Journal of Population Economics

, Volume 20, Issue 1, pp 101–120 | Cite as

Scholastic ability vs family background in educational success: evidence from Danish sample survey data

Original Paper

Abstract

This research examines the role of scholastic ability and family background variables in the determination of educational attainment in Denmark. A categorical representation of the highest level of education attained by the individual is the dependent variable. It is analyzed by procedures that take account of the presence of unobservable factors. Parent’s education and occupation, along with an indicator of scholastic ability which is represented by a set of aptitude tests, explain a small but significant portion of the variation in their children’s educational success. Women are shown to respond differently to their environments than men, and including these test scores does not remove the need to deal with unmeasured attributes. On the basis of the available data, family background variables as a group contribute more to the explained variation in the data than the test scores. Finally, credit constraints do not appear to be a factor in educational attainments.

Keywords

Educational mobility Test scores Denmark Unobservable heterogeneity 

JEL Classification

I21 C25 

References

  1. Arrow KJ, Bowles S, Durlauf S (2000) Meritocracy and economic inequality. Princeton University Press, PrincetonGoogle Scholar
  2. Becker GS, Tomes N (1979) An equilibrium theory of the distribution of income and intergenerational mobility. J Polit Econ 87(5):1153–1189CrossRefGoogle Scholar
  3. Behrman JR, Rosenzweig MR (2002) Does increasing women’s schooling raise the schooling of the next generation? Am Econ Rev 92(1):323–334CrossRefGoogle Scholar
  4. Belzil C, Hansen J (2003) Structural estimates of the intergenerational education correlation. J Appl Econ 18(6):679–696CrossRefGoogle Scholar
  5. Blau P, Duncan OD (1967) The American occupational structure, New York, John WileyGoogle Scholar
  6. Bowles S, Gintis H, Osborne M (2001) The determinants of earnings: a behavioral approach. J Econ Lit 39:1137–1176Google Scholar
  7. Breen R, Jonsson JO (2000) Analyzing educational careers: a multinomial transition model. Am Sociol Rev 65(5):754–772CrossRefGoogle Scholar
  8. Cameron SV, Heckman JJ (1998) Life cycle schooling and dynamic selection bias: models and evidence for five cohorts of American males. J Polit Econ 106(2):262–333CrossRefGoogle Scholar
  9. Cameron S, Heckman J (2001) The dynamics of educational attainment for Black, Hispanic, and White males. J Polit Econ 109(3):455–499CrossRefGoogle Scholar
  10. Carniero P, Heckman JJ (2001) The evidence on credit constraints in post-secondary schooling. Econ J 112(3):705–734CrossRefGoogle Scholar
  11. Conley D (2001) Capital for college: parental assets and post-secondary schooling. Sociol Educ 74(1):59–72CrossRefGoogle Scholar
  12. Davies R, Heinesen E, Holm A (2002) The relative risk aversion hypothesis of educational choice. J Popul Econ 15(4):683–713CrossRefGoogle Scholar
  13. Dearden L (1999) The effects of families and ability on men’s educational attainment in Britain. Labour Econ 6(4):551–567CrossRefGoogle Scholar
  14. Dearden L, Machin S, Reed H (1997) Intergenerational mobility in Britain. Econ J 107(440):47–66CrossRefGoogle Scholar
  15. Deb P, Trivedi P (1997) Demand For medical care by the elderly: a finite mixture approach. J Appl Econ 12(3):313–336CrossRefGoogle Scholar
  16. Ermisch J, Francesconi M (2001) Family matters: impacts of family background on educational attainment. Economica 68(270):137–156CrossRefGoogle Scholar
  17. Featherman DL, Hauser RM (1975) Schooling and achievement in American society. Academic, New YorkGoogle Scholar
  18. Fischer CS, Hout M, Lucas SR, Jankowski MS, Swidler A, Voss K (1996) Inequality by design: cracking the Bell Curve myth. Princeton University Press, PrincetonGoogle Scholar
  19. Hansen EJ (1995) En generation blev voksen, report 95: 8 (English summary 1996). The Danish National Institute of Social Research, CopenhagenGoogle Scholar
  20. Hansen MN (1997) Social and economic inequality in the educational career: Do the effects of social background characteristics decline? Eur Sociol Rev 13(3):305-320Google Scholar
  21. Hansen M, Kreiner S (1996) De kloge og de rige og de dumme og de fattige-en kritisk lae sning af The bell Curve, bogen om den intellektuelle elite. Report 4. University of Copenhagen, Department of Sociology, CopenhagenGoogle Scholar
  22. Haveman R, Wolfe B (1995) The determinants of childrens’ attainments: a review of methods and findings. J Econ Lit 33(4):1829–1878Google Scholar
  23. Heckman JJ (2000) Policies to foster human capital. Res Econ 54(1):3–56CrossRefGoogle Scholar
  24. Heckman J, Singer B (1984) A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 52(2):271–320CrossRefGoogle Scholar
  25. Herrnstein R, Murray C (1994) The Bell Curve. Free Press, New YorkGoogle Scholar
  26. Keane MP, Wolpin KI (2001) The effect of parental transfers and borrowing constraints on educational attainment. Int Econ Rev 42(4):1051–1103CrossRefGoogle Scholar
  27. Korenman S, Winship C (2000) A re-analysis of the Bell Curve: intelligence, family background, and schooling. In: Arrow KJ, Bowles S, Durlauf S (eds) Meritocracy and economic inequality. Princeton University Press, Princeton, pp 137–178Google Scholar
  28. Lauer C (2003) Family background, cohort, and education: a French–German comparison based on a multivariate ordered probit model of educational attainment. Labour Econ 10(2):231–251CrossRefGoogle Scholar
  29. Lillard LA, Willis RJ (1994) Intergenerational educational mobility: effects of family and state in Malaysia. J Hum Resour XXIX(4):1127–1166Google Scholar
  30. Mare RD (1980) Social background and school continuation decisions. J Am Stat Assoc 75(370):295–305CrossRefGoogle Scholar
  31. Mare R (1981) Change and stability in educational stratification. Am Sociol Rev 46(1):72–87CrossRefGoogle Scholar
  32. Marks G, McMillan J (2003) Declining inequality? The changing impact of socio-economic background and ability on education in Australia. Br J Sociol 54(4):453–471CrossRefPubMedGoogle Scholar
  33. Neal DA, Johnson WR (1996) The role of premarket factors in Black–White wage differences. J Polit Econ 104(5):869–895CrossRefGoogle Scholar
  34. Nyborg H (2003) The scientific study of general intelligence. Pergamon, AmsterdamGoogle Scholar
  35. Nyborg H (1990) Good, bad, and ugly questions about heredity. Behav Brain Sci 13(1):142–143Google Scholar
  36. Nørgaard E, Markussen I, Christensen DC (1978) Beskrivelse af officielle og alment accpeterede m/aa ls/ae tninger og undervisningsmetoder i uddannelsessystemets hoveddele 1920–1977. U90, vol 2. Ministry of Education, CopenhagenGoogle Scholar
  37. Peters EH, Mullis NC (1997) The role of family income and sources of income in adolescent achievement. In: Duncan GC, Brooks-Gunn J (eds) Consequences of growing up poor. Russel Sage, New York, pp 340–381Google Scholar
  38. Plomin R, Fulker DW, Corley R, DeFries JC (1997) Nature, nurture, and cognitive development from 1 to 16 years: a parent–offspring adoption study. Psychol Sci 8(6):442–447CrossRefGoogle Scholar
  39. Shavit Y, Blossfeld H-P (1993) Persistent inequality: changing educational attainment in thirteen countries. Boulder Co, WestviewGoogle Scholar
  40. Todd PE, Wolpin KI (2003) On the specification and estimation of the production function for cognitive achievement. Econ J 113(485):F3–F33CrossRefGoogle Scholar
  41. Voung QH (1988) Likelihood ratio tests for model selection and non-nested hypothesis. Econometrica 57(2):307–333CrossRefGoogle Scholar
  42. Wedel M, Desarbo WS, Bult JR, Ramaswamy V (1993) A latent class poisson regression model for heterogeneous count data. J Appl Econ 8(4):397–411CrossRefGoogle Scholar
  43. Ørum B (1971) Social baggrund, intellektuelt niveau og placering i skolesystemet, Study 20. The Danish National Institute of Social Research, CopenhagenGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Economics DepartmentConcordia UniversityMontrealCanada
  2. 2.The Danish National Institute of Social ResearchCopenhagen K,Denmark

Personalised recommendations