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Labour market dynamics in the era of technological advancements: The system-wide impacts of labour augmenting technological change

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  • Ross, Andrew G.
  • McGregor, Peter G.
  • Swales, J Kim
Abstract
The employment impact of future technological change is much debated. Some commentators predict devastating job losses, while others are more sanguine, claiming that technological change raises living standards without reducing total employment. Our analysis a use a combination of partial equilibrium and dynamic computable general equilibrium (CGE) analysis with neo-Keynesian characteristics. These models are employed to assess the implications of skill-biased labour-augmenting technological change. They are calibrated on recent German Social Accounting Matrix data and parameterised using the best available empirical results from developed economies. The numerical CGE multi-sectoral model simulates the system-wide impacts of pervasive technical change affecting all sectors of the economy. The model allows investigation of alternative scenarios based around a common economic structure and set of parameter values. The results suggest that labour-augmenting technological change typically stimulates GDP growth and long-run total employment. However, there are negative short- and medium-run employment and real wage implications which might require government policy intervention. The simulations indicate that for open developed economies, improving the efficiency of skilled workers, as opposed to unskilled workers, is the most beneficial. This improvement has positive long-run impacts that are spread across both skill classifications. On the other hand, increasing the efficiency of the unskilled produces negative impacts on skilled employment and real wages. Additionally, the openness of the economy to migration and trade are important in determining the scale of these system-wide effects.

Suggested Citation

  • Ross, Andrew G. & McGregor, Peter G. & Swales, J Kim, 2024. "Labour market dynamics in the era of technological advancements: The system-wide impacts of labour augmenting technological change," Technology in Society, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:teinso:v:77:y:2024:i:c:s0160791x24000873
    DOI: 10.1016/j.techsoc.2024.102539
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    1. Robert J. Gordon, 2016. "The Rise and Fall of American Growth: The U.S. Standard of Living since the Civil War," Economics Books, Princeton University Press, edition 1, number 10544.
    2. Simonetta Longhi & Peter Nijkamp & Jacques Poot, 2006. "Spatial Heterogeneity And The Wage Curve Revisited," Journal of Regional Science, Wiley Blackwell, vol. 46(4), pages 707-731, October.
    3. Layard, Richard & Nickell, Stephen & Jackman, Richard, 2005. "Unemployment: Macroeconomic Performance and the Labour Market," OUP Catalogue, Oxford University Press, number 9780199279173.
    4. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    5. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    6. Konstantin Stadler & Richard Wood & Tatyana Bulavskaya & Carl†Johan Södersten & Moana Simas & Sarah Schmidt & Arkaitz Usubiaga & José Acosta†Fernández & Jeroen Kuenen & Martin Bruckner & Stefan, 2018. "EXIOBASE 3: Developing a Time Series of Detailed Environmentally Extended Multi†Regional Input†Output Tables," Journal of Industrial Ecology, Yale University, vol. 22(3), pages 502-515, June.
    7. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    8. Daron Acemoglu & Pascual Restrepo, 2020. "Unpacking Skill Bias: Automation and New Tasks," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 356-361, May.
    9. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    10. Brücker, Herbert & Hauptmann, Andreas & Jahn, Elke J. & Upward, Richard, 2014. "Migration and imperfect labor markets: Theory and cross-country evidence from Denmark, Germany and the UK," European Economic Review, Elsevier, vol. 66(C), pages 205-225.
    11. Peter G. McGregor & John K. Swales & Ya Ping Yin, 1996. "Migration Equilibria in Regional Economies: A Multi-Period CGE Analysis of an Improvement in Local Amenities," Advances in Spatial Science, in: Jeroen C. J. M. Bergh & Peter Nijkamp & Piet Rietveld (ed.), Recent Advances in Spatial Equilibrium Modelling, chapter 17, pages 346-358, Springer.
    12. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    13. Melanie Arntz, 2010. "What Attracts Human Capital? Understanding the Skill Composition of Interregional Job Matches in Germany," Regional Studies, Taylor & Francis Journals, vol. 44(4), pages 423-441.
    14. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    15. Patrizio Lecca & Peter G. McGregor & J. Kim Swales & Ya Ping Yin, 2014. "Balanced Budget Multipliers For Small Open Regions Within A Federal System: Evidence From The Scottish Variable Rate Of Income Tax," Journal of Regional Science, Wiley Blackwell, vol. 54(3), pages 402-421, June.
    16. D Holden & J K Swales, 1993. "Factor Subsidies, Employment Generation, and Cost per Job: A Partial Equilibrium Approach," Environment and Planning A, , vol. 25(3), pages 317-338, March.
    17. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    18. Bohringer, Christoph & Boeters, Stefan & Feil, Michael, 2005. "Taxation and unemployment: an applied general equilibrium approach," Economic Modelling, Elsevier, vol. 22(1), pages 81-108, January.
    19. Alessandra Faggian & Isha Rajbhandari & Kathryn R. Dotzel, 2017. "The interregional migration of human capital and its regional consequences: a review," Regional Studies, Taylor & Francis Journals, vol. 51(1), pages 128-143, January.
    20. Connolly, Kevin, 2020. "The regional economic impacts of offshore wind energy developments in Scotland," Renewable Energy, Elsevier, vol. 160(C), pages 148-159.
    21. Francesco Caselli & Alan Manning, 2019. "Robot Arithmetic: New Technology and Wages," American Economic Review: Insights, American Economic Association, vol. 1(1), pages 1-12, June.
    22. David L. McCollum & Ajay Gambhir & Joeri Rogelj & Charlie Wilson, 2020. "Energy modellers should explore extremes more systematically in scenarios," Nature Energy, Nature, vol. 5(2), pages 104-107, February.
    23. Turner, Karen & Hanley, Nick, 2011. "Energy efficiency, rebound effects and the environmental Kuznets Curve," Energy Economics, Elsevier, vol. 33(5), pages 709-720, September.
    24. Bajzik, Jozef & Havranek, Tomas & Irsova, Zuzana & Schwarz, Jiri, 2019. "Estimating the Armington Elasticity: The Importance of Data Choice and Publication Bias," MPRA Paper 95031, University Library of Munich, Germany.
    25. Nickell, Stephen & Bell, Brian, 1995. "The Collapse in Demand for the Unskilled and Unemployment across the OECD," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 11(1), pages 40-62, Spring.
    26. Pedersen, Torben & Scedrova, Anna & Grecu, Alina, 2022. "The effects of IT investments and skilled labor on firms’ value added," Technovation, Elsevier, vol. 116(C).
    27. Bianca Biagi & Kathryn R. Dotzel, 2018. "Theoretical Advances on Interregional Migration Models," Advances in Spatial Science, in: Bianca Biagi & Alessandra Faggian & Isha Rajbhandari & Viktor A. Venhorst (ed.), New Frontiers in Interregional Migration Research, chapter 0, pages 21-47, Springer.
    28. Harrigan, Frank & McGregor, Peter G. & Dourmashkin, Neil & Perman, Roger & Swales, Kim & Yin, Ya Ping, 1991. "AMOS : A macro-micro model of Scotland," Economic Modelling, Elsevier, vol. 8(4), pages 424-479, October.
    29. Peter G. McGregor & Andrew G. Ross & J. Kim Swales, 2021. "How fiscal policies affect energy systems: the importance of an ‘environmental social wage’," Regional Studies, Taylor & Francis Journals, vol. 55(8), pages 1354-1364, August.
    30. Bajzik, Josef & Havranek, Tomas & Irsova, Zuzana & Schwarz, Jiri, 2020. "Estimating the Armington elasticity: The importance of study design and publication bias," Journal of International Economics, Elsevier, vol. 127(C).
    31. Allan, Grant J. & Ross, Andrew G., 2019. "The characteristics of energy employment in a system-wide context," Energy Economics, Elsevier, vol. 81(C), pages 238-258.
    32. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    33. David G. Blanchflower & Andrew J. Oswald, 1995. "An Introduction to the Wage Curve," Journal of Economic Perspectives, American Economic Association, vol. 9(3), pages 153-167, Summer.
    34. Peter G. McGregor & John K. Swales & Ya Ping Yin, 1996. "Erratum to: Migration Equilibria in Regional Economies: A Multi-Period CGE Analysis of an Improvement in Local Amenities," Advances in Spatial Science, in: Jeroen C. J. M. Bergh & Peter Nijkamp & Piet Rietveld (ed.), Recent Advances in Spatial Equilibrium Modelling, pages 393-394, Springer.
    35. Baltagi, Badi H. & Blien, Uwe & Wolf, Katja, 2012. "A dynamic spatial panel data approach to the German wage curve," Economic Modelling, Elsevier, vol. 29(1), pages 12-21.
    36. Lecca, Patrizio & McGregor, Peter G. & Swales, J. Kim, 2013. "Forward-looking and myopic regional Computable General Equilibrium models: How significant is the distinction?," Economic Modelling, Elsevier, vol. 31(C), pages 160-176.
    37. Figus, Gioele & Swales, J.Kim & Turner, Karen, 2018. "Can Private Vehicle-augmenting Technical Progress Reduce Household and Total Fuel Use?," Ecological Economics, Elsevier, vol. 146(C), pages 136-147.
    38. Tobias Emonts-Holley & Alastair Greig & Patrizio Lecca & Katerina Lisenkova & Peter G. McGregor & J. Kim Swales, 2019. "A Scandinavian ‘high-tax, high-spend’ model for regions? The impact of enhanced regional fiscal autonomy," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(3), pages 321-338, July.
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