Human Capital Investment, Inequality and Economic Growth
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- Bu səhifədəki naviqasiya:
- Table 1 Distributions of Grade Point Averages First Year Students at Four-Year Colleges and Universities
- Academic Year Major Gender (%) First Year Grade Point Average
- Table 2 Wage Elasticities of Average Weekly Hours, 1970-72 through 2010-12 By Intervals of the Male and Female Weekly Wage Distributions
- Wage Percentiles
Conclusion Over the past 40 plus years there has been a substantial rise in wage inequality for both men and women. When viewed in the context of a labor market equilibrium in which skill prices are determined by the interaction of supply and demand, the recent history has a simple explanation—rising relative wages for more skilled workers reflects the fact that the demand for skilled labor has outpaced growth in the supply of skilled labor. For purposes of understanding the evolution of inequality it is important to distinguish multiple dimensions on which the relative supply of skilled labor responds to a rise in its relative price. Different margins have very different effects on inequality. Investments on the extensive margin mitigate the impact of rising demand on the skill price and thereby mitigate the resulting rise in inequality. In contrast, while investments on intensive margins—by which we mean greater skill accumulation by those that choose to become skilled as well as more intensive application of skills in producing market income—also mitigate the rise in the skill price, these investments magnify the growth in inequality because they increase the quantity of human capital each skilled worker employs in the market. This contrast is particularly important for U.S. after 1980. The evidence indicates that the human capital supply response on the extensive margin has fallen far short of what would be required to prevent the skill price (measured by, say, the college premium) from rising. The rising skill premium then leads to more investment on the intensive margin and exacerbates the growth in inequality. The shortfall of investment on the extensive margin therefore not only contributes to inequality directly by driving up the price of skill but also sets in motion supply responses on the intensive margins that cause further growth in inequality. This suggests that the failure to “produce” a sufficient number of high skilled workers has contributed both directly and indirectly to the observed rise in inequality. The consequences of these behavioral responses are likely to be even broader since slower growth in skilled labor will be associated with slower rates of economic growth when TFP growth is generated by technical change that augments skilled labor. Finally, as should be obvious, our analysis indicates that efforts to combat inequality by capping the returns to skill or otherwise artificially compressing the wage distribution will reduce human capital investment and utilization, exacerbate the underlying scarcity of skills that is the root cause of rising inequality, and reduce economic growth. Our analysis points to remedies to the inequality problem that lie on the supply side, specifically in policies that encourage or enable the acquisition of skills or encourage the immigration of highly skilled individuals. References Acemoglu, Daron, Modern Economic Growth, Princeton University Press, Princeton, N.J., 2009 Autor, David H. “Skill Biased Technical Change and Rising Inequality: What is the Evidence? What are the Alternatives?” Class Notes, MIT, July 2002. Blau, Francine and Kahn, Lawrence M. “International Differences in Male Wage Inequality: Institutions Versus Market Forces.” Journal of Political Economy, 104 (1996): 791-836. Becker, Gary S., Kevin M. Murphy and Robert Tamura, “Human Capital, Fertility, and Economic Growth”, Journal of Political Economy 98: S12-S37 (1990) Becker, Gary S., William H. J. Hubbard and Kevin M. Murphy; "Explaining the Worldwide Boom in Higher Education of Women." Journal of Human Capital, 2010, 4(3), pp. 203-41. Broda, Christian and John Romalis. “The Welfare Implications of Rising Price Dispersion.” Working Paper, University of Chicago Booth School of Business, (July, 2009) Edin, Per-Anders and Topel, Robert H. “Wage Policy and Restructuring: The Swedish Labor Market Since 1960.” in Richard B. Freeman, Robert Topel and Birgitta Swedenborg (eds.), The Welfare State in Transition: Reforming the Swedish Model, University of Chicago Press, 1997: 155-202. Fredricksson, Peter and Topel, Robert H. “Wage Determination and Employment in Sweden Since the Early 1990s: Wage Formation in a New Setting.” in Richard B. Freeman, Birgitta Swedenborg and Robert Topel (eds.), Reforming the Welfare State: Recovery and Beyond in Sweden, University of Chicago Press, 2005: 83-126. Dew-Becker, Ian and Robert J. Gordon “Where Did the Productivity Growth Go? Inflation Dynamics and the Distribution of Income” Brookings Papers on Economic Activity 2005, no. 2, pp. 67-127
Heckman, James J., “Schools, Skills, and Synapses.” Economic Inquiry, Vol. 46, No. 3 (July 2008): 289-324. Juhn, Chinhui. “Decline of Male Labor Force Participation: The Role of Declining Market Opportunities.” Q.J.E. 107 (February 1992): 79-121. Juhn, Chinhui; Murphy, Kevin M.; and Topel, Robert H. “Why Has the Natural Rate of Unemployment Increased over Time?” Brookings Papers on Economic Activity, no. 2 (1991): 75-142. Juhn, Chinhui, Murphy, Kevin M.; and Topel, Robert H. “Current Unemployment, Historically Contemplated.” Brookings Papers on Economic Activity, no. 1 (2002): 79-116. Katz, Lawrence, and Murphy, Kevin M. “Changes Relative Wages 1963-87: Supply and Demand Factors.” Q.J.E. 107 (February 1992): 35-78 Juhn, Chinhui; Murphy, Kevin M.; and Pierce, Brooks, “Wage Inequality and the Rise in Returns to Skill.” Journal of Political Economy, Vol. 101, No. 3 (June 1993): 410-442. Karabarbounis, Loukas and Brent Neiman. “The Global Decline of the Labor Share.” Working Paper, University of Chicago Booth School of Business, (October, 2013). Piketty, Thomas. Capital in the Twenty-First Century, Harvard University Press, 2014. Topel, Robert H. “Factor Proportions and Relative Wages: The Supply-Side Determinants of Wage Inequality.” Journal of Economic Perspectives, vol. 11, no. 2 (Spring, 1997): 55-74. Topel, Robert H. “Labor Markets and Economic Growth.” Handbook of Labor Economics, vol. 3, Edited by O. Ashenfelter and D. Card, Elsevier Science B.V. (1999): 2943-2984. Topel, Robert H. “Comment on Dew-Becker and Gordon”, Brookings Papers on Economic Activity, 2005, no. 2, pp. 135-144. Murphy, Kevin M. and Topel, Robert H. “Unemployment and Nonemployment.” American Economic Review, vol. 87, no. 2 (May, 1997), 295-300. National Research Council. At What Price?: Conceptualizing and Measuring Cost of Living and Price Indexes, 2002. Peracchi, Franco. “Earnings Inequality in International Perspective.” The Causes and Consequences of Increasing Inequality, Edited by Finis Welch, University of Chicago Press, Chicago and London (2001): 117-52. Rosen, Sherwin, “Short Run Employment Variation on Class I Railroads in the United States, 1947-1963,” Econometrica, (July, 1968): 511-29. Violante, Giovanni. “Skill Biased Technical Change”, The New Palgrave of Economics, 2nd Edition, Steven Durlauf and Laurence Bloom (eds.) (2008) Figure 1A 
Figure 1B 
Notes: Authors’ calculations from March Current Population Surveys, 1963-2013. Samples are individuals aged 18-64 who worked more than 30 weeks and more than 30 hours per week during the indicated calendar years. Figure 2
Notes: See notes to Figures 1A & 1B Figure 3
Note: Figure shows the fraction of individuals that turned 18 in the indicated years with either some college (at least 1 year of post-secondary schooling) or with at least 4 years of college. Figure 4A
Figure 4B 
Figure 5A 
Figure 5B 
Figure 6
Source: National Center for Education Statistics. Figure 7A Figure 7B Table 1 Distributions of Grade Point Averages First Year Students at Four-Year Colleges and Universities 1995-96 & 2003-04, by Intended Major
Source: National Center for Education Statistics, Beginning Postsecondary Students Surveys.
Note: Calculated from data underlying Figures 7a and 7b. See text for description of calculations. 1 Juhn, Murphy and Topel (1991); Juhn (1992); Katz and Murphy (1992). Peracchi (2001) summarizes international trends. 2 Violante (2008), Karabarbounis and Neiman (2013). 3 Peracchi (2001), Edin and Topel (1997), Fredericksson and Topel (2005), Piketty (2014), Gottschalk and Smeeding (1997). 4 We define “full time” as working at least 30 weeks during the previous year with average weekly hours of at least 30. 5 The absence of a decline in real wages for men near the bottom of the wage distribution may due to selection, as those with the lowest skill and earnings potential leave the labor force. Then real wages at a given level of skill may be declining, but selection causes means that workers at a given percentile of the wage distribution (say the 10th) are more skilled than in the past. See Juhn, Murphy and Topel (1991, 2002) for evidence on this point. 6 For these calculations we pool individuals from the March CPS files of 1970-72 and 2010-12. 7 See, for example, Piketty (2014) or Dew-Becker and Gordon (2005). 8 National Research Council (2002) 9 Broda and Romalis (2009) 10 The importance of different price indexes for high and low skilled labor is less important on the demand side since the cost to firms of utilizing labor would be deflated by the same price index regardless of which type of labor (or other inputs) is used. 11 For the wage calculations in Figures 4A and 4B “full time” refers to individuals aged 25-40 who worked at least 48 weeks in the previous year, with usual weekly hours of at least 30. We measure college completion rates among individuals age 23-28 for each cohort. Figures 5A and 5B indicate that reported college completion continues to rise after age 30, especially among younger cohorts for whom the returns were highest. 12 See Becker, Hubbard and Murphy (2010) for some potential explanations. 13 Source: National Center for Education Statistics, Beginning Postsecondary Students survey. 14 Note that hours decline in lower percentiles, especially among men. In our papers with Chinhui Juhn (1991, 2001) and in Murphy and Topel (1997) we provide evidence of declining real wages of less skilled men, many of whom have left the labor force as a result of declining opportunities. In this paper we select on individuals who worked at least 30 weeks in the previous year and whose average weekly hours exceeded 30. Thus, especially for low-wage categories, our criteria mean that (say) the first decile of the distribution is unlikely to contain a population of constant relative skill. We would overstate wage growth for a constant-skill population. 15 It is possible to endogenize the return on capital without substantially altering our results. In particular, if we allow the return to be a function of the growth rate (as in the neoclassical growth model) then the growth impacts we discuss below would go in the same direction. 16 E.g. Kouraboubanis and Neiman (2013), Rosen (1968). 17 We treat SBTC as exogenous, raising the skill premium and hence inequality so long as >1. Models of directed technical change allow the skill bias in technical change to be endogenous, as summarized in Acemoglu (2009). Then causality may be reversed—an increase in the proportion of skilled workers S/U may drive R&D toward technologies that exploit the greater abundance of skill. If the technological response is large enough, an increase in the skill ratio—say as occurred before 1980—can increase inequality. The facts seem inconsistent with this, however—the continued increase in inequality after 1980 coincides with stagnation in the growth of skills, at least as measured by educational attainment. 18 As is shown shortly, our two-skill setup yields a full income distribution because we assume individuals have heterogeneous abilities to acquire human capital. A multiple or continuous skill hierarchy will reinforce this effect by allowing different rates of skill-biased technical change across the endogenous skill distribution. 19 Some forms of utilization that we embed in T can be varied over short periods, such as hours worked. Others, such as occupational choice are more similar to H, which is the result of a forward-looking investment decision. Our model abstracts from an explicit treatment of time, however, so differences in these responses do not come into play. 20 With a hierarchy of skill types, there will be multiple ability cutoffs and this condition will hold for each one, under the same cost conditions stated in the text. 21 Formally, these supply shifts change the density g(a) over abilities or changes in the costs of acquiring skills, and may be positive or negative. For example, low skilled immigration would cause because the density g(a) shifts to the left, while a more educated cohort of parents or government investments in education would cause 22 Terms in (20) involving the change in the skilled/unskilled ability cutoff a 23 Yüklə 152,78 Kb. Dostları ilə paylaş:
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