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----Tp;gvij;kgg Measuring the consumption value of higher education Annette Alstadsæter ∗ Statistics Norway † NHH Discussion Paper SAM 04/2004 Abstract The consumption value of education is an important, but rather ignored factor behind the individual's educational choice. This paper suggests a method for measuring the consumption value of education in a compensating di ff erentials framework when the ability bias is corrected for. As an example, the willingness to pay for the consumption value of attending teacher's college during the 1960's is estimated on unique Norwegian panel data. The ex-ante price of the consumption value of teacher's college is estimated to be 38 % of the present value of the individual's potential lifetime income. The ex-post price of this consumption value is for the same individuals estimated to be about 46 % of the present value of the potential lifetime income. JEL-classi fi cations: J24; J31; J33; I21; H89. ∗ Acknowledgements: Jarle Møen provided me with the idea in the fi rst place, and I thank him and my advisor Professor Agnar Sandmo for inte resting discussions and help along the way. Professor Kjell G. Salvanes, Knut R. Wangen, Erik Sørensen, and seminar participants at the Norwegian School of Economics and Business Admi nistration provided most appreciated comments. Grant 140731/510 from the Research Council of Norway is gratefully acknowledged. † Research Department, P.b. 8131 Dep., N-0033 Oslo. E-mail: aal@ssb.no 1 1Introduction. Higher education can be viewed both as a consumption good for which the individual is willing to pay, and as an investment alternative that yields higher wages later in life. The factors determining the individual's educational choice can be divided into three groups: preferences, returns, and costs. The costs of attending higher education are e ff ort, time and money, both direct monetary outlays and forgone labor income 1 . The return to higher education comes both as pecuniary and non-pecuniary returns. As higher education increases the skill level, and thus also the productivity of the individual, he is paid a higher wage in the labor market. Also, higher skilled individuals qualify for di ff erent types of jobs than lower skilled individuals. High- skilled jobs often o ff er various fringe bene fi ts, which are not paid as money, but which are all equivalent to a wage increase. Fringe bene fi ts 2 and the wage premium constitute the pecuniary return to higher education. The individual speci fi cnon- pecuniary return to higher education is the intrinsic or the consumption value of education , which is de fi ned in section 3. This paper suggests a method for measuring the consumption value of education in a compensating di ff erentials framework when the ability bias is corrected for. The identi fi cation strategy is to compare two individuals who attended teacher's college and business school respectively in Norway during the 1960's. In this period these two types of education required the same minimum average grade level from high school for admittance, but they generated very di ff erent wage returns. The wage return from attending business school in this period is used as a benchmark for the potential wage return of the teacher's college graduates. Using the Norwegian 1970 census, cross section wage pro fi les are estimated for those business school and teacher's college graduates with di ff erent levels of working experience. These wage pro fi les are interpreted as the expected future wages of the individuals attending business school and teacher's college during the 1960's. The ex-ante price of the consumption value of teacher's college is estimated to be 38 % of the present value of the individual's potential lifetime income. Using unique Norwegian panel data the actual wage pro fi les for the individuals acquiring their education during the 1960's are estimated. The ex-post price of this consumption value of teacher's college turned 1 Costs are disregarded in the following analysis. 2 Fringe bene fi ts are here de fi ned to be bene fi ts with a clear monetary equivalent, such as a company car, free newspaper subscrip tions, and a company health insurance. 2 Bishop,J.(1994):Schooling,learningandworkerproductivity.InRitaAsplund (ed.): HumanCapitalCreationinanEconomicPerspective .Physica-Verlag. Bound, J. and G. Solon (1999): Double trouble: on the value of twins-based es- timation of the return to schooling. Economics of Education Review 18(2) , 169-82. Bowles, S. (1972): Schooling and inequality from generation to generation. Journal of Political Economy 80, 219-251. Card, D. (1999): The causal e ff ect of education on earnings. Handbook of Labor Economics , volume 3, 1801-1863. Chiswick, B.R. (2003): Jacob Mincer, experience and the distribution of earnings. IZA Discussion Paper No. 847. Croix, D. de la (2001): Growth dynamics and educational spending: The role of inherited tastes and abilities. European Economic Review 45 , 1415-38. Dolton, P.J., G.H. Makepeace, and W. van der Klaauw (1989): Occupational choice and earnings determination: The role of sample selection and non-pecuniary factors. Oxford Economic Papers 41 , 573-594. Duncan, G.J. (1976): Earnings functions and nonpecuniary bene fi ts. Journal of Human Resources 11(4) , 462-483. Gullason, E.T. (1989): The consumption value of schooling. An empirical estimate of one aspect. Journal of Human Resources 24(2 ), 287-98. Hægeland, T., T.J. Klette, and K.G. Salvanes (1999): Declining returns to educa- tion in Norway? Comparing estimates across cohorts, sectors and over time. Scandinavian Journal of Economics 101 , 555-576. Judd, K. (2001): The impact of tax reform in modern dynamic economies. In K.A. Hasset and R.G. Hubbard: Transition costs of fundamental tax reform. The AEI Press, Washington D.C. Keane, M.P. and K.I. Wolpin (1997): The career decision of young men. Journal of Political Economy 105(3) , 473-522. 28 Klette, T.J. and J. Møen (2002): Vitenskapelig forskning og næringsutvikling. In E. Hope (ed.): Næringspolitikk for en ny økonomi. Fagbokforlaget. Kodde, D.A. and J.M.M. Ritzen (1984): Integrating consumption and investment motivesinaneoclassicalmodelofdemandforeducation. Kyklos 37(4) , 598- 605. Lazear, E. (1977): Education, consumption or production? Journal of Political Economy 85(3) , 569-597. Lleras-Muney, A. (2002): The relationship between education and adult mortality in the United States. NBER Working Paper No. 8986. Lochner, L. and E. Moretti (2001): The e ff ects of education on crime: Evidence from prison inmates, arrests, and self-reports. NBER Working Paper No. 8605. Lucas, R.E. (1988): On the mechanics of economic development. Journal of Mon- etary Economics 22 ,3-42. Marshall, A. (1920): Principles of economics, 8th ed. Macmillian, London. Mincer, J. (1974): Schooling, experience and earnings. Columbia University Press, New York. Milligan, K., E. Moretti and P. Oreopoulos (2003): Does education improve citi- zenship? Evidence from the U.S. and U.K. NBER Working Paper No. 9584. Nerdrum, L. (1999): The economics of human capital. A theoretical analysis illus- trated empirically by Norwegian data. Scandinavian University Press. Oosterbeek, H. and H.V. Ophem (2000): Schooling choices: Preferences, discount rates, and rates of return. Empirical Economics 25 , 15-34. Oosterbeek, H. and D. Webbink (1995): Enrolment in higher education in the Netherlands. De Economist 143 , 367-380. Oreopoulos, P. (2003): Do dropouts drop out too soon? International evidence from changes in shool-leaving laws. INBER Working Paper No. 10155. Persson, M. and A. Sandmo (2002): Taxation and tournaments. NHH Discussion Paper No. 10. 29 Romer, P.M. (1990): Endogenous technological change. Journal of Political Econ- omy 98 , 71-102. Rosen, S. (1986): The theory of equalizing di ff erences. In O. Ashenfelter and R. Layard (eds.): Handbook of Labour Economics, Vol. 1, 641-692. Sandmo, A. (1983): Ex post welfare economics and the theory of merit goods. Economica 50 , 19-33. Schaafsma, J. (1976): The consumption and investment aspects of the demand for education. Journal of Human Resources 11(2) ,233-42. Schultz, T.W. (1960): Capital formation by education. Journal of Political Economy 68 , 571-582. Scitovsky, T. (1976): The joyless economy. Oxford University Press. Scott, A. (2001): Eliciting GP's preferences for pecuniary and non-pecuniary job characteristics. Journal of Health Economics 20 , 329-347. Smith, A. (1776): The Wealth of Nations . Reprint 1999, Penguin Classics, London. Stern, S. (1999): Do scientists pay to be scientists?, NBER Working Paper No. 7410 . Wangen, K.R. (2003): En konsuments kvaler ved vanedannelse og nåtidsskjevhet. Økonomisk Forum 57(4). Ward, M.E. and P.J. Sloane (2000): Non-pecuniary advantages versus pecuniary disadvantages; job satisfaction among male and female academics in Scottish universities. Scottish Journal of Political Economy 47(3) , 273-303. Weisbrod, B.A. (1962): Education and investment in human capital. Journal of Political Economy 70(5) , Supplement, October, Part II, Investment in Human Beings, 106-123. Willis, R.J. and S. Rosen (1979): Education and self-selection, Journal of Political Economy 87(5) ,7-36. 30 7 Appendix 7.1 Documentation of admission requirements. It is a general perception that during the 1960's it was just as di ffi cult to be admitted to teacher's college as to business school in Norway. Aarrestad (1969) stated 24 on page 69: " The demand for teacher's college education far exceeds the supply. The mini- mum requirement for admission has the last years been above 60 grade points (from high school)." Also,onpage75hestates: "The admission requirements for the Norwegian School of Economics and Business Administration are not quite clear. With maximum awarded additional points, it is today possible to be admitted with about 60 grade points from high school." It proved di ffi cult to fi nd formal evidence for these admittance requirements. In the archives of the Norwegian School of Economics and Business Administration 25 and of the Teacher's Council 26 I found indications that the last student admitted to teacher's college and to business school had about the same grade levels, but no o ffi cial statistics are available on this issue. Another problem with comparing the two is that the di ff erent institutions had di ff erent regulations for giving so-called additional points to the applicants, such that their total competitive grade score varied from their high school graduation grade score. Additional points were awarded for previous education and work experience, and for extracurricular activities, but the praxis varied among the institutions. 7.2 Data The 1970 Household Censu s covers all Norwegian households and individuals (identi fi ed by their personal identi fi cation number). The census contains information on among other things on gross income, sex, age, marital status, type and level of education, and personal income. 24 The following quotations are translated from Norwegian. 25 For a long time this was the only business school in Norway, but at the and of the 1960's another one was founded as well. 26 From about 1967 admission to all teacher's colleges in Norway was organized centrally by the Teacher's Council (Lærerutdanningsrådet). Befo re that time the admission was organized by each school, and the requirements varied from school to school. 31 The Earnings Register covers all Norwegian adults and contains gross indi- vidual earnings based on pension rights earned over the period 1967-2000. The Core Administrative Register contains information on all Norwegians in the years 1986-2000. It has among many other variables age, sex, marital status, type and length of highest completed education, graduation date. The income history of the individuals can be extended by including the earnings history of the individuals from the earnings register. The cleaned sample for calculation of the ex-ante wage pro fi les. Individ- uals with missing observations on either educational type or income are removed from the sample. Beyond that all individuals who graduated from teacher's college or business school in the period 1941-1970 are included in the sample, in order to estimate the full income pro fi le for 0-29 years of working experience in 1970 for the two groups. Even individuals who for some reason were not active in the labor force are included. When a young person makes his educational choice, the future wage return is uncertain for many reasons, and one of them is that he might become ill and be unable to work. If one type of education leads to more stressful jobs than the other, more individuals will become ill, and the wage level while still at work needs to be higher in order to compensate for this. Hence the income of those not currently in the labor force in 1970, but with potential labor experience between 0 and 29 years, needs to be included to get the full picture. The full sample of males in the 1970 census counts 2269 business school graduates and 7089 teacher's college graduates. The cleaned sample for calculation of the ex-post wage pro fi les. The fi rst challenge was to identify who acquired the two educational types during the 1960's, as well as to fi nd their potential working experience. In principle, I could use the graduation date in the core administrative register to establish when the individual most likely started working, and thus fi nd the potential working experience in years. Unfortunately, all who completed their education prior to November 1970 are listed with this as their graduation date. Therefore I use their date of birth, add 19 years (to complete high-school) to fi nd the time when they most likely started their higher education, and add another 2 or 3 years to fi nd graduation date. Finally I added another year for the mandatory military service (some did this before and others 32 after their education, but most did it before they started working) to fi nd when they most likely started their professional careers. This procedure identi fi ed the individuals acquiring their education during the 1960's, as well as their entry into the labor force. By merging the core administrative data with the earnings register, I got the gross income series for these individuals from 1967 to 2000. From this the earnings history of the individuals from 0 to 29 years of potential experience was extracted. When the panel was cleaned for entries missing information on annual earnings, the fi nal sample consisted of 465 business school graduates with a total of 13110 observation entries, and 1805 teacher's college graduates with a total of 50153 ob- servation entries. 33 Figure 7: Results, ordinary least squares regression, teachers, 1970-census. Source | SS df MS Number of obs = 7089 -------------+------------------------------ F( 2, 7086) = 1380.21 Model | 678.480338 2 339.240169 Prob > F = 0.0000 Residual | 1741.66185 7086 .245789141 R-squared = 0.2803 -------------+------------------------------ Adj R-squared = 0.2801 Total | 2420.14219 7088 .341442183 Root MSE = .49577 ------------------------------------------------------------------------------ lnW | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- pexp | .0958324 .0026617 36.00 0.000 .0906146 .1010501 sqpexp | -.0024115 .0001008 -23.93 0.000 -.002609 -.002214 _cons | 9.796686 .0126202 776.27 0.000 9.771947 9.821426 ------------------------------------------------------------------------------ Figure 8: Results, ordinary least squares regression, business school graduates, 1970- census. Source | SS df MS Number of obs = 2269 -------------+------------------------------ F( 2, 2266) = 540.54 Model | 263.700785 2 131.850392 Prob > F = 0.0000 Residual | 552.728836 2266 .243922699 R-squared = 0.3230 -------------+------------------------------ Adj R-squared = 0.3224 Total | 816.429621 2268 .359977787 Root MSE = .49389 ------------------------------------------------------------------------------ lnW | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- pexp | .0999329 .0043521 22.96 0.000 .0913984 .1084674 sqpexp | -.0025426 .0001625 -15.64 0.000 -.0028613 -.0022238 _cons | 10.22356 .0222558 459.37 0.000 10.17992 10.26721 ------------------------------------------------------------------------------ Figure 9: Results, random e ff ects regression, teachers, earnings register. Random-effects GLS regression Number of obs = 50153 Group variable (i) : pid Number of groups = 1805 R-sq: within = 0.2680 Obs per group: min = 1 between = 0.0206 avg = 27.8 overall = 0.2021 max = 33 Random effects u_i ~ Gaussian Wald chi2(2) = 17749.49 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ lnW | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- pexp | .0783991 .0007973 98.34 0.000 .0768365 .0799617 sqpexp | -.0018876 .0000258 -73.24 0.000 -.0019382 -.0018371 _cons | 9.94585 .007921 1255.63 0.000 9.930326 9.961375 -------------+---------------------------------------------------------------- sigma_u | .25123219 sigma_e | .357633 rho | .33042589 (fraction of variance due to u_i) ------------------------------------------------------------------------------ 34 Figure 10: Results, random e ff ects regression, business school graduates, earnings register. Random-effects GLS regression Number of obs = 13110 Group variable (i) : pid Number of groups = 465 R-sq: within = 0.3778 Obs per group: min = 13 between = 0.0593 avg = 28.2 overall = 0.3069 max = 30 Random effects u_i ~ Gaussian Wald chi2(2) = 7694.71 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ lnW | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- pexp | .1192716 .0020061 59.45 0.000 .1153397 .1232035 sqpexp | -.0027991 .0000687 -40.75 0.000 -.0029337 -.0026645 _cons | 10.17645 .0185267 549.28 0.000 10.14014 10.21276 -------------+---------------------------------------------------------------- sigma_u | .29900569 sigma_e | .4930236 rho | .26890429 (fraction of variance due to u_i) ------------------------------------------------------------------------------ 35 http://repec.org/esAUSM04/up.17298.1075484224
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