Social Indicators Research

, Volume 138, Issue 3, pp 853–872 | Cite as

The Evolution of Educational Inequalities in Spain: Dynamic Evidence from Repeated Cross-Sections

  • Álvaro Choi
  • María Gil
  • Mauro MediavillaEmail author
  • Javier Valbuena
Original Research


A lack of longitudinal data prevents many countries from estimating dynamic models and, thus, from obtaining valuable evidence for policymaking in the field of education. This is the case of Spain, where recent education reforms have targeted secondary schools, but their design has been based on incomplete information regarding the evolution of student performance and far from robust evidence concerning just when educational inequalities are generated. This paper addresses the absence of longitudinal data required for performing such analyses by using a dynamic model with repeated cross-sectional data. We are able to link the reading competencies of students from the same cohort that participated in two international assessments at different ages (9/10 and 15/16) and so identify when educational gaps—in terms of gender, socio-economic status and place of birth—emerge. Our results suggest that educational inequalities in Spain originate in lower educational levels. These results stress the importance of early intervention for improving performance during the compulsory education and for tackling educational inequalities.


Academic achievement Educational inequalities Pseudo-panel PIRLS PISA 



The authors gratefully acknowledge the financial support from the Areces Foundation through its XIII National Contest for Research in Social Sciences and the Spanish Ministry of Economy and Competitiveness (project EDU2016-76414-R).


  1. Almond, D., & Currie, J. (2011). Human capital development before age five. In O. Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol. 4B, pp. 1315–1486). Amsterdam: Elsevier.Google Scholar
  2. Arellano, M., & Meghir, C. (1992). Female labour supply and on-the-job search: An empirical model estimated using complementary data sets. The Review of Economic Studies, 59, 537–559.CrossRefGoogle Scholar
  3. Bali, V., Anagnostopoulos, D., & Roberts, R. (2005). Toward a political explanation of grade retention. Educational Evaluation and Policy Analysis, 27, 133–155.CrossRefGoogle Scholar
  4. Bedard, K., & Dhuey, E. (2006). The persistence of early maturity: International evidence of long-run age effects. The Quarterly Journal of Economics. doi: 10.1093/qje/121.4.1437.Google Scholar
  5. Black, S., Devereux, P., & Salvanes, K. (2011). Too young to leave the nest? The effects of school starting age. The Review of Economics and Statistics., 93, 455–467. doi: 10.1162/REST_a_00081.CrossRefGoogle Scholar
  6. Brown, G., Micklewright, J., Schnepf, S., & Waldmann, R. (2007). International surveys of educational achievement: How robust are the findings? Journal of the Royal Statistical Society Series A. doi: 10.1111/j.1467-985X.2006.00439.x.Google Scholar
  7. Bukodi, E., & Goldthorpe, J. (2012). Decomposing ‘social origins’: The effects of parents’ class, status and educational on the educational attainment of their children. European Sociological Review, 29, 1024–1039.CrossRefGoogle Scholar
  8. Carneiro, P., & Heckman, J. (2004). Human capital policy. In J. Heckman & A. Krueger (Eds.), Inequality in America: What role for human capital policies (pp. 77–240). Cambridge, MA: MIT Press.Google Scholar
  9. Chen, X., Liu, C., Zhang, L., Shi, Y., & Rozelle, S. (2010). Does taking one step back get you two steps forward? Grade retention and school performance in poor areas in rural China. International Journal of Educational Development, 30, 544–559.CrossRefGoogle Scholar
  10. Choi, Á., & Calero, J. (2013). Determinantes del riesgo de fracaso escolar en España en PISA-2009 y propuestas de reforma. Revista de Educación, 362, 562–593.Google Scholar
  11. Choi, Á., & Jerrim, J. (2016). The use and (misuse) of PISA in guiding policy reform: evidence from Spain. Comparative Education. doi: 10.1080/03050068.2016.1142739.Google Scholar
  12. Contini, D., & Grand, E. (2015). On estimating achievement dynamic models from repeated cross sections. Sociological Methods & Research. doi: 10.1177/0049124115613773.Google Scholar
  13. Crawford, C., Dearden, L. & Greaves, E. (2007a). The impact of age within academic year on adult outcomes. IFS Working Paper W13/07.Google Scholar
  14. Crawford, C., Dearden, L. & Greaves, E. (2013). When you are born matters: evidence for England. Report R80. London: Institute for Fiscal Studies.Google Scholar
  15. Crawford, C., Dearden, L. & Meghir, C. (2007b). When you are born matters: the impact of date of birth on child cognitive outcomes in England. Report. London: Centre for the Economics of EducationGoogle Scholar
  16. Cunha, F., & Heckman, J. (2007). The technology of skill formation. American Economic Review, 92, 31–47.CrossRefGoogle Scholar
  17. Cunha, F., & Heckman, J. (2008). Formulating, identifying and estimating the technology of cognitive and noncognitive skill formation. Journal of Human Resources, 43, 738–782.CrossRefGoogle Scholar
  18. Cunha, F., Heckman, J., & Schennach, S. (2010). Estimating the technology of cognitive and noncognitive skill formation. Econometrica. doi: 10.3982/ECTA6551.Google Scholar
  19. Datar, A. (2006). Does delaying kindergarten entrance give children a head start? Economics of Education Review, 25, 43–62.CrossRefGoogle Scholar
  20. De Simone, G. (2013). Render unto primary the things which are primary’s: Inherited and fresh learning divides in Italian secondary education. Economics of Education Review. doi: 10.1016/j.econedurev.2013.03.002.Google Scholar
  21. Elder, T., & Lubotsky, D. (2009). Kindergarten entrance age and children’s achievement: Impact of state policies, family background and peers. The Journal of Human Resources. doi: 10.3368/jhr.44.3.641.Google Scholar
  22. Feinstein, L. (2003). Inequality in the early cognitive development of British children in the 1970 cohort. Economica, 70, 73–98.CrossRefGoogle Scholar
  23. Ferguson, P., Jimerson, S., & Dalton, M. (2001). Sorting out successful failures: Exploratory analyses of factors associated with academic and behavioral outcomes of retained students. Psychology in the Schools, 38, 327–341.CrossRefGoogle Scholar
  24. Fernández, M. (2014). The evolution of inequality of educational opportunities: A systematic review of analyses of the Spanish case. Revista Española de Investigaciones Sociológicas. doi: 10.5477/cis/reis.147.107.Google Scholar
  25. Fernández-Macías, E., Antón, J., Braña, F., & Bustillo, R. (2013). Early School-leaving in Spain: evolution, intensity and determinants. European Journal of Education. doi: 10.1111/ejed.12000.Google Scholar
  26. Fletcher, J., & Kim, T. (2016). The effect of changes in kindergarten entry age policies on educational achievement. Economics of Education Review. doi: 10.1016/j.econedurev.2015.11.004.Google Scholar
  27. Fredriksson, P., & Öckert, B. (2014). Life-cycle effects of age at school start. Economic Journal, 124, 977–1004.CrossRefGoogle Scholar
  28. Frey, N. (2005). Retention, social promotion and academic redshirting: What do we know and need to know? Remedial and Special Education, 26, 332–346.CrossRefGoogle Scholar
  29. Gomes-Neto, J., & Hanushek, E. (1994). Causes and consequences of grade repetition: Evidence from Brazil. Economic Development and Cultural Change, 43, 117–148.CrossRefGoogle Scholar
  30. Guio, J., Choi, Á & Escardíbul, J-O. (2017). Labor markets, academic performance and school dropout risk: Evidence for Spain. International of Manpower,  10.1108/IJM-08-2016-0158.
  31. Hanushek, E., & Wößmann, L. (2011). The economics of international differences in educational achievement. In E. A. Hanushek, S. Machin, & L. Wößmann (Eds.), Handbook of economics of education (Vol. 3, pp. 89–200). St. Louis, MO: Elsevier.Google Scholar
  32. Heckman, J. (2011). The economics of inequality: The value of early childhood education. American Educator, 35, 31–35.Google Scholar
  33. Holmlund, H., Lindahl, M., & Plug, E. (2011). The causal effects of parents’ schooling on children’s schooling: A comparison of estimation methods. Journal of Economic Literature. doi: 10.1257/jel.49.3.615.Google Scholar
  34. Hox, J. (1995). Applied multilevel analysis. Amsterdam: TT-Publikaties.Google Scholar
  35. Jerrim, J., & Choi, Á. (2014). The mathematics skills of school children: how does England compare to the high-performing East Asian jurisdictions? Journal of Education Policy. doi: 10.1080/02680939.2013.831950.Google Scholar
  36. Jerrim, J., Choi, Á., & Simancas, R. (2016). Two-sample two-stage least squares (TSTSLS) estimates of earnings mobility: How consistent are they? Survey Research Methods. doi: 10.18148/srm/2016.v10i2.6277.Google Scholar
  37. Jerrim, J., & MacMillan, L. (2015). Income inequality, intergenerational mobility, and the Great Gatsby Curve: Is education the key? Social Forces, 94, 505–533.CrossRefGoogle Scholar
  38. Jerrim, J., & Micklewright, J. (2014). Socioeconomic gradients in Children’s cognitive skills: Are cross-country comparisons robust to who reports family background? European Sociological Review. doi: 10.1093/esr/jcu072.Google Scholar
  39. Le Donné, N. (2014). European variations in socioeconomic inequalities in students’ cognitive achievement: The role of educational policies. European Sociological Review. doi: 10.1093/esr/jcu040.Google Scholar
  40. Levels, M., Dronkers, J., & Kraaykamp, G. (2008). Immigrant children’s educational achievement in western countries: Origin, destination, and community effects on mathematical performance. American Sociological Review, 73, 835–853.CrossRefGoogle Scholar
  41. Liddell, C., & Rae, G. (2001). Predicting early grade retention: A longitudinal investigation of primary school progress in a sample of Rural South African children. British Journal of Educational Psychology, 71, 413–428.CrossRefGoogle Scholar
  42. Machin, S., & Pekkarinen, T. (2008). Global sex differences in test score variability. Science, 322, 1331–1332.CrossRefGoogle Scholar
  43. McEwan, P., & Shapiro, J. (2008). The benefits of delayed primary school enrollment: discontinuity estimates using exact birth dates. Journal of Human Resources, 43, 1–29.CrossRefGoogle Scholar
  44. MEC (2016). TIMSS 2015. Estudio Internacional de Tendencias en Matemáticas y Ciencias. Informe Español: Resultado y Contexto. MEC: Madrid.Google Scholar
  45. Moffitt, R. (1993). Identification and estimation of dynamic models with a time series of repeated cross-sections. Journal of Econometrics. doi: 10.1016/0304-4076(93)90041-3.Google Scholar
  46. Mullis, I., Martin, M., Kennedy, A. & Foy. P. (2007). PIRLS 2006 International Report. Boston. MA: Lynch School of Education, Boston CollegeGoogle Scholar
  47. OECD. (2014a). PISA 2012 results in focus: What 15-year-olds know and what they can do with what they know. Paris: OECD.Google Scholar
  48. OECD. (2014b). PISA 2012 technical report. Paris: OECD.Google Scholar
  49. OECD. (2016). PISA 2015 results (volume I): Excellence and equity in education. Paris: OECD.Google Scholar
  50. Puhani, P., & Weber, A. (2007). Does the early bird catch the worm? Instrumental variable estimates of early educational effects of age of school entry in Germany. Empirical Economics, 32, 359–386.CrossRefGoogle Scholar
  51. Robertson, E. (2011). The effects of quarter of birth on academic outcomes at the elementary school level. Economics of Education Review. doi: 10.1016/j.econedurev.2010.10.005.Google Scholar
  52. Royston, P., & White, I. (2011). Multiple imputation by chained equations (MICE): Implementation in STATA. Journal of Statistical Software. doi: 10.18637/jss.v045.i04.Google Scholar
  53. Schütz, G., Ursprung, H., & Wößmann, L. (2008). Education policy and equality of opportunity. Kyklos. doi: 10.1111/j.1467-6435.2008.00402.x.Google Scholar
  54. Smith, J. (2009). Can regression discontinuity help answer an age-old question in education? The effect of age on elementary and secondary school achievement. The B.E. Journal of Economics Analysis & Policy, 9, 1–30.Google Scholar
  55. StataCorp. (2013). Stata Multiple Imputation: Reference Manual. Release 13. Texas: Stata Press.Google Scholar
  56. Verbeek, M., & Vella, F. (2005). Estimating dynamic models from repeated cross-sections. Journal of Econometrics. doi: 10.1016/j.jeconom.2004.06.004.Google Scholar
  57. Willms, D. (2006). Learning divides. Ten policy questions about the peroformance and equity of schools and schooling systems. Montreal: UNESCO.Google Scholar
  58. Wilson, V., & Hughes, J. (2009). Who is retained in first grade? A psychosocial perspective. Elementary School Journal, 109, 251–266.CrossRefGoogle Scholar
  59. Zinovyeva, N., Felgueroso, F., & Vazquez, P. (2014). Immigration and student achievement in Spain: evidence from PISA. SERIEs. doi: 10.1007/s13209-013-0101-7.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Faculty of Economics and BusinessUniversity of BarcelonaBarcelonaSpain
  2. 2.Faculty of Economics and BusinessUniversity Autónoma of MadridMadridSpain
  3. 3.Faculty of Economics and BusinessUniversity of ValenciaValenciaSpain
  4. 4.ZaragozaSpain

Personalised recommendations