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2020 | nr 54 | 19
Tytuł artykułu

What Will Drive Long-Run Growth in the Digital Age?

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper considers the prospective sources of long-run growth in the future. Historically, in the industrial era and at the early stage of the digital era (which began approximately in the 1980s) the main growth engine is R&D. If in the future all essential production or R&D tasks will eventually be subject to automation, though, the engine of growth will be shifted to the accumulation of programmable hardware (capital), and R&D will lose its prominence. By contrast, if neither production nor R&D tasks will be fully automated, R&D will remain the main growth engine. Additional mechanisms potentially accelerating and sustaining growth are the accumulation of R&D capital (particularly important under partial automation), and hardware-augmenting technical change. (original abstract)
Rocznik
Numer
Strony
19
Opis fizyczny
Twórcy
  • SGH Warsaw School of Economics
Bibliografia
  • Acemoglu D., (2009): Introduction to Modern Economic Growth, Princeton University Press.
  • Acemoglu D., D. Autor, (2011): Skills, Tasks and Technologies: Implications for Employment and Earnings, in: Handbook of Labor Economics, eds.: O. Ashenfelter, D. Card, vol. 4, chap. 12, pp. 1043-1171. Elsevier.
  • Acemoglu D., P. Restrepo (2018): The Race Between Man and Machine: Implications of Technology for Growth, Factor Shares and Employment, American Economic Review, 108, 1488-1542.
  • Aghion P., B.F. Jones, C.I. Jones, (2019): Artificial Intelligence and Economic Growth, in: The Economics of Artificial Intelligence: An Agenda, eds.: A. Agrawal, J. Gans, A. Goldfarb, pp. 237-282. University of Chicago Press.
  • Andrews D., C. Criscuolo, P.N. Gal, (2016): The Global Productivity Slowdown, Technology Divergence and Public Policy: A Firm Level Perspective, Working party no. 1 on macroeconomic and structural policy analysis, OECD.
  • Arntz M., T. Gregory, U. Zierahn, (2016): The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis, OECD Social, Employment and Migration Working Paper No 189, OECD Publishing, Paris.
  • Autor, D., D. Dorn, L. F. Katz, C. Patterson, J. Van Reenen (2017): The Fall of the Labor Share and the Rise of Superstar Firms, Working Paper No. 23396, NBER.
  • Autor, D. H., D. Dorn, (2013): The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market, American Economic Review, 103 (5),1553-97.
  • Barkai, S. (2017): Declining Labor and Capital Shares, Job market paper, University of Chicago. Barro, R. J., X. X. Sala-i-Martin (2003): Economic Growth. MIT Press.
  • Benzell, S. G., E. Brynjolfsson (2019): Digital Abundance and Scarce Genius: Implications for Wages, Interest Rates, and Growth, Working Paper, MIT Initiative on the Digital Economy.
  • Benzell, S. G., L. J. Kotlikoff, G. LaGarda, J. D. Sachs, (2015): Robots Are Us: Some Economics of Human Replacement, Working Paper No.20941, NBER.
  • Berg, A., E. F. Buffie, L.-F. Zanna, (2018): Should We Fear the Robot Revolution? (The Correct Answer is Yes), Journal of Monetary Economics, 97,117-148.
  • Brynjolfsson, E., A. McAfee, (2014):The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W.W. Norton & Co.
  • Brynjolfsson, E., D. Rock, C. Syverson, (2019): Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics, in: The Economics of Artificial Intelligence: An Agenda, ed. by A. Agrawal, J. Gans, A. Goldfarb, pp. 23-57. University of Chicago Press.
  • DeCanio S.J., (2016): Robots and Humans - Complements or Substitutes?, Journal of Macroeconomics, 49, 280-291.
  • Frey, C. B., M. Osborne, (2017): The Future of Employment: How Susceptible Are Jobs to Computerisation?, Technological Forecasting and Social Change,114, 254-280.
  • Gillings, M. R., M. Hilbert, D. J. Kemp (2016): Information in the Biosphere: Biological and Digital Worlds, Trends in Ecology and Evolution, 31,180-189.
  • Gordon, R. J. (2016):The Rise and Fall of American Growth: The U.S. Standard of Living since the Civil War, Princeton University Press.
  • Grace, K. (2013): Algorithmic Progress in Six Domains, Technical report 2013-3, Berkeley, CA: Machine Intelligence Research Institute.
  • Graetz, G., G. Michaels, (2018): Robots at Work, Review of Economics and Statistics, 100, 753-768.
  • Groth, C., K.-J. Koch, T. Steger (2010): When Economic Growth is Less Than Exponential, Economic Theory, 44, 213-242.
  • Growiec J., (2019): The Hardware-Software Model: A New Conceptual Frame-work of Production, R&D and Growth with AI, KAE Working Paper 2019/042, SGH Warsaw School of Economics.
  • Growiec J., (2020): Automation, Partial and Full, SGH KAE Working Paper 2020/048, SGH Warsaw School of Economics.
  • Hanson, R., E. Yudkowsky, (2013):The Hanson-Yudkowsky AI-Foom Debate. Machine Intelligence Research Institute.
  • Hemous, D., M. Olsen (2018): The Rise of the Machines: Automation, Hor-izontal Innovation and Income Inequality, Working Paper, University of Zurich.
  • Hernandez, D., T. B. Brown, (2020): Measuring the Algorithmic Efficiency of Neural Networks, Preprint arxiv:2005.04305, arXiv.
  • Hilbert M., P. Lopez, (2011): The World's Technological Capacity to Store, Communicate, and Compute Information, Science, 332, 60-65.
  • Jones C.I., (1995): R&D-Based Models of Economic Growth, Journal of Political Economy, 103, 759-84.
  • Jones C.I., (2002): Sources of U.S. Economic Growth in a World of Ideas, American Economic Review, 92, 220-239.
  • Jones C.I., J. Kim, (2018): A Schumpeterian Model of Top Income Inequality, Journal of Political Economy, 126, 1785-1826.
  • Jones, L.E., R.E. Manuelli, (1990): A Convex Model of Equilibrium Growth: Theory and Policy Implications, Journal of Political Economy, 98,1008-1038.
  • Klump R., P. McAdam, A. Willman, (2007): Factor Substitution and Factor Augmenting Technical Progress in the US, Review of Economics and Statistics, 89, 183-192.
  • Klump R., P. McAdam, A. Willman, (2012): Normalization in CES Production Functions: Theory and Empirics, Journal of Economic Surveys, 26, 769-799.
  • Kurzweil R., (2005):The Singularity is Near. New York: Penguin.
  • Nordhaus W.D. (2017): Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth, Working Paper, Cowles Foundation, Yale University.
  • Romer P.M., (1986): Increasing Returns and Long-run Growth, Journal of Political Economy, 94 (5), 1002-1037.
  • Romer P.M., (1990): Endogenous Technological Change, Journal of Political Economy, 98, S71-S102.
  • Sachs J.D., S.G. Benzell, G. LaGarda, (2015): Robots: Curse or Blessing? A Basic Framework, Working Paper Np. 21091, NBER.
  • Silver D., T. Hubert, J. Schrittwieser, et al. (2018): A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play, Science, 362, 1140-1144.
  • Tegmark M., (2017):Life 3.0: Being Human in the Age of Artificial Intelligence. New York: Knopf.
  • Yudkowsky E., (2013): Intelligence Explosion Microeconomics, Technical Report 2013-1, Machine Intelligence Research Institute.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171607885

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