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Why Are There Still So Many Jobs? The History and Future of Workplace Automation
Open accessAbstract
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 leads to higher demand for labor, and interacts with adjustments in labor supply. 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 a “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; however, I also argue, this polarization and is unlikely to continue very far into 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.
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“With each wave, however, human effort has not been removed, but rather redirected.”
From The views of health guideline developers on the use of automation in health evidence synthesis (2021) · cites this paper as [15]
In the citing paper
“From the late nineteenth century onwards, there have been repeated waves of automation of production and consequent population-level job panic [15].”
From The views of health guideline developers on the use of automation in health evidence synthesis (2021) · cites this paper as [15]
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“In some cases, job opportunities have expanded rather than contracted as a by-product of widespread automation.”
From The views of health guideline developers on the use of automation in health evidence synthesis (2021) · cites this paper as [15]
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“For instance, even though present-day machines can conceive messages perceived as more empathic than messages conceived by humans 170,171 , such messages may be perceived differently once recipients become aware of their artificial origin.”
From Machine culture (2023) · cites this paper as [170]
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“More specifically, Autor (2015) argues that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine and codifiable tasks while amplifying the comparative advantage of workers in supplying problemsolving skills, adaptability, and…”
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“Autor (2015) rejoinder insists that while some jobs will disappear, new ones will emerge.”
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“…McKinsey Global Institute 2015), to women- and childcare-friendly public policies (Connelly 1992; Gornick, Meyers, and Ross 1998), and to technological change (Autor and Dorn 2013; Autor 2015) favoring the growth of sectors in which employment is more gender equal—has been extensively documented.”
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“A recurrent fantasy—or anxiety—at the heart of industrial development, the fully automated society can manifest as a dystopia of unemployment or a utopia of abundance and liberation from work (Autor 2015; Wajcman 2017).”
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“Evidence shows that technology is the most important driving force behind current changing employment structure and tends to outperform international trade as an explanation of the rise in inequality and job polarization observed in recent years (Autor, 2015; Goos et al., 2014).”
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“…to certain activities is, by and large, underpinned by a belief in human exceptionalism: i.e. the idea that important human capacities are the result of non-routine processes that will never be fully explicable, and thus, never fully replicable by (human-designed) machines (e.g. Autor, 2015).”
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“Importantly, the supply chain versus B2C framework helps explain the job polarization in services identified in prior work (Autor and Dorn, 2013; Autor, 2015).”
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“In manufacturing, robotics and automation e ff orts, which aimed to mechanise intelligence, have utilised AI for years as automated machines and computers are relatively more productive and cost-e ffi cient than humans (Autor 2015; Frey and Osborne 2017).”
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“In contrast, research has shown that employees can adapt sufficiently to the changes on the labor market and that the negative effects of digitization and automation might be exaggerated, as many jobs may change but also new jobs will be created (Autor 2015; Arntz et al. 2016).”
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“Research on soft skills exists across different fields such as labor economics [7]; employability, workforce development, and human resource development [6,8]; management and communication [4,29]; and in industry or subject specific literature such as information technology [20,30]; STEM [31]; education [15], or libra…”
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Yu, H. C., Shum, C. W. S., Alcorn, M., Sun, J., & He, Z. (2022) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-10-2021-1312 · Cited by 137
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Edin, P., Fredriksson, P., Nybom, M., & Öckert, B. (2017) · American Economic Journal Applied Economics · doi:10.1257/app.20190199 · Cited by 137
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“A lot of attention goes to possible negative effects, such as in the literature on automation that focuses on the types of jobs that might be displaced or under threat (e.g., Acemoglu & Restrepo, 2019; Autor, 2015; Bessen et al., 2019; Frey & Osborne, 2016).”
BackgroundThe Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand
Acemoğlu, D., & Restrepo, P. (2019) · National Bureau of Economic Research · doi:10.3386/w25682 · Cited by 148
“17See Dreyfus (1992) and Autor (2015).”
Micro-work, artificial intelligence and the automotive industry
Tubaro, P., & Casilli, A. A. (2019) · Journal of Industrial and Business Economics · doi:10.1007/s40812-019-00121-1 · Cited by 122
“…policymakers have echoed the dire job loss predictions of a few prospective studies (Brynjolfsson and McAfee 2014; Frey and Osborne 2017), although historical evidence suggests that dynamics of complementarity rather than substitution between labor and machinery might also be in place (Autor 2015).”
BackgroundTrust me, i'm a chatbot: how artificial intelligence in health care fails the Turing Test
Powell, J. (2019) · Journal of Medical Internet Research · doi:10.2196/16222 · Cited by 115
“As various studies into the future of work have shown, automation in the workplace will not eliminate all human tasks [20].”
BackgroundThe next wave of digital technological change and the cultural industries
Peukert, C. (2018) · Journal of Cultural Economics · doi:10.1007/s10824-018-9336-2 · Cited by 121
“Summary and research questions A projection based on the evidence related to the introduction of new technologies throughout history may be that there will be winners and losers, and not only losers, in the next wave of digital technological change (Autor 2015; Deming 2017).”
Automation anxiety and translators
Vieira, L. N. (2018) · Translation Studies · doi:10.1080/14781700.2018.1543613 · Cited by 131
Identifying the Challenges to Adopting Robotics in the US Construction Industry
Pradhananga, P., ElZomor, M., & Kasabdji, G. S. (2021) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002007 · Cited by 120
Wang, K.-L., Sun, T.-T., & Xu, R.-Y. (2022) · Economic Change and Restructuring · doi:10.1007/s10644-022-09467-4 · Cited by 129
“For example, Autor (2015) argued that the wide application of AI will not only significantly improve production efficiency, but also create new jobs, further increasing labor demand.”
BackgroundDanaher, J. (2016) · Science and Engineering Ethics · doi:10.1007/s11948-016-9770-5 · Cited by 125
Tax Policy and Local Labor Market Behavior
Garrett, D. G., Ohrn, E., & Serrato, J. C. S. (2020) · American Economic Review Insights · doi:10.1257/aeri.20190041 · Cited by 120
Artificial Intelligence, Jobs and the Future of Work: Racing with the Machines
Bruun, E. P. G., & Duka, A. (2018) · Basic Income Studies · doi:10.1515/bis-2018-0018 · Cited by 125
“While an invention that replaces workers with machines in a specific field could have a positive effect on society (Autor, 2015) – reducing the price of goods, increasing real income – the same benefits will not exist if deskilling and shrinking employment occurs across all fields simultaneously.”
BackgroundMorgan, J. (2019) · Economy and Society · doi:10.1080/03085147.2019.1620027 · Cited by 122
“For an archive of Professor Frey’s media activity see: https://www.oxfordmartin.ox.ac.uk/ people/453 24 Autor (2015), for example, is the main contrasting paper and that had 1,026 citations, whilst the OECD paper Arntz et al. (2016) had 702 (see later).”
BackgroundWork of the Past, Work of the Future
Autor, D. (2019) · National Bureau of Economic Research · doi:10.3386/w25588 · Cited by 117
Self-citationTalent Development for Industry 4.0
Karaçay, G. (2018) · Springer series in advanced manufacturing · doi:10.1007/978-3-319-57870-5_7 · Cited by 94
Hannola, L., Richter, A., Richter, S., & Stocker, A. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1445877 · Cited by 117
Friend or Foe? Teaming Between Artificial Intelligence and Workers with Variation in Experience
Wang, W., Gao, G. G., & Agarwal, R. (2019) · Management Science · doi:10.1287/mnsc.2021.00588 · Cited by 148
Acemoğlu, D., & Restrepo, P. (2018) · National Bureau of Economic Research · doi:10.3386/w24421 · Cited by 114
Artificial intelligence and work: a critical review of recent research from the social sciences
Deranty, J.-P., & Corbin, T. (2024) · AI & SOCIETY · doi:10.1007/s00146-022-01496-x · Cited by 118
“…are supported by the analyses of historians who directly contradict optimistic readings like Mokyr’s, and emphasise the slowing down of innovation under financial capitalism, where profit maximisation occurs through speculation rather than changes in industrial paradigms (Gordon 2014, 2015).”
InfluentialSzalavetz, A. (2019) · Post-Communist Economies · doi:10.1080/14631377.2019.1578584 · Cited by 124
“At the same time, as argued by labour economists (e.g. Autor, 2015), DT creates new kinds of technological and organisational problems to be addressed by new high-skilled, specialised and labour-intensive tasks, often performed in new occupations.”
BackgroundAI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity
Gao, X., & Feng, H. (2023) · Sustainability · doi:10.3390/su15118934 · Cited by 113
“Autor [48] argues that while automation can replace some of people’s routine and codable tasks, people still have a comparative advantage in areas such as solving complex problems and producing ideas.”
Background
Metadata
- Authors
- Autor, D. H.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Autor, David H.
- ORCID
- Autor, David H.
- Venue
- Journal of Economic Perspectives
- Publisher
- American Economic Association
- Published
- 2015-08-01
- Volume
- 29
- Issue
- 3
- Pages
- 3-30
- ISSN
- 0895-3309 (ISSN-L)
- OpenAlex
- W1821977771
- Cited by
- 3,917
- References
- 46
- Citations by year
- 2026: 697 · 2025: 579 · 2024: 457 · 2023: 364 · 2022: 309 · 2021: 345 · 2020: 349 · 2019: 312 · 2018: 263 · 2017: 153 · 2016: 72 · 2015: 9
- FWCI
- 398.92
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Economic Perspectives (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic Growth and Productivity
- Keywords
- workplace automation, labor substitution, labor complementarity, labor demand, labor supply, labor market polarization, wage inequality, artificial intelligence, robotics, career change, job creation, routine tasks, problem-solving skills, comparative advantage
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