Fluent Essays

Here are the articles and questions you will need for this assignment.Please read and answer carefully, not google the question for answers. Thank you! ._why_are_there_still_so_many_jobs_by_autor_1_.pdf econ343_w2020_assignment4_1_.pdf Unformatted Attachment Preview Journal of Economic Perspectives—Volume 29, Number 3—Summer 2015—Pages 3–30 Why Are There Still So Many Jobs? The History and Future of Workplace Automation† David H. Autor T here have been periodic warnings in the last two centuries that automation and new technology were going to wipe out large numbers of middle class jobs. The best-known early example is the Luddite movement of the early 19th century, in which a group of English textile artisans protested the automation of textile production by seeking to destroy some of the machines. A lesser-known but more recent example is the concern over “The Automation Jobless,” as they were called in the title of a TIME magazine story of February 24, 1961: The number of jobs lost to more efficient machines is only part of the problem. What worries many job experts more is that automation may prevent the economy from creating enough new jobs. . . . Throughout industry, the trend has been to bigger production with a smaller work force. . . . Many of the losses in factory jobs have been countered by an increase in the service industries or in office jobs. But automation is beginning to move in and eliminate office jobs too. . . . In the past, new industries hired far more people than those they put out of business. But this is not true of many of today’s new industries. . . . Today’s new industries have comparatively few jobs for the unskilled or semiskilled, just the class of workers whose jobs are being eliminated by automation. Concerns over automation and joblessness during the 1950s and early 1960s were strong enough that in 1964, President Lyndon B. Johnson empaneled a ■ David H. Autor is Professor of Economics, Massachusetts Institute of Technology, Cambridge, Massachusetts. From 2009 to 2014, he was Editor of the Journal of Economic Perspectives. † To access the Data Appendix and disclosure statement, visit http://dx.doi.org/10.1257/jep.29.3.3 doi=10.1257/jep.29.3.3 4 Journal of Economic Perspectives “Blue-Ribbon National Commission on Technology, Automation, and Economic Progress” to confront the productivity problem of that period—specifically, the problem that productivity was rising so fast it might outstrip demand for labor. The commission ultimately concluded that automation did not threaten employment: “Thus technological change (along with other forms of economic change) is an important determinant of the precise places, industries, and people affected by unemployment. But the general level of demand for goods and services is by far the most important factor determining how many are affected, how long they stay unemployed, and how hard it is for new entrants to the labor market to find jobs. The basic fact is that technology eliminates jobs, not work” (Bowen 1966, p. 9). However, the Commission took the reality of technological disruption as severe enough that it recommended, as one newspaper (The Herald Post 1966) reported, “a guaranteed minimum income for each family; using the government as the employer of last resort for the hard core jobless; two years of free education in either community or vocational colleges; a fully administered federal employment service, and individual Federal Reserve Bank sponsorship in area economic development free from the Fed’s national headquarters.” Such concerns have recently regained prominence. In their widely discussed book The Second Machine Age, MIT scholars Erik Brynjolfsson and Andrew McAfee (2014, p. 11) offer an unsettling picture of the likely effects of automation on employment: Rapid and accelerating digitization is likely to bring economic rather than environmental disruption, stemming from the fact that as computers get more powerful, companies have less need for some kinds of workers. Technological progress is going to leave behind some people, perhaps even a lot of people, as it races ahead. As we’ll demonstrate, there’s never been a better time to be a worker with special skills or the right education, because these people can use technology to create and capture value. However, there’s never been a worse time to be a worker with only ‘ordinary’ skills and abilities to offer, because computers, robots, and other digital technologies are acquiring these skills and abilities at an extraordinary rate. Clearly, the past two centuries of automation and technological progress have not made human labor obsolete: the employment‐to‐population ratio rose during the 20th century even as women moved from home to market; and although the unemployment rate fluctuates cyclically, there is no apparent long-run increase. But those concerned about automation and employment are quick to point out that past interactions between automation and employment cannot settle arguments about how these elements might interact in the future: in particular, the emergence of greatly improved computing power, artificial intelligence, and robotics raises the possibility of replacing labor on a scale not previously observed. There is no fundamental economic law that guarantees every adult will be able to earn a living solely on the basis of sound mind and good character. Whatever the future holds, the present clearly offers a resurgence of automation anxiety (Akst 2013). David H. Autor 5 In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for labor—as it is typically intended to do. However, automation also complements labor, raises output in ways that lead to higher demand for labor, and interacts with adjustments in labor supply. Indeed, a key observation of the paper is that journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor. Changes in technology do alter the types of jobs available and what those jobs pay. In the last few decades, one noticeable change has been “polarization” of the labor market, in which wage gains went disproportionately to those at the top and at the bottom of the income and skill distribution, not to those in the middle. I will offer some evidence on this phenomenon. However, I will also argue that this polarization is unlikely to continue very far into the foreseeable future. The final section of this paper reflects on how recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. I argue that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity. The frontier of automation is rapidly advancing, and the challenges to substituting machines for workers in tasks requiring flexibility, judgment, and common sense remain immense. In many cases, machines both substitute for and complement human labor. Focusing only on what is lost misses a central economic mechanism by which automation affects the demand for labor: raising the value of the tasks that workers uniquely supply. How Automation and Employment Interact In 1900, 41 percent of the US workforce was employed in agriculture; by 2000, that share had fallen to 2 percent (Autor 2014), mostly due to a wide range of technologies including automated machinery. The mass-produced automobile drastically reduced demand for many equestrian occupations, including blacksmiths and stable hands. Successive waves of earth-moving equipment and powered tools displaced manual labor from construction. In more recent years, when a computer processes a company’s payroll, alphabetizes a list of names, or tabulates the age distribution of residents in each Census enumeration district, it is replacing a task that a human would have done in a previous era. Broadly speaking, many—perhaps most—workplace technologies are designed to save labor. Whether the technology is tractors, assembly lines, or spreadsheets, the first-order goal is to substitute mechanical power for human musculature, machine-consistency for human handiwork, and digital calculation for slow and error-prone “wetware.” 6 Journal of Economic Perspectives Given that these technologies demonstrably succeed in their labor saving objective and, moreover, that we invent many more labor-saving technologies all the time, should we not be somewhat surprised that technological change hasn’t already wiped out employment for the vast majority of workers? Why doesn’t automation necessarily reduce aggregate employment, even as it demonstrably reduces labor requirements per unit of output produced? These questions underline an economic reality that is as fundamental as it is overlooked: tasks that cannot be substituted by automation are generally complemented by it. Most work processes draw upon a multifaceted set of inputs: labor and capital; brains and brawn; creativity and rote repetition; technical mastery and intuitive judgment; perspiration and inspiration; adherence to rules and judicious application of discretion. Typically, these inputs each play essential roles; that is, improvements in one do not obviate the need for the other. If so, productivity improvements in one set of tasks almost necessarily increase the economic value of the remaining tasks. An iconic representation of this idea is found in the O‑ring production function studied by Kremer (1993).1 In the O-ring model, failure of any one step in the chain of production leads the entire production process to fail. Conversely, improvements in the reliability of any given link increase the value of improvements in all of the others. Intuitively, if n − 1 links in the chain are reasonably likely to fail, the fact that link n is somewhat unreliable is of little consequence. If the other n − 1 links are made reliable, then the value of making link n more reliable as well rises. Analogously, when automation or computerization makes some steps in a work process more reliable, cheaper, or faster, this increases the value of the remaining human links in the production chain. As a contemporary example, consider the surprising complementarities between information technology and employment in banking, specifically the experience with automated teller machines (ATMs) and bank tellers documented by Bessen (2015). ATMs were introduced in the 1970s, and their numbers in the US economy quadrupled from approximately 100,000 to 400,000 between 1995 and 2010. One might naturally assume that these machines had all but eliminated bank tellers in that interval. But US bank teller employment actually rose modestly from 500,000 to approximately 550,000 over the 30-year period from 1980 to 2010 (although given the growth in the labor force in this time interval, these numbers do imply that bank tellers declined as a share of overall US employment). With the growth of ATMs, what are all of these tellers doing? Bessen observes that two forces worked in opposite directions. First, by reducing the cost of operating a bank branch, ATMs indirectly increased the demand for tellers: the number of tellers per branch fell by more than a third between 1988 and 2004, but the number of urban bank branches (also encouraged by a wave of 1 The name of the O-ring production function refers to the 1986 accident of Space Shuttle Challenger, which exploded and crashed back to earth less than two minutes after takeoff, killing its seven crew members. The proximate cause of the Challenger crash was an inexpensive and seemingly inconsequential rubber O-ring seal in one of its booster rockets that failed after hardening and cracking during the icy Florida weather on the night before takeoff. Why Are There Still So Many Jobs? 7 bank deregulation allowing more branches) rose by more than 40 percent. Second, as the routine cash-handling tasks of bank tellers receded, information technology also enabled a broader range of bank personnel to become involved in “relationship banking.” Increasingly, banks recognized the value of tellers enabled by information technology, not primarily as checkout clerks, but as salespersons, forging relationships with customers and introducing them to additional bank services like credit cards, loans, and investment products. This example should not be taken as paradigmatic; technological change is not necessarily employment-increasing or Pareto-improving. Three main factors can mitigate or augment its impacts. First, workers are more likely to benefit directly from automation if they supply tasks that are complemented by automation, but not if they primarily (or exclusively) supply tasks that are substituted. A construction worker who is expert with a shovel but cannot drive an excavator will generally experience falling wages as automation advances. Similarly, a bank teller who can tally currency but cannot provide “relationship banking” is unlikely to fare well at a modern bank. Second, the elasticity of labor supply can mitigate wage gains. If the complementary tasks that construction workers or relationship bankers supply are abundantly available elsewhere in the economy, then it is plausible that a flood of new workers will temper any wage gains that would emanate from complementarities between automation and human labor input. While these kinds of supply effects will probably not offset productivity‑driven wage gains fully, one can find extreme examples: Hsieh and Moretti (2003) document that new entry into the real estate broker occupation in response to rising house prices fully offsets average wage gains that would otherwise have occurred. Third, the output elasticity of demand combined with income elasticity of demand can either dampen or amplify the gains from automation. In the case of agricultural products over the long run, spectacular productivity improvements have been accompanied by declines in the share of household income spent on food. In other cases, such as the health care sector, improvements in technology have led to ever-larger shares of income being spent on health. Even if the elasticity of final demand for a given sector is below unity—meaning that the sector shrinks as productivity rises—this does not imply that aggregate demand falls as technology advances; clearly, the surplus income can be spent elsewhere. As passenger cars displaced equestrian travel and the myriad occupations that supported it in the 1920s, the roadside motel and fast food industries rose up to serve the “motoring public” ( Jackson 1993). Rising income may also spur demand for activities that have nothing to do with the technological vanguard. Production of restaurant meals, cleaning services, haircare, and personal fitness is neither strongly complemented nor substituted by current technologies; these sectors are “technologically lagging” in Baumol’s (1967) phrase. But demand for these goods appears strongly income-elastic, so that rising productivity in technologically leading sectors may boost employment nevertheless in these activities. Ultimately, this outcome requires that the elasticity of substitution between leading and lagging sectors is less than or equal to unity (Autor and Dorn 2013). 8 Journal of Economic Perspectives Over the very long run, gains in productivity have not led to a shortfall of demand for goods and services: instead, household consumption has largely kept pace with household incomes. We know this because the share of the population engaged in paid employment has generally risen over (at least) the past century despite vast improvements in material standards of living. An average US worker in 2015 wishing to live at the income level of an average worker in 1915 could roughly achieve this goal by working about 17 weeks per year.2 Most citizens would not consider this tradeoff between hours and income desirable, however, suggesting that consumption demands have risen along with productivity. Of course, citizens in high-income countries work fewer annual hours, take more vacations, and retire earlier (relative to death) than a century ago—implying that they choose to spend part of their rising incomes on increased leisure. This is clearly good news on many fronts, but does it also imply that consumption demands are approaching satiation? I think not. In high-income countries, consumption and leisure appear to be complements; citizens spend much of their leisure time consuming—shopping, traveling, dining, and, less pleasantly, obtaining medical care.3 What about the Marxian concern that automation will immiserate workers by obviating the demand for labor? In simple economic models, this outcome cannot really occur because capital is owned by the economic agents who are presumably also the workers; but, alternatively, the returns could accrue to a narrow subset of agents. Sachs and Kotlikoff (2012) and Sachs, Benzell, and LaGarda (2015) explore multigenerational economic environments in which a burst of robotic productivity can enrich one generation of capital owners at the expense of future generations. These later generations suffer because the fruits of the productivity surge are consumed by the old, while the young face diminished demand for their labor and, in some cases, also experience credit constraints that inhibit their human capital investments. In these models, the fundamental threat is not technology per se but misgovernance; an appropriate capital tax will render the technological advance broadly welfare-improving, as these papers stress. Thus, a key takeaway is that rapid automation may create distributional challenges that invite a broad policy response, a point to which I will return. 2 Douglas (1930; reproduced in US Bureau of the Census 1949) reports average annual earnings across all sectors in 1915 at $633. Inflating this to 2015 dollars using the US Bureau of Labor Statistics historical Consumer Price Index calculator yields a current dollar equivalent of $14,711. The BLS employment report from April 2015 reports mean weekly private nonfarm earnings of $858. Thus, it would take 17 weeks of work at the average US weekly wage to earn a full-time annual 1915 income. 3 This outcome is a modern version of the “coal paradox” posed by William Stanley Jevons in his 1865 book The Coal Question. Jevons argued that as we became more efficient in mining coal, we would use more of it, not less. Modern environmental economists term this idea the “rebound effect.” In this discussion, the broad parallel is that greater efficiency of production of all goods and services means that we consume more of them, not the same or less. David H. Autor 9 Figure 1 Average Change per Decade in US Occupational Employment Shares for Two Periods: 1940–1980 and 1980–2010 3.0 2.5 2.0 1.5 1.0 0.5 0.0 −0.5 Agricultural occupations Service occupations Operative/ laborers Skilled blue Clerical/sales collar (craft) Managers Professionals/ technicals −1.0 −1.5 −2.0 −2.5 −3.0 −3.5 1940–1980 1980–2010 −4.0 Source: Based on Katz and Margo (2014), table 1.6, panel A, which is based upon the 1920 through 2000 Census of population IPUMS and 2010 American Community Survey. Notes: Observed long changes in US occupational employment shares over 1940–1980 and 1980–2010 are scaled by the number of intervening decades to yield average change per decade. Occupations are classified into occupational groups based on 1950 occupation codes using the consistent coding of occupations in all years into 1950 codes (the OCC1950 variable) in the IPUMS. Additional details are found in Katz and Margo (201 ... Purchase answer to see full attachment