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Automation, productivity, and innovation in information technology

Published online by Cambridge University Press:  15 February 2022

Unni Pillai
Unni Pillai*
Affiliation:
College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY 12309, USA
*
Email: upillai@sunypoly.edu. Phone: 1 845 706 9955
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Abstract

The rate of innovation in Information Technology (IT) has slowed down over time. The slowdown is evident both in the data on quality-adjusted prices of computers, and performance of microprocessors used in computers. The model in this paper shows that an IT–labor elasticity of substitution that is greater than 1 can explain the slowdown. With an elasticity of substitution greater than 1, however, slowing innovation can result in sustained labor productivity and output growth. Sustained growth is possible because an IT–labor elasticity of substitution greater than 1 results in a continuously increasing share of IT in production costs, which counteracts the effect of slowing innovation on labor productivity and output growth. In this environment of slowing innovation, increasing IT share and sustained growth, employment can increase or decrease, depending on the values of the IT–labor elasticity of substitution and the price elasticity of demand for IT-enabled consumption goods.

Keywords

Information technologyautomationproductivityemployment
Type
Articles
Information
Macroeconomic Dynamics , Volume 27 , Issue 4 , June 2023 , pp. 879 - 905
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

I thank Dave Byrne, Charles Jones, Samuel Kortum, Dan Sichel, Chad Syverson, and Evsen Turkay for their valuable comments and suggestions.

References

Acemoglu, D. and Restrepo, P. (2018a) Modeling automation. NBER Working Papers 24321, National Bureau of Economic Research.CrossRefGoogle Scholar
Acemoglu, D. and Restrepo, P. (2018b) The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review 108, 1488–1542.CrossRefGoogle Scholar
Aghion, P., Jones, B. F. and Jones, C. I. (2017) Artificial intelligence and economic growth, NBER Working Papers 23928, National Bureau of Economic Research.CrossRefGoogle Scholar
Aizcorbe, A. M. (2006) Why did semiconductor price indexes fall so fast in the 1990s? A decomposition. Economic Inquiry 44, 485–496.CrossRefGoogle Scholar
Aizcorbe, A. M., Flamm, K. and Khurshid, A. (2002) The role of semiconductor inputs in IT hardware price decline: Computers vs. Communications. In: Berndt, E. R. and Hulten, C. R. (eds.), Hard to Measure Goods and Services. National Bureau of Economic Research.Google Scholar
Aizcorbe, A., Oliner, S. D. and Sichel, D. E. (2008) Shifting trends in semiconductor prices and the pace of technological progress. Business Economics 43, 23–39.Google Scholar
Autor, D. H. (2015) Paradox of abundance. In: Rangan, S. (ed.), Performance and Progress: Essays on Capitalism, Business, and Society. Oxford University Press, Oxford.Google Scholar
Bloom, N., Jones, C., Van Reenen, J. and Webb, M. (2020) Are ideas getting harder to find? American Economic Review 110(4), 1104–1144.CrossRefGoogle Scholar
Boltzmann, L. (1872) Weitere Studien uber das Warmegleichgewicht unter Gasmolekulen. Sitzungsberichte Akad. Wiss. 66, 275–370.Google Scholar
Brynjolfsson, E. and McAfee, A. (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W.Norton and Company.Google Scholar
Byrne, D. and Corrado, C. (2017) ICT services and their prices: What do they tell us about productivity and technology. International Productivity Monitor 33, 150–181.Google Scholar
Byrne, D., Oliner, S. D. and Sichel, D. E. (2017) How fast are semiconductor prices falling? Finance and Economics Discussion Series 2017-005. Board of Governors of the Federal Reserve System (U.S).CrossRefGoogle Scholar
Doms, M. E., Dunn, W. F., Oliner, S. D. and Sichel, D. E. (2004) How fast do personal computers depreciate? Concepts and new estimates. In: Tax Policy and the Economy Volume 18, NBER Chapters, National Bureau of Economic Research, pp. 37–80.CrossRefGoogle Scholar
Eden, M. and Gaggl, P. (2018) On the welfare implications of automation. Review of Economic Dynamics 29, 15–43.CrossRefGoogle Scholar
Fernald, J. G. (2014) Productivity and potential output before, during, and after the great recession. In: J. Parker and M. Woodford (eds.), NBER Macroeconomics Annual. CrossRefGoogle Scholar
Frey, C. B. and Osborne, M. A. (2017) The future of employment: How susceptible are jobs to computerization ? Technological Forecasting and Social Change 114, 254–280.CrossRefGoogle Scholar
Friedman, A. (1994) Approximation to Boltzmann transport in semicondcutor devices. In: Mathematics in Industrial Problems. The IMA Volumes in Mathematics and Applications, Vol. 57, New York, NY: Springer.CrossRefGoogle Scholar
Gordon, R. and Sayed, H. (2020) Transatlantic technologies: The role of ICT in the evolution of productivity growth in the U.S. and Europe. International Productivity Monitor 38, 55–80.Google Scholar
Gordon, R. J. (2015) Secular stagnation: A supply-side view. American Economic Review 105, 54–59.CrossRefGoogle Scholar
Helpman, E. and Grossman, G. M. (1991) Quality ladders in the theory of growth. Review of Economic Studies 58, 43–61.Google Scholar
Huff, H. R. (2001) John Bardeen and transistor physics. In: AIP Conference Proceedings, Volume 550.CrossRefGoogle Scholar
Hutcheson, D. (2005) The R&D Crisis. Technical Report, Santa Clara: VLSI Research.Google Scholar
Jorgenson, D. W. (2001) Information technology and the U.S economy. American Economic Review 91, 1–32.CrossRefGoogle Scholar
Jorgenson, D. W., Ho, M. S. and Samuels, J. D. (2014) What will revive U.S. economic growth? Lessons from a prototype industry-level production account for the United States. Journal of Policy Modeling 36, 674–691.CrossRefGoogle Scholar
Jorgenson, D. W., Ho, M. S. and Stiroh, K. J. (2008) A retrospective look at the U.S. productivity growth resurgence. Journal of Economic Perspectives 22, 3–24.CrossRefGoogle Scholar
Jorgenson, D. W., Ho, M. S., , J. D. Samuels and Stiroh, K. J. (2007) Industry origins of the american productivity resurgence. Economic Systems Research 9, 229–252.CrossRefGoogle Scholar
Klette, T. J. and Kortum, S. (2004) Innovating firms and aggregate innovation. Journal of Political Economy 112, 986–1018.CrossRefGoogle Scholar
Lapedus, M. (2015) Semiconductor R&D Crisis. Semiconductor Engineering. https://semiengineering.com/semiconductor- rd-crisis/. Accessed 2019-09-01.Google Scholar
Lecuyer, C. and Brock, D. C. (2009) From nuclear physics to semiconductor manufacturing: the making of ion implantation. History and Technology 25, 193–217.CrossRefGoogle Scholar
Mokyr, J., Vickers, C. and Ziebarth, N. L. (2015) The history of technological anxiety and the future of economic growth: Is this time different? Journal of Economic Perspectives 29, 31–50.CrossRefGoogle Scholar
Moore, S. K. (2018) Global foundries halts 7nm chip development. IEEE Spectrum. Accessed 2019-09-01.Google Scholar
Nordhaus, W. D. (2021) Are we approaching an economic singularity? Information technology and the future of economic growth. American Economic Journal: Macroeconomics 13, 299–332.Google Scholar
Oliner, S. D. and Sichel, D. E. (2000) The resurgence of growth in the late 1990s: Is information technology the story? Journal of Economic Perspectives 14, 3–22.CrossRefGoogle Scholar
Paluka, T. (2002) History of electron microscopy. Materials Research Activities, Dibner Institute for the History of Science and Technology. Accessed 2019-09-01.Google Scholar
Pillai, U. (2013) A model of technological progress in the microprocessor industry. Journal of Industrial Economics 61, 877–912.CrossRefGoogle Scholar
Pines, D. (2013) Biographical Memoirs: John Bardeen. National Academy of Sciences. http://www.nasonline.org/ publications/biographical-memoirs/memoir-pdfs/bardeen-john.pdf. Accessed 2019-09-01.Google Scholar
Postek, M. T. (1994) Critical issues in scanning electron microscope metrology. Journal of Research of the National Institute of Standards and Technology 99, 641–671.CrossRefGoogle Scholar
Rashid, M. A., Hossain, L. and Patrick, J. D. (2002) The evolution of ERP systems: A historical perspective. In: Rashid, M. A., Hossain, L. and Patrick, J. D. (eds.). Enterprise Resource Planning: Global Opportunities and Challenges. Igi Global.CrossRefGoogle Scholar
Rosenberg, N. and R. R., Nelson (1994) American universities and technical advance in industry. Research Policy 23, 323–348.CrossRefGoogle Scholar
Sachs, J. D. and Kotlikoff, L. J. (2012) Smart machines and long-term misery. NBER Working Papers 18629. National Bureau of Economic Research.CrossRefGoogle Scholar
Schmalensee, R. (2018) Puzzles and surprises in employment and productivity in U.S. manufacturing after the great recession. International Productivity Monitor 35, 5–27.Google Scholar
Zeira, J. (1998) Workers, machines, and economic growth. The Quarterly Journal of Economics 113, 1091–1117.CrossRefGoogle Scholar