Main Article Content

Abstract

Purpose ― Most global economies are dealing with the issue of skill bias. In developing and underdeveloped countries, skill bias poses a problem by preventing the educated from participating in the economy's production function, especially in the long run. This paper expands on the skill-wage relationship and investigates this issue in the case of Iran from 1981 to 2021.
Methods- Applying Impulse Responses from VECM and the Structural VAR model separates the relationship between skill and wage into short- and long-term effects. The structural wage model was estimated using the structural vector auto-regression model.
Findings ― The results show that skill played a significant role in wage determination only for three periods in the short run, and the effect was neutral in the long run. This means that skill accumulation through advancement in graduate and postgraduate study is unlikely to increase wages in the long run.
Implication ― According to the findings, skill bias implies that education attainment in the Iranian labour market can only improve wages to a minimum extent. This also proves that factors other than education determine wage growth in the economy.
Originality ― The skill-wage relationship has not been a focus of studies in education outcome fields. Moreover, in the case of Iran, this investigation is novel, and there is a lack of studies on the relationship between compensation and skill.

Keywords

skill bias long-run wage model human capital bargaining

Article Details

Author Biography

Akbar Komijani, Faculty of Economics, University of Tehran, Tehran, Iran

A Professor at the Faculty of Economics, University of Tehran, Tehran, Iran

How to Cite
Mohebi, M., & Komijani, A. (2024). Skill bias in the labour market: Evidence from Iran. Economic Journal of Emerging Markets, 16(2), 136–150. https://doi.org/10.20885/ejem.vol16.iss2.art4

References

  1. Barro, R. J., & Lee, J. W. (2001). International data on educational attainment: Updates and implications. Oxford Economic Papers, 53(3), 541–563. https://doi.org/10.1093/oep/53.3.541
  2. Barro, R., & Lee, J. W. (2000). International data on educational attainment: Updates and implications (Issue 7911). https://doi.org/10.3386/w7911
  3. Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press.
  4. Blanchard, O., & Perotti, R. (2002). An empirical characterization of changes in Government spending and taxes on output. Quarterly Journal of Economics, 117(4), 1329–1368. https://doi.org/10.1162/003355302320935043
  5. Carbonero, F., Offermanns, C. J., & Weber, E. (2022). The fall of the labor income share: The role of technological change and hiring frictions. Review of Economic Dynamics. https://doi.org/10.1016/j.red.2022.02.004
  6. Cunha, F., Heckman, J., & Schennach, S. (2010). Estimating the technology of cognitive and noncognitive skill formation (Issue 15664). https://ideas.repec.org/p/nbr/nberwo/15664.html
  7. Hendricks, L. (2002). How important is human capital for development? Evidence from immigrant earnings. American Economic Review, 92(1), 198–219. https://doi.org/10.1257/000282802760015676
  8. Herrendorf, B., & Schoellman, T. (2018). Wages, human capital, and barriers to structural transformation. American Economic Journal: Macroeconomics, 10(2), 1–23. https://doi.org/10.1257/mac.20160139
  9. Holmstrom, B. (2017). Pay for performance and beyond. American Economic Review, 107(7), 1753–1777. https://doi.org/10.1257/aer.107.7.1753
  10. Hutter, C., & Weber, E. (2021). Labour market miracle, productivity debacle: Measuring the effects of skill-biased and skill-neutral technical change. Economic Modelling, 102, 105584. https://doi.org/10.1016/j.econmod.2021.105584
  11. Hutter, C., & Weber, E. (2022). Labour market effects of wage inequality and skill-biased technical change. Applied Economics, 55(27), 3063–3084. https://doi.org/10.1080/00036846.2022.2108751
  12. Jones, B. F. (2014). The human capital stock: A generalized approach. American Economic Review, 104(11), 3752–3777. https://doi.org/10.1257/aer.104.11.3752
  13. Kalman, R. E. (2006). Time series analysis. Statistical information theory, and other special. Journal of Advanced Nursing, 2(133), 43. https://doi.org/10.1111/j.1365-2648.2006.03681.x
  14. Klenow, P. J., & Blis, M. (2000). Does schooling cause growth? American Economic Review, 90(5), 1160–1183. https://doi.org/10.1257/aer.90.5.1160
  15. Lazear, E. P., & Oyer, P. (2007). Personnel economics (Issue 13480). https://doi.org/10.3386/w13480
  16. Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407–437. https://doi.org/10.2307/2118477
  17. Manuelli, R. E., & Seshadri, A. (2014). Human capital and the wealth of nations: Dataset. American Economic Review, 104(9), 2736. https://doi.org/10.1257/aer.104.9.2736
  18. Mincer, J. (1974). Progress in human capital analysis of the distribution of earnings (53; National Bureau of Economic Research Working Paper Series). https://doi.org/10.3386/w0053
  19. Rosen, S. (1976). A theory of life earnings. Journal of Political Economy, 84(4), 45–67.
  20. Sims, C. A. (1999). The role of interest rate policy in the generation and propagation of business cycles: What has changed since the 30s? Federal Reserve Bank of Boston Conference Series, 42, 121–160.
  21. Tassaeva, I. V. (2021). The changing education distribution and income inequality in Great Britain. Review of Income and Wealth, 67(3), 659–683. https://doi.org/10.1111/roiw.12486
No Related Submission Found