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AUTOMATION, PARTIAL AND FULL

Published online by Cambridge University Press:  15 February 2021

Jakub Growiec Open the ORCID record for Jakub Growiec [Opens in a new window]
Jakub Growiec*
Affiliation:
SGH Warsaw School of Economics
*
Address correspondence to: Jakub Growiec, Szkoła Główna Handlowa w Warszawie, Katedra Ekonomii Ilościowej, al. Niepodległości 162, 02-554 Warszawa, Poland. Phone/Fax: (+48) 225649326. e-mail: [email protected].
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Abstract

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When some steps of a complex, multi-step task are automated, the demand for human work in the remaining complementary sub-tasks goes up. In contrast, when the task is fully automated, the demand for human work declines. Upon aggregation to the macroeconomic scale, partial automatability of complex tasks creates a bottleneck of development, where further growth is constrained by the scarcity of essential human work. This bottleneck is removed once the tasks become fully automatable. Theoretical analysis using a two-level nested constant elasticity of substitution production function specification demonstrates that the shift from partial to full automation generates a non-convexity: humans and machines switch from complementary to substitutable, and the share of output accruing to human workers switches from an upward to a downward trend. This process has implications for inequality, the risk of technological unemployment, and the likelihood of a secular stagnation.

Keywords

AutomationTaskComplementarityEconomic Growth
Type
Articles
Information
Macroeconomic Dynamics , Volume 26 , Issue 7 , October 2022 , pp. 1731 - 1755
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© Cambridge University Press 2021

Footnotes

Financial support from the Polish National Science Center (Narodowe Centrum Nauki) under grant OPUS 14 No. 2017/27/B/HS4/00189 is gratefully acknowledged. The author thanks two anonymous Referees as well as Michał Gradzewicz and Jakub Mućk for their useful comments which helped substantially improve the paper. The author declares that he has no relevant or material financial interests that relate to the research described in this paper.

References

Acemoglu, D. and Autor, D. (2011) Skills, tasks and technologies: Implications for employment and earnings. In Ashenfelter, O. and Card, D. (eds.), Handbook of Labor Economics, Volume 4, Chapter 12, pp. 1043–1171. North Holland: Elsevier.Google Scholar
Acemoglu, D. and Restrepo, P. (2018) The race between man and machine: Implications of technology for growth, factor shares and employment. American Economic Review 108, 1488–1542.CrossRefGoogle Scholar
Acemoglu, D. and Restrepo, P. (2019a) Artificial intelligence, automation, and work. In Agrawal, A., Gans, J. S. and Goldfarb, A. (eds.), The Economics of Artificial Intelligence: An Agenda, pp. 197–236. Chicago: University of Chicago Press.Google Scholar
Acemoglu, D. and Restrepo, P. (2019b) Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives 33, 3–30.CrossRefGoogle Scholar
Aghion, P., Jones, B. F. and Jones, C. I. (2019) Artificial intelligence and economic growth. In Agrawal, A., Gans, J. S. and Goldfarb, A. (eds.), The Economics of Artificial Intelligence: An Agenda, pp. 237–282. Chicago: University of Chicago Press.Google Scholar
Andrews, D., Criscuolo, C. and Gal, P. N. (2016) The Global Productivity Slowdown, Technology Divergence and Public Policy: A Firm Level Perspective. Working party no. 1 on macroeconomic and structural policy analysis. Paris: OECD Press.Google Scholar
Arntz, M., Gregory, T. and Zierahn, U. (2016) The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis. OECD Social, Employment and Migration Working Paper No. 189. Paris: OECD Press.Google Scholar
Autor, D. (2019) Work of the past, work of the future: Richard T. Ely Lecture. American Economic Association: Papers and Proceedings 109, 1–32.Google Scholar
Autor, D. and Salomons, A. (2018) Is automation labor-displacing? Productivity growth, employment, and the labor share. Brookings Papers on Economic Activity, Spring 2018, 1–63.Google Scholar
Autor, D., Dorn, D., Katz, L. F., Patterson, C. and Van Reenen, J. (2020) The fall of the labor share and the rise of superstar firms. Quarterly Journal of Economics 135, 645–709.Google Scholar
Autor, D. and Dorn, D. (2013) The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review 103, 1553–1597.Google Scholar
Barkai, S. (2020) Declining labor and capital shares. Journal of Finance 75, 2421–2463.CrossRefGoogle Scholar
Benzell, S. G. and Brynjolfsson, E. (2019) Digital Abundance and Scarce Genius: Implications for Wages, Interest Rates, and Growth. NBER Working Paper No. 25585. National Bureau of Economic Research.Google Scholar
Benzell, S. G., Kotlikoff, L. J., LaGarda, G. and Sachs, J. D. (2015) Robots Are Us: Some Economics of Human Replacement. NBER Working Paper No. 20941. National Bureau of Economic Research.CrossRefGoogle Scholar
Berg, A., Buffie, E. F. and Zanna, L.-F. (2018) Should we fear the robot revolution? (The correct answer is yes). Journal of Monetary Economics 97, 117–148.CrossRefGoogle Scholar
Boucekkine, R. and Crifo, P. (2008) Human capital accumulation and the transition from specialization to multi-tasking. Macroeconomic Dynamics 12, 320–344.Google Scholar
Brynjolfsson, E. and McAfee, A. (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton & Co. Google Scholar
Cantore, C., Ferroni, F. and León-Ledesma, M. A. (2017) The dynamics of hours worked and technology. Journal of Economic Dynamics and Control 82, 67–82.CrossRefGoogle Scholar
DeCanio, S. J. (2016) Robots and humans – Complements or substitutes? Journal of Macroeconomics 49, 280–291.CrossRefGoogle Scholar
Frey, C. B. and Osborne, M. (2017) The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change 114, 254–280.CrossRefGoogle Scholar
Gillings, M. R., Hilbert, M. and Kemp, D. J. (2016) Information in the biosphere: biological and digital worlds. Trends in Ecology and Evolution 31, 180–189.CrossRefGoogle ScholarPubMed
Gordon, R. J. (2016) The Rise and Fall of American Growth: The U.S. Standard of Living since the Civil War. Princeton: Princeton University Press.CrossRefGoogle Scholar
Grace, K. (2013) Algorithmic Progress in Six Domains. Technical report 2013-3, Berkeley, CA: Machine Intelligence Research Institute.Google Scholar
Graetz, G. and Michaels, G. (2018) Robots at work. Review of Economics and Statistics 100, 753–768.CrossRefGoogle Scholar
Growiec, J. (2019). The Hardware–Software Model: A New Conceptual Framework of Production, R&D and Growth with AI. RCEA WP 19-18, SGH Warsaw School of Economics and Rimini Centre of Economic Analysis.Google Scholar
Growiec, J. and Mućk, J. (2020) Isoelastic elasticity of substitution production functions. Macroeconomic Dynamics 24, 1597–1634.CrossRefGoogle Scholar
Growiec, J. and Schumacher, I. (2008) On technical change in the elasticities of resource inputs. Resources Policy 33, 210–221.CrossRefGoogle Scholar
Hemous, D. and Olsen, M. (2018) The Rise of the Machines: Automation, Horizontal Innovation and Income Inequality . Working paper, University of Zurich.Google Scholar
Hernandez, D. and Brown, T. B. (2020) Measuring the Algorithmic Efficiency of Neural Networks. Preprint arXiv:2005.04305. OpenAI.Google Scholar
Hilbert, M. and López, P. (2011) The world’s technological capacity to store, communicate, and compute information. Science 332, 60–65.CrossRefGoogle ScholarPubMed
Jones, C. I. (2002) Sources of U.S. economic growth in a world of ideas. American Economic Review 92, 220–239.CrossRefGoogle Scholar
Jones, C. I. and Kim, J. (2018) A Schumpeterian model of top income inequality. Journal of Political Economy 126, 1785–1826.Google Scholar
Kemnitz, A. and Knoblach, M. (2020) Endogenous Sigma-Augmenting Technological Change: An R&D-Based Approach. CEPIE Working Paper No. 02/20, Technical University Dresden.Google Scholar
Klump, R. and de La Grandville, O. (2000) Economic growth and the elasticity of substitution: Two theorems and some suggestions. American Economic Review 90, 282–291.Google Scholar
Klump, R., McAdam, P. and Willman, A. (2012) Normalization in CES production functions: Theory and empirics. Journal of Economic Surveys 26, 769–799.CrossRefGoogle Scholar
Kurzweil, R. (2005) The Singularity is Near. New York: Penguin.Google Scholar
Lu, C.-H. (in press) Artificial intelligence and human jobs. Macroeconomic Dynamics.Google Scholar
McAdam, P. and Willman, A. (2013) Medium run redux. Macroeconomic Dynamics 17, 695–727.CrossRefGoogle Scholar
Miyagiwa, K. and Papageorgiou, C. (2007) Endogenous aggregate elasticity of substitution. Journal of Economic Dynamics and Control 31, 2899–2919.CrossRefGoogle Scholar
Nordhaus, W. D. (2017) Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth . Working paper, Cowles Foundation, Yale University.Google Scholar
Prettner, K. (2019) A note on the implications of automation for economic growth and the labor share. Macroeconomic Dynamics 23, 1294–1301.CrossRefGoogle Scholar
Sachs, J. D., Benzell, S. G. and LaGarda, G. (2015) Robots: Curse or Blessing? A Basic Framework. NBER Working Paper No. 21091, National Bureau of Economic Research.Google Scholar
Silver, D., Hubert, T., Schrittwieser, J., et al. (2018) A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science 362, 1140–1144.CrossRefGoogle ScholarPubMed
Tegmark, M. (2017) Life 3.0: Being Human in the Age of Artificial Intelligence. New York: Knopf.Google Scholar
Xue, J. and Yip, C. K. (2013) Aggregate elasticity of substitution and economic growth: A synthesis. Journal of Macroeconomics 38, 60–75.CrossRefGoogle Scholar
Yudkowsky, E. (2013) Intelligence Explosion Microeconomics. Technical report 2013-1, Berkeley, CA: Machine Intelligence Research Institute.Google Scholar