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Computerizing Industries and Routinizing Jobs: Explaining Trends in Aggregate Productivity

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  • Sangmin Aum
  • Sang Yoon (Tim) Lee
  • Yongseok Shin

Abstract

Aggregate productivity growth in the U.S. has slowed down since the 2000s. We quantify the importance of differential productivity growth across occupations and across industries, and the rise of computers since the 1980s, for the productivity slowdown. Complementarity across occupations and industries in production shrinks the relative size of those with high productivity growth, reducing their contributions toward aggregate productivity growth, resulting in its slowdown. We find that such a force, especially the shrinkage of occupations with above-average productivity growth through “routinization,” was present since the 1980s. Through the end of the 1990s, this force was countervailed by the extraordinarily high productivity growth in the computer industry, of which output became an increasingly more important input in all industries (“computerization”). It was only when the computer industry's productivity growth slowed down in the 2000s that the negative effect of routinization on aggregate productivity became apparent. We also show that the decline in the labor income share can be attributed to computerization, which substitutes labor across all industries.

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  • Sangmin Aum & Sang Yoon (Tim) Lee & Yongseok Shin, 2018. "Computerizing Industries and Routinizing Jobs: Explaining Trends in Aggregate Productivity," NBER Working Papers 24357, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24357
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    Cited by:

    1. Bárány, Zsófia L. & Siegel, Christian, 2020. "Biased technological change and employment reallocation," Labour Economics, Elsevier, vol. 67(C).
    2. Zsófia L. Bárány & Christian Siegel, 2019. "Job Polarization, Structural Transformation and Biased Technological Change," Travail et Emploi, La DARES, vol. 0(1), pages 25-44.
    3. Sen, A., 2024. "Structural Change at a Disaggregated Level: Sectoral Heterogeneity Matters," Janeway Institute Working Papers 2410, Faculty of Economics, University of Cambridge.
    4. Sotiris Blanas & Gino Gancia & Sang Yoon (Tim) Lee, 2019. "Who is afraid of machines?," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 627-690.
    5. Pablo Casas & José L. Torres, 2023. "Automation, automatic capital returns, and the functional income distribution," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 32(1), pages 113-135, January.
    6. Julieta Caunedo & Elisa Keller & Yongseok Shin, 2023. "Technology and the Task Content of Jobs across the Development Spectrum," The World Bank Economic Review, World Bank, vol. 37(3), pages 479-493.
    7. Aum, Sangmin, 2022. "The Impact of COVID-19 on Jobs in Korea: Does Contact-intensiveness Matter?," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 44(2), pages 1-28.
    8. Cebreros Alfonso & Heffner-Rodríguez Aldo & Livas René & Puggioni Daniela, 2020. "Automation Technologies and Employment at Risk: The Case of Mexico," Working Papers 2020-04, Banco de México.
    9. Pablo Casas & José L. Torres, 2024. "Government size and automation," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 31(3), pages 780-807, June.
    10. Kobosko, Monika, 2021. "Ginące zawody jako konsekwencja zmian technologicznych na polskim rynku pracy," Studia z Polityki Publicznej / Public Policy Studies, Warsaw School of Economics, vol. 8(4), pages 1-21, December.
    11. Zsofia Barany & Christian Siegel, 2021. "Engines of sectoral labor productivity growth," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 39, pages 304-343, January.
    12. Julieta Caunedo & David Jaume & Elisa Keller, 2023. "Occupational Exposure to Capital-Embodied Technical Change," American Economic Review, American Economic Association, vol. 113(6), pages 1642-1685, June.
    13. Pena, Werner & Siegel, Christian, 2023. "Routine-biased technical change, structure of employment, and cross-country income differences," CEPR Discussion Papers 18366, C.E.P.R. Discussion Papers.
    14. Bertin Martens & Songul Tolan, 2018. "Will this time be different? A review of the literature on the Impact of Artificial Intelligence on Employment, Incomes and Growth," JRC Working Papers on Digital Economy 2018-08, Joint Research Centre.
    15. L. Rachel Ngai & Orhun Sevinc, 2020. "A Multisector Perspective on Wage Stagnation," Discussion Papers 2026, Centre for Macroeconomics (CFM).
    16. Pugsley, Benjamin, 2018. "Comment on “Structural transformation and the rise of information technology” by Gallipoli and Makridis," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 111-116.
    17. Jakub Growiec, 2019. "The Hardware–Software Model: A New Conceptual Framework of Production, R&D, and Growth with AI," Working Paper series 19-18, Rimini Centre for Economic Analysis.
    18. Jakub Growiec & Peter McAdam & Jakub Mućk, 2022. "Are Ideas Really Getting Harder To Find? R&D Capital and the Idea Production Function," KAE Working Papers 2022-071, Warsaw School of Economics, Collegium of Economic Analysis.
    19. Lin Shao & Rongsheng Tang, 2021. "Allocative Efficiency and Aggregate Productivity Growth in Canada and the United States," Staff Working Papers 21-1, Bank of Canada.
    20. Jakub Growiec & Peter McAdam & Jakub dup Muck, 2023. "R&D Capital and the Idea Production Function," Research Working Paper RWP 23-05, Federal Reserve Bank of Kansas City.
    21. Chen, Chaoran, 2020. "Capital-skill complementarity, sectoral labor productivity, and structural transformation," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    22. Kehrig, Matthias, 2018. "Comment on “Computerizing industries and routinizing jobs: Explaining trends in aggregate productivity” by Sangmin Aum, Sang Yoon (Tim) Lee and Yongseok Shin," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 22-28.
    23. Wang, Tianxi & Wright, Greg C., 2020. "Increasing returns to scale within limits: A model of ICT and its effect on the income distribution and occupation choice," Journal of Economic Theory, Elsevier, vol. 189(C).
    24. Seunghoon Na & Hyunseung Oh, 2020. "Computerizing Households and the Role of Investment-Specific Productivity in Business Cycles," International Finance Discussion Papers 1292, Board of Governors of the Federal Reserve System (U.S.).
    25. Sangmin Aum & Yongseok Shin, 2020. "Why Is the Labor Share Declining?," Review, Federal Reserve Bank of St. Louis, vol. 102(4), pages 413-428, October.

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    More about this item

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E25 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Aggregate Factor Income Distribution
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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