Hostname: page-component-89b8bd64d-4ws75 Total loading time: 0 Render date: 2026-05-07T06:55:25.588Z Has data issue: false hasContentIssue false
  • English
  • Français

Robotic capital - skill complementarity

Part of: Productivity Measurement and Analysis

Published online by Cambridge University Press:  06 November 2024

Michele Battisti ,
Massimo Del Gatto ,
Antonio Francesco Gravina  and
Christopher F. Parmeter
Michele Battisti
Affiliation:
RCEA, University of Palermo, Palermo, Italy
Massimo Del Gatto
Affiliation:
G. d'Annunzio, University of Chieti-Pescara, Pescara, Italy; LUISS, Rome, Italy and CRENoS, Cagliari, Italy
Antonio Francesco Gravina
Affiliation:
University of Palermo, Palermo, Italy
Christopher F. Parmeter*
Affiliation:
Department of Economics, University of Miami, Carol Gables, USA
*
Corresponding author: Christopher F. Parmeter; Email: c.parmeter@bus.miami.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Relying upon an original (country-sector-year) measure of robotic capital ($RK$), we investigate the degree of complementarity/substitutability between robots and workers at different skill levels. We employ nonparametric methods to estimate elasticity of substitution patterns between $RK$ and skilled/unskilled labor over the period 1995–2009. We show that: i) on average, $RK$ exhibits less substitutability with skilled workers compared to unskilled workers, indicating a phenomenon of “RK-Skill complementarity”. This pattern holds in a global context characterized by significant heterogeneity; ii) the dynamic of “RK-Skill complementarity” has increased since the early 2000s; iii) the observed strengthening is more prominent in OECD countries, as opposed to non-OECD countries, and in the Manufacturing sector, compared to non-Manufacturing industries.

Keywords

Automationelasticity of substitutionrobotizationtechnologyC23E24J31O33O47

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 29 , 2025 , e54
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Acemoglu, D. (1998) Why do new technologies complement skills? Directed technical change and wage inequality. The Quarterly Journal of Economics 113(4), 1055–1089.CrossRefGoogle Scholar
Acemoglu, D. (2002) Directed technical change. The Review of Economic Studies 69(4), 781–809.CrossRefGoogle Scholar
Acemoglu, D. and Autor, D. (2011) Skills, tasks and technologies: Implications for employment and earnings, Handbook of Labor Economics, vol. 4, pp. 1043–1171, Elsevier.Google Scholar
Acemoglu, D. and Restrepo, P. (2020) Robots and jobs: Evidence from US labor markets. Journal of Political Economy 128(6), 2188–2244.CrossRefGoogle Scholar
Ackerberg, D. A., Caves, K. and Frazer, G. (2015) Identification properties of recent production function estimators. Econometrica, 83(6), 2411–2451.CrossRefGoogle Scholar
Alesina, A., Battisti, M. and Zeira, J. (2018) Technology and labor regulations: Theory and evidence. Journal of Economic Growth 23(1), 41–78.CrossRefGoogle Scholar
Autor, D., Goldin, C. and Katz, L. F. (2020, May) Extending the race between education and technology. AEA Papers and Proceedings. 110, 347–351.CrossRefGoogle Scholar
Autor, D. H. (2015) Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives 29(3), 3–30.CrossRefGoogle Scholar
Autor, D. H. and Dorn, D. (2013) The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review 103(5), 1553–1597.CrossRefGoogle Scholar
Autor, D. H., Levy, F. and Murnane, R. J. (2003) The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics 118(4), 1279–1333.CrossRefGoogle Scholar
Battisti, M., Gatto, M. D. and Parmeter, C. F. (2022) Skill-biased technical change and labor market inefficiency. Journal of Economic Dynamics and Control. 139, 104428.CrossRefGoogle Scholar
Berg, A., Buffie, E. F. and Zanna, L.-F. (2018) Should we fear the robot revolution? (The correct answer is yes). Journal of Monetary Economics. 97, 117–148.CrossRefGoogle Scholar
Blackorby, C. and Russell, R. R. (1981) The Morishima elasticity of substitution; symmetry, constancy, separability, and its relationship to the Hicks and Allen elasticities. The Review of Economic Studies 48(1), 147–158.CrossRefGoogle Scholar
Bonfiglioli, A., Crino’, R., Gancia, G. and Papadakis, I. (2022) Robots, Offshoring and Welfare. In: Bonfiglioli, A., Crino’, R., Gancia, G. and Papadakis, I.. (eds.), Robots and AI: A New Economic Era, Taylor & Francis.Google Scholar
Brynjolfsson, E. (1993) The productivity paradox of information technology. Communications of the ACM 36(12), 66–77.CrossRefGoogle Scholar
Caselli, F. and Manning, A. (2019) Robot arithmetic: New technology and wages. American Economic Review: Insights 1(1), 1–12.Google Scholar
Chu, B. (2023) Local linear regression with nonparametrically generated covariates for weakly dependent data. Journal of Statistical Planning and Inference, 225, 89–109.CrossRefGoogle Scholar
Costinot, A. and Werning, I. (2020) Robots, trade, and luddism: A sufficient statistic approach to optimal technology regulation, National Bureau of Economic Research, Technical report.Google Scholar
David, P. A. and Van de Klundert, T. (1965) Biased efficiency growth and capital-labor substitution in the us. The American Economic Review 1960, 357–394.Google Scholar
Diamond, P., McFadden, D. and Rodriguez, M. (1978) Measurement of the elasticity of factor substitution and bias of technical change, Contributions to economic analysis, vol. 2, pp. 125–147, Elsevier.Google Scholar
Division, U. N. S. (2008) International Standard Industrial Classification of all Economic Activities (ISIC), vol. Number 4. New York: United Nations Publications, United Nations Statistics Division.Google Scholar
Duffy, J., Papageorgiou, C. and Perez-Sebastian, F. (2004) Capital-skill complementarity? Evidence from a panel of countries. Review of Economics and Statistics 86(1), 327–344.CrossRefGoogle Scholar
Eden, M. and Gaggl, P. (2018) On the welfare implications of automation. Review of Economic Dynamics. 29, 15–43.CrossRefGoogle Scholar
Estlund, C. (2021) Automation Anxiety: Why and How to Save Work. Oxford: Oxford University Press.CrossRefGoogle Scholar
Faber, M. (2020) Robots and reshoring: Evidence from Mexican labor markets. Journal of International Economics 127, 103384.CrossRefGoogle Scholar
Fajgelbaum, P. D. and Khandelwal, A. K. (2022) The economic impacts of the US–China trade war. Annual Review of Economics 14(1), 205–228.CrossRefGoogle Scholar
Fallon, P. R. and Layard, P. R. G. (1975) Capital-skill complementarity, income distribution, and output accounting. Journal of Political Economy 83(2), 279–302.CrossRefGoogle Scholar
Ford, M. (2015) Rise of the Robots: Technology and the Threat of a Jobless Future. Hachette, NY: Basic Books.Google Scholar
Furman, J. (2016) Is this time different? The opportunities and challenges of artificial intelligence, presentation, AI Now: The Social and Economic Implications of Artificial Intelligence Technologies in the Near Term, New York, NY Google Scholar
Gandhi, A., Navarro, S. and Rivers, D. A. (2020) On the identification of gross output production functions. Journal of Political Economy, 128(8), 2973–3016.CrossRefGoogle Scholar
Gechert, S., Havranek, T., Irsova, Z. and Kolcunova, D. (2022) Measuring capital-labor substitution: The importance of method choices and publication bias. Review of Economic Dynamics. 45, 55–82.CrossRefGoogle Scholar
Goldin, C. D. and Katz, L. F. (2009) The race between education and technology. Cambridge, MA: Harvard University Press.Google Scholar
Graetz, G. and Michaels, G. (2018) Robots at work. Review of Economics and Statistics 100(5), 753–768.CrossRefGoogle Scholar
Griliches, Z. (1969) Capital-skill complementarity. Review of Economics and Statistics 51(4), 465–468.CrossRefGoogle Scholar
Guerreiro, J., Rebelo, S. and Teles, P. (2022) Should robots be taxed? The Review of Economic Studies 89(1), 279–311.CrossRefGoogle Scholar
Hall, P. G. and Racine, J. S. (2015) Infinite order cross-validated local polynomial regression. Journal of Econometrics 185(2), 510–525.CrossRefGoogle Scholar
Hémous, D. and Olsen, M. (2022) The rise of the machines: Automation, horizontal innovation, and income inequality. American Economic Journal: Macroeconomics 14(1), 179–223.Google Scholar
Henderson, D. J. (2009) A non-parametric examination of capital–skill complementarity. Oxford Bulletin of Economics and Statistics 71(4), 519–538.CrossRefGoogle Scholar
Henderson, D. J., Kumbhakar, S. C., Li, Q. and Parmeter, C. F. (2015) Smooth coefficient estimation of a seemingly unrelated regression. Journal of Econometrics 189(1), 148–162.CrossRefGoogle Scholar
Inklaar, R. and Timmer, M. P. (2014) The relative price of services. Review of Income and Wealth 60(4), 727–746.CrossRefGoogle Scholar
International Federation of Robotics (2005) World robotics industrial robots. Technical report.Google Scholar
International Federation of Robotics (2019) World robotics industrial robots. Technical report.Google Scholar
Jaimovich, N., Saporta-Eksten, I., Siu, H. and Yedid-Levi, Y. (2021) The macroeconomics of automation: Data, theory, and policy analysis. Journal of Monetary Economics. 122, 1–16.CrossRefGoogle Scholar
Jaimovich, N. and Siu, H. E. (2020) Job polarization and jobless recoveries. Review of Economics and Statistics 102(1), 129–147.CrossRefGoogle Scholar
Jovanovic, B. and Rousseau, P. L. (2002) Moore’s law and learning by doing. Review of Economic Dynamics 5(2), 346–375.CrossRefGoogle Scholar
Jurkat, A., Klump, R. and Schneider, F. (2022) Tracking the rise of robots: The ifr database. Jahrbücher für Nationalökonomie und Statistik 242(5-6), 669–689.CrossRefGoogle Scholar
Katz, L. F. and Murphy, K. M. (1992) Changes in relative wages, 1987: Supply and demand factors. The Quarterly Journal of Economics 107(1), 35–78, 1963.CrossRefGoogle Scholar
Knoblach, M. and Stöckl, F. (2020) What determines the elasticity of substitution between capital and labor? A literature review. Journal of Economic Surveys 34(4), 847–875.CrossRefGoogle Scholar
Koch, M., Manuylov, I. and Smolka, M. (2021) Robots and firms. The Economic Journal 131(638), 2553–2584.CrossRefGoogle Scholar
Krugman, P. (1979) A model of innovation, technology transfer, and the world distribution of income. Journal of Political Economy 87(2), 253–266.CrossRefGoogle Scholar
Krusell, P., Ohanian, L. E., Ríos-Rull, J.-V. and Violante, G. L. (2000) Capital-skill complementarity and inequality: A macroeconomic analysis. Econometrica 68(5), 1029–1053.CrossRefGoogle Scholar
Kunst, D., Freeman, R. B. and Oostendorp, R. (2022) Occupational skill premia around the world: New data, patterns and drivers. Labour Economics. 79, 10–2255.CrossRefGoogle Scholar
Leduc, S. and Liu, Z. (2020) Can pandemic-induced job uncertainty stimulate automation?, San Francisco: Federal Reserve Bank of San Francisco Working Paper 2020-19.Google Scholar
Li, Q. and Racine, J. S. (2007) Nonparametric econometrics: theory and practice. Princeton, NJ: Princeton University Press.Google Scholar
Mack, C. A. (2011) Fifty years of moore’s law. IEEE Transactions on semiconductor manufacturing 24(2), 202–207.CrossRefGoogle Scholar
Mammen, E., Rothe, C. and Schienle, M. (2012) Nonparametric Regression with Nonparametrically Generated Covariates. The Annals of Statistics, 40(2), 1132–1170.CrossRefGoogle Scholar
Marschak, J. and Andrews, W. H. (1944) Random simultaneous equations and the theory of production. Econometrica, Journal of the Econometric Society 12(3/4), 143–205.CrossRefGoogle Scholar
Michaels, G., Natraj, A. and Van Reenen, J. (2014) Has ICT polarized skill demand? Evidence from eleven countries over twenty-five years. Review of Economics and Statistics 96(1), 60–77.CrossRefGoogle Scholar
Mokyr, J., Vickers, C. and Ziebarth, N. L. (2015) The history of technological anxiety and the future of economic growth: Is this time different? Journal of Economic Perspectives 29(3), 31–50.CrossRefGoogle Scholar
Moll, B., Rachel, L. and Restrepo, P. (2021) Uneven growth: automation’s impact on income and wealth inequality, National Bureau of Economic Research, Technical report.CrossRefGoogle Scholar
Muro, M., Maxim, R. and Whiton, J. (2020) The robots are ready as the COVID-19 recession spreads. Brookings Metro’s COVID-19 Analysis. 24.Google Scholar
O’Mahony, M. and Timmer, M. P. (2009) Output, input and productivity measures at the industry level: the EU KLEMS database. The Economic Journal 119(538), F374–F403.CrossRefGoogle Scholar
Okyere, M. A., Forson, R. and Essel-Gaisey, F. (2020) Positive externalities of an epidemic: the case of the coronavirus (COVID-19) in China. Journal of Medical Virology 92(9), 1376–1379.CrossRefGoogle ScholarPubMed
Olley, G. S. and Pakes, A. (1996). The Dynamics of Productivity in the Telecommunications Equipment Industry. Econometrica, 64(6), 1263–1297.CrossRefGoogle Scholar
Oxley, L. and McAleer, M. (1993) Econometric issues in macroeconomic models with generated regressors. Journal of Economic Surveys 7(1), 1–40.CrossRefGoogle Scholar
Prettner, K. and Bloom, D. (2020) Automation and its Macroeconomic Consequences. Cambridge, MA: Academic Press.Google Scholar
Racine, J. and Li, Q. (2004) Nonparametric estimation of regression functions with both categorical and continuous data. Journal of Econometrics 119(1), 99–130.CrossRefGoogle Scholar
Raveh, O. and Reshef, A. (2016) Capital imports composition, complementarities, and the skill premium in developing countries. Journal of Development Economics. 118, 183–206.CrossRefGoogle Scholar
Redding, S. J. and Weinstein, D. E. (2020) A unified approach to estimating demand and welfare changes. The Quarterly Journal of Economics 135(1), 503–560.CrossRefGoogle Scholar
Schaller, R. R. (1997) Moore’s law: Past, present and future. IEEE spectrum 34(6), 52–59.CrossRefGoogle Scholar
Schivardi, F. and Schmitz, T. (2018) The IT revolution and southern Europe’s two lost decades. Journal of the European Economic Association 18(5), 2441–2486.CrossRefGoogle Scholar
Singer, P. W. (2009) Military robots and the laws of war. The New Atlantis 23, 25–45.Google Scholar
Susskind, D. (2020) A World Without Work: Technology, Automation and how We Should Respond. London: Penguin UK.Google Scholar
Taniguchi, H. and Yamada, K. (2022) ICT capital-skill complementarity and wage inequality: Evidence from OECD countries. Labour Economics 10, 2151.Google Scholar
Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R. and De Vries, G. J. (2015) An illustrated user guide to the world input–output database: The case of global automotive production. Review of International Economics 23(3), 575–605.CrossRefGoogle Scholar
Tinbergen, J. (1974) Substitution of graduate by other labour. Kyklos 27(2), 217–226.CrossRefGoogle Scholar
vom Lehn, C. (2020) Labor market polarization, the decline of routine work, and technological change: A quantitative analysis. Journal of Monetary Economics. 110, 62–80.CrossRefGoogle Scholar
West, D. M. (2018) The Future of Work: Robots, AI, and Automation. Washington D.C: Brookings Institution Press.Google Scholar