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Economic growth and convergence during the transition to production using automation capital

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  • Martin Labaj
  • Daniel Dujava

Abstract

This paper examines the implications of automation capital in a Solow growth model withtwo types of labour. We study the transition from standard production to production usingautomation capital which substitutes low-skilled workers. We assume that despite advancesin technology, AI and machine learning, certain tasks can be performed only by high-skilledlabour and are not automatable. We show that under these assumptions, automation capitaldoes not generate endogenous growth without technological progress. However, assumingpresence of technological progress augmenting both effective number of workers and effectivenumber of industrial robots, automation increases rate of long-run growth. We analyse asituation in which some countries do not use robots at all and other group of countries startsthe transition to the economy where industrial robots replace low-skilled labour. We showthat this has potential non-linear effects on?-convergence and that the model is consistentwith temporary divergence of incomes per capita. We derive a set of estimable equationsthat allows us to test the hypotheses in a Mankiw-Romer-Weil framework.

Suggested Citation

  • Martin Labaj & Daniel Dujava, 2019. "Economic growth and convergence during the transition to production using automation capital," Department of Economic Policy Working Paper Series 017, Department of Economic Policy, Faculty of National Economy, University of Economics in Bratislava.
  • Handle: RePEc:brt:depwps:017
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    References listed on IDEAS

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    1. 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.
    2. David Hémous & Morten Olsen, 2022. "The Rise of the Machines: Automation, Horizontal Innovation, and Income Inequality," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 179-223, January.
    3. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, Oxford University Press, vol. 70(1), pages 65-94.
    4. Prettner, Klaus, 2016. "The implications of automation for economic growth and the labor share," Hohenheim Discussion Papers in Business, Economics and Social Sciences 18-2016, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    5. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    6. Lankisch, Clemens & Prettner, Klaus & Prskawetz, Alexia, 2019. "How can robots affect wage inequality?," Economic Modelling, Elsevier, vol. 81(C), pages 161-169.
    7. David Cass, 1965. "Optimum Growth in an Aggregative Model of Capital Accumulation," Review of Economic Studies, Oxford University Press, vol. 32(3), pages 233-240.
    8. Prettner, Klaus, 2019. "A Note On The Implications Of Automation For Economic Growth And The Labor Share," Macroeconomic Dynamics, Cambridge University Press, vol. 23(3), pages 1294-1301, April.
    9. Prettner, Klaus & Strulik, Holger, 2017. "The lost race against the machine: Automation, education and inequality in an R&D-based growth model," Hohenheim Discussion Papers in Business, Economics and Social Sciences 08-2017, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    10. Gasteiger, Emanuel & Prettner, Klaus, 2017. "A note on automation, stagnation, and the implications of a robot tax," Discussion Papers 2017/17, Free University Berlin, School of Business & Economics.
    11. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
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    Cited by:

    1. Klarl, Torben, 2022. "Fragile robots, economic growth and convergence," Economic Modelling, Elsevier, vol. 112(C).
    2. Torben Klarl, 2022. "Fragile Robots, Economic Growth and Convergence," Bremen Papers on Economics & Innovation 2202, University of Bremen, Faculty of Business Studies and Economics.

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    More about this item

    Keywords

    Automation; Economic growth; Income inequality; Convergence; Robots;
    All these keywords.

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • E25 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Aggregate Factor Income Distribution
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

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