Abstract
This paper examines how human capital-based approaches explain the distribution of earnings. It assesses traditional, quasi-experimental, and new micro-based structural models, the latter of which gets at population heterogeneity by estimating individual-specific earnings function parameters. The paper finds one’s ability to learn and one’s ability to retain knowledge are most influential in explaining earnings variations. Marketable skills actually acquired in school depend on these two types of ability. However, schools may also implement ability-enhancing interventions which can play a role in improving learning outcomes. Policy initiatives that improve these abilities would be a possible strategy to increase earnings and lower earnings disparity.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
One reason for the increased attention, at least in the USA, stems from the rising share going to labor until the 1970s (Krueger 1999; Armenter 2015). However, of late, there has been a reversal of this trend and a renewed interest in the functional distribution of income, only now dealing with the rising share to capital, especially since the 2000s (Mike Elsby et al. 2013; Karabarbounis and Neiman 2013). One new theory attributes this change to firm heterogeneity. In particular, Autor et al. (2017) describe “superstar firms” where labor’s share fell relatively more.
- 2.
These tables update data previously presented in Polachek (2008).
- 3.
Human capital investment comprises of general and specific training. Specific training enhances productivity in the firm and nowhere else. Firms provide such training because of its limited applicability. However, to reduce turnover, incentive compatible contracts can arise in which firms equally share with its employees the costs and benefits of such training (Kuratani 1973). This survey deals mainly with general training which enhances productivity throughout the economy. The cost of general training is usually borne by the individual, though because of its social value, much of schooling is subsidized by the government. Bishop (1997) presents evidence that employers can pay for general (as opposed to only specific) training. Acemoglu and Pischke (1999) argue firms can pay for general training because even general training can have a specific component. In this chapter, we concentrate on the costs and benefits of that part of human capital an individual purchases either in school or on the job.
- 4.
Initial human capital is determined by genetics as well as initial parental and other investments. We examine parental investments later in this chapter.
- 5.
Ben-Porath (1967) assumed a more general production function employing “goods” inputs such as teachers and books \( q_{t} = \beta K_{t}^{{b_{1} }} D_{t}^{{b_{2} }} \) where Dt equals other inputs. Because goods inputs are difficult to measure, most analyses subsequent to Ben-Porath omit this factor. These include Haley (1976), Johnson (1978), and Wallace and Ihnen (1975).
- 6.
Parameters b, β, r, δ, and \( E_{0} \) are assumed constant throughout one’s life. Obviously, this need not be the case, but is consistent with the notion that IQ remains constant (Tucker-Drob 2009). Of the parameters, skill depreciation seems most likely to increase as one ages, but to our knowledge, no one has estimated how skill depreciation increases with age in the context of a life-cycle human capital model.
- 7.
A derivation of the exact function is given as Eq. (7) in Polachek et al. (2015) and derived in their Appendix 1. Their specification differs slightly from Haley (1976) in that it assumes a two-term Taylor expansion for the third term in Haley’s earnings function, thus enabling them to identify all five earnings function parameters. We present this equation in Appendix 1 because it is used later in this chapter to assess the importance of ability \( E_{0} ,\beta ,b \) compared to schooling and experience which is used in more traditional approaches to explain earnings.
- 8.
- 9.
Of course, there were other biases but these were considered later.
- 10.
Also Tom Johnson (1970).
- 11.
These include a Gompertz specification as well as various interaction terms.
- 12.
Murphy and Welch (1990) experiment with cubic and quadratic functional forms. Heckman and Polachek (1974) use Box-Cox and Box-Tidwell transformations to show the log-linear fit works best when compared to a set of other common functional forms. Heckman et al. (2003) modify the Mincer model to incorporate individuals choosing their education levels to maximize their present value of lifetime earnings. They also relax other restrictions such as the constraint that log earnings increase linearly with schooling and the constraint that log earnings-experience profiles are parallel across schooling classes, but Mincer also relaxes these latter constraints in a number of his specifications which contain an interaction term between experience and schooling. Indeed, he finds (1974, pp. 92–93) nonparallel profile shifts, as well.
- 13.
These five aspects are related to, but not exactly the same as, PDT’s five parameters. The coefficients \( \alpha_{0} = \ln E_{0} - k_{0} [1 + \frac{{k_{0} }}{2}] \), \( \beta_{1} = r_{t} k_{0} + \frac{{k_{0} }}{T}\left( {1 + k_{0} } \right) \) and \( \beta_{2} = - \left[ {\frac{{r_{t} k_{0} }}{2T} + \frac{{(k_{0} )^{2} }}{2T}} \right] \) assuming a linearly declining post-school investment function \( k_{t} = k_{0} - \frac{{k_{0} }}{T}t \) where \( k_{0} \) is initial and \( k_{t} \) concurrent “time-equivalent” investment and T is the total period of positive investments. Mincer also considered three other specifications for kt. These entail (1) a linear declining dollar specification, (2) an exponentially declining dollar specification, and (3) an exponentially declining time-equivalent investment specification. These yielded nonlinear in the parameters less popular earnings functions that by and large have been ignored in the literature.
- 14.
The parameters are the initial human capital stock (E0), the rate of return to schooling (rs), the rate of return to post-school human capital investment (rt), and the time when gross human capital investment just equals depreciation which is the experience level at which net human capital investment goes to zero (T).
- 15.
The computation results from solving the following equations: \( { \ln }\,E_{0} - k\left( {1 + \frac{k}{2}} \right) = 6.2;r_{s} = .107;r_{t} k + \frac{k}{T}\left( {1 + k} \right) = .081; \) \( - r_{t} \frac{k}{2T} + \frac{{k^{2} }}{{2T^{2} }} = - .0012;r_{s} = r_{t} ;\text{for}\,T,k,r_{s} ,r_{t} \,\text{and}\,Y. \)
- 16.
- 17.
Some use panel data, but one can question how these adjust for price changes. Another exception is in executive pay late in some individuals’ career paths.
- 18.
Proof is given in Mincer (1974, p. 103).
- 19.
- 20.
Assuming a linear human capital investment function \( k(t) = a_{i} + b_{i} t \) where ai is the initial “time-equivalent” investment and bi is the rate of change in investment taking place in one of the n work/non-work segments i yields \( \ln E_{t} = \ln E_{0} + r_{s} S + r_{p} \sum\nolimits_{i = 1}^{n} {\int_{0}^{{e_{i} }} {(a_{i} + b_{i} t){\text{d}}t} } \). For the three-period case (n = 3), the earnings function is a quadratic in each work/non-work segment:
$$ \ln E_{t} = \ln E_{0} + r_{s} S + r_{p} \left( {a_{1} e_{1} + \frac{1}{2}b_{1} e_{1}^{2} + a_{2} e_{2} + \frac{1}{2}b_{2} e_{2}^{2} + a_{3} e_{3} + \frac{1}{2}b_{3} e_{3}^{2} } \right) $$Taking a linear approximation of the quadratic in each segment and denoting segment e2 as h (since it represents time at home out of the labor force) yields (6).
- 21.
See Polachek (1975a) for a derivation.
- 22.
Based on data from: https://www.dol.gov/wb/resources/Womens_Earnings_and_the_Wage_Gap_17.pdf.
- 23.
Other studies concentrate on heterogeneity by allowing ARMA processes to vary across individuals (e.g., Browning and Ejrnӕs 2013). Some present decile ranges of key parameters illustrating that heterogeneity affects the speed individuals respond to shocks (e.g., Browning et al. 2010; Browning and Ejrnæs 2013). In other realms, Greene (2005, 2010) examines heterogeneity by using fixed- and random-effects models.
- 24.
AFQT scores are computed using the Standard Scores from four ASVAB subtests: Arithmetic Reasoning (AR), Mathematics Knowledge (MK), Paragraph Comprehension (PC), and Word Knowledge (WK).
- 25.
Atrophy is zero when Nt is 1, but is \( \xi E_{t} \) when Nt is 0.
- 26.
Heckman, Layne-Farrar, and Todd (1996) also claim heterogeneity in human capital. They do so by exploiting three interactions: (1) between school quality and education, (2) between regional labor shocks and education, and (3) between place of birth and place of residence.
- 27.
Heckman et al. (1998) adopt an alternative identification strategy to determine R. Their approach exploits the fact that all observed earnings changes (adjusted for hours) between two time periods must be attributed to rental rates changes when in “flat periods” a time when human capital stock (Et) remains constant. Typically, flat spots occur late in life, usually around the mid-fifties, an age greater than any current respondent in the NLSY. Bowlus and Robinson (2012), who apply the flat spot identification approach with CPS data, obtain similar results to PDT.
- 28.
Another similar approach is to compute the percent impact on earnings of a standard deviation increase in each variable.
- 29.
Computed as 1-(0.093/0.134) from row (2) of column (3) in the lower panel of Table 10.
- 30.
Computed as 1-(0.065/0.134) from row (3) of column (3) in the lower panel of Table 10.
References
Acemoglu, Daron, and Jorn-Steffen Pischke. 1999. The structure of wages and investment in general training. Journal of Political Economy 107 (3): 539–572.
Albrecht, J.W., P.A. Edin, M. Sundstrom, and S.B. Vroman. 1999. Career interruptions and subsequent earnings: A reexamination using Swedish data. Journal of Human Resources 34 (2): 294–311.
Anger, Silke, and Daniel Schnitzlein. 2017. Cognitive skills, non-cognitive skills, and family background: Evidence from sibling correlations. Journal of Population Economics 30 (2): 591–620.
Angrist, Joshua, and Alan Krueger. 1991. Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics 106 (4): 979–1014.
Armenter, Roc. 2015. A bit of a miracle no more: The decline of the labor share. Business Review, Third Quarter, Federal Reserve Bank of Philadelphia Research Department.
Ashenfelter, Orley, and Alan Krueger. 1994. Estimating the returns to schooling using a new sample of twins. American Economic Review 84: 1157–1173.
Ashenfelter, Orley, and Cecilia Rouse. 1998. Income, schooling, and ability: Evidence from a new sample of identical twins. The Quarterly Journal of Economics 113 (1): 253–284.
Ashenfelter, Orley, C. Harmon, and H. Oosterbeek (eds.). 1999. Economic returns to schooling: New evidence. Special Issue of Labour Economics 6 (4).
Autor, David, David Dorn, Lawrence F. Katz, Christina Patterson, and John van Reenen. 2017. Concentrating on the fall of the labor share, IZA DP #10539.
Baldwin, M.L., L.A. Zeager, and P.R. Flacco. 1994. Gender differences in wage losses from impairments: Estimates from the survey of income and program participation. Journal of Human Resources 29 (3): 865–887.
Barro, Robert, and X. Sala-i-Martin. 1999. Economic growth. Cambridge, MA: MIT Press.
Baum, Charles L. 2002. The effect of work interruptions on women’s wages. Labour 16 (1): 1–36.
Becker, Gary. 1964. Human capital: A theoretical and empirical analysis, with special preferences to education. Chicago: University of Chicago Press.
Becker, Gary, and Barry Chiswick. 1966. Education and the distribution of earnings. American Economic Review 56: 358–369.
Benmelech, Efraim, and Claude Berrebi. 2006. Attack assignments in terror organizations and the productivity of suicide bombers. Working Paper, Harvard University.
Ben-Porath, Yoram. 1967. The production of human capital over the life cycle. Journal of Political Economy 75: 352–365.
Bergmann, Barbara R. 1971. The effect on white incomes of discrimination in employment. Journal of Political Economy 79 (2): 294–313.
Bergmann, Barbara R. 1974. Occupational segregation, wages and profits when employers discriminate by race or sex. Eastern Economic Journal 1 (2): 103–110.
Bhuller, Manudeep, Magne Mogstad, and Kjell Salvanes. 2014. Life cycle earnings, education premiums and internal rates of return. NBER Working Papers no. 20250.
Bishop, John. 1997. What we know about employer provided training: A review of the literature. Research in Labor Economics 16: 19–88.
Blanco, German, Carlos A. Flores, and Alfonso Flores-Lagunes. 2013. Bounds on average and quantile treatment effects of job corps training on wages. Journal of Human Resources 48 (3): 659–701.
Blau, Francine, and Lawrence M. Kahn. 1992. The gender earnings gap: Learning from international comparisons. American Economic Review 82 (2): 533–538.
Blau, Francine, and Lawrence M. Kahn. 2005. Do cognitive test scores explain higher U.S. wage inequality? The Review of Economics and Statistics 87 (1): 184–193.
Blundell, Richard. 2014. Income dynamics and life-cycle inequality: Mechanisms and controversies. The Economic Journal 124: 289–318.
Boissiere, M., J.B. Knight, and R.H. Sabot. 1985. Earnings, schooling, ability, and cognitive skills. The American Economic Review 75 (5): 1016–1030.
Booth, Alison L., and Pamela Katic. 2013. Cognitive skills, gender and risk preferences. Economic Record 89 (284): 19–30.
Borjas, George J. 1982. The earnings of male Hispanic immigrants in the United States. Industrial and Labor Relations Review 35 (3): 343–353.
Borjas, George J. 1985. Assimilation, changes in cohort quality, and the earnings of immigrants. Journal of Labor Economics 3 (4): 463–489.
Borjas, George J. 1993. The intergenerational mobility of immigrants. Journal of Labor Economics 11 (1): 113–135.
Bound, John, and David A. Jaeger. 1996. On the validity of season of birth as an instrument in wage equations: A comment on Angrist & Krueger’s “Does compulsory school attendance affect schooling and earnings”. NBER Working Paper 5835.
Bound, John, David A. Jaeger, and Regina M. Baker. 1995. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variables is weak. Journal of the American Statistical Association 90 (430): 443–450.
Bowles, Samuel, and Herbert Gintis. 2002. The inheritance of inequality. Journal of Economic Perspectives 16 (3): 3–30.
Bowles, Samuel, Herbert Gintis, and Melissa Osborne. 2001. The determinants of earnings: A behavioral approach. Journal of Economic Literature 39 (4): 1137–1176.
Bowlus, Audra, and Chris Robinson. 2012. Human capital Prices, productivity, and growth. American Economic Review 102 (7): 3483–3515.
Brown, Charles. 1980. The ‘overtaking’ point revisited. Review of Economics and Statistics 62 (2): 309–313.
Brown, Heather, and Marjon van der Pol. 2015. Intergenerational transfer of time and risk preferences. Journal of Economic Psychology 49: 187–204.
Browning, Martin, and Mette Ejrnæs. 2013. Heterogeneity in the dynamics of labor earnings. Annual Review of Economics 5: 219–245.
Browning, Martin, Lars Hansen, and James Heckman. 1999. Micro data and general Equilibrium models. In Handbook of macroeconomics, vol. 1A, ed. John Taylor and Michael Woodford, 543–633. Amsterdam: Elsevier.
Browning, Martin, Mette Ejrnæs, and Javier Alvarez. 2010. Modelling income processes with lots of heterogeneity. Review of Economic Studies 77 (4): 1353–1381.
Burgess, Simon, and Carol Propper. 1998. Early health-related behaviours and their impact on later life chances: Evidence from the US. Health Economics 7 (5): 381–399.
Card, David. 1995. Using geographic variation in college proximity to estimate the return to schooling. In Aspects of labor market behaviour: Essays in honour of John Vanderkamp, ed. L.N. Christofides, E.K. Grant, and R. Swidinsky. Toronto: University of Toronto Press.
Card, David. 1999. The causal effect of education on earnings. In Handbook of labor economics, vol. 3A, ed. O. Ashenfelter and D. Card. Amsterdam: Elsevier.
Card, David. 2001. Estimating the return to schooling: Progress on some persistent econometric problems. Econometrica 69 (5): 1127–1160.
Card, David, and Alan B. Krueger. 1992. School quality and black-white relative earnings: A direct assessment. Quarterly Journal of Economics 107 (1): 151–200.
Carneiro, Pedro, and James Heckman. 2002. The evidence on credit constraints in post-secondary schooling. The Economic Journal 112: 989–1018.
Catalyst. 2003. Workplace flexibility is still a women’s advancement issue. http://64.233.167.104/u/Catalyst?q=cache:BGumQKH8saEJ:www.catalystwomen.org/bookstore/files/view/Workplace%2520Flexibility%2520Is%2520Still%2520a%2520Women%27s%2520Advancement%2520Issue.pdf+mba+and+men+and+women&hl=en&ie=UTF-8.
Cawley, John, James Heckman, and Edward Vytlacil. 2001. Three observations on wages and measured cognitive ability. Labour Economics 8 (4): 419–442.
Chetty, Raj, John N. Friedman, and Jonah Rockoff. 2014. Measuring the impact of teachers II: Teacher value-added and student outcomes in adulthood. American Economic Review 104 (9): 2633–2679.
Chetty, Raj, John N. Friedman, Nathaniel Hilger, Emmanuel Saez, Diane Whitmore Schanzenbach, and Danny Yagan. 2011. How does your kindergarten classroom affect your earnings? Evidence from project star. Quarterly Journal of Economics 126 (4): 1593–1660.
Chiswick, Barry R. 1978. The effect of americanization on the earnings of foreignborn men. Journal of Political Economy 86 (5): 897–921.
Chiswick, Barry, and Jacob Mincer. 1972. Time-series changes in personal income inequality in the United States. Journal of Political Economy 80 (3): S34–S66.
Clark, Damon, and Paco Martorell. 2014. The signaling value of a high school diploma. Journal of Political Economy 122 (2): 282–318.
Cobb-Clark, Deborah A., and Michelle Tan. 2011. Noncognitive skills, occupational attainment, and relative wages. Labour Economics 18 (1): 1–13.
Coleman, James S., Ernest Q. Campbell, Carol J. Hobson, James McPartland, Alexander M. Mood, Frederic D. Weinfeld, and Robert L. York. 1966. Equality of education opportunity. U.S. Department of Health, Education, and Welfare, Office of Education, U.S. Government Printing Office Washington.
Conneely, K., and R. Uusitalo. 1997. Estimating heterogeneous treatment effects in the Becker schooling model. Industrial Relations Section, Princeton University.
Corcoran, Mary, and Greg Duncan. 1979. Work history, labor force attachment, and earnings differences between the races and sexes. Journal of Human Resources 14 (1): 3–20.
Corcoran, Mary, Greg Duncan, and Michael Ponza. 1983. Longitudinal analysis of white women’s wages. Journal of Human Resources 18 (4): 497–520.
Cortès, Patricia, and Jessica Pan. 2016. Prevalence of long hours and women’s job choices: Evidence across countries and within the U.S. Paper presented at the 2016 ASSA Annual Conference.
Croson, Rachel, and Uri Gneezy. 2009. Gender differences in preferences. Journal of Economic Literature 47 (2): 448–474.
Cseh, Attila. 2008. The effects of depressive symptoms on earnings. Southern Economic Journal 75 (2): 383–409.
Cunha, Flavio, and James Heckman. 2007. The technology of skill formation. The American Economic Review 97 (2): 31–47.
Cunha, Flavio, and James Heckman. 2010. Investing in our young people. NBER Working Paper No. 16201.
Cunha, Flavio, James J. Heckman, Lance Lochner, and Dimitriy V. Masterov. 2006. Interpreting the evidence on life cycle skill formation. In Handbook of the economics of education, ed. Eric Hanushek and Finis Welch, 698–812. Amsterdam: North Holland.
Cunha, Flavio, James J. Heckman, and Susanne M. Schennach. 2010. Estimating the technology of cognitive and noncognitive skill formation. Econometrica 78 (3): 883–931.
Czarnitzki, Dirk, and Thorsten Doherr. 2009. Genetic algorithms for econometric optimization. Working Paper.
Dechter, Evgenia K. 2015. Physical appearance and earnings, hair color matters. Labour Economics 32: 15–26.
Dohmen, Thomas, and Armin Falk. 2001. Performance pay and multidimensional sorting: Productivity, preferences, and gender. American Economic Review 101 (2): 556–590.
Dorsey, Robert, and Walter Mayer. 1995. Genetic algorithms for estimation problems with multiple optima, nondifferentiability, and other irregular features. Journal of Business & Economic Statistics 13 (1): 53–66.
Duflo, Esther. 2001. Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment. American Economic Review 91 (4): 795–813.
Elsby, Mike, Bart Hobijn, and Aysegul Sahin. 2013. The decline of the U.S. labor share. Brookings Papers on Economic Activity, 1–42.
Fletcher, Jason M. 2014. The effects of childhood ADHD on adult labor market outcomes. Health Economics 23 (2): 159–181.
Fletcher, Jason M., and Barbara Wolfe. 2016. The importance of family income in the formation and evolution of non-cognitive skills in childhood. La Follette School Working Paper no. 2016-001, University of Wisconsin.
Flores, C., and Alfonso Flores-Lagunes. 2013. Partial identification of local average treatment effects with an invalid instrument. Journal of Business and Economic Statistics 31 (4): 534–545.
Fryer, R. G., Jr., and S. D. Levitt. 2004. Understanding the black-white test score gap in the first two years of school. The Review of Economics and Statistics 86 (2): 447–464.
Fryer, Roland, G. Jr., and Steven D. Levitt. 2013. Testing for racial differences in the mental ability of young children. American Economic Review 103 (2): 981–1005.
Fuchs, Victor. 1967. Hourly earnings differentials by region and size of city. Monthly Labor Review 94 (5): 9–15.
Gabriel, Paul E. 2016. The doughboy premium: An empirical assessment of the relative wages of American veterans of World War I. Applied Economics Letters 23 (2): 93–96.
Gibson, John, and Osaiasi Koliniusi Fatai. 2006. Subsidies, selectivity and the returns to education in urban Papua New Guinea. Economics of Education Review 25 (2): 133–146.
Goldberg, David. 1989. Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.
Goldin, Claudia. 2014. A grand gender convergence: Its last chapter. American Economic Review 104 (4): 1091–1119.
Goldin, Claudia, and Solomon Polachek. 1987. Residual differences by sex: Perspectives on the gender gap in earnings. American Economic Review 77 (2): 143–151.
Goldsmith, Arthur, Jonathan Veum, and William Darity. 1997. Unemployment, joblessness, psychological well-being and self-esteem: Theory and evidence. Journal of Socio-Economics 26 (2): 133–158.
Greene, William. 2005. Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics 126 (2): 269–303.
Greene, William. 2010. Distinguishing between heterogeneity and inefficiency: Stochastic frontier analysis of the World Health Organization’s panel data on national health care systems. Health Economics 13 (10): 959–980.
Gronau, Reuben. 1988. Sex-related wage differentials and women’s interrupted labor careers-The chicken or the egg. Journal of Labor Economics 6 (3): 277–301.
Grossman, Michael. 1972. On the concept of health capital and the demand for health. The Journal of Political Economy 80 (2): 223–255.
Groves, Melissa O. 2005. How important is your personality? Labor market returns to personality for women in the US and UK. Journal of Economic Psychology 26 (6): 827–841.
Haley, William J. 1976. Estimation of the earnings profile from optimal human capital accumulation. Econometrica 44 (6): 1223–1238.
Hamermesh, Daniel, and Jeff E. Biddle. 1994. Beauty and the labor market. American Economic Review 84: 1174–1194.
Hamermesh, Daniel, Xin Meng, and Junsen Zhang. 2002. Dress for success—Does primping pay? Labour Economics 9 (3): 361–373.
Hammitt, James K., and Tuba Tuncel. 2015. Preferences for life-expectancy gains: Sooner or later? Journal of Risk and Uncertainty 51 (1): 79–101.
Hansen, Karsten T., James Heckman, and Kathleen J. Mullen. 2004. The effect of schooling and ability on achievement test scores. Journal of Econometrics 121 (1–2): 39–98.
Harmon, Colm, and Ian Walker. 1995. Estimates of the economic return to schooling for the United Kingdom. American Economic Review 85 (5): 1278–1286.
Hartog, Joop, Gerard Pfann, and Geert Ridder. 1989. (Non-)graduation and the earnings function: An inquiry on self-selection. European Economic Review 33 (7): 1373–1395.
Heckman, James. 1975. Estimates of aging capital production function embedded in a life-cycle model of labor supply. In Household production and consumption, ed. Nester Terleckyj, 227–258. New York: Columbia University Press for the National Bureau of Economic Research.
Heckman, James. 1976. A life-cycle model of earnings, learning, and consumption. Journal of Political Economy 84 (4): S11–S44.
Heckman, James. 2008. School, skills, and synapses. Economic Inquiry 46 (3): 289–324.
Heckman, James, Anne Layne-Farrar, and Petra Todd. 1996. Human capital pricing equations with an application to estimating the effect of schooling quality on earnings. The Review of Economics and Statistics 78 (4): 562–610.
Heckman, James, Lance Lochner, and Christopher Taber. 1998. Explaining rising wage inequality: Explorations with a dynamic general equilibrium model of labor earnings with heterogeneous agents. Review of Economic Dynamics 1: 1–58.
Heckman, James, Lance Lochner, and Petra Todd. 2003. Fifty years of Mincer earnings regressions. IZA Discussion Paper No. 775.
Heckman, James, Lance Lochner, and Petra Todd. 2006. Earnings functions, rates of return and treatment effects: The Mincer equation and beyond. In Handbook of the economics of education, ed. Eric A. Hanushek and Finis Welch, 307–458. Amsterdam: North Holland.
Heckman, James, and Solomon Polachek. 1974. The functional form of the income-schooling relation. Journal of the American Statistical Association 69: 350–354.
Henderson, Daniel, Solomon Polachek, and Le Wang. 2011. Heterogeneity in schooling rates of return. Economics of Education Review 30 (6): 1202–1214.
Herrnstein, R. J., and C. Murray. 1994. The bell curve: Intelligence and class structure in American life. New York: The Free Press.
Hoffmann, Florian. 2016. HIP, RIP and the robustness of empirical earnings processes. Vancouver School of Economics, University of British Columbia Version.
Holland, J.H. 1975. Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
Hosios, Arthur. 1990. On the efficiency of matching and related models of search and unemployment. The Review of Economic Studies 57 (2): 279–298.
Hotchkiss, Julie, and Melinda Pitts. 2003. At what level of labour market intermittency are women penalized? American Economic Review Papers and Proceedings 93 (2): 233–237.
Hotchkiss, Julie, and Melinda Pitts. 2005. Female labour force intermittency and current earnings: Switching regression model with unknown sample selection. Applied Economics 37 (5): 545–560.
Hull, Charles R. (ed.). 1899. The economic writings of Sir William Petty. Cambridge: Cambridge University Press.
Ichino, Andrea, and Rudolf Winter-Ebmer. 2004. The long-run educational cost of World War II. Journal of Labor Economics 22 (1): 57–86.
Imbens, Guido, and Paul Rosenbaum. 2005. Robust, accurate confidence intervals with a weak instrument: Quarter of birth and education. Journal of the Royal Statistical Society A 168 (1): 109–126.
Jacobsen, J.P., and L.M. Levin. 1995. Effects of intermittent labour force attachment on women’s earnings. Monthly Labour Review, September 14–19.
Johnson, Thomas. 1970. Returns from investment in human capital. American Economic Review 60 (4): 546–560.
Johnson, Thomas. 1978. Time in school: The case of the prudent patron. American Economic Review 68 (5): 862–872.
Johnson, Thomas, and Frederick Hebein. 1974. Investments in human capital and growth in personal income 1956–1966. American Economic Review 64 (4): 604–615.
Jovanovic, Boyan. 1979. Job matching and the theory of turnover. Journal of Political Economy 87 (5): 972–990.
Kane, Thomas J., and Cecilia E. Rouse. 1995. Labor market returns to two- and four-year colleges: Is a credit a credit and do degrees matter? American Economic Review 85 (3): 600–614.
Kao, Charng, Solomon Polachek, and Phanindra Wunnava. 1994. Male-female wage differentials in Taiwan: A human capital approach. Economic Development and Cultural Change 42 (2): 351–374.
Karabarbounis, Loukas, and Brent Neiman. 2013. The global decline of the labor share. Quarterly Journal of Economics 129 (1): 61–103.
Kiker, B.F. 1966. The historical roots and the concept of human capital. Journal of Political Economy 74: 481–799.
Kiker, B.F., and M. Mendes de Oliveira. 1992. Optimal allocation of time and estimation of market wage functions. Journal of Human Resources 27 (3): 445–471.
Kim, Moon K., and Solomon Polachek. 1994. Panel estimates of male-female earnings functions. Journal of Human Resources 27 (3): 445–471.
King, Robert, and Sergio Rebelo. 1999. Resuscitating real business cycles. In Handbook of macroeconomics, vol. 1B, ed. John Taylor and Michael Woodford, 927–1007. Amsterdam: Elsevier.
Kitagawa, Toru. 2015. A test for instrument validity. Econometrica 83 (5): 2043–2063.
Klawitter, Marieka. 2015. Meta-analysis of the effects of sexual orientation on earnings. Industrial Relations 54 (1): 4–32.
Kleibergen, F. 2002. Pivotal statistics for testing structural parameters in instrumental variables regression. Econometrica 70: 1781–1803.
Korenman, Sanders, and David Neumark. 1992. Marriage, motherhood, and wages. Journal of Human Resources 27 (2): 233–255.
Krueger, Alan. 1999. Measuring labor’s share. American Economic Review, Papers and Proceedings 89 (2): 45–51.
Kuhn, Peter, and Catherine Weinberger. 2004. Leadership skills and wages. Santa Barbara Working Paper, University of California. http://econ.ucsb.edu/~weinberg/Leader.pdf.
Kumbhakar, Subal. 1996. A farm-level study of labor use and efficiency wages in Indian agriculture. Journal of Econometrics 72: 177–195.
Kuratani, Masatoshi. 1973. A theory of training, earnings and employment in japan. Ph.D. dissertation, Columbia University.
Lazear, Edward. 1995. Personnel economics. Cambridge, MA: The MIT Press.
Lazear, Edward, and Sherwin Rosen. 1981. Rank-order tournaments as optimum labor contracts. Journal of Political Economy 89 (5): 841–864.
Lazear, Edward, and Sherwin Rosen. 1990. Male-female wage differentials in job ladders. Journal of Labor Economics 8 (1): S106–S123.
Lee, David S. 2009. Training, wages, and sample selection: Estimating sharp bounds on treatment effects. Review of Economic Studies 76 (3): 1071–1102.
Lemieux, Thomas, and David Card. 1998. Education, earnings, and the ‘Canadian G.I. Bill’. National Bureau of Economic Research Working Paper no. 6718.
Lewis, H. Gregg. 1963. Unionism and relative wages in the United States: An empirical inquiry. Chicago: University of Chicago Press.
Lewis. H. Gregg. 1986. Union relative wage effects: A survey. Chicago: University of Chicago Press.
Licht, G., and V. Steiner. 1991. Male-female wage differentials, labor force attachment, and human capital accumulation in Germany. Working Paper no. 65 (institut fr Volkswirtschafslehre der Universit Augburf).
Light, Audrey, and M. Ureta. 1995. Early-career work experience and gender wage differentials. Journal of Labor Economics 13 (1): 121–154.
Liu, Huju. 2009. Life cycle human capital formation, search intensity and wage dynamics. Working Paper, University of Western Ontario.
Lochner, Lance, and Enrico Moretti. 2004. The effect of education on crime: Evidence from prison inmates, arrests, and self-reports. American Economic Review 94 (1): 155–189.
Lundborg, Petter, Paul Nystedt, and Dan-Olof Rooth. 2014. Height and earnings: The role of cognitive and noncognitive skills. Journal of Human Resources 49 (1): 141–166.
Magnac, Thierry, Nicolas Pistolesi, and Sébastien Roux. 2018. Post-schooling human capital investments and the life cycle of earnings. Journal of Political Economy 126 (3): 1219–1249.
Maluccio, John. 1997. Endogeneity of schooling in the wage Function. Working Paper, Yale University Department of Economics.
Manski, Charles F., and John V. Pepper. 2000. Monotone instrumental variables: With an application to the returns to schooling. Econometrica 689 (4): 997–1010.
Manski, Charles F., and John V. Pepper. 2009. More on monotone instrumental variables. The Econometrics Journal 12 (1): S200–S216.
Mariotti, Martine, and Juergen Meinecke. 2015. Partial identification and bound estimation of the average treatment effect of education on earnings for South Africa. Oxford Bulletin of Economics and Statistics 77 (2): 210–233.
Meghir, Costas, and Luigi Pistaferri. 2011. Earnings, consumption and life cycle choices. In Handbook of labor economics, vol. 4B, ed. O. Ashenfelter and D. Card, 774–854. Amsterdam: Elsevier North Holland.
Meghir, Costas, and Mårten Palme. 1999. Assessing the effect of schooling on earnings using a social experiment. IFS Working Papers no. W99/10, Institute for Fiscal Studies.
Michael, Robert. 1973. Education in nonmarket production. Journal of Political Economy 81 (2): 306–327.
Miller, Carloe. 1993. Actual experience, potential experience or age, and labor force participation by married women. Atlantic Economic Journal 21 (4): 60–66.
Mincer, Jacob. 1958. Investment in human capital and the personal income distribution. Journal of Political Economy 66: 281–302.
Mincer, Jacob. 1974. Schooling, experience, and earnings. New York: Columbia University Press for the National Bureau of Economic Research.
Mincer, Jacob, and Haim Ofek. 1982. Interrupted work careers: Depreciation and restoration of human capital. Journal of Human Resources 17: 3–24.
Mincer, Jacob, and Solomon Polachek. 1974. Family investments in human capital. Journal of Political Economy 82: S76–S108.
Mincer, Jacob, and Solomon Polachek. 1978. Women’s earnings reexamined. Journal of Human Resources 13 (1): 118–134.
Montenegro, Claudio E., and Harry Anthony Patrinos. 2014. Comparable estimates of returns to schooling around the world. Policy Research Working Paper no. WPS 7020, World Bank Group, Washington, DC.
Moreira, Marcelo J. 2003. A conditional likelihood ratio test for structural models. Econometrica 71 (4): 1027–1048.
Mueller, Gerrit, and Erik J.S. Plug. 2006. Estimating the effect of personality on male and female earnings. Industrial and Labor Relations Review 60 (1): 3–22.
Murnane, R.J., J.B. Willett, and F. Levy. 1995. The growing importance of cognitive skills in wage determination. Review of Economics and Statistics 77 (2): 251–266.
Murphy, Kevin, and Finis Welch. 1990. Empirical age-earnings profiles. Journal of Labor Economics 8: 202–229.
Okumura, Tsunao, and Emiko Usui. 2014. Concave-monotone treatment response and monotone treatment selection: With an application to the returns to schooling. Quantitative Economics 5 (1): 175–194.
Oreopoulos, Philip, and Uros Petronijevic. 2013. Making college worth it: A review of research on the returns to higher education. NBER Working Paper no. 19053.
Patrinos, H. A., and G. Psacharopoulos. 2010. Returns to education in developing countries. In International encyclopedia of education, ed. P. Penelope, B. Eva, and M. Barry, 305–312. Oxford: Elsevier.
Phipps, S., P. Burton, and L. Lethbridge. 2001. In and out of the labour market: Long-term income consequences of child-related interruptions to women’s paid work. Canadian Journal of Economics 34 (2): 411–429.
Polachek, Dora, and Solomon Polachek. 1989. An indirect test of children’s influence on efficiencies in parental consumer behavior. Journal of Consumer Affairs 23 (1): 91–110.
Polachek, Solomon. 1975a. Differences in expected post-school investment as a determinant of market wage differentials. International Economic Review 16: 451–470.
Polachek, Solomon. 1975b. Potential biases in measuring discrimination. Journal of Human Resources 6: 205–229.
Polachek, Solomon. 1979. Occupational segregation among women: Theory, evidence, and a prognosis. In Women in the labor market, ed. C. Lloyd, E. Andrews, and C. Gilroy, 137–157. New York: Columbia University Press.
Polachek, Solomon. 1981. Occupational self-selection: A human capital approach to sex differences in occupational structure. Review of Economics and Statistics 63 (1): 60–69.
Polachek, Solomon. 1987. Occupational segregation and the gender wage gap. Population Research and Policy Review 6: 47–67.
Polachek, Solomon. 2003. Mincer’s overtaking point and the life cycle earnings distribution. Review of Economics of the Household 1: 273–304.
Polachek, Solomon. 2008. Earnings over the life cycle: The Mincer earnings function and Its applications. Foundations and Trends in Microeconomics 4 (3): 165–272.
Polachek, Solomon. 2012. Introduction to a life cycle approach to migration: Analysis of the perspicacious peregrinator. Research in Labor Economics 35: 341–347.
Polachek, Solomon, Tirthatanmoy Das, and Rewat Thamma-Apiroam. 2015. Micro- and macroeconomic implications of heterogeneity in the production of human capital. Journal of Political Economy 123 (6): 1410–1455.
Polachek, Solomon, and Francis Horvath. 2012. A life cycle approach to migration: Analysis of the perspicacious peregrinator. Research in Labor Economics 35: 349–395.
Polachek, Solomon, and John Robst. 2001. Trends in the male-female wage gap: The 1980s compared with the 1970s. Southern Economic Journal 67 (4): 869–888.
Psacharopoulos, George. 2006. The value of investment in education: Theory, evidence, and policy. Journal of Education Finance 32 (2): 113–136.
Psacharopoulos, George, and Harry Anthony Patrinos. 2004. Returns to investment in education: A further update. Education Economics 12 (2): 111–134.
Rai, Jyoti, and Jean Kimmel. 2015. Gender differences in risk preferences: An empirical study using attitudinal and behavioral specifications of risk aversion. Research in Labor Economics 42: 61–92.
Riddell, W. Craig, and Xueda Song. 2017. The role of education in technology use and adoption: Evidence from the Canadian workplace and employee survey. Industrial and Labor Relations Review 70 (5): 1219–1253.
Robins, Philip, Jenny F. Homer, and Michael T. French. 2011. Beauty and the labor market: Accounting for the additional effects of personality and grooming. Labour 25 (2): 228–251.
Rosen, Sherwin. 1976. A theory of life earnings. Journal of Political Economy 84 (4): S45–S67.
Rosen, Sherwin. 1981. The economics of superstars. American Economic Review 71 (5): 845–858.
Roy, A.D. 1950. The distribution of earnings and of individual output. Economic Journal 60 (239): 489–505.
Rummery, S. 1992. The contribution of intermittent labour force participation to the gender wage differential. Economic Record 68 (203): 351–364.
Sabia, Joseph J. 2015. Fluidity in sexual identity, unmeasured heterogeneity, and the earnings effects of sexual orientation. Industrial Relations 54 (1): 33–58.
Saiz, Albert, and Elena Zoido. 2005. Listening to what the world says: Bilingualism and earnings in the United States. The Review of Economics and Statistics 87 (3): 523–538.
Sandell, Steven, and David Shapiro. 1980. Work expectations, human capital accumulation and the wages of young women. Journal of Human Resources 15 (3): 335–353.
Scholz, John Karl, and Kamil Sicinski. 2015. Facial attractiveness and lifetime earnings: Evidence from a cohort study. The Review of Economics and Statistics 97 (1): 14–28.
Sen, B. 2001. Revisiting women’s preferences about future labor force attachment: What effects do they have on earnings and what are they affected by? Worker Wellbeing in a Changing Labor Market, Research in Labor Economics 20: 311–337.
Simpson, Wayne. 2000. Intermittent work activities and earnings. Applied Economics 32 (14): 1777–1786.
Slichter, David P. 2015. The employment effects of the minimum wage: A selection ratio approach to measuring treatment effects. Working Paper.
Song, Xueda, and John Jones. 2006. The effects of technological change on life-cycle human capital investment. Working Paper.
Spivey, Christy. 2005. Time off at what price? The effects of career interruptions on earnings. Industrial and Labor Relations Review 59 (1): 119–140.
Stafford, Frank, and Marianne Sundstrom. 1996. Time out for childcare: Signalling and earnings rebound effects for men and women. Labour 10 (3): 609–629.
Staiger, Douglas, and James H. Stock. 1997. Instrumental variables regression with weak instruments. Econometrica 65 (3): 557–586.
Steen, Todd. 2004. The relationship between religion and earnings: Recent evidence from the NLS Youth Cohort. International Journal of Social Economics 31 (5/6): 572–581.
Sternberg, R.J. 1985. Beyond IQ: A triarchic theory of human intelligence. New York: Cambridge University Press.
Stratton, Leslie. 1995. The effect of interruptions in work experience have on wages. Southern Economic Journal 61 (4): 955–970.
Suter, Larry E., and Herman P. Miller. 1973. Income difference between men and career women. American Journal of Sociology 78 (4): 962–974.
Theeuwes, J., C. Koopmans, R. Van Opstal, and H. Van Reijn. 1985. Estimation of optimal human capital accumulation parameters for the Netherlands. European Economic Review 29 (2): 233–257.
Tracey, Marlon, and Solomon Polachek. 2018. If looks could heal: Child health and paternal investment. Journal of Health Economics 57: 179–190.
Trostel, Philip, Ian Walker, and Paul Woolley. 2002. Estimates of the economic return to schooling for 28 countries. Labour Economics 9 (1): 1–16.
Tucker-Drob, Elliot. 2009. Differentiation of cognitive abilities across the life span. Developmental Psychology 45 (4): 1097–1118.
Verdon, Andrew. 2018. Human capital: Homogenous or heterogeneous? An empirical test. Working Paper, Binghamton University.
Von Weizsäcker, Robert. 1993. A theory of earnings distribution. Cambridge: Cambridge University Press.
Wallace, T. Dudley, and Lauren Ihnen. 1975. Full-time schooling in life cycle models of human capital accumulation. Journal of Political Economy 83 (1): 137–155.
Walsh, John. 1935. Capital concept applied to man. Quarterly Journal of Economics 49: 255–285.
Webber, Douglas. 2014. The lifetime earnings premia of different majors: Correcting for selection based on cognitive, noncognitive, and unobserved factors. Labour Economics 28: 14–23.
Weinberger, Catherine J. 2014. The increasing complementarity between cognitive and social skills. The Review of Economics and Statistics. 96 (5): 849–861.
Weiss, Yoram, and Reuben Gronau. 1981. Expected interruptions in labor force participation and sex related differences in earnings growth. Review of Economic Studies 48 (4): 607–619.
Welch, Finis. 1974. Black white differences in returns to schooling. American Economic Review 63 (5): 893–907.
Wiswall, Matthew, and Basit Zafar. 2016. Preference for the workplace, human capital, and gender. NBER Working Papers no. 22173.
Wu, Houying. 2007. Can the human capital approach explain life-cycle wage differentials between races and sexes? Economic Inquiry 45 (1): 24–39.
Acknowledgements
The authors thank Thijs ten Raa as well as an anonymous referee for valuable comments that substantially improved the quality of this chapter.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix 1
Appendix 1
Optimally producing human capital to maximize lifetime earnings entails equating the marginal costs and marginal benefits of human capital creation in each year of one’s life. This process yields a nonlinear (in the parameters) earnings function (Polachek et al. 2015)
where \( W = \beta R^{1 - b} \), \( E = \frac{{E_{0} }}{{\beta^{{\left( {\frac{1}{1 - b}} \right)}} }}, \) t* is the age at which the individual graduates from school, N is the anticipated retirement age which PDT take to be 65, and E0 is the human capital stock when training begins. To identify each parameter \( b_{i} ,\beta_{i} ,E_{{0_{i} }} ,r_{i} ,\delta_{i} \,{\text{and}}\,R \), PDT adopt a two-step process. First, they estimate (9) separately for approximately 1700 individuals in the NLSY79 to obtain b, W, E, d, and r for each individual. Their dependent variable is each of 1700 individual’s weekly earnings adjusted to 1982–1984 dollars. The independent variable is each individual’s age (t). Years of completed school for each individual are denoted as t* and remain constant throughout each person’s life because PDT’s sample omits intermittently schooled individuals.
Second, to identify \( \beta_{i} \), \( E_{{0_{i} }} \), and the population-wide R, PDT first specify \( \beta_{i} \) to equal \( \beta e_{i} \), where β is the population average and \( e_{i} \) is the individual deviation. Second, they rewrite the W = βR1–b coefficient in (9) for individual i as \( W_{i} = R^{{1 - b_{i} }} \beta e_{i} \). They then take the logarithm which yields \( \text{ln}\,W_{i} = \left( {1 - b_{i} } \right)\text{ln}\,R + { \ln }\beta + { \ln }\,e_{i} \) which they then estimate using each individual’s values for \( \widehat{W}_{l} \,{\text{and}}\,\hat{b}_{l} \) obtained from estimating (9) above. They obtain βi by taking the antiloge of the sum of the latter two terms \( { \ln }\beta + { \ln }\,e_{i} \) in the above equation. Utilizing \( b_{i} \) and βi values along with the coefficient \( \widehat{E}_{l} = {{E_{{0_{i} }} } \mathord{\left/ {\vphantom {{E_{{0_{i} }} } {\beta_{i}^{{1/1 - b_{i} }} }}} \right. \kern-0pt} {\beta_{i}^{{1/1 - b_{i} }} }} \) obtained from estimating (9) yields individual-specific \( E_{{0_{i} }} \). The population-wide rental rate is the coefficient of \( \left( {1 - b_{i} } \right) \).
Rights and permissions
Copyright information
© 2019 The Author(s)
About this chapter
Cite this chapter
Das, T., Polachek, S.W. (2019). Microfoundations of Earnings Differences. In: ten Raa, T., Greene, W. (eds) The Palgrave Handbook of Economic Performance Analysis. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-23727-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-23727-1_2
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-23726-4
Online ISBN: 978-3-030-23727-1
eBook Packages: Economics and FinanceEconomics and Finance (R0)