Acemoglu, D. (1998) “Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality,” Quarterly Journal of Economics, Vol. 113, No. 4, November.
Autor, D., L. Katz, and A. Krueger (1998) “Computing Inequality: Have Computers Changed the Labor Market?” Quarterly Journal of Economics, November.
Berman, E., J. Bound and S. Machin (1998) “Implications of Skill-Biased Technological Change: International Evidence,” Quarterly Journal of Economics, Vol. 113, No. 4, November.
Blankenau, W. (1999) “A Welfare Analysis of Policy Responses to the Skilled Wage Premium,” Review of Economic Dynamics, Vol. 2, 209–849.
Bound, J. and G. Johnson (1992) “Changes in the Structure of Wages in the 1980’s: An Evaluation of Alternative Explanations,” American Economic Review, Vol. 83, No 3, June.
Devroye, D. and R. Freeman (2001) “Does Inequality in Skills Explain Inequality of Earnings across Advanced Countries?,” NBER Working Paper 8140, February.
DiNardo, J., N. Fortin and T. Lemieux (1996) “Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semi-parametric Approach,” Econometrica, Vol. 64, No. 5, 1001–1044.
Fernandez, D. (2000) “Education or Occupation? International Trends of Wage Inequality,” Dissertation, The University of Chicago, June.
Galor, O. and O. Moav (2000) “Ability-Biased Technological Transition, Wage Inequality and Economic Growth,” Quarterly Journal of Economics, Vol. 115 No. 2, May.
Gottschalk, P. and T. Smeeding (1997) “Cross-National Comparisons of Earnings and Income Inequality,” Journal of Economic Literature, Vol. XXXV, pp. 633–687.
Haskel, J. and M. Slaughter (1998) “Does The Sector Bias of Skill-Biased Technical Change Explain Changing Wage Inequality?” NBER Working Paper 6565, May.
Katz, L. and K. M. Murphy (1992) “Changes in Relative Wages, 1963-1987: Supply and Demand Factors,” Quarterly Journal of Economics, Vol. 107 No. 1, February.
Krusell, P., Ohanian, L., Rios-Rull, J. and G. Violante (2000) “Capital-Skill Complementarity and Inequality: A Macroeconomic Analysis,” Econometrica, Vol. 68, No. 5, September.
Machin, S. and J. Van Reenen (1998) “Technology and Changes in Skill Structure: Evidence from Seven OECD Countries,” Quarterly Journal of Economics, Vol. 113, No. 4, November.
Murphy, K. M., G. Riddell and P. M. Romer (1998) “Wages, Skills, and Technology in The United States and Canada” NBER Working Paper 6638, July.
Prasad, E. S. (2000) “The Unbearable Stability of the German Wage Structure: Evidence and Interpretation,” IMF Working Paper, WP/00/22, February.
We thank David Goldsbrough for his thoughtful comments and Gustavo Ramirez for his excellent assistance in compiling the data. All errors and omissions are our own.
Galor and Moav (2000), Acemoglu (1998), Krusell, Ohanian, Rios-Rull and Violante (2002) develop theoretical models explaining the implications of the positive link between investment in human capital and investment in new technologies.
This view is compatible with learning-by-doing theories which explain the wage profile of young workers.
The function F has the standard properties of the Neoclassical production functions, that is, F(K, AL) is increasing, concave in both arguments, and homogeneous of degree one. We can
thus rewrite it as: Yt =AtLtf(kt), where f(·) is increasing and concave and
Given wages ws,t and wu,t we have that
Equating the marginal product of capital to the international interest rate, we have:
We assume that α (xt) is an increasing and weakly concave function bounded by unity.
Our assumption is also consistent with Acemoglu (1998)’s view that a high proportion of skilled workers implies a larger market size for skill-complementary technologies and encourages faster upgrading of productivity of skilled workers.
We assume that Γ(.) is an increasing, concave, and continuous function.
The results are not specific to a particular mechanism used to cap the skill premium. We consider here a common income redistribution policy that shifts income from those well-off (according to earnings) to those in need (on a per-capita basis).
Here we do not investigate optimal size issues for the education subsidy, which would require using an inter-temporal budget constraint and a government welfare function to bring in intergenerational preferences. Although human capital in this framework is an input fully consumed in the one period when individual are active workers (i.e. human capital has a depreciation rate of 100 percent), the existence of cross-generation externalities, implied by xt+1 = Γ(st), may determine an optimal budget deficit. Nonetheless, given that we ask the narrower question of what would be the effect of an education subsidy on skill acquisition and growth, the use of a budget balance for every period does not alter the qualitative implications of the results.
This observation is robust to various parameter specifications (i.e. education costs, growth function parameters).
In the case of the United Kingdome, Prasad (2001) points out to evidence that rising wage dispersion is consistent with skill-biased technological progress. Similarly, Murphy, Riddell and Romer (1998) show evidence of relative demand shift for more-educated labor in the United States and Canada.
See DiNardo, Fortin and Lemieux (1994) and Card and Krueger (1995).