Eder, Andreas and Koller, Wolfgang and Mahlberg, Bernhard (2024): Industrial robots and employment change in manufacturing: A combination of index and production-theoretical decomposition analysis.
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Abstract
This paper investigates the contribution of industrial robots to employment change in manufacturing in a sample of 17 European countries and the USA over the period 2004 to 2019. We combine index decomposition analysis (IDA) and production-theoretical decomposition analysis (PDA). First, we use IDA to decompose employment change in the manufacturing industry into changes in (aggregate) manufacturing output, changes in the sectoral structure of the manufacturing industry, and changes in labour intensity which is a composite index of labour intensity change within each of the nine sub-sectors of total manufacturing. Second, we use PDA to further decompose labour intensity change to isolate the contribution of technical efficiency change, technological change, human capital change, change in non-robot capital intensity and change in robot capital intensity to employment change. In almost all of the countries considered, the labour intensity is falling in entire manufacturing, which has a dampening effect on employment. Robotisation contributes to this development by reducing labour intensities and employment in all countries and sub-sectors, though to varying degrees. Manufacturing output, in turn, grows in all countries (except Greece, Spain and Italy), which increases employment and counteracts or in some countries even more than offsets the dampening effect of declining labour intensities. The structural change within manufacturing has an almost neutral effect in many countries.
Item Type: | MPRA Paper |
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Original Title: | Industrial robots and employment change in manufacturing: A combination of index and production-theoretical decomposition analysis |
English Title: | Industrial robots and employment change in manufacturing: A combination of index and production-theoretical decomposition analysis |
Language: | English |
Keywords: | automation; robotisation; decomposition; structural change; data envelopment analysis |
Subjects: | C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C43 - Index Numbers and Aggregation J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J21 - Labor Force and Employment, Size, and Structure J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J24 - Human Capital ; Skills ; Occupational Choice ; Labor Productivity O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes |
Item ID: | 121128 |
Depositing User: | Bernhard Mahlberg |
Date Deposited: | 16 Jun 2024 17:30 |
Last Modified: | 16 Jun 2024 17:30 |
References: | Abeliansky, A., K. Prettner (2023). Automation and population growth: Theory and cross-country evidence. Journal of Economic Behavior & Organization 208, 345-358. (https://doi.org/10.1016/j.jebo.2023.02.006) Abeliansky, A., K. Prettner (2024). Automation and ageing. In: D. E. Bloom, A. Sousa-Poza, U. Sunde (eds.), The Routledge Handbook of the Economics of Ageing. Routledge, Taylor & Francis Group, Abingdon, New York, pp. 317-328. (https://doi.org/10.4324/9781003150398-20) Albinowski, M., P. Lewandowski (2024), The impact of ICT and robots on labour market outcomes of demographic groups in Europe. Labour Economics 87, 102481. (https://doi.org/10.1016/j.labeco.2023.102481) Acemoglu, D., P. Restrepo (2019a). Artificial Intelligence, Automation and Work. In: Agrawal, A., Gans, J., Goldfarb, A., The Economics of Artificial Intelligence: An Agenda. The University of Chicago Press, Chicago, pp. 197 – 236. (https://doi.org/10.7208/chicago/9780226613475.001.0001) Acemoglu, D., P. Restrepo (2019b). Automation and New Tasks: How Technology Displaces and Reinstates Labor. Journal of Economic Perspectives 33, 3–30. (https://doi.org/10.1257/jep.33.2.3) Acemoglu, D., P. Restrepo (2022). Demographics and automation. Review of Economic Stud¬ies 89, 1–44. (https://doi.org/10.1093/restud/rdab031) Ang, B.W. (2004). Decomposition analysis for policymaking in energy: which is the preferred method? Energy Policy 32, 1131-1139. (https://doi.org/10.1016/S0301-4215(03)00076-4) Ang, B.W. (2005). The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33, 867:871. (https://doi.org/10.1016/j.enpol.2003.10.010) Badunenko, O., D. Romero-Ávila (2013). Financial development and the sources of growth and convergence. International Economic Review 54, 629-663. (https://doi.org/10.1111/iere.12009) Barbieri, L., C. Mussida, M. Piva, M. Vivarelli (2020). Testing the Employment and Skill Impact of New Technologies, in: K. F. Zimmermann (ed.), Handbook of Labor, Human Re¬sources and Population Economics, Springer, Cham. (https://doi.org/10.1007/978-3-319-57365-6_1-1) Borjas, G.J., R. B. Freeman (2019). From Immigrants to Robots: The Changing Locus of Substitutes for Workers. RSF: The Russell Sage Foundation Journal of the Social Sciences 5(5), 22–42. (https://doi.org/10.7758/RSF.2019.5.5.02) Carbonero, F., E. Ernst, E. Weber (2018). Robots worldwide: the impact of automation on employment and trade. International Labour Office (ILO), Research Department Working paper No. 36 (https://ilo.userservices.exlibrisgroup.com/discovery/delivery/41ILO_INST:41ILO_V2/1256489950002676?lang=en&viewerServiceCode=DigitalViewer) Caves, D.W., L.R. Christensen, W.E. Diewert (1982). The economic theory of index numbers and the measurement of inputs, output, and productivity. Econometrica 50, 1393-1414. (https://doi.org/10.2307/1913388) Dahlin, E. (2019). Are robots stealing our jobs? Socius: Sociological Research for a Dynamic World 5, 1–14. (https://doi.org/10.1177/2378023119846249) Dauth, W., S. Findeisen, J. Südekum, N. Woessner (2021). The Adjustment of Labor Markets to Robots. Journal of the European Economic Association 19, 3104–3153. (https://doi.org/10.1093/jeea/jvab012) De Backer, K., T. De Stefano, C. Menon, J.R. Suh (2018). Industrial robotics and the global organisation of production. OECD Science, Technology and Industry Working Papers 2018/03. Paris: Organisation for Economic Co-operation and Development. (https://www.oecd-ilibrary.org/deliver/dd98ff58-en.pdf?itemId=%2Fcontent%2Fpaper%2Fdd98ff58-en&mimeType=pdf) de Vries, G. J., E. Gentile, S. Miroudot, K. M. Wacker (2020). The rise of robots and the fall of routine jobs. Labour Economics 66, 101885. (https://doi.org/10.1016/j.labeco.2020.101885) Diewert, W.E. (1980). Capital and the theory of productivity measurement. American Eco¬nomic Review 70, 260-267. (https://doi.org/10.2307/1913388) Dyson, R.G., R. Allen, A.S. Camanho, V. V. Podinovski, C.S. Sarrico, E.A. Shale (2001). Pit-falls and protocols in DEA. European Journal of Operational Research 132, 245-259. (https://doi.org/10.1016/S0377-2217(00)00149-1) Eder, A., W. Koller, B. Mahlberg (2023). The contribution of industrial robots to labor produc-tivity growth and economic convergence: a production frontier approach. Journal of Productiv¬ity Analysis (forthcoming). (https://doi.org/10.1007/s11123-023-00707-x) Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statisti¬cal Society 120, 253-290. (https://doi.org/10.2307/2343100) Färe, R., Grosskopf, B., Norris, M., & Zhang, Z. (1994). Productivity growth. technical pro¬gress. and efficiency change in industrialized countries. American Economic Review 84, 66-83. Färe, R., S. Grosskopf, C.A. Pasurka (2018). Pollution abatement and employment. Empirical Economics 54, 259-285. (https://doi.org/10.1007/s00181-016-1205-2) Feenstra, R.C., R. Inklaar, M.P. Timmer (2015). The next generation of the Penn World Ta¬ble. The American Economic Review 105, 3150-3182. (https://doi.org/10.1257/aer.20130954) Fu, X.M., Q. Bao, H. Xie, X. Fu (2021). Diffusion of industrial robotics and inclusive growth: labour market evidence from cross country data, Journal of Business Research 122, 670-684 (https://doi.org/10.1016/j.jbusres.2020.05.051) Graetz, G., G. Michaels (2018). Robots at Work, Review of Economics and Statistics 100, 753–768. (https://doi.org/10.1162/rest_a_00754) Henderson, D.J., R.R. Russell (2005). Human capital and convergence: A production-frontier approach. International Economic Review 46, 1167-1205. (https://doi.org/10.1111/j.1468-2354.2005.00364.x) IFR (2006). World Robotics 2006. International Federation of Robotics Statistical Department, VDMA Services GmbH, Frankfurt am Main, Germany. Jestl, S. (2024). Industrial robots, and information and communication technology: the em-ployment effects in EU labour markets, Regional Studies (forthcoming) (https://doi.org/10.1080/00343404.2023.2292259) Jung, J.H., D.-G. Lim (2020). Industrial robots, employment growth, and labor cost: A simul-taneous equation analysis, Technological Forecasting & Social Change 159, 120202 (https://doi.org/10.1016/j.techfore.2020.120202) Kariel, J (2021). Job Creators or Job Killers? Heterogeneous Effects of Industrial Robots on UK Employment, LABOUR 35, 52–78 (https://doi.org/10.1111/labr.12192) Klenert, D., E. Fernández-Macías, J.-I. Antón (2023). Do robots really destroy jobs? Evidence from Europe, Economic and Industrial Democracy 44, 280–316 (https://doi.org/10.1177/0143831X211068891) Kopidou, D., Tsakanikas, D., Diakoulaki, D. (2016). Common trend and drivers of CO2 emis-sion and employment: a decomposition analysis in the industrial sector of selected European Union countries. Journal of Cleaner Production 112, 4159-4172. (https://doi.org/10.1016/j.jclepro.2015.06.079) Kromann, L., N. Malchow-Møller, J.R. Skaksen, A. Sørense A (2020). Automation and productivity: A cross-country, cross-industry comparison. Industrial and Corporate Change 29(2), 265–287. (https://doi.org/10.1093/icc/dtz039) Kumar, S., Russell, R.R. (2002). Technological change, technological catch-up, and capital deepening: Relative contributions to growth and convergence. American Economic Review 92, 527-548. (https://doi.org/10.1257/00028280260136381) Lin, B., K. Du (2014). Decomposing energy intensity change: A combination of index decom-position analysis and production-theoretical decomposition analysis. Applied Energy 129, 158-165. (https://doi.org/10.1016/j.apenergy.2014.04.101) Los, B., & Timmer, M.P. (2005). The ‘appropriate technology‘ explanation of productivity growth differentials: An empirical approach. Journal of Development Economics 77, 517-531. (https://doi.org/10.1016/j.jdeveco.2004.04.001) Montobbio, F., J. Staccioli, M. E. Virgillito, M. Vivarelli, (2023). The empirics of technology, employment and occupations: Lessons learned and challenges ahead. Journal of Economic Surveys, 1–34. (https://doi.org/10.1111/joes.12601) Müller, C., N. Kutzbach (2020). World Robotics 2020 – Industrial Robots. IFR Statistical De-partment, VDMA Services GmbH, Frankfurt am Main, Germany. Olislager, L., P. Konijn (2016). Estimating purchasing power parities for the production side of GDP, EURONA — Eurostat Review on National Accounts and Macroeconomic Indicators, 2/2016, 45-71. (https://ec.europa.eu/eurostat/documents/3217494/7784358/KS-GP-16-002-EN-N.pdf/af4b1474-cc3a-4453-9814-bfbcb74e31d0?t=1483966712000) Stemmler, H. (2019), Does automation lead to de-industrialization in emerging economies? Evidence from Brazil, cege Discussion Papers, No. 382, University of Göttingen, Center for European, Governance and Economic Development Research (cege), Göttingen (https://www.econstor.eu/bitstream/10419/203326/1/1677065451.pdf) Walheer, B. (2016a). A multi-sector nonparametric production-frontier analysis of the eco¬nomic growth and the convergence of the European countries, Pacific Economic Review 21, 498-524. (https://doi.org/10.1111/1468-0106.12195) Walheer, B. (2016b). Growth and Convergence of the OECD countries: A Multi-Sector Production-Frontier Approach, European Journal of Operational Research 252, 665-675. (https://doi.org/10.1016/j.ejor.2016.01.030) Walheer, B. (2020). A sector-based nonparametric decomposition of economic growth. Eco-nomics and Business Letters 9, 48-55. (https://doi.org/10.17811/ebl.9.2.2020.48-55) Wang, C. (2007). Decomposing energy productivity change: a distance function approach. Energy 32, 1326–33. (https://doi.org/10.1016/j.energy.2006.10.001) Wang, C. (2011). Sources of energy productivity growth and its distribution dynamics in China. Resource and Energy Economics 33, 279–92. Wang, C. (2013). Changing energy intensity of economies in the world and its decomposition. Energy Economics 40, 637-644. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/121128 |