployment insurance records typically track the workers’ industry, and not their occupation (although several states are experimenting with tracking the latter). 108 Regular TAA defines eligible workers as being from a firm that is certified as trade-impacted, or from a secondary supplier. The TTAA program will need to create a new model. A questionnaire-based approach would first query a worker’s separating employer (as identified by the unemployment insurance system) to determine whether the worker meets the the standard occupational definition. Similar to how UI handles typical employer queries, if there is no response from the employer, the state agency would ask the separated employee directly.

The capacity of artificial intelligence, automation, and related technologies to replace labor is rapidly increasing. While the first set of studies forecast large society-wide losses of jobs, more recent research has centered on a smaller set of jobs where most of the tasks could be replaced by technology. Establishing a technology-related certification for TAA benefits would provide a powerful tool for either scenario. But it would be particularly powerful if increasing productivity in the economy creates new demand for workers who can be retrained into new careers. Just as the institution of a universal high school education and the expansion of higher education prepared America for the opportunities of the twentieth-century technology revolution, greater government investments in readjustment programs like the one outlined above can make a major difference in America.

Acknowledgements

The author would like to thank Amanda Novello for research assistance, and the following commenters: Annette Bernhardt, Mark Muro, Neil Ridley, Gerri Fiala, Kelly Ross, Brad Markell, Armando Viramontes, Jen Mishory, Jon Cardinal, Denise Forte, Conor McKay, Monica Rondon, and Roy Houseman.

Explanation: This estimate starts with the total number of workers employed in the United States as of January 2018. It then uses the OECD estimates of job loss in the United States based on those jobs at high risk of automation (9 percent), and uses McKinsey’s aggressive estimate that technological adoption will move fast enough to eliminate 20 percent of these jobs. Based on World Economic Forum research, it surmises that fifty percent of these workers can be reskilled into different job classifications and won’t suffer displacement. Among those displaced, take up proxies are used. First, an estimate is created of how many workers would exhaust UI, a good proxy for the percent who might be interested in this program and would not apply on their own. Finally, it’s assumed that a smaller share of these likely participants would participate in the five-year pilot than do in an established program like UI.

The cost estimates look at these anticipated 20,500 annual participants. We assume 15,500 will access training and TRA and 5,000 will opt for wage insurance. A 5-percent administrative cost is added for case management and similar supports, creating an annual cost of $343 million and $1.8 billion over five years.

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  • Petitioning could be limited to state workforce areas as designated by the WIOA, with a required state sign-off.
  • TAA allows up to as few as three workers to apply for eligibility. Eligible entities for a TTAA petition could be a professional society, a worker center, or a similar organization, and the law would establish a minimum requirement of membership for these non-union organizations.
  • These would be detailed occupations as defined by O*Net and data from the bureau of Labor Statistics.
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Tags: trade , automation , trade adjustment assistance

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Technology has always fueled economic growth, improved standards of living, and opened up avenues to new and better kinds of work. Recent advances in artificial intelligence and machine learning, which brought us Watson and self-driving cars, mark the beginning of a seismic shift in the world as we know it. To navigate the unstable labormarket and seize the plentiful opportunities offered by new technologies, we must find a way to more quickly adapt. By continually updating our skills and seeking alternative work arrangements, we can “race with the machines.” Whether we like it or not, change is coming, and the worst move of all would be to ignore it.

Introduction

Recent advances in artificial intelligence and machine learning, which brought us Watson and self-driving cars, mark the beginning of a seismic shift in the world as we know it. But major innovations (defined as widely-used technologies that improve over time and have spillover effects that provoke further advancements) have been around since the beginning of recorded history. From the first metal tools, to the wheel and the printing press, these innovations (dubbed general purpose technologies, or GPTs¹ ) have changed the course of history. GPTs “interrupt and accelerate the normal march of economic progress”. 1 In other words, they make humans more productive and increase standards of living. They also help open avenues to new kinds of work.

Erik Brynjolfsson and Andrew McAfee succinctly divide historical progress into two machine “ages”. 2 The first machine age dates back to the invention of the steam engine, by James Watt in 1775. This brought about an explosion of innovation, and resulted in an increase of living standards to such an extent, that the average American today has a quality of life that was unimaginable to even the wealthiest nobles of that era. The “second machine age” began in the 1990s, and is characterized by three factors: (1) exponential increases in computing power, known as Moore’s Law; (2) the agility and power of digital technologies (including their ability to replicate ideas and products at zero or low cost); and (3) our creative ability to build off of ideas like building blocks, in order to create innovations (called recombinant growth). 3

Even more fascinating, is that “Moore’s Law” ––which has driven so many changes in technological progress–– has held up amazingly well over the years. In 1965, Gordon Moore, who was then the director of research and development (R&D) at Fairchild Semiconductor, predicted that the overall processing power for computers (or the number of transistors in an integrated circuit) would double every year. This prediction became known as Moore’s Law. (Moore then revised the prediction in 1975 to every two years. He also later became the CEO of Intel.) While Moore originally made his prediction for a period of ten years, exponential increases in computing have continued even up to present day. What’s more, William Nordhaus has actually traced back Moore’s Law back to the earliest adding machines from circa 1850. 4 The exponential growth of computing power makes it seem as though new technologies come out of nowhere; in reality, however, they’ve been around (albeit also very expensive and rare) for quite a while. In the past, only the rich could benefit from the latest innovations.

For example, Brynjolfsson and McAfee illustrate this astonishing speed of progress by comparing the technologies available in 1996 and 2006: In 1996, the ASCI Red was the world’s fastest supercomputer. It cost $55 million to develop, and occupied 80% of the space in a tennis court at Sandia National Laboratories in New Mexico. It took as much electricity as was needed to run 800 homes. In 1997, it hit 1.8 teraflops. In 2006, ten years after the ASCI Red was introduced, the Sony Playstation 3 hit 1.8 teraflops. It cost only $500, took up less than onetenth of a square meter, and drew as much power as one lightbulb. 5 McAfee and Brynjolfsson provide another example, displaying the rapid adoption of smartphones: “By 2015, only eight years after the iPhone was introduced, more than 40% of the adults in 21 emerging and developing countries surveyed by the Pew Research Center reported owning a smartphone. In 2016, approximately 1.5 billion more were sold.” 6

Generally speaking, technology has increased the size of “economic surplus pie” and redistribute much of its consumers.

As a thought experiment, let us consider the computing power we will have in our hands in twenty years if Moore’s Law continues to hold (as we have no reason to think otherwise, given its faultless track record). Suppose the cost of computing falls in half every 18 months. Then, a thousand dollars of computing power today (approximately the cost of an unlocked iPhone 8 Plus with 256GB memory in 2017) would cost less than ten cents by 2037. 7 While that may seem astonishing, ask yourself how much would you pay for a cell phone made twenty years ago? If we assume that consumers, twenty years from now, would be willing to pay a thousand dollars for whatever smartphones are on the market at that time, what would be the cost of such technologies, if we could get them today? A little more than ten million dollars. 8 Imagine if you could have a smartphone with ten million dollars of computing power –––that is a rough approximation of what will be in everyone’s hands in about two decades. In other words, as Hal Varian, chief economist at Google, says, “A simple way to forecast the future is to look at what rich people have today.” 9

Technology and Labor

The average American today has better medical care, better access to information and education, and better ways to communicate and travel than the richest people in the world in the not-very-distant past. We have experienced a dramatic increase in living standards, whose “single most important determinant […] across countries and over time” is labor productivity. 10 Productivity—equal to output divided by inputs (such as capital, labor, energy, materials, and services)—increases when we deploy technology. 11 The Council of Economic Advisers gives us an example of incredible improvements in agricultural productivity over the past two centuries: “In 1830, it took 250-300 hours for a farmer to produce 100 bushels of wheat. In 1890, with horse-drawn machines, it took only 40-50 hours to produce the same amount. By 1975, with large tractors and combines, a farmer could produce 100 bushels of wheat in only 3-4 hours.” 12 By producing more output, given the same value of inputs, agricultural machines decreased production costs. As a result, food became more affordable and people became less likely to die of starvation. In addition, the increased productivity from the automation of agricultural work led farm workers to migrate to cities, where they then helped the industrial economy develop and grow. New goods and services were created, and consumption increased. Productivity rose even more as automation drove down costs, thereby making transportation, healthcare, education, and government more affordable. 13

Generally speaking, technology has increased the size of ‘economic surplus pie’ and redistributed much of it to consumers. Consider one example: When Amazon offers free same-day or next-day delivery, that delivery is not actually free ––it costs Amazon notable resources to achieve this. The gains from Amazon’s investments in automation and improvements in its supply chain are reflected as a combination of lower prices, greater variety, and faster delivery, as the firm competes to win over consumers. From this perspective, we can understand how it’s understandable that William Nordhaus had estimated a whopping 96% of gains from technology go to consumers, not producers. 14

As wonderful as the gains of technology have been, they are also occurring against a backdrop of rising inequality, a shrinking middle class, and difficulties in finding employment. From the 1940s to the 1970s, incomes at all levels grew at approximately the same rate in the United States. However, since then, the wealthiest Americans have seen significant gains in their income and share of wealth, whereas the rest of the income distribution has seen much more modest gains. Consequently, as America’s middle class has shrunk, an unfortunate opioid epidemic has ravaged the country in areas with high unemployment. 15

Thus, it is important to remind ourselves that automation does not have a universal effect on employment; a machine can be either a substitute or a complement to human labor.

A machine can substitute for human labor when it has the ability to produce more than the worker for the same cost (such as his or her wages), or as much as the worker for a fraction of the price. This is most likely to occur when a worker’s tasks are routine and codifiable—that is, when the instructions for the tasks can be translated into code for a computer to carry out. In addition, automation is more able to replace workers in simplified, controlled environments. While computers can perform the most complex calculations in milliseconds, it is much more difficult to get a machine to write novels or care for children as effectively as humans do.

Machines complement labor when they allow workers to be more productive, but cannot—at least cannot fully —replace the worker. In other words, automation that complements human labor makes it easier for people to do their jobs and concentrate on what humans excel in, such as idea generation, problem solving, pattern recognition, and complex communication—all of which constitute computers’ weaknesses. 16 For example: calculators, spreadsheets, and bookkeeping software all made accountants’ jobs much simpler. For the most part, however, humans are still the ones making insights and providing strategic advice to the businesses they work at.

Various kinds of automation already complement human labor (e.i., big data-collecting robots that allow people to do more valuable work ––and telescopes, which have helped humans make discoveries that would have otherwise been impossible). Tom Davenport and Julia Kirby refer to this “mutually-empowering” relationship between humans and machines as augmentation , which they distinguish from the process of automation, which simply substitutes for labor. 17 Additionally, as David Autor points out, because machines increase labor productivity and lower production costs, they allow us to more easily create goods and services.

BBVA-OpenMind-Libro 2018-Perplejidad-Saunders-Almacen_Amazon-Workers sorting Christmas presents at the Amazon warehouse in Milton Keynes, Bucks.

The Current Labor Market

Throughout history, machines have helped workers to produce more output. In spite of concerns that automation would get rid of jobs or cause mass unemployment, technology has continually led to the creation of new jobs. In fact, history has proven that as labor productivity grew, so too did job growth. (This has not been the case recently however, a point we will return to). 18

The employment-to-population ratio (i.e., the share of the total US working-age population, aged 16 and above, that is employed) increased during the 20th century, even as more women entered the labor force. 19 The development of machines increased productivity and decreased production costs, allowing the creation of mass production. The subsequent surge in economic growth during this period led to the evolution of consumerism, and thus, resulted in increased job creation. However, as Autor has noted, “there is no apparent long-run increase” in the ratio which has fluctuated over the years—and falling especially during recessions. 20

BBVA-OpenMind-Libro 2018-Perplejidad-Saunders-Mineria-Chart 1

One phenomenon masked by the unemployment rate is the trend of people leaving the workforce. People categorized as part of the workforce are those who are either employed or “unemployed” (meaning they don’t have jobs, are available for work, and have actively looked for jobs in the past four weeks). For example, the most recent statistics indicate there are still roughly 6.8 million people unemployed in the United States. 22 However, there are about 1.6 million others not in the workforce—that is, they have no job and are not currently looking for work—but are considered “marginally attached”, since they want a job, and are available for work, and have looked for a job in the last 12 months. 23 Almost half a million of these workers are considered “discouraged”, because they have given up the search since “they believe no jobs are available for them.” 24

Take, for example, a coal worker with a high school education in West Virginia who used to earn an annual salary of $80,000, but was recently laid off because more sophisticated technologies were deployed in mines. It is unlikely that such a job will make a comeback ––at least in West Virginia. Will this worker want to work for less than one-third his previous wage as a cashier? Suppose, instead, this miner stops looking for work because he is tired of finding nothing available and becomes increasingly unmotivated. This discouraged worker doesn’t get factored into the unemployment rate, since, technically speaking, he is no longer a part of the labor force ––despite how much he would actually want to work, if only he could have his old job back.

The airline industry is an interesting example of automation. The majority of people benefit from its advancements, but a growing number of employees simultaneously suffer painful job losses.

Now consider the future of a much larger group of workers: once self-driving cars are deployed more widely, many of the 3.5 million truck drivers in the United States could lose their jobs. 25 Some long-haul truck drivers make as much as $150,000 per year. 26 As is the concern with coal miners, will these drivers find jobs with similar salaries if they do not have more than a high-school education?

People may wonder, “Where have all the jobs gone and why have they disappeared?” Some blame immigrants, trade agreements, or advancing technology ––while some blame a combination of all three. The reality, however, is more complicated. At least with regard to technology, automation has both created and taken away jobs. There are both winners and losers. Workers in Silicon Valley, as well as those with backgrounds in statistics and economics, are thriving in the current economy. As Google’s Chief Economist, Hal Varian, remarked, “the sexy job in the next ten years will be statisticians.” 27

The airline industry is an interesting example of automation. The majority of people benefit from its advancements, but a growing number of employees simultaneously suffer painful job losses. Automation has affected almost every job in the industry, from the flight booking process, all the way to border control. Most of us book flights online, use automated check-in counters and passport scanners, fly to our destination primarily by computers on airplanes, and pass through border control with self-serve kiosks. While there are still people who assist us, many jobs have also been removed from each stage of the process. On the other hand, the increased automation has, for the most part, made flights safer and cheaper. 28 Moreover, the Internet has empowered travelers by allowing them to much more easily compare ticket prices charged by various airlines for various routes. This transparency has led to increased competition that have helped airline ticket prices drop by 50% in 30 years 29 –––serving as another illustration supporting Nordhaus’s study, in which consumers receive 96% of the gains from technological innovation. 30

On July 31 , 2009 (right after the Great Recession) job openings in the United States had hit a low of 2.2 million ––while civilian unemployment was as high as 14.6 million. 31 Yet, 32 job openings have been increasing, particularly in professional and business services, healthcare and social assistance, and construction. 33 In fact, in August 2017, they surged to an all-time high of 6.1 million. 34 As far as we can tell, there are more jobs available today, than

there have been in the last seventeen years —which is when the BLS first began to measure them.

BBVA-OpenMind-Libro 2018-Perplejidad-Saunders-Mineria-tuneladora-Engineer inspecting a tunnel boring machine which has completed the excavation of a deep access power cable tunnel in London.

So, even as labor productivity has increased—an effect usually accompanied by job growth—private employment has essentially remained stagnate since 2000. 35 (See Figure 2.) (Real median family income and real GDP per capita also “decoupled” from labor productivity in the early 1980s and the 2000s, respectively.) Brynjolfsson and McAfee call this effect the ‘Great Decoupling’, and attribute a portion of these effects to the emergence of digital technologies. 36 They don’t see these gaps closing anytime soon.

BBVA-OpenMind-Libro 2018-Perplejidad-Saunders-chart 2

Alternative Work Arrangements

Lawrence Katz and Alan Krueger discovered that on top of the slowdown in employment, 94% of the net job growth from 2005 to 2015 was simply in temporary or unsteady work—as opposed to the previous decade, during which there was almost no growth in such “alternative work”. 37 As increasingly more tasks are being handled by machines—which are not only simply more efficient than people, but also, unlike humans, don’t demand high wages, vacations, health insurance, and pension plans—companies now require fewer workers or fewer hours from their workers (or both). But while there might not be as many (full-time) jobs left, there is still a lot of work. As Diane Mulcahy explains, “Work is being disaggregated from jobs and reorganized into a variety of alternative arrangements, such as consulting projects, freelance assignments, and contract opportunities.” 38

Millions of people have been affected by these rearrangements. As of September 2017, there were 5.1 million “involuntary part-time workers” who weren’t able to find full-time jobs or whose work hours had been reduced by their employers. 39 However, according to the McKinsey Global Institute, 20% to 30% of the American working-age population (of approximately 206 million) 40 perform some type of independent work, which amounts to approximately 40 to 60 million people 41 –––and this share, Mulcahy notes, is growing. 42

Because jobs are no longer as stable as they have been, people have been turning to the “gig economy” to seek alternate forms of work. Thanks to two-sided platforms such as Uber, Lyft, Airbnb, Etsy, Samasource, Postmates, and TaskRabbit, people can now work whenever they want, as often as they’d like, more easily than ever. This benefits people young and old who do low-skill to high-skill work. Former taxi drivers can now dictate their own schedules with Uber and Lyft. Stay-at-home parents and people with disabilities are able to more easily find work and develop their skills with Samasource. Young artists can now sell their self-made products directly to their customers on Etsy. Elderly empty-nesters now have the ability to rent out their rooms on Airbnb to help give a boost to their own retirement funds. Freelance designers and coders also have the opportunity to contract out their work and take time off for their families and for vacations whenever they wish.

Clearly, the advantages of the gig economy go beyond providing cost-savings to firms and offering some sort of employment to workers. The gig economy offers “choice, autonomy, flexibility, and control”, that which full-time jobs don’t. 43 These benefits influence work satisfaction. They’re why 74% of surveyed freelancers wish to remain independent workers and “have no intention of returning to a full-time job”. 44 Indeed, says Mulcahy, “independent workers are more satisfied with nearly every aspect of their working lives than employees,” and for these reasons, she advises her MBA students to seek “plentiful work, not increasingly scarce jobs” and to prepare to be “independent workers, not full-time employees.” 45

Job Polarization

In addition to affecting the quantity of jobs, technology can also have a great impact on job quality. 46 Some have concerns that automation steals jobs, while others insist that it actually improves them. In reality, both of these are true. Machines have affected jobs all across the skill spectrum—both increasing and decreasing the demand for jobs of different skill levels. 47

Low-Skill Jobs

On the low side of the skill spectrum, the demand for jobs ( i.e.: milkmen, switchboard operators, mail-sorters, dishwashers, ice-cutters, weavers, and assembly line workers) has fallen drastically ––or even disappeared–– because of technologies such as refrigerators, cell phones, and industrial machines. Although the invention of these technologies has driven out jobs, it’s also allowed us to make certain forms of work more bearable. For example, by investing in industrial dishwashing machines, restaurants don’t require as many human dishwashers. Consequently, the demand for dishwashing jobs would decrease, though some would still remain. These remaining jobs would then be simplified. Instead of doing the actual washing by hand, human dishwashers would only have to load and unload dishes.

While it’s easy to imagine other low-skill jobs dying out due to automation—as robots now have the ability to vacuum rooms, patrol buildings, and flip burgers (to name but a few tasks) —machines still aren’t replacing low-skilled jobs in cleaning, security, and food service. 48 This 49 is because although certain tasks may be automated, robots aren’t able to take over entire jobs. For example, while dishwashing machines do an excellent job washing dishes, humans are not completely replaced in the process, as machines don’t load or unload themselves. Humans still outperform machines, especially in jobs that involve manual skills and varying environments. 50 Therefore, there still is (and will be) a demand for low-skill jobs. In fact, as we’ll see later, demand is actually increasing.

Middle-Skill Jobs

The middle part of the spectrum is a little more complicated. Middle-skill jobs (which include blue-collar production and operative positions, as well as whitecollar clerical sales positions) are more likely to be codifiable. As a result, they’ve been disappearing, even though low-skill jobs haven’t.

Some forms of automation force people to perform mind-numbing tasks. Think of how most artisans and craftspeople were replaced by assembly line workers. In this process of “deskilling”, middle-skill jobs get replaced by low-skill jobs. Meanwhile, some jobs simply die out, forcing workers to resort to lower-skill jobs. For example, most manufacturing job losses have been due to automation (rather than international trade, as politicians tend to suggest). 51 Workers previously in employed in the manufacturing sector have since had to turn to lower-skill and lower-paying in service sector to get by. 52 This increases job growth in low-skill work. According to the Organisation for Economic Co-operation and Development (OECD), about one-third of medium-skill jobs that have disappeared worldwide have been replaced by low-skill jobs. 53

However, much like in low-skill jobs, other forms of automation can take out the danger and drudgery out of certain tasks, thereby allowing us to do safer and more meaningful work. For instance, although removing humans from coal mines might rob them of their incomes and jobs, fewer people now have to suffer from black lung disease or be threatened by deadly mine collapses. And while many bank employees may have been replaced as more customers use ATMs to conduct routine transactions, those employees who do remain can now, instead of counting cash, do potentially more important work, such as recommending financial services to clients. The OECD estimates that two-thirds of lost middle-skill jobs have been replaced by jobs that require higher-skill work, such as analysts and managers. 54

High-Skill Jobs

Although technology has been widely known to displace lower-skill and bluecollar workers, high-skill occupations have, for the most part, been protected because jobs that require more training and more complex cognitive skills (such as analysis, problem-solving, and decision-making) are much less codifiable. As David Autor and others have noted, this makes white-collar professionals and knowledge workers such as doctors, programmers, engineers, marketing executives, and sales managers difficult to replace. 55 Therefore, even though recent developments in automation have targeted high-skill work, there is still growth on this side of the spectrum. 56 After all, to get the most out of their technological investments, firms have to hire workers who are more highly skilled and educated. 57

Thus, we have ended up with a polarized workforce—an effect that’s been occurring around the world. 58 As Autor has observed, job growth has increasingly become concentrated on the two opposite sides of the skill spectrum, while medium-skill jobs are shrinking. 59 Indeed, the share of US workers in low-skill and high-skill jobs both increased from 1979 to 2016. 60 (See Figure 3.) On the other hand, although just over 61% of US workers were employed in middle-skill jobs in 1979, this share fell to 43% in 2016. 61

BBVA-OpenMind-Libro 2018-Perplejidad-Saunders-chart 3

As a result, those who aren’t able to find employment could be facing two types of options—neither of which are good. 62 On one hand, there is a set of available jobs that aren’t as rewarding or as satisfying as they were before, since they require fewer skills or offer lower wages. On the other hand, there is another set of jobs that could be more desirable, but these jobs are unattainable because they require a higher level of skill or education than the worker has achieved.

Race with the Machines

It’s important to consider how technology has changed the labor market and the economy for the better for some, but for the worse for others. We should focus on finding solutions to the issues that have arisen (by ensuring job security, and supplying healthcare and retirement plans) while taking advantage of new opportunities (through new technologies, data and analytics, platforms, etc.) and remaining flexible as the times change.

Whether or not we like it, technology, and the increased competition from globalization of the workforce has changed labor markets. The days of steady, long-term, full-time jobs ––especially with one single firm for one’s career–are coming to an end sooner than we think. This is certainly difficult to accept for those who had been prospering in fields now rampant with automation. Regulation, trade barriers, or otherwise fighting and racing against machines will not be fruitful in the long term. Instead, as Brynjolfsson and McAfee like to say, we should continuously be investing in new skills to race with the machines. 63

So how do we race with the machines? Davenport and Kirby, as well as Autor recommend that people focus on becoming tech-literate and on improving their manual and abstract skills. 64 Learning how to code in various computer languages and knowing how to collect and analyze data, for example, would be immensely helpful in the race with machines. Manual skills such as dexterity and flexibility will also still be valuable in the near future, and further developing innate human qualities (i.e., abstract skills that machines aren’t good at—such as creativity, persuasion, empathy, pattern recognition, and complex communication) would certainly be beneficial. 65

Davenport and Kirby identify five different ways for both people and companies to use such skills to succeed in the second machine age: 66

Stepping up: Let machines do your dirty work, so to speak, thereby allowing you to focus your time and energy on making big-picture insights (e.g., managing investment portfolios).

Stepping aside: Use abstract skills, such as creativity or empathy, to do things that machines aren’t good at or to explain decisions that computers made (e.g., communicating negative news).

BBVA-OpenMind-Libro 2018-Perplejidad-Saunders-oficinas-Workers in an office building at night.

Stepping narrowly: Do things that would be too costly to be automated, such as specializing in a very particular area of a field (e.g., specializing in the legal issues pertaining to malfunctioning garage doors, or in connecting buyers and sellers of Dunkin’ Donuts franchises).

Stepping in: Use tech skills to improve machines’ decision-making abilities and to make sure that they function well (e.g., providing feedback to programmers by identifying bugs and suggesting modifications to be made).

Stepping forward: Use tech skills and entrepreneurial thinking to create advanced cognitive technologies (e.g., becoming a machine learning engineer).

The better that individuals and companies become at finding such complementary and “mutually empowering” 67 relationships that augment human labor with machines (or vice versa), the more likely it is that employment growth and job quality will improve. With more fitting skills, there would more people employed in more satisfying and meaningful jobs.

However, there will still be those who are left behind and who aren’t able to find jobs in the increasingly unstable labor market. There’s been much debate on whether a safety net in the form of a universal basic income should be provided to address the Great Decoupling, particularly the stagnant wages Americans have experienced for three decades. 68 Nevertheless, a guaranteed income won’t fix all the issues we’ve been dealing with. Employment is important for one’s well-being, providing many with a sense of purpose. As Voltaire once said, “Work saves us from three great evils: boredom, vice and need.”

The fear that machines are taking over our jobs isn’t a new one, and Autor compares the situation today to when many jobs in the agriculture sector were becoming automated a hundred years ago. 69 In 1900, farming used to make up 41% of the American workforce. A century later, the share decreased to just 2%. 70 If Americans had been told a century ago that due to new technologies and innovation, farmers as a share of the workforce would fall by 95%, most would probably consider it to be frightening news. What are people going to do? How could they have possibly imagined that people would become social media managers, app developers, cloud computing specialists, information security analysts, drone operators, solar and wind energy technicians, genetic counselors, vloggers, yoga instructors, and sustainability managers? Likewise, how can we expect to understand exactly what can be done in a world that doesn’t yet exist with technologies that haven’t even been invented?

It is who we decide what becomes of technology

As agriculture became automated, the United States responded to these changes by investing in its youth ––they put children in school to prepare them for industry jobs. Similar actions must be taken today. As Mulcahy argues, the education system, now outdated, needs an overhaul. Instead of teaching children to prepare for jobs of the past, we should be preparing them for work in the gig economy of the future. So, schools and universities should prepare youths to be agile and adaptable, and have a more significant focus on the skills Davenport and Kirby, as well as Autor, have recommended. Additionally, businesses and government policies should help retrain adults who have been left behind. Along with education, Brynjolfsson and McAfee recommend four other areas of focus to help create an economic environment that would “make the best use of the new digital technologies”: infrastructure, entrepreneurship, immigration, and basic research. 71

As the futurist Ray Kurzweil reminds us, we always underestimate the pace of technological change, because the technologies that will be invented in twenty years will not be invented with today’s technology. It will be invented with the technologies available in twenty years, which of course, haven’t been dreamed of yet. We can begin to imagine such a world, with smartphones in everyone’s hands, that today, would cost us ten million dollars apiece. As David Autor suggests, (1) it’s very difficult to predict the future and (2) it’s arrogant to bet against human ingenuity. 72

As many have pointed out, technology is just a tool. 73 It will not necessarily lead us to a utopian or dystopian world because we, as human beings, have a say in the matter. It is we who decide what becomes of technology. To paraphrase electrical engineer and physicist Dennis Gabor, we cannot predict the future, but we can invent it. 74

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Thompson, D. (2013, February 28). How airline ticket prices fell 50% in 30 years (and why nobody noticed). The Atlantic . Retrieved from http://www.theatlantic.com/business/archive/2013/02/how-airline-ticket-prices-fell-50-in-30-years-and-why-nobody-noticed/273506/

Traufetter, G. (2009, July 31). Will increasing automation make jets less safe? Der Spiegel . Retrieved from http://www.spiegel.de/international/world/the-computer-vs-the-captain-will-increasing-automation-make-jets-less-safe-a-639298.html

World Economic Forum. (2016, December). Renewable infrastructure investment handbook: A guide for institutional investors. Retrieved from http://www3.weforum.org/docs/WEF_Renewable_Infrastructure_Investment_Handbook.pdf

Varian, H.R. (2009, January). Hal Varian on how the web challenges managers. McKinsey&Company. Retrieved August 20, 2017 from http://www.mckinsey.com/industries/high-tech/our-insights/hal varian-on-how-the-web-challenges-managers

Varian, H.R. (2011, August 15). Micromultinationals will run the world. Foreign Policy . Retrieved from http://foreignpolicy.com/2011/08/15/micromultinationals-will-run-the-world/

1 See Brynjolfsson and McAfee (2014, p. 76).

2 See Brynjolfsson and McAfee (2014).

3 See Brynjolfsson and McAfee (2014).

4 See Nordhaus (2007).

5 See Brynjolfsson and McAfee (2014, pp. 49-50).

6 See McAfee and Brynjolfsson (2017, pp. 17-18).

7 x1,000 * 0.5^(20/1.5) = 0.10.

8 x10,321,273.24 * 0.5^(20/1.5)=1,000.

9 See Varian (2011).

10 See Council of Economic Advisers (2016, p. 58).

11 The only ways to increase output are by increasing inputs such as population (i.e., by increasing the number of hours worked given the same level of productivity) or through productivity growth (i.e., by increasing output per hour, or the amount of output given the same level of inputs) (Brynjolfsson and Saunders, 2010).

12 See Council of Economic Advisers (2007, pp. 47-48).

13 See Pearlstein (2016).

14 See Nordhaus (2005).

15 See Case and Deaton (2017) and Hollingsworth et al. (2017).

16 See Davenport and Kirby (2016) and Autor (2015).

17 See Davenport and Kirby (2016).

18 See Brynjolfsson and McAfee (2014).

19 See Autor (2015).

20 See Autor (2015, p. 4).

21 See BLS Series LNS12300000, Employment-population ratio, 16 years and older.

22 See: How the Government Measures Unemployment – Bureau of Labor Statistics, at http://www.bls.gov/cps/cps_htgm.htm . The statistics come from http://www.bls.gov/charts/employment-situation/civilian-unemployment.htm

23 See: The Employment Situation – Bureau of Labor Statistics, accessed at http://www.bls.gov/news.release/pdf/empsit.pdf

24 See the statistics at http://www.bls.gov/charts/employment-situation/persons-not-in-the-labor-force-selected-indicators.htm

25 See: Reports, Trends & Statistics – American Trucking Associations, accessed at http://www.trucking.org/News_and_Information_Reports_Industry_Data.aspx

26 See: Tons of trucking jobs … that nobody wants – CNN Money, accessed at http://money.cnn.com/2012/07/24/news/economy/trucking-jobs/index.htm

27 See Varian (2009).

28 See Traufetter (2009) and Thompson (2013).

29 See Thompson (2013).

30 See Nordhaus (2005).

31 See job openings statistics at http://www.bls.gov/opub/ted/2017/job-openings-rose-to-6-point-2million-in-june-2017.htm

32 See civilian unemployment statistics at http://www.bls.gov/charts/employment-situation/civilian-unemployment.htm

33 http://www.bls.gov/news.release/jolts.nr0.htm

34 http://www.bls.gov/opub/ted/2017/job-openingsrose-to-6-point-2-million-in-june-2017.htm

35 See Brynjolfsson and McAfee (2014).

37 See Katz and Krueger (2016).

38 See Mulcahy (2016a, b).

39 See: The Employment Situation – Bureau of Labor Statistics, accessed at http://www.bls.gov/news.release/pdf/empsit.pdf

40 OECD, Working Age Population: Aged 15-64: All Persons for the United States – Federal Reserve Bank of St. Louis, accessed at http://fred.stlouisfed.org/series/LFWA64TTUSM647S on August 20, 2017.

41 See McKinsey Global Institute (2016) and Mulcahy (2016a).

42 See Mulcahy (2016a).

43 See Mulcahy (2016a).

44 See Mulcahy (2016a) and Future Workplace and Field Nation (2016).

45 See Mulcahy (2016a).

46 See Autor (2015).

47 See Goos and Manning (2003).

48 See Autor (2015).

49 See, for instance: DC security robot quits job by drowning itself in a fountain – The Verge , accessed at http://www.theverge.com/tldr/2017/7/17/15986042/dc-security-robot-k5-fallsinto-water on August 20, 2017.

50 See Autor (2015).

51 Only about 13.4% of job losses in manufacturing have been a result of direct imports or import substitutions (Hicks and Devaraj, 2017). The rest have been due to the increased productivity brought about by automation ( ibid ). Indeed, even in China, factory workers such as in Foxconn have been replaced by machines (Davenport and Kirby, 2016).

52 See OECD (2017).

55 See Autor (2015).

56 See Davenport and Kirby (2016) and Autor (2015).

57 See Bresnahan, Brynjolfsson, and Hitt (2002) and Brynjolfsson and Saunders (2010).

58 See Autor (2015) and OECD (2017).

59 For a discussion on how job polarization has affected wages, see Autor (2015).

60 See Autor (2016).

62 See Autor (2015).

63 See Brynjolfsson and McAfee (2014).

64 See Autor (2015) and Davenport and Kirby (2016).

66 See Davenport and Kirby (2016).

68 See Brynjolfsson and McAfee (2014) and Davenport and Kirby (2016).

69 See Autor (2015).

70 See Autor (2014).

71 See Bernstein and Raman (2015).

72 See Autor (2015).

73 See Brynjolfsson and McAfee (2014) and Davenport and Kirby (2016).

74 See Gabor (1963).

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Unemployment Rate Due to Impact of Technology Essay

Introduction.

Advancements in technology have led to the development of smart machines that have made work easier. Various economic sectors have adopted technology in their routine activities and business transactions. Financial technologies have bolstered the internet and mobile phones’ use for fiscal transactions. Meanwhile, adopting artificial intelligence and other smart systems has led to the automation of business and industrial activities. Although technology has remained relevant to economic growth, increased unemployment is its greatest detriment. Therefore, unemployment is a macroeconomic issue facilitated by technological integration.

Topic Relevance

Technological integration and unemployment is a relevant topic to the academic and corporate world. The topic offers broad socio-economic understanding making it relevant in macroeconomics. Understanding the impact of technology on the unemployment rate helps determine an effective mechanism for automating business and industrial activities without affecting the employees. The topic helps the businesses identify opportunities through technological integration, and help their employees’ career development. Moreover, the topic explores the flipside of the technology by discussing its detriments on employment rate. Furthermore, by understanding how technology leads to unemployement, the workers seek necessary skills and knowledge to become competitive.

Interest in the Public

Unemployment is a socio-economic issue that affects society largely. Increased unemployment rate leads to high crime rates, among other social vices. Consequently, the public must understand the causes of unemployment and how to mitigate them. By understanding the role of technology on unemployment, the public can develop innovative mechanisms to overcome the issue. Moreover, the public will know how to accommodate those who have lost their jobs to technology. Therefore, technological integration and unemployment is a topic of high public interest.

Articles’ Summary

“the fear of technology-driven unemployment and its empirical base”.

The article was written by Kerstin Hötte, Melline Somers, and Angelos Theodorakopoulos on10th June 2022, on the VoxEU website. The authors explore the impact of new technologies on human labor. According to the article, human labor is only needed when workers use the technologies (Hötte et al.). The columnists suggest that technology boosts productivity, increasing disposable income and expanding demand-induced employment. The column identified 127 relevant studies with evidence on the technology’s impact on employment. While the column reviews existing literature on the technology’s impact on employment, the authors state that the labor market has been positively influenced.

“Economists Pin More Blame on Tech for Rising Inequality”

The article was written by Steve Lohr on January 11, 2022, in The New York Times. Mr.Lohr suggests that business and industrial automation have widened job disparities. The author alludes to the recent economic research and recommendations against “excessive automation.” While investing in software and machines is profitable, the investments lead to inequality (Lohr). The digitization has led to the job displacement of the workers, especially those without college degrees. Furthermore, the author supports the economists’ argument that computerization has increased the widening gaps in income in the U.S. Mr. Lohr states that, unlike the current generation, the post-war years were golden age since technology forged ahead and workers enjoyed rising income and job opportunities.

“China’s Tech Layoffs Could Become a Self-Inflicted Headache for Xi”

The article by Laura He was published on the CNN Business on March 31, 2022. The author focuses on the impact of technology adoption among Chinese corporations on employment. While the Chinese government states that the country’s unemployment rate is stable, fluctuating between 5% and 5.5%, private surveys show that jobs are being lost in the tech-based industries (He). The author uses big Chinese corporations to illustrate the impact of technology on employment. For instance, he illustrates how JD.Com, Alibaba, and Tencent have dismissed a considerable percentage of their workforce, attracting public attention. According to Laura, the menace is ‘self-inflicted’ since China has embraced technological adoption across its economic sectors. Like China, many developed nations embrace technology, notwithstanding its impact on the labor market.

“Robots: Stealing Our Jobs or Solving Labour Shortages?”

Martin Ford wrote the article on October 2, 2021, on the Guardian News website. The author explores the impact of robots on employment post-COVID-19 pandemic. According to Martin, although the end of the pandemic relieved the economy, it was a nightmare for the employment sector. Many organizations automated and adopted robots to conduct activities normally executed by the workers (Ford). However, the organizations realized that robots execute the activities better than humans. Moreover, computerized systems are costly and error-free. Consequently, the firms are upholding the use of robots, risking the employment of many people.

“Don’t fear AI. It will lead to long-term job growth.”

Mohamed Kande and Sonmez Murat wrote the article on the World Economic Forum on October 26, 2020. The article alludes to the World Economic Forum’s ‘Future of Jobs Report 2020’, which estimates the displacement of 85 million jobs and the creation of 97 million across 26 countries by 2025 (Kande and Sonmez). The authors suggest that although automation has destroyed jobs, it will create more. According to Kande and Sonmez, a similar concern was created during the growth of internet, but it has created million of jobs that contribute to 10% of the U.S. GDP. Therefore, while technology quickly displaces workers, it is likely to have a long-term positive employment impact.

Textbook Connection

Unemployment is a macroeconomic issue that is largely influenced by technological adoption. Automating business activities and digitizing fiscal transactions have led to the displacement of many works. The article by Kerstin Hötte is consistent with the textbook view on the positive impact of technology on business activities (Chiang 221). Moreover, Steve Lohr’s argument that labor inequality, as facilitated by technology integration, bolsters unemployment is consistent with Chiang’s affirmation (Chiang 203). The textbook does not suggest that technology creates more jobs than it destroys, as suggested by Kande and Sonmez. Therefore, the textbook and the articles articulate that technology integration risks the labor market.

Evaluation and Conclusion

The impact of technology on the labor market is relevant to my present and future life. Upon understanding how technology causes unemployment, I am convinced to sharpen my technical skills to avoid future job displacement. Consequently, I am currently enrolled in an online class on business technology. Moreover, I am motivated to work harder and become the few most competitive employees in the labor market in the future. My future career is at risk since new technologies are invented daily. I am obliged to get updated on trending technological skills in the future. As a business manager, the technology will help me dismiss less competitive works and employ those with broad skills and knowledge in business and technology. Therefore, technological integration and unemployment is a relevant topic in my present and future life, and my career as a business manager.

Works Cited

Chiang, Eric. Macroeconomics: Principles for a Changing World . 5th ed., MacMillan, 2020.

Ford, Martin. “Robots: Stealing Our Jobs or Solving Labour Shortages?” The Guardian , Web.

He, Laura. “Analysis: China’s Tech Layoffs Could Become a Self-Inflicted Headache for Xi.” CNN, Web.

Hötte, Kerstin, et al. “The Fear of Technology-Driven Unemployment and Its Empirical Base.” VoxEU.org , Web.

Kande, Mohamed, and Murat Sonmez. “Don’t Fear AI. The Tech Will Lead to Long-Term Job Growth.” World Economic Forum , Web.

Lohr, Steve. “Economists Pin More Blame on Tech for Rising Inequality.” The New York Times , Web.

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IvyPanda. (2023, May 16). Unemployment Rate Due to Impact of Technology. https://ivypanda.com/essays/unemployment-rate-due-to-impact-of-technology/

"Unemployment Rate Due to Impact of Technology." IvyPanda , 16 May 2023, ivypanda.com/essays/unemployment-rate-due-to-impact-of-technology/.

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IvyPanda . 2023. "Unemployment Rate Due to Impact of Technology." May 16, 2023. https://ivypanda.com/essays/unemployment-rate-due-to-impact-of-technology/.

1. IvyPanda . "Unemployment Rate Due to Impact of Technology." May 16, 2023. https://ivypanda.com/essays/unemployment-rate-due-to-impact-of-technology/.

Bibliography

IvyPanda . "Unemployment Rate Due to Impact of Technology." May 16, 2023. https://ivypanda.com/essays/unemployment-rate-due-to-impact-of-technology/.

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Band 7 essay sample | Technology is responsible for unemployment

by Manjusha Nambiar · August 18, 2016

Since the 18th century technological advances have replaced people in the workplace. With today’s technology this process is happening at a greater rate. Technology is increasingly responsible for unemployment.

To what extent do you agree or disagree with this statement?

Band 7 essay sample

Modern technology helps people live a lot easier, although it has a number of disadvantages. The fantasy vision of robots controlling major parts of human lives is slowly becoming a reality. Unemployed people blame the government for allowing this situation to spiral out of control but opinions are divided.

Unemployment in 21th century is far higher than what it was a couple of decades ago. Inventors work really hard to develop new machines and robots. The trend of saving money on labour has dramatically increased and this can be noticed on a daily basis. People use all of the inventions every day, but they are starting to see the downsides of machines as well. New self-checkout machines in most supermarkets have replaced over 50 percent of old fashion cashiers. In some countries the train drivers has been replaced by self-driving trains. Now many of us are withdrawing money through cash machines. Looking at this trend one might assume that technology has increased the number of people without jobs. However, it is not quite true.

The machines have considerably improved our standard of life. They haven’t killed jobs. Actually, it is safe to claim that the new technologies have created new professions. Every machine requires regular service and repairs, so engineers are in high demand. Most of the new technologies need to be constantly improved by inventors. This gives new career opportunities for the youngsters, and university teachers.  Fewer labourers means less costs and lower final product price.

The number of advantages is far higher than disadvantages. Using all of the inventions and blaming them for the loss of jobs is hypocrisy. Laziness is the major problem, and the increase in machine usage is only a ridiculous excuse.

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Home — Essay Samples — Information Science and Technology — Impact of Technology — The Impact of Technology on Employment: the Future of Job

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The Impact of Technology on Employment: The Future of Job

  • Categories: Employment Impact of Technology Unemployment

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Words: 1617 |

Published: Aug 14, 2023

Words: 1617 | Pages: 4 | 9 min read

Table of contents

Transformation of labor in the technological era, trends and causes of technological unemployment, economic circular flow and income generation, reference list.

  • A.P. Bartel and N. Sicherman (October 1998), “ Technological change and the skill Acquisition of Young workers.” Available at : https://www.nber.org/reporter/summer99/bartel.html (Accessed : 21 February 2019)
  • Susan N.L (2014) Economics revision Guide. Published by second edition. Endorsed by Cambridge International examination.
  • Leonard W.F(2018) Impact of Technology on employment & Unemployment. Available at : http://il4syrians.org/impact-of-technology-on-employment-%26-unemployment. (Accessed : 21 February 2019)
  • Geoff R.L (2007) understanding the circular flow of income and spendin. Available at : https://www.tutor2u.net/economics/reference/circular-flow-of-income-and-spending. (Accessed : 21 February 2019)
  • https://en.m.wikipedia.org/wiki/Creative_industries. (2019) (Accessed : 21 February 2019)
  • https://www.totaljobs.com/salary-checker/average-creative-designer-salary. (2017) (Accessed : 22 February 2019)
  • Fin C.R (2017)What’s so great about working in the creative industry? Available at: https://www.dusted.com/dusted/whats-great-working-creative-industry/. (Accessed : 22 February 2019)
  • Tejvan (2015) National minimum wage. Available at: https://www.economicshelp.org/labour-markets/minimum-wage/. ( Accessed : 22 February 2019)
  • Milton F.D (1966) Minimum wage rate. Available at: https://www.aei.org/publication/milton-friedman-in-a-1966-newsweek-op-ed-the-minimum-wage-law-is-a-monument-to-the-power-of-superficial-thinking/

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technology unemployment essay

The Impact of Technology on Unemployment Essay Example

The Impact of Technology on Unemployment Essay Example

  • Pages: 3 (642 words)
  • Published: February 12, 2017
  • Type: Essay

Advances in technology will result in a growth in unemployment. Technological advances allow society to produce more output from the existing mix of resources. These advances may take the form of less costly methods of producing existing output or may result in the production of new (or substantially improved) commodities (such as DVD players, HDTV, anti-lock braking systems, and similar innovations). Society clearly gains from the production of either more output or more highly valued output.

Nevertheless, how do these technological advances affect employment? Virtually all types of technological change result in increases in the demand for labor in some labor markets and decreases in the demand for labor in other labor markets. The introduction of assembly line production methods and the production of interchangeable parts resulted in a substantial increase in labo

r productivity. This technological innovation also resulted in an increase in the demand for unskilled workers and a decrease in the demand for skilled artisans.

The introduction of automated manufacturing processes, on the other hand, has resulted in a decrease in the demand for unskilled workers and an increase in the demand for quality control technicians and computer programmers. In general, technological change will alter the composition of the demand for labor, raising the demand for some types of labor and reducing the demand for other types of labor. Those who lose jobs because of technological change that reduces the demand for that category of labor are said to be structurally unemployed.

Even though technological change may adversely affect the demand for labor in some labor markets, the overall effect of technological change on total employment may b

positive. Technological change tends to increase the rate of economic growth. Higher rates of economic growth are generally associated with lower unemployment rates. While there is some doubt about the exact magnitude of this effect, there is substantial empirical evidence that unemployment rates tend to fall when the rate of economic growth is higher.

An increase in the pace of technological change can have two profound side effects in the labour market. It can increase the rate and the average duration of unemployment. Because firms may not consider it cost-effective to retrain some types of workers to keep up with change, notably the less-educated and older employees, these workers may be jobless for long periods, with some of them perhaps never working again. If technological change causes workers to ecome unemployed more often and for longer periods, not only will the level of unemployment increase, but the "natural rate of unemployment," the hypothesized minimum sustainable rate of unemployment, will increase as well. While the effect of technological change on the unemployment rate is ambiguous, this may be little consolation to those workers whose job skills have been rendered obsolete because of technological change. One of the issues that every industrialized society has to deal with is the extent to which the government should be involved in the retraining of structurally unemployed workers.

A good deal of recent debate has involved the related question of whether the widespread use of computers in the workplace has enhanced productivity. Preliminary studies suggested that the introduction of computers had no significant effect on productivity. Studies that are more recent have generated mixed results. It is clear, though,

that the widespread introduction of computers has, to date, had a less dramatic effect on productivity and economic growth than resulted from the widespread introduction of such earlier innovations as the steam engine, electricity, and the internal combustion engine.

Technology both eliminates jobs and creates jobs. Generally, it destroys lower wage, lower productivity jobs, while it creates jobs that are more productive, high-skill and better paid. Historically, the income-generating effects of new technologies have proved more powerful than the labour-displacing effects: technological progress has been accompanied not only by higher output and productivity, but also by higher overall employment.

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Unemployment Essay

500+ words essay on unemployment.

Unemployment is a serious problem among young people. There are thousands of people who do not have any work to do and cannot find work for themselves. Unemployment refers to the situation where a person wants to work but cannot find employment in the labour market. One of the major reasons that contribute to unemployment is the large population of India and the limited availability of resources. In this essay on unemployment, we will discuss all these issues responsible for unemployment in India and how we can overcome this problem. Students must go through this unemployment essay to get ideas on how to write an effective essay on the topic related to unemployment. Also, they can practice more CBSE essays on different topics to boost their writing skills.

Unemployment is measured by the unemployment rate, defined as the number of people actively looking for a job as a percentage of the labour force. The unemployment rate for the year 2013-14 in rural India was 4.7%, whereas it was 5.5% for urban India. In the short term, unemployment significantly reduces a person’s income and, in the long term, it reduces their ability to save for retirement and other goals. Unemployment is a loss of valuable productive resources to the economy. The impact of job loss in rural and regional areas flows through the local community, damaging businesses.

Reason for Unemployment

An unemployed person is one who is an active member of the labour force and is seeking work but is unable to find any work for himself. There are multiple reasons behind the unemployment of a person. One of them is the slow economic growth, due to which jobs in adequate numbers are not created. Excessive dependence on agriculture and slow growth of non-farm activities also limit employment generation. Unemployment in urban areas is mainly the result of substantial rural migration to urban areas. This has also resulted in a labour workforce in cities. The lack of technology and proper machinery has also contributed to unemployment.

The present educational system is based on theoretical knowledge instead of practical work. Thus, it lacks the development of aptitude and technical qualifications required for various types of work among job seekers. This has created a mismatch between the need and availability of relevant skills and training. This results in unemployment, especially among the youth and educated people with high degrees and qualifications. Apart from it, the lack of investment and infrastructure has led to inadequate employment opportunities in different sectors.

Steps to Eliminate Unemployment

Various strategies and proposals have been implemented to generate employment. Many Employment programmes and policies have been introduced and undertaken to boost self-employment and help unemployed people engage in public works. The Government of India has taken several policy measures to fight the problem of unemployment. Some of the measures are the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), National Skill Development Mission, Swarna Jayanti Shahari Rozgar Yojana (SJSRY), Regional Rural Banks (RRBs).

Despite the measures taken by the government, India remains a country experiencing severe unemployment problems. It can be resolved by imparting education in such a way that youth get the necessary skills so as to get employment easily. Setting up various vocational training and vocational courses for undergraduate and postgraduate students will help in finding employment for youth. The government needs to emphasise these courses at the primary level and make them a compulsory part of the curriculum to make students proficient in their early stages of life. Career counselling should be provided within schools and colleges so that students can choose a better career option based on their interests and ability. Government should create more job opportunities for the youth and graduates.

India is a fast-growing economy. There is an enormous scope for improvement in the unemployment sector. The various measures and steps taken by the government to increase the employment rate have succeeded to a great extent. The widespread skill development programmes have gained popularity across the nation. With better enforcement of the strategies, the employment level can be significantly improved. Although, we have to go a long way before we can say that all the people in India will get employment.

We hope this essay on unemployment must have helped students in boosting their essay-writing skills. Keep learning and visiting the BYJU’S website for more study material.

Frequently Asked Questions on Unemployment Essay

Is unemployment still an existing problem in india.

Yes, unemployment is still a serious issue in our country. Steps need to be taken by the government and also by the youngsters in India to improve this situation.

Is it necessary for schoolchildren to be informed about unemployment?

Students at this young age should definitely be informed about this topic as it will motivate them to study and aim for higher scores in exams.

What points are to be added to an essay topic on Unemployment?

Add details about different age groups of people suffering from this state of employment. You can focus on the fact that poverty is an indirect reason for unemployment and vice-versa. Then, suggest steps that can be taken to bring about an improvement in education and increase the percentage of literacy.

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How Technology Can Help Us Remember Better

My photo camera creates best pictures of Spring

I n the digital age, we have the technology to document our lives in extraordinary detail via photographs, voice recordings, and social media posts. In theory, this ability to effortlessly capture the important moments of our lives should enrich our ability to remember those moments. But in practice, people often tell me they experience the opposite.

I study the neuroscience of memory and one question I hear again and again is whether technology is making us “dumber” —or, more precisely, whether it’s hurting our ability to remember. For some, the question is motivated by worry about the amount of time their children spend on screens or mobile devices. For others, it reflects concerns about their own memory problems.

A common fear is that there might be a “use it or lose it” principle at play—that an increasing dependence on our devices for reminders will lead us to lose our own capabilities to remember. This might be true for certain skills. If, for instance, you always rely on navigation apps in new or unfamiliar neighborhoods, you might not attend to features in the environment to create a mental map that would allow you to learn to navigate on your own. However, there is no reason to think that relying on technology to store important information will somehow lead your brain to wither in ways that are bad for memory. In fact, I’m all for outsourcing mundane memory tasks, like memorizing phone numbers, passwords, email addresses, and appointments. I don’t have a photographic memory—but my phone does.

So, if technology can help us “free up space” for the things we want to remember when we need to remember them, why do so many of us feel like its presence in our lives is leading us to form blurry, fragmented, and impoverished memories?

The short answer: technology isn’t the problem—it’s how we interact with it.

To form lasting memories, we need to focus on what is distinct about the present moment, those immersive sensory details we can call back up to reconstruct an experience when we remember. As we go about our daily lives, we usually do a pretty good job of focusing on what’s relevant, and for that, we can thank a part of the brain called the prefrontal cortex. The prefrontal cortex helps us focus attention on and meaningfully process what we need to learn, to search for memories that are “in there somewhere,” and to keep our recollections accurate when we manage to remember the right thing.

But, in a world where our conversations, activities, and meetings are routinely interrupted by text messages, emails, and phone calls, these abilities get swamped—and we often compound the problem by splitting our attention between multiple goals. Multitasking can make us feel that we’re being more efficient. Many of us even pride ourselves on our ability to switch from one task to another, but it comes at a cost.

Read More: Why Multitasking Is Bad for You

Each time we are routinely distracted or intentionally toggle between different media streams (such as reading a text message while maintaining a conversation), prefrontal resources are sucked up to regain our focus. The result is that we remain one step behind, and after all is done, we are only left with blurry, fragmented memories.

Outside of the workplace, we often use technology to document our lives. The proliferation of “Instagram walls” and the throngs of people at concerts recording the action with their smartphones illustrate how technology has changed our lives. The ubiquity of smartphone cameras enables us to easily document our experiences, yet for most of us this hasn’t translated into a more expansive memory for the personal past. Again, the problem isn’t necessarily with the technology, but rather that we are filtering our experiences through the lens of a camera.

Taking photos does not necessarily have a good or bad effect on memory. The critical factors involve how you direct your attention and whether you meaningfully engage with the subject matter. Our brains are designed to do more with less, by engaging meaningfully with a little bit of high-quality information rather than amassing a massive catalog of information. When we focus on “documenting” over “experiencing,” we don’t pay attention to what is distinctive in the moment, the sights, sounds, smells, and feelings that make an experience unique—and memorable. Without those immersive details, something that was so vivid when we experienced it (a family vacation or child’s violin recital) can wind up feeling as distant to us as a story we read in a book. By trying to record every moment, we don’t focus on any one facet of the experience in enough detail to form distinctive memories that we will retain.

The negative potential of technology is exacerbated by a culture of sharing experiences on social media platforms. Social media engagement can have a negative effect on memory, partly because it involves multitasking (e.g., switching between recording the moment and engagement with social media platforms) and increases the potential for distraction.

Social media itself isn’t bad for memory, per se. Like most forms of technology, it’s a tool that when used properly can even enhance our memory of an event, but the images we post are often accompanied by captions with brief descriptions, rather than a thorough reflection on the event. Some platforms like Snapchat and Instagram stories, feature photo posts that disappear within 24 hours—an apt metaphor for the way in which mindless documentation can leave us bereft of lasting memories for our experiences.

Read More: How to Make Your Mind Happy, According to Neuroscience

Technology can enhance memory if it is used consistently with principles that help us remember. Thoughtfully taking pictures or videos at opportune moments can orient us to what is interesting and distinctive around us. My daughter, for instance, likes to selectively photograph plants and flowers that catch her eye on our nature walks, which allows her to pause and fully take in those aspects of the scenery as we are experiencing them in the moment.

After you take those pictures and videos, organize them in a way that will allow you to find them later (as we used to in the old days with photo albums) and make sure to revisit them later on. In the following weeks, revisit those photos and use them as cues to mentally re-experience those events, bringing back as many details as possible. By using the photos almost like a “test” of your memory, and spacing out those tests, you can enhance your ability to retain memories of the entire event, not only what is in the photo. Journaling can be another way to enhance memory because it allows us to test our memory for an event and also integrate it in a meaningful way, so that we can shape our narrative of the experience.

As with memory itself, a key principle for technology is that “less is more.” All the life-logging in the world will not enable us to remember all our experiences, nor is that a desirable goal in the first place. Our memories for events are selective, but they also can have a great deal of detail, meaning, and emotion. By mindfully using technology in ways that allow us to access those aspects of our past experiences, we can hold on to what matters.

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Volkswagen Leans on Electric Vehicles and Nostalgia to Grow in U.S.

It and other foreign automakers are trying to exploit upheaval caused by new technology to gain market share from their dominant rivals.

An older Scout sport utility vehicle is parked next to an outdoor stage where a man is standing in front of a microphone. Flags for Scout, the United States and South Carolina fly from the backdrop of the stage.

By Jack Ewing

Reporting from Columbia, S.C.

Probably only Americans of a certain age remember when the Volkswagen Beetle was the best-selling imported car in the United States and the hippest ride to a Grateful Dead concert was a Volkswagen Microbus.

Volkswagen is trying to tap some of that nostalgia in its latest push to regain the status and sales it enjoyed in the United States during the Beetle’s and Microbus’s heydays in the 1960s. But this time it hopes its top models will be electric.

The German carmaker is second only to Toyota globally but is a niche player in the United States. Part of its plan to revive its fortunes here is to lean on a new electric model that resembles the Microbus, the ID.Buzz, and to revive the Scout brand with a line of electric pickups and sport utility vehicles.

Last week, as giant earth movers kicked up clouds of dust, Volkswagen executives and local officials gathered near Columbia, S.C., to inaugurate the site of a factory that will build vehicles bearing the Scout badge for the first time since 1980.

Volkswagen is one of several foreign automakers that see electric cars and the upheaval they are causing as a way to challenge the dominant players in the United States. Volkswagen, which also owns Audi, Porsche, Bentley and Lamborghini, is aiming to at least double its market share in the United States by the end of the decade from a meager 4 percent now.

“This market is turning electric, and everybody’s starting from scratch,” Arno Antlitz, the chief financial officer of Volkswagen, said in an interview. “This is our unique opportunity to grow.”

Electric vehicles have already shaken the industry rankings, emboldening Volkswagen and other foreign automakers. Battery-powered S.U.V.s and sedans helped Hyundai Motor and its sister brand Kia overtake Stellantis, the maker of Jeep, Dodge, Chrysler and Ram, as the fourth-largest carmaker by sales in the United States last year.

“Electric vehicles are helping our brand to be seen as a technology leader,” said José Muñoz, chief operating officer of Hyundai. They also attract a better-educated, more affluent customer than has been the case for the South Korean company’s gasoline vehicles, he said in an interview.

The list of companies that dominate electric car sales looks a lot different from the top rankings for overall U.S. sales, hinting at a future when a different group of companies rule.

The top five companies in the United States for all engine types are General Motors, Toyota, Ford Motor, Hyundai and Stellantis. In electric cars, Tesla is No. 1 by a wide margin, followed by Hyundai, G.M., Ford and Volkswagen. Toyota is a minor player in electric cars.

“Just because you’ve been around for 120 years doesn’t mean you’re going to have anything in this new market ,” said Steven Center, the chief operating officer of Kia America.

Volvo Cars is another company hoping to take advantage of the changes wrought by electric vehicles. The Swedish carmaker, which is majority owned by Geely Holding Group of China, reported a 26 percent increase in U.S. sales last year.

Much of that growth came from hybrids that have a gasoline engine and can travel shorter distances on batteries. But Mike Cottone, president of Volvo Car for the United States and Canada, said he saw hybrids as a pathway to fully electric vehicles.

Later this year, Volvo will begin selling a Chinese-made, all-electric compact S.U.V., the EX30, which will start at $35,000. The company will also begin delivering the EX90, a seven-seat S.U.V. that is made in South Carolina and will start around $80,000.

Especially for luxury car buyers, Mr. Cottone said, “there’s a lot of room for growth in the E.V. segment over the next few years.”

Volkswagen has tried and failed since the 1970s to become a bigger presence in the United States, and analysts are skeptical that this time will be different. “I’ve seen Volkswagen set these goals before,” said Michelle Krebs, executive analyst at Cox Automotive.

The established carmakers will not be pushovers. G.M. and Ford are also investing heavily in electric vehicles, while Toyota has said it will start producing a large electric S.U.V. in Kentucky next year.

Ms. Krebs pointed out that auto sales in the U.S. were growing slowly, making the fight for market share largely a zero-sum game. “There’s this little bit of growth that everybody is going after,” she said.

Volkswagen’s last big push in the United States ended in scandal. In the early 2000s, the company tried to sell Americans on cars with “ clean diesel ” engines. It advertised the fuel, which was used in European passenger cars much more than in American cars, as more environmentally friendly than gasoline.

But the campaign collapsed in 2015 when U.S. regulators discovered that Volkswagen had used software in the vehicles to cheat on emissions tests. In reality, the cars polluted as much as long-haul trucks.

The scandal had one benefit for Volkswagen. It prompted the company to invest early in electric vehicle technology and build cars that were designed from the ground up to run on batteries, rather than make awkward modifications to gasoline models. In Europe, Volkswagen’s various electric brands together outsell Tesla, according to Schmidt Automotive Research.

The person responsible for doubling Volkswagen sales in the United States is Pablo Di Si, president of Volkswagen Group of America. Mr. Di Si, originally from Argentina, said he planned to use the same strategy he deployed while overseeing the company’s operations in Brazil, where Volkswagen’s market share rose to more than 16 percent from 9 percent.

“You look at the segments that you think are going to be successful 10 years from now,” Mr. Di Si said in an interview. “What are your gaps in the product portfolio? And then you start adding products for those particular markets.”

In the United States, he said, that is likely to include gasoline cars and hybrids as well as all-electric vehicles. Volkswagen plans to import the ID.7, an electric sedan, and the ID.Buzz. Mr. Di Si hinted that there might also be a new electric vehicle that references the design of the Beetle. The last version of that car sold in the United States was the 2019 Beetle .

Volkswagen is building a $5 billion factory in Ontario to supply batteries to its factories in Chattanooga, Tenn., and Puebla, Mexico, which together will produce at least 80 percent of the company’s cars sold in North America. That will help buyers of cars from its Volkswagen, Audi and other brands qualify for federal tax credits of up to $7,500 per car.

Scout will fill a major gap in Volkswagen’s portfolio: pickups, among the most popular vehicles in the United States. By reviving Scout, which was one of the first passenger vehicles that could navigate rough dirt tracks as well as city streets, Volkswagen hopes to attract buyers who typically buy off-road-capable vehicles from U.S. brands like Chevrolet, Ford and Jeep.

The South Carolina factory will underscore the made-in-America vibe when the first Scouts go on sale in late 2026. Volkswagen inherited the Scout brand when the company’s truck subsidiary, Traton, acquired Navistar, a U.S. company previously known as International Harvester, in 2021.

The new Scouts may borrow some parts used in other Volkswagen vehicles, company executives said, but the design will be distinct from existing vehicles like the electric ID.4 S.U.V. made in Chattanooga. Scout plans to reveal prototypes this year.

A stronger presence in the United States is “a strategic necessity,” Scott Keogh, the chief executive of Volkswagen’s Scout Motors division, said in South Carolina last week.

Outside the United States, Volkswagen is a behemoth, with a 26 percent share of the European market and 15 percent in China. But the company is under severe pressure in China, where sales of electric vehicles have been growing fast, allowing BYD and other Chinese carmakers to gain market share from foreign automakers. Volkswagen needs growth in the United States to compensate.

Volkswagen “wants to have a strong global footprint,” Mr. Keogh said, “not have an isolated footprint, where it’s only sitting strong in one region.”

Jack Ewing writes about the auto industry with an emphasis on electric vehicles. More about Jack Ewing

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OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

  • Will Douglas Heaven archive page

OpenAI has built a striking new generative video model called Sora that can take a short text description and turn it into a detailed, high-definition film clip up to a minute long.

Based on four sample videos that OpenAI shared with MIT Technology Review ahead of today’s announcement, the San Francisco–based firm has pushed the envelope of what’s possible with text-to-video generation (a hot new research direction that we flagged as a trend to watch in 2024 ).

“We think building models that can understand video, and understand all these very complex interactions of our world, is an important step for all future AI systems,” says Tim Brooks, a scientist at OpenAI.

But there’s a disclaimer. OpenAI gave us a preview of Sora (which means sky in Japanese) under conditions of strict secrecy. In an unusual move, the firm would only share information about Sora if we agreed to wait until after news of the model was made public to seek the opinions of outside experts. [Editor’s note: We’ve updated this story with outside comment below.] OpenAI has not yet released a technical report or demonstrated the model actually working. And it says it won’t be releasing Sora anytime soon. [ Update: OpenAI has now shared more technical details on its website.]

The first generative models that could produce video from snippets of text appeared in late 2022. But early examples from Meta , Google, and a startup called Runway were glitchy and grainy. Since then, the tech has been getting better fast. Runway’s gen-2 model, released last year, can produce short clips that come close to matching big-studio animation in their quality. But most of these examples are still only a few seconds long.  

The sample videos from OpenAI’s Sora are high-definition and full of detail. OpenAI also says it can generate videos up to a minute long. One video of a Tokyo street scene shows that Sora has learned how objects fit together in 3D: the camera swoops into the scene to follow a couple as they walk past a row of shops.

OpenAI also claims that Sora handles occlusion well. One problem with existing models is that they can fail to keep track of objects when they drop out of view. For example, if a truck passes in front of a street sign, the sign might not reappear afterward.  

In a video of a papercraft underwater scene, Sora has added what look like cuts between different pieces of footage, and the model has maintained a consistent style between them.

It’s not perfect. In the Tokyo video, cars to the left look smaller than the people walking beside them. They also pop in and out between the tree branches. “There’s definitely some work to be done in terms of long-term coherence,” says Brooks. “For example, if someone goes out of view for a long time, they won’t come back. The model kind of forgets that they were supposed to be there.”

Impressive as they are, the sample videos shown here were no doubt cherry-picked to show Sora at its best. Without more information, it is hard to know how representative they are of the model’s typical output.   

It may be some time before we find out. OpenAI’s announcement of Sora today is a tech tease, and the company says it has no current plans to release it to the public. Instead, OpenAI will today begin sharing the model with third-party safety testers for the first time.

In particular, the firm is worried about the potential misuses of fake but photorealistic video . “We’re being careful about deployment here and making sure we have all our bases covered before we put this in the hands of the general public,” says Aditya Ramesh, a scientist at OpenAI, who created the firm’s text-to-image model DALL-E .

But OpenAI is eyeing a product launch sometime in the future. As well as safety testers, the company is also sharing the model with a select group of video makers and artists to get feedback on how to make Sora as useful as possible to creative professionals. “The other goal is to show everyone what is on the horizon, to give a preview of what these models will be capable of,” says Ramesh.

To build Sora, the team adapted the tech behind DALL-E 3, the latest version of OpenAI’s flagship text-to-image model. Like most text-to-image models, DALL-E 3 uses what’s known as a diffusion model. These are trained to turn a fuzz of random pixels into a picture.

Sora takes this approach and applies it to videos rather than still images. But the researchers also added another technique to the mix. Unlike DALL-E or most other generative video models, Sora combines its diffusion model with a type of neural network called a transformer.

Transformers are great at processing long sequences of data, like words. That has made them the special sauce inside large language models like OpenAI’s GPT-4 and Google DeepMind’s Gemini . But videos are not made of words. Instead, the researchers had to find a way to cut videos into chunks that could be treated as if they were. The approach they came up with was to dice videos up across both space and time. “It’s like if you were to have a stack of all the video frames and you cut little cubes from it,” says Brooks.

The transformer inside Sora can then process these chunks of video data in much the same way that the transformer inside a large language model processes words in a block of text. The researchers say that this let them train Sora on many more types of video than other text-to-video models, varied in terms of resolution, duration, aspect ratio, and orientation. “It really helps the model,” says Brooks. “That is something that we’re not aware of any existing work on.”

“From a technical perspective it seems like a very significant leap forward,” says Sam Gregory, executive director at Witness, a human rights organization that specializes in the use and misuse of video technology. “But there are two sides to the coin,” he says. “The expressive capabilities offer the potential for many more people to be storytellers using video. And there are also real potential avenues for misuse.” 

OpenAI is well aware of the risks that come with a generative video model. We are already seeing the large-scale misuse of deepfake images . Photorealistic video takes this to another level.

Gregory notes that you could use technology like this to misinform people about conflict zones or protests. The range of styles is also interesting, he says. If you could generate shaky footage that looked like something shot with a phone, it would come across as more authentic.

The tech is not there yet, but generative video has gone from zero to Sora in just 18 months. “We’re going to be entering a universe where there will be fully synthetic content, human-generated content and a mix of the two,” says Gregory.

The OpenAI team plans to draw on the safety testing it did last year for DALL-E 3. Sora already includes a filter that runs on all prompts sent to the model that will block requests for violent, sexual, or hateful images, as well as images of known people. Another filter will look at frames of generated videos and block material that violates OpenAI’s safety policies.

OpenAI says it is also adapting a fake-image detector developed for DALL-E 3 to use with Sora. And the company will embed industry-standard C2PA tags , metadata that states how an image was generated, into all of Sora’s output. But these steps are far from foolproof. Fake-image detectors are hit-or-miss. Metadata is easy to remove, and most social media sites strip it from uploaded images by default.  

“We’ll definitely need to get more feedback and learn more about the types of risks that need to be addressed with video before it would make sense for us to release this,” says Ramesh.

Brooks agrees. “Part of the reason that we’re talking about this research now is so that we can start getting the input that we need to do the work necessary to figure out how it could be safely deployed,” he says.

Update 2/15: Comments from Sam Gregory were added .

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Ai for everything: 10 breakthrough technologies 2024.

Generative AI tools like ChatGPT reached mass adoption in record time, and reset the course of an entire industry.

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Our writers look at the four hot trends to watch out for this year

  • Melissa Heikkilä archive page

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

Deploying high-performance, energy-efficient AI

Investments into downsized infrastructure can help enterprises reap the benefits of AI while mitigating energy consumption, says corporate VP and GM of data center platform engineering and architecture at Intel, Zane Ball.

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screenshot saying 'the site is now under control of law enforcement'

Seized ransomware network LockBit rewired to expose hackers to world

Four arrested and LockBit victims will get help to recover data after joint operation in UK, US and Europe

The entire “command and control” apparatus for the ransomware group LockBit is now in possession of law enforcement, the UK’s National Crime Agency has revealed, after it emerged that it had seized the criminal gang’s website in a coordinated international operation.

The flood of data hacked back from the hackers has already led to four arrests, and the authorities promised on Tuesday to repurpose the technology to expose the group’s operations to the world.

The joint operation, between the NCA, the FBI, Europol and a coalition of international police agencies, was revealed with a post on LockBit’s own website , which read: “This site is now under the control of the National Crime Agency of the UK, working in close cooperation with the FBI and the international law enforcement taskforce Operation Cronos.”

Europol said that two LockBit actors had been arrested in Poland and Ukraine, and that a further two defendants, thought to be affiliates, had been arrested and charged in the US. Two more individuals have been named, and are Russian nationals still at large. Authorities have also frozen more than 200 cryptocurrency accounts linked to the criminal organisation.

Disruption to the LockBit operation is significantly greater than first revealed. As well as taking control of the public-facing website, the NCA seized LockBit’s primary administration environment, the infrastructure that allowed it to manage and deploy the technology that it used to extort businesses and individuals around the world.

“Through our close collaboration, we have hacked the hackers; taken control of their infrastructure, seized their source code, and obtained keys that will help victims decrypt their systems,” said Graeme Biggar, the NCA’s director general.

“As of today, LockBit are locked out. We have damaged the capability and most notably, the credibility of a group that depended on secrecy and anonymity.”

The organisation is a pioneer of the “ransomware as a service” model, whereby it outsources the target selection and attacks to a network of semi-independent “affiliates”, providing them with the tools and infrastructure and taking a commission on the ransoms in return.

As well as ransomware, which typically works by encrypting data on infected machines and demanding a payment for providing the decryption key, LockBit copied stolen data and threatened to publish it if the fee was not paid, promising to delete the copies on receipt of a ransom.

However, the NCA said that promise was false. Some of the data it discovered on LockBit’s systems belonged to victims who had paid the ransom.

The home secretary, James Cleverly, said: “The NCA’s world-leading expertise has delivered a major blow to the people behind the most prolific ransomware strain in the world.

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“The criminals running LockBit are sophisticated and highly organised, but they have not been able to escape the arm of UK law enforcement and our international partners.”

The “hack back” campaign also recovered more than 1,000 decryption keys earmarked for victims of LockBit’s attacks, and will be contacting those victims to aid them in the recovery of encrypted data.

In a blogpost last month, the former National Cybersecurity Centre boss, Ciaran Martin, said the involvement of Russian hackers in cybercrime undercut many common tactics of law enforcement. “Impose costs when we can: there are things we can do to harass and harry cybercriminals,” he warned. “But this will not be a strategic solution for as long as the Russia safe haven exists.”

  • NCA (National Crime Agency)
  • Data and computer security

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Technical Writing

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technical writing in 11 reviews

It's an amazing begin for peoples that need to have a first contact with the technical writing.

I enjoyed learning technical writing course from Coursera.

The entire "first week" is background information about the university and instructors, along with a short animation related to technical writing with no commentary that seems like it was made to be inserted into a video but was uploaded alone instead.

You can skip straight to Week 2 "Characteristics of Technical Writing" and not be any worse off for it.

", could've easily been combined with the course information in a text document or with "Characteristics of Technical Writing" for a more robust understanding of what technical writers do and how it relates to their writing.)

For how much the course tries to impress that technical writing should be clear, concise, and well-presented, it fails in nearly all of those aspects.It's not unwatchable and there's solid information to be found, but those are only part of what makes a quality educational video.

Great course and got great insights about Technical Writing.

Adding some Technical Writing Tools would have been even nicer.

This course was a great opportunity to learn the pros and cons of technical writing.

This course is very helpful for developing technical writing skill.

Is a product description an example of marketing or technical writing?

I think the information you learn about the reasons for technical writing as well as the types, and intricate parts of a body of work are valuable information.

This course is a great gateway to the world of technical writing.

The course was very well structured... a perfect balance between theoretical and practical aspects of Technical Writing.

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Technical File Reviewer $49k

Logistics Specialist / Technical Support $76k

Technical Field Representative 3 $81k

Technical Service Representative 1 2 $91k

Americas Technical Analyst $104k

Team IT Technical Analyst Lead 3 $108k

Technical Research and Development Engineer $109k

Technical Implementation Specialist Lead 2 $113k

Technical Marketing Mgr $114k

Technical Onsite Project Manager $117k

Distiguished Member of Technical Staff 3 $146k

Senior Program Manager/Technical Project Manager $156k

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