1,086
Views
2
CrossRef citations to date
0
Altmetric
Articles

Can public subsidy on education reduce wage inequality in the presence of automation?

Pages 6850-6866 | Received 10 Jun 2021, Accepted 10 Mar 2022, Published online: 28 Mar 2022

References

  • Acemoglu, D., & Autor, D. (2012). What does human capital do? A review of Goldin and Katz’s The race between education and technology. Journal of Economic Literature, 50(2), 426–463. https://doi.org/10.1257/jel.50.2.426
  • Acemoglu, D., & Restrepo, P. (2018a). Low-skill and high-skill automation. Journal of Human Capital, 12(2), 204–232. https://doi.org/10.1086/697242
  • Acemoglu, D., & Restrepo, P. (2018b). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. https://doi.org/10.1257/aer.20160696
  • Anwar, S. (2006). Factor mobility and wage inequality in the presence of specialisation-based external economies. Economics Letters, 93(1), 88–93. https://doi.org/10.1016/j.econlet.2006.03.042
  • Anwar, S. (2008). Labor inflow induced wage inequality and public infrastructure. Review of Development Economics, 12(4), 792–802. https://doi.org/10.1111/j.1467-9361.2008.00453.x
  • Autor, D. H. (2014). Skills, education, and the rise of earnings inequality among the “other 99 percent". Science (New York, N.Y.), 344(6186), 843–851. https://doi.org/10.1126/science.1251868
  • 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. https://doi.org/10.1257/jep.29.3.3
  • Autor, D. H., & 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. https://doi.org/10.1257/aer.103.5.1553
  • Autor, D. H., Katz, L. F., & Kearney, M. S. (2006). The polarization of the U.S. labor market. American Economic Review, 96(2), 189–194. https://doi.org/10.1257/000282806777212620
  • Autor, D. H., Katz, L. F., & Kearney, M. S. (2008). Trends in U.S. wage inequality: Revising the revisionists. Review of Economics and Statistics, 90(2), 300–323. https://doi.org/10.1162/rest.90.2.300
  • Beladi, H., Chakrabarti, A., & Marjit, S. (2010). Skilled-unskilled wage inequality and urban unemployment. Economic Inquiry, 48(4), 997–1007. https://doi.org/10.1111/j.1465-7295.2009.00247.x
  • Beladi, H., Chaudhuri, S., & Yabuuchi, S. (2008). Can international factor mobility reduce wage inequality in a dual economy? Review of International Economics, 16(5), 893–903. https://doi.org/10.1111/j.1467-9396.2008.00751.x
  • Beladi, H., Marjit, S., & Broll, U. (2011). Capital mobility, skill formation and polarization. Economic Modelling, 28(4), 1902–1906. https://doi.org/10.1016/j.econmod.2011.03.019
  • Bentaouet Kattan, R., Macdonald, K., & Patrinos, H. A. (2021). The role of education in mitigating automation’s effect on wage inequality. LABOUR, 35(1), 79–104. https://doi.org/10.1111/labr.12187
  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
  • Chaudhuri, S. (2014). Foreign capital, non-traded goods and welfare in a developing economy in the presence of externalities. International Review of Economics & Finance, 31, 249–262. https://doi.org/10.1016/j.iref.2014.02.003
  • Chaudhuri, S., Ghosh, A., & Banerjee, D. (2018). Can public subsidy on education necessarily improve wage inequality? International Review of Economics & Finance, 54, 165–177. https://doi.org/10.1016/j.iref.2017.08.005
  • Chaudhuri, S., Kumar Dwibedi, J., & Biswas, A. (2017). Subsidizing healthcare in the presence of market distortions. Economic Modelling, 64, 539–552. https://doi.org/10.1016/j.econmod.2017.04.011
  • Goldin, C., & Katz, L. (2008). The race between education and technology. Harvard University Press.
  • Gupta, M. R., & Dutta, P. B. (2010). Skilled–unskilled wage inequality, nontraded good and endogenous supply of skilled labour: A theoretical analysis. Economic Modelling, 27(5), 923–934. https://doi.org/10.1016/j.econmod.2010.05.013
  • Harris, J. R., & Todaro, M. P. (1970). Migration, unemployment and development: A two-sector analysis. American Economic Review, 60, 126–142.
  • Jones, R. W. (1965). The structure of simple general equilibrium models. Journal of Political Economy, 73(6), 557–572. https://doi.org/10.1086/259084
  • Jones, R. W. (1971). A three factor model in theory, trade, and history. In J. N. Bhagwati, R. W. Jones, R. A. Mundell & J. Vanek (Eds.), Trade, balance of payments and growth: Papers in international economics in Honor of Charles P. Kindleberger (pp. 3–21). North-Holland.
  • Kar, S., & Guha-Khasnobis, B. (2006). Economic reform, skill formation and foreign capital. The World Economy, 29(1), 79–94. https://doi.org/10.1111/j.1467-9701.2006.00759.x
  • Lankisch, C., Prettner, K., & Prskawetz, A. (2019). How can robots affect wage inequality? Economic Modelling, 81, 161–169. https://doi.org/10.1016/j.econmod.2018.12.015
  • Levy, F., & Murnane, R. (2013). Dancing with robots: Human skills for computerized work. NEXT report, Third Way.
  • Marjit, S., & Kar, S. (2005). Emigration and wage inequality. Economics Letters, 88(1), 141–145. https://doi.org/10.1016/j.econlet.2005.02.003
  • Okada, K. (2020). Dynamic analysis of education, automation, and economic growth. Graduate School of Economics and Osaka School of International Public Policy (OSIPP). Osaka University Discussion Papers in Economics and Business, 20, 1–31.
  • Pan, L. (2014). The impacts of education investment on skilled–unskilled wage inequality and economic development in developing countries. Economic Modelling, 39, 174–181. https://doi.org/10.1016/j.econmod.2014.02.040
  • Pi, J., & Fan, Y. (2019). Urban bias and wage inequality. Review of Development Economics, 23(4), 1788–1799. https://doi.org/10.1111/rode.12603
  • Pi, J., & Zhang, P. (2018). Skill-biased technological change and wage inequality in developing countries. International Review of Economics & Finance, 56, 347–362. https://doi.org/10.1016/j.iref.2017.11.004
  • Pi, J., & Zhang, P. (2020). Organized crime and wage inequality. Scottish Journal of Political Economy, 67(3), 344–361. https://doi.org/10.1111/sjpe.12238
  • Pi, J., & Zhou, Y. (2012). Public infrastructure provision and skilled-unskilled wage inequality in developing countries. Labour Economics, 19(6), 881–887. https://doi.org/10.1016/j.labeco.2012.08.007
  • Prettner, K. (2019). A note on the implications of automation for economic growth and the labor share. Macroeconomic Dynamics, 23(3), 1294–1301. https://doi.org/10.1017/S1365100517000098
  • Prettner, K., & Strulik, H. (2020). Innovation, automation, and inequality: Policy challenges in the race against the machine. Journal of Monetary Economics, 116, 249–265. https://doi.org/10.1016/j.jmoneco.2019.10.012
  • Taresh, A. A., Sari, D. W., & Purwono, R. (2021). Analysis of the relationship between income inequality and social variables: Evidence from Indonesia. Economics & Sociology, 14(1), 103–119. https://doi.org/10.14254/2071-789X.2021/14-1/7
  • Tawada, M., & Sun, S. (2010). Urban pollution, unemployment and national welfare in a dualistic economy. Review of Development Economics, 14(2), 311–322. https://doi.org/10.1111/j.1467-9361.2010.00554.x
  • Wang, D. (2019). International labour movement, public intermediate input and wage inequality: A dynamic approach. Economic Research-Ekonomska Istraživanja, 32(1), 1–16. https://doi.org/10.1080/1331677X.2018.1546123
  • Yabuuchi, S., & Chaudhuri, S. (2009). Skill formation, capital adjustment cost and wage inequality. Review of Urban & Regional Development Studies, 21(1), 2–13. https://doi.org/10.1111/j.1467-940X.2009.00156.x
  • Zhang, P. (2019a). Automation, wage inequality and implications of a robot tax. International Review of Economics & Finance, 59, 500–509. https://doi.org/10.1016/j.iref.2018.10.013
  • Zhang, P. (2019b). Skill formation, environmental pollution, and wage inequality. The Annals of Regional Science, 62(2), 405–424. https://doi.org/10.1007/s00168-019-00901-6