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Building on work by Paul David, Syverson discussed the slowdown in productivity growth in the historical context of electrification of the production process at the end of the 19th century. The first wave arrived quickly and reflected the adoption of electrification within the existing organization of production.

The second wave, delayed by a few decades, reflected new ways of organizing production around this new technology. Similarly, while the first power looms allowed weavers to produce 2. Reinsdorf, , Does the United States have a productivity slowdown or a measurement problem? Hitt, , Computing productivity: Firm-level evidence, Review of Economics and Statistics Hitt, , IT, workplace organization and the demand for skilled labor: A firm-level analysis, Quarterly Journal of Economics 1: David, , The dynamo and the computer: An historical perspective on the modern productivity paradox, American Economic Review 80 2: Bessen, , Learning By Doing: In a related manner, there is evidence that adopting new technologies requires organizational changes and restructuring of business practices that take time.

This perspective may help reconcile the observation of the apparently rapid changes in technology outlined in Chapter 2 with the current sluggish growth in productivity. Yet there are more pessimistic views about the prospects for productivity and economic growth. Some have suggested that recent post innovations in information and other advanced technology simply do not have the same high payoff as innovations in earlier periods. The argument is that earlier innovations were in the form of general purpose technologies that had wide application to many industries.

Firm-level evidence for the United States and the Organisation for Economic Co-operation and Development shows a widening gap between the most and least productive firms within industries in the post period. Firm-level evidence, Review of Economics and Statistics 85 4: Causes, Consequences, and Policies, September The latter has been shown to be an important part of the process of productivity growth, and is discussed further in Chapter 4.

From this perspective, the hypothesis is that while the changes in technology outlined in Chapter 2 are indeed occurring, they are slow to show up in economic growth due to slowing diffusion or business dynamism. All of these hypotheses are active areas of research. The discussion of future research directions in Chapter 6 emphasizes the significance of exploring such critically important questions. It is useful to note that future productivity growth cannot be predicted simply by extrapolating past trends because there is little serial correlation in growth rates from one decade to the next.

Instead, future trends will depend on the invention and deployment of new and improved technologies and on the co-inventions by the workforce, organizations, and institutions needed to effectively use them. In the past few years, U. For instance, by early , the unemployment rate fell below 5 percent. However, much of this employment growth can be interpreted as a recovery from the Great Recession, which has been slow despite the fact that it officially ended in Furthermore, jobs lost in the recession are very different from those that appeared during the recovery.

There have also been substantial shifts in employment in various occupational categories. For instance, the employment rate in clerical and. Center on Education and the Workforce, https: Despite the low unemployment rate, the overall U. It began to decline in the post period, with a sharp drop during the Great Recession, from which it has recovered slightly. Some of this trend can be accounted for by the aging of the population. However, declines in the employment rate are especially large for young and less educated individuals.

Employment rates of prime age year-old males are still low 84 percent in , near the year low of 81 percent in , as compared to a high of 95 percent in , according to the. Predictions that new technologies will make workers largely or almost entirely redundant are as old as technological change itself. Although the story might be apocryphal, the famous Roman historian Pliny the Elder recounts how the Roman Emperor Tiberius killed an inventor who had supposedly invented unbreakable glass for fear of what this would do to the glassmaking trade.

It is not only emperors and queens who have feared the implications of new technologies for employment. More famously, British textile workers. More recently, the economist John Maynard Keynes predicted that the introduction of new technologies would create considerable wealth but would also generate widespread technological unemployment as machines replaced humans. More and more workers will be replaced by machines. I do not see that new industries can employ everybody who wants a job.

However, predictions of widespread, technologically induced unemployment have not come to pass, at least so far. To be sure, technologies did and will continue to decimate particular occupations. As the Luddites feared, artisans lost their jobs in spinning and then weaving as new technologies automated tasks they had previously performed. Straume, Globalisation and union opposition to technological change, Journal of International Economics 68 1: Heilbroner, , Men and machines in perspective, National Affairs , Fall, pp.


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Leontief, , Machines and man, Scientific American 87 3: For a more recent perspective on this questions, see E. Similarly, the replacement of horses by automobiles eliminated the need for blacksmiths. But as these jobs disappeared, new ones sprang up to operate, manage, and service the new technologies.

For instance, in the late s, the replacement of the stagecoach by the railroad went hand in hand with the creation of new work for managers, engineers, machinists, repairmen, and conductors. Simultaneously, there was a boom in a range of new service occupations, from teaching to entertainment to sales.

Nonetheless, simultaneous automation of a broader range of tasks could create unemployment or perhaps reduce aggregate levels of employment for an extended period of time. As noted in the previous section, over the past 20 years, the share of people working—the employment-to-population ratio—has declined. While there are many factors at work, it is possible that technological substitution for certain types of labor is part of the explanation. As compensation falls for tasks that can increasingly be done by machines, some people may choose to work less or not at all, finding other alternatives, including increased leisure or family time, applying for disability benefits, or investing in education, to become relatively more attractive.

Over the longer term, there may be a continuation of the long-term decline in the share of hours worked as society as a whole becomes wealthier and leisure becomes relatively more attractive. What happens depends, in part, on whether new technologies automate and replace workers in existing tasks more rapidly than they create new demands for labor. Which will be the case is difficult to answer, because it is easier to see how new technologies coming down the line will automate existing tasks than it is to imagine tasks that do not yet exist and how new technologies may stimulate greater consumer demand.

Further, the future of employment is not only a question of the availability or necessity of tasks to be performed, but how they are organized, compensated, and more generally valued by society. These are matters of business strategy, social organization, and political choices and not simply driven by technologies themselves. In principle, driving and delivery occupations could be automated with the use of such technologies over the next several decades. However, there are numerous social and cultural as well as technological roadblocks to such an outcome.

These include such factors as consumer trust; the fact that there will be a long period of mixed-use road use, with both autonomous driving and manual driving cars sharing the roads; and the infrastructure require-. The new jobs are likely to rely more heavily on analytic, cognitive, and technical skills.

Indeed, even in the near term, as self-driving technologies are being developed, the occupation of trucking 37 is likely to be transformed. For example, additional IT-based capabilities for driver simulation training can help improve the skill sets of more drivers than would be possible otherwise.

In the longer term, increased automation will reduce the need for additional drivers and ultimately reduce overall demand for truck drivers. As transportation costs drop due to partial automation, it is possible that lower per-unit costs will lead to increased demand e. Self-driving cars also offer a good illustration of the variable and mixed impact of technology on employment, as well as the long and often uneven march of technology development, which complicates the ability to make accurate long-term projections.

In addition to eliminating some jobs while creating others, technological developments can create new occupations without reducing employment in older occupations. New medical imaging technologies are a case in point. Prior to the development of computer-controlled imaging modalities such as ultrasound, computed tomography scanners, and magnetic resonance imaging, most technicians who worked in radiology departments operated standard X-ray machines and fluoroscopes.

The jobs associated with these technologies were not significantly altered by the arrival of digital imaging. Thus, in the case of medical imaging, the overall number. Nonetheless, technologies can also have an impact on how tasks are allocated and how job categories and tasks associated with particular organizational forms and structures are designed. For instance, they can shift the allocation of tasks across occupations such that some occupations contract as the work they once performed is shifted onto members of other occupations.

As recently as the s, administrative assistants answered phones, interacted with students, kept paper records of accounts, filed documents, and typed letters, memos, and manuscripts for faculty who often wrote first drafts by hand. Today, administrative assistants continue to answer phones and interact with students, but few type documents for faculty.

Professors now use a computer to create and revise their own documents. Some faculty also enter their own data on travel expenses and other activities directly into databases, tasks previously performed by administrative assistants. R Barley, , Technology as an occasion for structuring: Evidence From observations of CT scanners and the social order of radiology departments, Administrative Science Quarterly R Barley, , The alignment of technology and structure through roles and networks, Administrative Science Quarterly Wigand, , Electronic markets and virtual value chains on the information superhighway, Sloan Management Review Orlikowski, , A practice perspective on technology-mediated network relations: The use of Internet-based self-serve technologies, Information Systems Research 5 1: As noted in Chapter 2 , jobs involving physical labor will be increasingly affected by advances in robotics, although there is debate about the timeline.

The committee notes that the effects of technologies on employment can be shaped by interests and social dynamics beyond merely the technological dimension. For example, computer-mediated communications, especially those facilitated by the Web, such as e-mail, computer teleconferencing, and the ability to easily and almost instantaneously transfer documents of all kinds across space and hence time zones , were initially thought of as simply more efficient ways to communicate.

But because these technologies did not require co-location, companies began using such technologies to both outsource and offshore a variety of tasks and even jobs, ranging from clerical to engineering work. There is nothing about computer-mediated communication technologies that preordained such developments. Instead, they are the result of choices strategic or otherwise by decision makers in organizations about how the technologies would be deployed and what they would be used to achieve, along with market forces encouraging the adoption of cost-efficient processes.

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Choices regarding the development of technologies can also be influenced by the same interests and social dynamics. For example, it has been suggested that the decision to develop technologies that automate rather than augment the human role in the machine tool industry was driven by the combined interests of the U. Air Force and the Massachusetts Institute of Technology servomechanisms laboratory. Consideration of whether technology can replace human workers has prompted discussion about the subtle complexity of human skills.

Income and Wealth Inequality: Crash Course Economics #17

A recent paper 43 by economist David Autor invoked the philosopher Michael Polanyi: The skill of a driver cannot be replaced by a thorough schooling in the theory of the motorcar; the knowledge I have of my own body differs altogether from. Currently, computational systems are far from being able to use creativity, intuition, persuasion, and imaginative problem solving, or to coordinate and lead teams. Autor and others have argued that many highly valued and important human capabilities may never be automated. Educational programs, even those in vocational disciplines like business and engineering, may need to add interpersonal and creative skills to their mix of hard analytical skills.

To what extent are these human attributes, including creativity, empathy, interpersonal skills, leadership, mentoring, and physical presence currently valued in the U. The rapidly growing attendance at research conferences on artificial intelligence AI , like the annual Neural Information Processing Systems and Association for the Advancement of Artificial Intelligence conferences, demonstrates that an increasing number of researchers are attempting to address these challenges, and most of them now focus on approaches that enable machines to learn how to do tasks, from recognizing and labeling objects to understanding speech, improving dexterity and mobility, and mastering increasingly complex games and puzzles.

Autor, , Why are there still so many jobs? The history and future of workplace automation, Journal of Economic Perspectives 29 3: It is generally understood that, by increasing productivity, IT will tend to increase overall income—although without a guarantee that these gains will be evenly distributed.

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Furthermore, while it is common to focus on average levels of income and income growth, the distribution of those gains can also have an effect on well-being. This is true not only because absolute levels directly affect the quality of life of particular groups, but also because broad perceptions of unfairness can have a negative psychological impact, and inequality can contribute to sociopolitical tensions.

Since the mids, the United States has experienced significant growth in inequality in both income and wealth. This is the subject of a large amount of literature and has been documented in great detail by Acemoglu, Autor, Katz, Piketty, Saez, and many others. Over the past several decades, IT and automation have been a significant driver of this increase in inequality, although there are also other forces at work. Much popular attention has been focused on the rising share of income of the top 1 percent of each of these distributions. While this increase has been substantial, with the share of income accruing to the top 1 percent of households increasing from about 10 percent to over 20 percent between and , there have also been increases in earnings inequality within the other 99 percent, accounted for largely by the increasing skills premium associated with a 4-year college degree.

For example, the absolute median earnings gap between those with a high school and a college degree approximately doubled from to , as the real wages of college graduates rose and those of less educated workers fell through about A related phenomenon is the falling share of GDP paid to labor relative to owners of capital illustrated in Figure 3. Kearney, , Trends in US wage inequality: Revising the revisionists, Review of Economics and Statistics Saez, , Top incomes and the Great Recession: Neiman, , The global decline of the labor share, Quarterly Journal of Economics 1: It suggests that trends in income are increasingly favoring those who have already accrued wealth.

This decline in the labor share of GDP, if sustained, will affect the distribution of wealth as well as that of income, expanding the share of total income flowing to wealth holders. Many factors are likely at work in this landscape of inequality; technological change, social biases, increased globalization and trade, the decline in labor union density and power, 51 declines in the real minimum wage, changing norms regarding executive compensation, growing economic deregulation, changes in tax rates, and growing oligopoly—or in some cases, simple monopoly 52 —are among the hypothesized causes of increased inequality of income and wealth over the past 40 years.


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As with employment, the case that technological advances have contributed to wage inequality is strong. For most of the 20th century, real median incomes—incomes of people at the 50th percentile—grew at least as fast as overall real GDP per person, suggesting that the benefits of improved technological progress were widely shared.

But since the late s, productivity and GDP per person have continued to grow, while median incomes have stagnated illustrated in Figure 3. There is a debate in the research literature, and indeed, among committee members, about how much of the increase in inequality should be attributed to technology.

There are three prominent narratives implicating technological change as a force toward greater inequality over the last several decades. First, many new technologies have replaced labor-intensive, routine, and physical tasks and expanded demand for labor in jobs that require social skills, numeracy, abstract thinking, and flexibility.

Second, as labor-intensive tasks are automated, the share of income going to capital relative to labor can increase, which may also help to explain the falling share of labor in overall GDP as illustrated in Figure 3. This in part reflects their improved ability to sell to not only customers in local markets, but also with greater ease to those in regional, national, and even global markets as improved communications technologies reduce the costs of reaching a broader audience.

Changes in IT also seem to be playing a role in the changing demand for skills and the earnings inequality for the other 99 percent.

National Bureau of Economic Research Studies in Income and Wealth

Technology can be a complement for highly skilled workers, as well as a substitute for low- or medium-skill workers. This is often called the skill-biased technological change hypothesis. Unless supply changes sufficiently, this will shift wages in favor of the more skilled group. It has been suggested that this divergence is exacerbated by an increasing reliance on technology in the workplace, as the skills required to work with these technologies are more readily. DiNardo, , Skill-biased change and rising wage inequality: Some problems and puzzles, Journal of Labor Economics Kearney, , Trends in U.

Revising the revisionists, Review of Economics and Statistics 90 2: The Evolution of U. In fact, many new technologies of the 19th century automated previously skilled occupations and expanded unskilled assembly work which paid lower wages than the prior forms of work.

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For instance, the Luddites may have been misguided in their tactics of smashing mechanical spinning and weaving machines, but they were right that the way these machines were used was bad news for them: In general, if there is a mismatch between the skills of the workforce and the skill requirements of new technologies, changes in the structure of pay will tend to follow. In the s and s, these changes in technology—along with complementary factors such as globalization, deregulation, and deunionization—have likely contributed to the reduction in demand for middle-level skills, and this has been reflected in both the quantity of jobs and in wages for middle-skill workers see Figure 3.

In particular, workers doing routine tasks such as production tasks in manufacturing or clerical tasks have seen their demand decline due to multiple factors, including changing technology. This is reflected in a decline in manufacturing employment even as output has grown to an all-time high. Globalization has further eroded the demand for such skills in advanced economies like the United States.

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In contrast, there have been expanding job opportunities in both high-skill, high-wage occupations like professional, technical, and managerial occupations and low-skill,. But changes in skill demand are far from the only factors at work. In addition, technology may be helping to drive a decline in the labor force participation rate and a broader shift in the labor-capital relationship, as advanced technology is embodied in capital equipment that replaces many workers. Acemoglu, , Changes in unemployment and wage inequality: An alternative theory and some evidence, American Economic Review 89 5: Acemoglu, , Good jobs versus bad jobs, Journal of Labor Economics 19 1: Karabarbounis, , The global decline of the labor share, Quarterly Journal of Economics 1: Another factor that is important in this context is that advances in IT often involve significant supply-side economies of scale: Thus companies whose software has more users will tend to have lower average costs per user.

Similar logic holds for many business-to-business platforms and, to some extent, even productivity software like word processors, spreadsheets, and presentation software, because they are compatible between users and enable file sharing and collaboration. Even if the gains are shared across all the workers within the winning firms, this still leads to concentration of the gains to a relatively small share of workers. That said, gains have not been evenly distributed, with the top employees in firms seeing the biggest gains on average.

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