Hostname: page-component-745bb68f8f-b6zl4 Total loading time: 0 Render date: 2025-01-13T11:28:25.831Z Has data issue: false hasContentIssue false
  • English
  • Français

Aggregate elasticity of substitution between skills: estimates from a macroeconomic approach

Published online by Cambridge University Press:  28 July 2022

Michał Jerzmanowski  and
Robert Tamura
Michał Jerzmanowski
Affiliation:
John E. Walker Department of Economics, Clemson University, Clemson, SC, USA
Robert Tamura*
Affiliation:
John E. Walker Department of Economics, Clemson University, Clemson, SC, USA
*
*Corresponding author. Email: rtamura@clemson.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

We estimate the elasticity of substitution between high-skill and low-skill workers using panel data from 32 countries during 1970–2015. Most existing estimates, which are based only on US microdata, find a value close to 1.6. We bring international data together with a theory-informed macro-approach to provide new evidence on this important macroeconomic parameter. Using the macro-approach, we find that the elasticity of substitution between tertiary-educated workers and those with lower education levels falls between 1.7 and 2.6, which is higher than previous estimates but within a plausible range. In some specifications, estimated elasticity is above the value required for strong skill-bias of technology, suggesting strong skill-bias is possible.

Keywords

Elasticity of substitutionhigh-skill laborlow-skill laborskill premiumstrong skill-biasendogenous-directed technologyE24E25O11J31
Type
Articles
Information
Macroeconomic Dynamics , Volume 27 , Issue 6 , September 2023 , pp. 1597 - 1627
Copyright
© The Author(s), 2022. 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.)

Footnotes

†

We would like to thank the participants of the Southern Economics Association’s Meetings in New Orleans, Simon Gilchrist, the editors, and two anonymous referees for helpful comments and suggestions.

References

Acemoglu, D. (1998) Why do new technologies complement skills? Directed technical change and wage inequality. Quarterly Journal of Economics 113(4), 1055–1090.CrossRefGoogle Scholar
Acemoglu, D. (2002) Directed technical change. Review of Economic Studies 69(4), 781–809.CrossRefGoogle Scholar
Acemoglu, D. (2009) Introduction to Modern Economic Growth. Princeton, NJ: Princeton University Press.Google Scholar
Acemoglu, D. and Autor, D. (2011) Skills, Tasks and Technologies: Implications for Employment and Earnings. In: Acemoglu, D. and Autor, D. (eds.), Handbook of Labor Economics, vol. 4, pp. 1043–1171, Elsevier B.V.Google Scholar
Acemoglu, D. and Restrepo, P. (2019) Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives 3(2), 3–30.CrossRefGoogle Scholar
Adarov, Amat and Stehrer, Robert (2019) Tangible and Intangible Assets in the Growth Performance of the EU, Japan and the US. https://wiiw.ac.at/p-5058.html. [Online; accessed 5/5/20].Google Scholar
Arellano, M. and Bond, S. (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297.CrossRefGoogle Scholar
Arellano, M., and Bover, O. (1995) Another look at the instrumental variable estimation of error-component models. Journal of Econometrics 68, 29–52.CrossRefGoogle Scholar
Barro, R. J. (2012) Convergence and modernization revisited. Working paper 18295. National Bureau of Economic Research, 10.3386/w18295, http://www.nber.org/papers/w18295 Google Scholar
Blundell, R. and Bond, S. (1998) Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143.CrossRefGoogle Scholar
Bowlus, A., Bozkurt, E., Lochner, L. and Robinson, C. (2017) Wages and employment: The canonical model revisited. Working paper 24069. National Bureau of Economic Research, 10.3386/w24069, http://www.nber.org/papers/w24069 Google Scholar
Caselli, F. and Antonio, C. (2019) The human capital stock: A generalized approach: Comment. American Economic Review 109.3, 1155–1174 CrossRefGoogle Scholar
Caselli, F. and Coleman, W. J. (2006) The world technology frontier. American Economic Review 96(3), 499–522.CrossRefGoogle Scholar
Ciccone, A. and Peri, G. (2005) The Long-Run substitutability between more and less educated workers: Evidence from U.S. States, 1950–1990. The Review of Economics and Statistics 87(4), 652–663.CrossRefGoogle Scholar
EU KLEMS project on Growth and Productivity in the European Union (2018) EU KLEMS Data. http://www.euklems.net/index.html. [Online; accessed 10/12/18].Google Scholar
Greene, W. H. (2003) Econometric Analysis, 5th edition, Pearson Education, isbn: 0-13-066189-9, http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm.Google Scholar
Heckman, J., Lochner, L. and Taber, C. (1998) Explaining rising wage inequality: Explanations with a dynamic general equilibrium model of labor earnings with heterogeneous agents. Review of Economic Dynamics 1.1, 1–58. doi: 10.1006/redy.1997.0008, https://ideas.repec.org/a/red/issued/v1y1998i1p1-58.html CrossRefGoogle Scholar
Hendricks, L. and Schoellman, T. (2020) Skilled labor productivity and cross-country income differences. Working Paper, https://lhendricks.org/Research/jones_cc/paper_jcc.pdf Google Scholar
Jäger, K. (July 2018) Conference Board Report. http://www.euklems.net/index.html. [Online; accessed 12/18/2019].Google Scholar
Jerzmanowski, M. (2007) TFP differences: Appropriate technology vs. efficiency. European Economic Review 51(8), 1963–2110.CrossRefGoogle Scholar
Jerzmanowski, M. and Tamura, R. (2019) Directed technological change and cross-country income differences: A quantitative analysis. Journal of Development Economics 141, 10.1016/j.jdeveco.2019.102372, https://ideas.repec.org/a/eee/deveco/v141y2019ics0304387818305820.html CrossRefGoogle Scholar
Johnson, G. (1970) The demand for labor by educational category. Southern Economic Journal 37(2), 190–204.CrossRefGoogle Scholar
Jones, B. F. (2014) The human capital stock: A generalized approach. American Economic Review 104.11, 3752–3777. doi: 10.1257/aer.104.11.3752, http://www.aeaweb.org/articles?id10.1257/aer.104.11.3752 CrossRefGoogle Scholar
Katz, L. F. and Murphy, K. M. (1992) Changes in relative wages, 1963-1987: Supply and demand factors. The Quarterly Journal of Economics 107.1, 35–78 CrossRefGoogle Scholar
Klenow, P. and Rodríguez-Clare, A. (1997) The Neoclassical Revival in Growth Economics: Has It Gone Too Far?, NBER Macroeconomics Annual 1997, vol. 12, pp. 73–114, NBER Chapters. National Bureau of Economic Research, Inc, https://ideas.repec.org/h/nbr/nberch/11037.html Google Scholar
Krusell, P., Ohanian, L. E., Rios-Rull, J.-V. and Violante, G. L. (2000) Capital-skill complementarity and inequality: A macroeconomic analysis. Econometrica 68(5), 1029–1054.CrossRefGoogle Scholar
O’Mahony, M. and Timmer, M. P. (June 2009) Output, input and productivity measures at the industry level: The EU KLEMS database. The Economic Journal 119(538), 374–403.CrossRefGoogle Scholar
Tamura, R. (2006) Human capital and economic development. Journal of Development Economics 79(1), 26–72.CrossRefGoogle Scholar
Tamura, R., Simon, C. J. and Murphy, K. M. (2016) Black and white fertility, differential baby booms: The value of equal education opportunity. Journal of Demographic Economics 82(1), 27–109.CrossRefGoogle Scholar
Timmer, M., van Moergastel, T., Stuivenwold, E., Ypma, G., O’Mahony, M. and Kangasniemi, M. (2007) EU KLEMS Growth and Productivity Accounts Version 1.0; Part I Methodology. http://www.euklems.net/data/EUKLEMS_Growth_and_Productivity_Accounts_Part_I_Methodology.pdf. [Online; accessed 12/12/19].Google Scholar