IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v59y2019icp500-509.html
   My bibliography  Save this article

Automation, wage inequality and implications of a robot tax

Author

Listed:
  • Zhang, Pengqing

Abstract

To examine how automation affects the skilled-unskilled wage gap and whether robots should be taxed, this paper embeds the use of robots within the specific-factor framework. In the basic model, we show that an acceleration in automation generates the displacement effect and the capital reallocation effect. If the elasticity of substitution between labor and capital in the robot-producing sector is not too small (resp. is sufficiently small), then the capital reallocation effect cannot (resp. can) counteract the displacement effect and thus the wage gap will be expanded (resp. will be narrowed down). In the extended models, the results of the basic model will still hold when a labor union is introduced into the robot-using sector, but the wage gap will be unconditionally expanded when a regulated wage rate is introduced into that sector. In both the basic model and the extended models, a tax on robots will unambiguously narrow down the wage gap.

Suggested Citation

  • Zhang, Pengqing, 2019. "Automation, wage inequality and implications of a robot tax," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 500-509.
    • Handle: RePEc:eee:reveco:v:59:y:2019:i:c:p:500-509
    DOI: 10.1016/j.iref.2018.10.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056018307251
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2018.10.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lankisch, Clemens & Prettner, Klaus & Prskawetz, Alexia, 2017. "Robots and the skill premium: An automation-based explanation of wage inequality," ECON WPS - Working Papers in Economic Theory and Policy 06/2017, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.
    2. Guy Michaels & Ashwini Natraj & John Van Reenen, 2010. "Has ICT Polarized Skill Demand? Evidence from Eleven Countries over 25 Years," CEP Discussion Papers dp0987, Centre for Economic Performance, LSE.
    3. Reza Oladi & John Gilbert, 2011. "Monopolistic Competition and North–South Trade," Review of International Economics, Wiley Blackwell, vol. 19(3), pages 459-474, August.
    4. Pi, Jiancai & Zhang, Pengqing, 2018. "Skill-biased technological change and wage inequality in developing countries," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 347-362.
    5. 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.
    6. Pi, Jiancai & Zhou, Yu, 2012. "Public infrastructure provision and skilled–unskilled wage inequality in developing countries," Labour Economics, Elsevier, vol. 19(6), pages 881-887.
    7. Daron Acemoglu & Pascual Restrepo, 2017. "Robots and Jobs: Evidence from US Labor Markets," Boston University - Department of Economics - Working Papers Series dp-297, Boston University - Department of Economics.
    8. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    9. Chaudhuri, Sarbajit & Banerjee, Dibyendu, 2008. "Fair Wage Hypothesis, International Factor Mobility and Skilled-Unskilled Wage Inequality in a Developing Economy," MPRA Paper 9303, University Library of Munich, Germany.
    10. Pi, Jiancai & Zhou, Yu, 2014. "Foreign capital, public infrastructure, and wage inequality in developing countries," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 195-207.
    11. Pi, Jiancai & Zhang, Pengqing, 2016. "Hukou system reforms and skilled-unskilled wage inequality in China," China Economic Review, Elsevier, vol. 41(C), pages 90-103.
    12. Joseph E. Stiglitz, 1974. "Alternative Theories of Wage Determination and Unemployment in LDC's: The Labor Turnover Model," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 88(2), pages 194-227.
    13. Hamid Beladi & Sarbajit Chaudhuri & Shigemi Yabuuchi, 2008. "Can International Factor Mobility Reduce Wage Inequality in a Dual Economy?," Review of International Economics, Wiley Blackwell, vol. 16(5), pages 893-903, November.
    14. Anwar, Sajid, 2006. "Factor mobility and wage inequality in the presence of specialisation-based external economies," Economics Letters, Elsevier, vol. 93(1), pages 88-93, October.
    15. Daron Acemoglu & Pascual Restrepo, 2018. "Low-Skill and High-Skill Automation," Journal of Human Capital, University of Chicago Press, vol. 12(2), pages 204-232.
    16. Jiancai Pi & Pengqing Zhang, 2017. "Social conflict and wage inequality," Journal of Economics, Springer, vol. 121(1), pages 29-49, May.
    17. 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.
    18. Maarten Goos & Alan Manning & Anna Salomons, 2014. "Explaining Job Polarization: Routine-Biased Technological Change and Offshoring," American Economic Review, American Economic Association, vol. 104(8), pages 2509-2526, August.
    19. Reza Oladi & Hamid Beladi, 2008. "Non-traded Goods, Technical Progress and Wages," Open Economies Review, Springer, vol. 19(4), pages 507-515, September.
    20. 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.
    21. 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.
    22. Joao Guerreiro & Sergio Rebelo & Pedro Teles, 2022. "Should Robots Be Taxed?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(1), pages 279-311.
    23. Pan, Lijun & Zhou, Yu, 2013. "International factor mobility, environmental pollution and skilled–unskilled wage inequality in developing countries," Economic Modelling, Elsevier, vol. 33(C), pages 826-831.
    24. Ronald W. Jones, 2018. "The Structure of Simple General Equilibrium Models," World Scientific Book Chapters, in: International Trade Theory and Competitive Models Features, Values, and Criticisms, chapter 4, pages 61-84, World Scientific Publishing Co. Pte. Ltd..
    25. 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.
    26. Chaudhuri, Sarbajit & Yabuuchi, Shigemi, 2007. "Economic liberalization and wage inequality in the presence of labour market imperfection," International Review of Economics & Finance, Elsevier, vol. 16(4), pages 592-603.
    27. 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.
    28. Mandal, Biswajit & Marjit, Sugata, 2010. "Corruption and wage inequality?," International Review of Economics & Finance, Elsevier, vol. 19(1), pages 166-172, January.
    29. Chaudhuri, Sarbajit, 2014. "Foreign capital, non-traded goods and welfare in a developing economy in the presence of externalities," International Review of Economics & Finance, Elsevier, vol. 31(C), pages 249-262.
    30. Sun, Sizhong & Anwar, Sajid, 2015. "Taxation of labour, product varieties and skilled–unskilled wage inequality: Short run versus long run," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 250-257.
    31. Calvo, Guillermo A, 1978. "Urban Employment and Wage Determination in LDC's: Trade Unions in the Harris-Todaro Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 19(1), pages 65-81, February.
    32. Guy Michaels & Ashwini Natraj & John Van Reenen, 2014. "Has ICT Polarized Skill Demand? Evidence from Eleven Countries over Twenty-Five Years," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 60-77, March.
    33. Sajid Anwar, 2008. "Labor Inflow Induced Wage Inequality and Public Infrastructure," Review of Development Economics, Wiley Blackwell, vol. 12(4), pages 792-802, November.
    34. Harris, John R & Todaro, Michael P, 1970. "Migration, Unemployment & Development: A Two-Sector Analysis," American Economic Review, American Economic Association, vol. 60(1), pages 126-142, March.
    35. Hamid Beladi & Avik Chakrabarti & Sugata Marjit, 2010. "Skilled‐Unskilled Wage Inequality And Urban Unemployment," Economic Inquiry, Western Economic Association International, vol. 48(4), pages 997-1007, October.
    36. Chaudhuri, Sarbajit & Kumar Dwibedi, Jayanta & Biswas, Anindya, 2017. "Subsidizing healthcare in the presence of market distortions," Economic Modelling, Elsevier, vol. 64(C), pages 539-552.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Uwe Thuemmel, 2023. "Optimal Taxation of Robots," Journal of the European Economic Association, European Economic Association, vol. 21(3), pages 1154-1190.
    2. Mr. Andrew Berg & Lahcen Bounader & Nikolay Gueorguiev & Hiroaki Miyamoto & Mr. Kenji Moriyama & Ryota Nakatani & Luis-Felipe Zanna, 2021. "For the Benefit of All: Fiscal Policies and Equity-Efficiency Trade-offs in the Age of Automation," IMF Working Papers 2021/187, International Monetary Fund.
    3. Mohammed M. Mabkhot & Pedro Ferreira & Antonio Maffei & Primož Podržaj & Maksymilian Mądziel & Dario Antonelli & Michele Lanzetta & Jose Barata & Eleonora Boffa & Miha Finžgar & Łukasz Paśko & Paolo M, 2021. "Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals," Sustainability, MDPI, vol. 13(5), pages 1-33, February.
    4. Jiang, Hong & Wang, Xue & Liu, Chongguang, 2024. "Automated machines and the labor wage gap," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    5. Maciej Cieślukowski & Przemysław Garsztka & Beata Zyznarska-Dworczak, 2022. "The Impact of Robotification on the Financial Situation of Microenterprises: Evidence from the Financial Services Sector in Poland," Risks, MDPI, vol. 10(2), pages 1-20, February.
    6. Raja Bentaouet Kattan & Kevin Macdonald & Harry Anthony Patrinos, 2021. "The Role of Education in Mitigating Automation’s Effect on Wage Inequality," LABOUR, CEIS, vol. 35(1), pages 79-104, March.
    7. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    8. KATO Hayato & Jonas LOEBBING, 2023. "Tax Competition for Automation Capital," Discussion papers 23078, Research Institute of Economy, Trade and Industry (RIETI).
    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. Živilė Stankevičiūtė & Eglė Staniškienė & Joana Ramanauskaitė, 2021. "The Impact of Job Insecurity on Employee Happiness at Work: A Case of Robotised Production Line Operators in Furniture Industry in Lithuania," Sustainability, MDPI, vol. 13(3), pages 1-20, February.
    11. Živilė Stankevičiūtė & Eglė Staniškienė & Joana Ramanauskaitė, 2021. "The Impact of Job Insecurity on Organisational Citizenship Behaviour and Task Performance: Evidence from Robotised Furniture Sector Companies," IJERPH, MDPI, vol. 18(2), pages 1-17, January.
    12. Luminita IONESCU & Maria ANDRONIE, 2019. "The Future of Jobs in the Digital World," International Conference on Economic Sciences and Business Administration, Spiru Haret University, vol. 5(1), pages 89-94, November.
    13. Oscar Afonso & Rosa Forte, 2023. "How powerful are fiscal and monetary policies in a directed technical change model with humans and robots?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3008-3032, July.
    14. Li, Jianjun & Wu, Zhouyi & Yu, Kaijia & Zhao, Wei, 2024. "The effect of industrial robot adoption on firm value: Evidence from China," Finance Research Letters, Elsevier, vol. 60(C).
    15. Nakatani, Ryota, 2024. "Optimal Taxation in the Automated Era," MPRA Paper 121347, University Library of Munich, Germany.
    16. Jacob, Tinu Iype & Paul, Sunil, 2024. "Labour income share, market power and automation: Evidence from an emerging economy," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 37-45.
    17. Nakatani, Ryota, 2022. "Optimal fiscal policy in the automated economy," MPRA Paper 115003, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pi, Jiancai & Zhang, Pengqing, 2018. "Structural change and wage inequality," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 699-707.
    2. Pi, Jiancai & Zhang, Pengqing, 2018. "Skill-biased technological change and wage inequality in developing countries," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 347-362.
    3. Pi, Jiancai & Zhang, Pengqing, 2016. "Hukou system reforms and skilled-unskilled wage inequality in China," China Economic Review, Elsevier, vol. 41(C), pages 90-103.
    4. Jiancai Pi & Yu Zhou, 2015. "The impacts of corruption on wage inequality and rural–urban migration in developing countries," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(3), pages 753-768, May.
    5. Pi, Jiancai & Zhang, Pengqing, 2021. "Redistribution and wage inequality," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 510-523.
    6. Pengqing Zhang, 2019. "Skill formation, environmental pollution, and wage inequality," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(2), pages 405-424, April.
    7. Jiancai Pi & Pengqing Zhang, 2017. "Social conflict and wage inequality," Journal of Economics, Springer, vol. 121(1), pages 29-49, May.
    8. Du, Longzheng & Lin, Weifen, 2022. "Does the application of industrial robots overcome the Solow paradox? Evidence from China," Technology in Society, Elsevier, vol. 68(C).
    9. Jiancai Pi & Xiangyu Huang, 2021. "Product Variety and Wage Inequality," Annals of Economics and Finance, Society for AEF, vol. 22(1), pages 135-151, May.
    10. Cirillo, Valeria & Evangelista, Rinaldo & Guarascio, Dario & Sostero, Matteo, 2021. "Digitalization, routineness and employment: An exploration on Italian task-based data," Research Policy, Elsevier, vol. 50(7).
    11. Pi, Jiancai & Zhang, Pengqing, 2018. "Privatization and wage inequality in developing countries," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 594-603.
    12. Chaudhuri, Sarbajit & Ghosh, Arnab & Banerjee, Dibyendu, 2018. "Can public subsidy on education necessarily improve wage inequality?," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 165-177.
    13. Gregory, Terry & Salomons, Anna & Zierahn, Ulrich, 2016. "Racing With or Against the Machine? Evidence from Europe," VfS Annual Conference 2016 (Augsburg): Demographic Change 145843, Verein für Socialpolitik / German Economic Association.
    14. Maarek, Paul & Moiteaux, Elliot, 2021. "Polarization, employment and the minimum wage: Evidence from European local labor markets," Labour Economics, Elsevier, vol. 73(C).
    15. Anwar, Sajid & Sun, Sizhong, 2015. "Taxation of labour income and the skilled–unskilled wage inequality," Economic Modelling, Elsevier, vol. 47(C), pages 18-22.
    16. Pi, Jiancai & Zhou, Yu, 2014. "Foreign capital, public infrastructure, and wage inequality in developing countries," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 195-207.
    17. Pi, Jiancai & Zhou, Yu, 2013. "Institutional quality and skilled–unskilled wage inequality," Economic Modelling, Elsevier, vol. 35(C), pages 356-363.
    18. Domini, Giacomo & Grazzi, Marco & Moschella, Daniele & Treibich, Tania, 2021. "Threats and opportunities in the digital era: Automation spikes and employment dynamics," Research Policy, Elsevier, vol. 50(7).
    19. Reijnders, Laurie S.M. & de Vries, Gaaitzen J., 2018. "Technology, offshoring and the rise of non-routine jobs," Journal of Development Economics, Elsevier, vol. 135(C), pages 412-432.
    20. Sun, Sizhong & Anwar, Sajid, 2015. "Taxation of labour, product varieties and skilled–unskilled wage inequality: Short run versus long run," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 250-257.

    More about this item

    Keywords

    Automation; Wage inequality; Robot taxation; General equilibrium approach;
    All these keywords.

    JEL classification:

    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reveco:v:59:y:2019:i:c:p:500-509. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.