The back of the coin in resilience: on the characteristics of advantaged low-achieving students

Abstract

In the field of Economics of Education the “resilience” term is used to designate those students from low socio-economic backgrounds who can overcome their initial situation and obtain high academic results. However, the opposite kind of student profile has been a less explored field, i.e., high socio-economic status students who perform poorly and are thus denoted as advantaged low-achieving students. Because of that, the current study intends to disentangle the characteristics which influence the likelihood of high socio-economic students to become low-achievers. In order to do this, we use census and longitudinal education data and a rich set of variables from secondary education students in the most populated region of Spain (Andalusia). Our results show that students’ use of time and self-confidence, together with parental engagement, when students were in primary education, are relevant variables in explaining the low achievement of advantaged students.

This is a preview of subscription content, access via your institution.

Notes

  1. 1.

    The differences in scores between Andalusia and Spain and also Andalusia and the OECD for reading, mathematics and science, are significant at 1%.

  2. 2.

    The regulation for these Diagnostic Assessments in Andalusia is described in the education law which was applicable in the courses under analysis (Ley Orgánica 2/2006, de 3 de mayo, de Educación, i.e., LOE; BOE 2006; art. 21, regulating the conduction of these DA in primary education; art. 29, for secondary education and art. 144 regulates the competences that Administrations have in this DA).

  3. 3.

    As a means of identifying those students who repeated we followed the applicable Spanish education law for the previous academic years to 2008–09, i.e., Ley Orgánica 10/2002, de 23 de diciembre, de Calidad de la Educación or LOCE (BOE 2002), from 2002 to 2006. This law indicates that students can only repeat once in primary education (BOE 2002, art. 17.3). The next education law (Ley Orgánica 2/2006, de 3 de mayo, de Educación, or LOE; BOE 2006) also indicated this (BOE 2006, art. 20.2) and it was applicable from 2006 to 2013.

  4. 4.

    In order to avoid losing the missing observations of the independent variables we employed a missing flag procedure. However, this procedure cannot be applied with the missing information of the dependent variable (i.e. being an advantaged low-achieving student), so we cannot avoid missing this information.

  5. 5.

    The reading competence is defined as “the use of language as an instrument of oral and written communication, of presentation, interpretation and comprehension of reality; to construct and communicate the knowledge, to organize and to auto-regulate thinking, emotions and behaviour” (AGAEVE 2009, p. 7).

  6. 6.

    The mathematics competence is defined as “the ability to use and relate numbers, their basic operations, symbols and expression forms and mathematic reasoning, to produce and interpret different types of information and to increase knowledge on quantitative and spatial aspects of reality and to solve problems related to daily life and to the labour world” (AGAEVE 2009, p. 7).

  7. 7.

    The socio-economic status variable is an index created by AGAEVE employing: the highest from the mother or father education; the highest from the mother or father occupation; the number of books at home and household possessions.

  8. 8.

    The definition of resilient student is the opposite: a student who is in the highest quartile of scores (alternatively, in reading or mathematics) and in the lowest quartile of socio-economic status.

  9. 9.

    This variable is derived from administrative records regarding the biological sex of the student, i.e. male or female.

  10. 10.

    Authors such as Agasisti et al. (2018) specified their resilience models using only the subsample of less affluent students. Nevertheless, instead of restricting our analysis to the most affluent students for our case (i.e. advantaged low-achievers), we believe that controlling by the high socio-economic status quartile might account for students’ socio-economic characteristics and, at the same time, it would not reduce the sample size. Therefore, we have added this control to our estimations.

  11. 11.

    We use a logit model instead of a linear probability model (i.e. Ordinary Least Squares estimation with a binary dependent variable) due to the problems of heteroscedasticity and the prediction of negative probabilities that the latter model presents.

  12. 12.

    This also applies for resilient students \({P}_{ijt}=P\left({R}_{ij}=1|{X}_{ijt-3},{Z}_{jt-3}\right)\) (where \({R}_{ijt}\) represents resilient student).

  13. 13.

    Furthermore, analysing the change of status of students between 5 and 8th grade (i.e. becoming an advantaged low-achieving student, stop being one or remaining the same) would mean using, for each grade, dependent and independent variables which are measured in the same moment of time (i.e. simultaneously); thus, this would be a correlational analysis which would omit many more relevant variables than those in the model employed in the present study and, therefore, might not contribute to the objective of our analysis.

  14. 14.

    Estimations have been performed using the information in 2011–2012 of those students who failed 8th grade in 2011–2012 and repeated that grade in 2012–2013. These estimations have been replicated using the information for these students in 2012–2013 (changing, then, our dependent variable) and results do not change. These estimations will be provided upon request to the authors.

  15. 15.

    Estimations have been replicated including the different groups of variables using a stepwise procedure and results do not change. These estimations will be provided upon request to the authors.

  16. 16.

    Estimations have been replicated using only non-repeater students and results do not change. These estimations are available upon request to the authors.

  17. 17.

    In order to facilitate the interpretation of the odd ratios which are lower than one we invert them. An example for the 0.560 coefficient of the “female” variable for reading would be 1/0.560 = 1.786, which would be the coefficient for “male”.

  18. 18.

    In order to interpret our results as probabilities the following procedure can be applied: (odds ratio-1) × 100. Then, for 1.8 times, it would mean that it is (1.8–1) × 100 = 80% more likely.

  19. 19.

    These estimations will be provided upon request to the authors.

  20. 20.

    The use of quintiles accounts for the existence of an “average category” in the middle of the socio-economic status index and reading and mathematics scores distributions. Although this “average category” has not been directly used in the current analysis, it indirectly allows delimiting the profile of those students who are advantaged low-achieving students, to the extent that we can narrow down who are these students by accounting for the existence of a “middle” group of students in the distribution of these variables.

References

  1. Abelev, M. S. (2009). Advancing out of poverty. Journal of Adolescent Research, 24(1), 114–141. https://doi.org/10.1177/0743558408328441.

    Article  Google Scholar 

  2. AGAEVE. (2009). Evaluación de Diagnóstico. Curso 2008–2009. Andalucía: Consejería de Educación, Junta de Andalucía.

    Google Scholar 

  3. Agasisti, T., Avvisati, F., Borgonovi, F., & Longobardi, S. (2018). Academic resilience: What schools and countries do to help disadvantaged students succeed in PISA. OECD Education Working Papers, No 167. Paris: OECD Publishing. https://doi.org/10.1787/e22490ac-en.

    Google Scholar 

  4. Agasisti, T., & Longobardi, S. (2014a). Educational institutions, resources, and students’ resiliency: An empirical study about OECD countries. Economics Bulletin, 34(2), 1055–1067.

    Google Scholar 

  5. Agasisti, T., & Longobardi, S. (2014b). Inequality in education: Can Italian disadvantaged students close the gap? Journal of Behavioral and Experimental Economics, 52, 8–20. https://doi.org/10.1016/j.socec.2014.05.002.

    Article  Google Scholar 

  6. Bernard, B. (2004). Resiliency: What we have learned. San Francisco: WestEd.

    Google Scholar 

  7. BOE (2002). Organic Law 10/2002, 23rd December, of the Quality of Education (LOCE). Spain: Nº 307, 24th December 2002, 45188–45220.

  8. BOE (2006). Organic Law 2/2006, 3rd May, of Education (LOE). Spain: Nº 106, 4th May 2006, 17158–17207.

  9. Borman, G. D., & Overman, L. T. (2004). Academic resilience in mathematics among poor and minority students. The Elementary School Journal, 104(3), 177–195.

    Article  Google Scholar 

  10. Brozo, W. G., Sulkunen, S., Shiel, G., Garbe, C., Pandian, A., & Valtin, R. (2014). Reading, gender, and engagement. Journal of Adolescent and Adult Literacy, 57(7), 584–593. https://doi.org/10.1002/jaal.291.

    Article  Google Scholar 

  11. Brune, N., & Garrett, G. (2005). The globalization Rorschach test: International economic integration, inequality, and the role of government. Annual Review of Political Science, 8, 399–423. https://doi.org/10.1146/annurev.polisci.6.121901.085727.

    Article  Google Scholar 

  12. Cassidy, S. (2015). Resilience building in students: The role of academic self-efficacy. Frontiers in Psychology, 6, 1–14. https://doi.org/10.3389/fpsyg.2015.01781.

    Article  Google Scholar 

  13. Cattani, L., & Pedrini, G. (2020). Opening the black-box of graduates’ horizontal skills: Diverging labour market outcomes in Italy. Studies in Higher Education. https://doi.org/10.1080/03075079.2020.1723527 ((in press)).

    Article  Google Scholar 

  14. Chandler, D. (2014). Resilience. The governance of complexity. London: Routledge.

    Google Scholar 

  15. Cheung, K.-C. (2017). The effects of resilience in learning variables on mathematical literacy performance: A study of learning characteristics of the academic resilient and advantaged low achievers in Shanghai, Singapore, Hong Kong, Taiwan and Korea. Educational Psychology, 37(8), 965–982. https://doi.org/10.1080/01443410.2016.1194372.

    Article  Google Scholar 

  16. Ciccone, A., & Garcia-Fontes, W. (2009). The quality of the Catalan and Spanish Education Systems: A perspective from PISA. IESE Business School Working Paper No. 810, 1–33. Navarre: IESE Business School. https://doi.org/10.2139/ssrn.1513214.

  17. Clark, C., & Rumbold, K. (2006). Reading for pleasure: A research overview. London: National Literacy Trust.

    Google Scholar 

  18. Cordero, J. M., Crespo-Cebada, E., Pedraja-Chaparro, F., & Santin, D. (2011). Exploring educational efficiency divergences across Spanish regions in PISA 2006. Revista de Economía Aplicada, 19(57), 117–145. https://www.redalyc.org/pdf/969/96922243005.pdf

  19. Cordero, J. M., Pedraja, F., & Simancas, R. (2015). Success factors for educational attainment in unfavorable socioeconomic conditions. Revista de Educación, 370, 172–198. https://doi.org/10.4438/1988-592X-RE-2015-370-302.

    Article  Google Scholar 

  20. Cosden, M., Morrison, G., Albanese, A. L., & Macias, S. (2001). When homework is not home work: After-school programs for homework assistance. Educational Psychologist, 36(3), 211–221. https://doi.org/10.1207/s15326985ep3603_6.

    Article  Google Scholar 

  21. Crespo-Cebada, E., Pedraja-Chaparro, F., & Santin, D. (2014). Does school ownership matter? An unbiased efficiency comparison for regions of Spain. Journal of Productivity Analysis, 41, 153–172. https://doi.org/10.1007/s11123-013-0338-y.

    Article  Google Scholar 

  22. Dolton, P., & Marcenaro-Gutierrez, O. D. (2005). Career progression: Getting-on, getting-by and going-nowhere. Education Economics, 13(2), 239–257. https://doi.org/10.1080/09645290500031447.

    Article  Google Scholar 

  23. Duncan, G. J., & Hoffman, S. D. (1981). The incidence and wage effects of overeducation. Economics of Education Review, 1(1), 75–86. https://doi.org/10.1016/0272-7757(81)90028-5.

    Article  Google Scholar 

  24. Escardíbul, J.-O., & Villarroya, A. (2009). The inequalities in school choice in Spain in accordance to PISA data. Journal of Education Policy, 24(6), 673–696. https://doi.org/10.1080/02680930903131259.

    Article  Google Scholar 

  25. Feng, X., Xie, K., Gong, S., Gao, L., & Cao, Y. (2019). Effects of parental autonomy support and teacher support on middle school students’ homework effort: Homework autonomous motivation as mediator. Frontiers in Psychology, 10, 1–11. https://doi.org/10.3389/fpsyg.2019.00612.

    Article  Google Scholar 

  26. Filippin, A., & Paccagnella, M. (2012). Family background, self-confidence and economic outcomes. Economics of Education Review, 31(5), 824–834. https://doi.org/10.1016/j.econedurev.2012.06.002.

    Article  Google Scholar 

  27. Finn, J. D., & Rock, D. A. (1997). Academic success among students at risk for school failure. Journal of Applied Psychology, 82(2), 221–234. https://doi.org/10.1037/0021-9010.82.2.221.

    Article  Google Scholar 

  28. García-Pérez, J. I., Hidalgo-Hidalgo, M., & Robles-Zurita, J. A. (2014). Does grade retention affect students’ achievement?. Some evidence from Spain. Applied Economics, 46(12), 1373–1392. https://doi.org/10.1080/00036846.2013.872761.

    Article  Google Scholar 

  29. Gil, J. (2014). Factores asociados a la brecha regional del rendimiento español en la evaluación PISA. Revista de Investigación Educativa, 32(2), 393–410. https://doi.org/10.6018/rie.32.2.192441.

    Article  Google Scholar 

  30. Gordon, K. A. (2001). Resilient students’ goals and motivation. Journal of Adolescence, 24(4), 461–472. https://doi.org/10.1006/jado.2001.0383.

    Article  Google Scholar 

  31. Green, F., & Henseke, G. (2017). Graduates and ‘graduate jobs’ in Europe: A picture of growth and diversification. Working paper no. 25, 1–43. England: Centre for Global Higher Education.

    Google Scholar 

  32. Hailpern, S. M., & Visintainer, P. F. (2003). Odds ratios and logistic regression: Further examples of their use and interpretation. The Stata Journal, 3(3), 213–225. https://doi.org/10.1177/1536867X0300300301.

    Article  Google Scholar 

  33. Hanushek, E. A., Kain, J. F., Markman, J. M., & Rivkin, S. G. (2003). Does peer ability affect student achievement? Journal of Applied Economics, 18(5), 527–544. https://doi.org/10.1002/jae.741.

    Article  Google Scholar 

  34. Hanushek, E., & Woessmann, L. (2011). The economics of international differences in educational achievement. In E. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the economics of education (Vol. 3, pp. 89–200). Amsterdam: Elsevier B.V.

    Google Scholar 

  35. Hemmerechts, K., Agirdag, O., & Kavadias, D. (2016). The relationship between parental literacy involvement, socio-economic status and reading literacy. Educational Review, 69(1), 85–101. https://doi.org/10.1080/00131911.2016.1164667.

    Article  Google Scholar 

  36. IECA (2020). Premature dropout rate by sex. http://www.juntadeandalucia.es/institutodeestadisticaycartografia/indsoc/indicadores/1038.htm. Accessed December 2020.

  37. Kane, T. J. (2004). The impact of after-school programs: Interpreting the results of four recent evaluations. Semantic Scholar. https://doi.org/10.1037/e680712012-001.

    Article  Google Scholar 

  38. Koedel, C., Mihaly, K., & Rockoff, J. E. (2015). Value-added modeling: A review. Economics of Education Review, 47, 180–195. https://doi.org/10.1016/j.econedurev.2015.01.006.

    Article  Google Scholar 

  39. Komarraju, M., & Nadler, D. (2013). Self-efficacy and academic achievement: Why do implicit beliefs, goals, and effort regulation matter? Learning and Individual Differences, 25, 67–72. https://doi.org/10.1016/j.lindif.2013.01.005.

    Article  Google Scholar 

  40. Krovetz, M. L. (2007). Fostering resilience: Expecting all students to use their minds and hearts well. Corwin: Corwin Press.

    Google Scholar 

  41. Kutner, M., Greenberg, E., Jin, Y., Boyle, B., Hsu, Y.-C., & Dunleavy, E. (2007). Literacy in everyday life: Results from the 2003. National Assessment of Adult Literacy (NCES 2007–480). Washington, DC: National Center for Education Statistics.

    Google Scholar 

  42. Lara-Porras, A. M., Rueda-García, M., & Molina-Muñoz, D. (2019). Identifying the factors influencing mathematical literacy in several Spanish regions. South African Journal of Education, 39(2), S1–S13. https://doi.org/10.15700/saje.v39ns2a1630.

    Article  Google Scholar 

  43. Lopez, F., Garcia, I., & Exposito-Casas, E. (2019). Educational effectiveness, efficiency, and equity in Spanish Regions: What does PISA 2015 reveal? Orbis Scholae, 12(2), 9–36. https://doi.org/10.14712/23363177.2018.291.

    Article  Google Scholar 

  44. Martin, A. J., & Marsh, H. W. (2006). Academic resilience and its psychological and educational correlates: A construct validity approach. Psychology in the Schools, 43(3), 267–281. https://doi.org/10.1002/pits.20149.

    Article  Google Scholar 

  45. Mayer, S. E., & Lopoo, L. M. (2008). Government spending and intergenerational mobility. Journal of Public Economics, 92(1–2), 139–158. https://doi.org/10.1016/j.jpubeco.2007.04.003.

    Article  Google Scholar 

  46. MECD. (2016). PISA 2015. Programa para la Evaluación Internacional de los Alumnos. Informe español. Madrid: Ministerio de Educación, Cultura y Deporte.

    Google Scholar 

  47. MEFP. (2018). Panorama de la educación Indicadores de la OCDE 2018. Informe Español. Madrid: Ministerio de Educación y Formación Profesional.

    Google Scholar 

  48. Morales, E. E. (2010). Linking strengths: Identifying and exploring protective factor clusters in academically resilient low-socioeconomic urban students of color. Roeper Review, 32(3), 164–175. https://doi.org/10.1080/02783193.2010.485302.

    Article  Google Scholar 

  49. OECD. (2012). Public and private schools: How management and funding relate to their socio-economic profile. Paris: OECD Publishing. https://doi.org/10.1787/9789264175006-en.

    Google Scholar 

  50. OECD. (2015). The ABC of gender equality in education: Aptitude, behaviour, confidence. Paris: OECD Publishing. https://doi.org/10.1787/9789264229945-en.

    Google Scholar 

  51. OECD. (2016). PISA 2015 results (Volume I): Excellence and equity in education. Paris: OECD Publishing. https://doi.org/10.1787/9789264266490-en.

    Google Scholar 

  52. Panozzo, G. (2002). Read all about it: The Moreland reading PROJECT and the UK national reading campaigns. Australasian Public Libraries and Information Services, 15(2), 52–60.

    Google Scholar 

  53. Patall, E. A., Cooper, H., & Robinson, J. C. (2008). Parent involvement in homework: A research synthesis. Review of Educational Research, 78(4), 1039–1101. https://doi.org/10.3102/0034654308325185.

    Article  Google Scholar 

  54. Pedraja-Chaparro, F., Santín, D., & Simancas, R. (2015). Determinants of grade retention in France and Spain: Does birth month matter? Journal of Policy Modeling, 37(5), 820–834. https://doi.org/10.1016/j.jpolmod.2015.04.004.

    Article  Google Scholar 

  55. Perrons, D., & Plomien, A. (2010). Why socio-economic inequalities increase? Facts and policy responses in Europe. Brussels: European Union.

    Google Scholar 

  56. Ritchie, S. J., & Bates, T. C. (2013). Enduring links from childhood mathematics and reading achievement to adult socioeconomic status. Psychological Science, 24(7), 1301–1308. https://doi.org/10.1177/0956797612466268.

    Article  Google Scholar 

  57. Sacker, A., Schoon, I., & Bartley, M. (2002). Social inequality in educational achievement and psychosocial adjustment throughout childhood: Magnitude and mechanisms. Social Science and Medicine, 55(5), 863–880. https://doi.org/10.1016/s0277-9536(01)00228-3.

    Article  Google Scholar 

  58. Stankov, L., Lee, J., Luo, W., & Hogan, D. J. (2012). Confidence: A better predictor of academic achievement than self-efficacy, self-concept and anxiety? Learning and Individual Differences, 22(6), 747–758. https://doi.org/10.1016/j.lindif.2012.05.013.

    Article  Google Scholar 

  59. Thomsen, K. (2002). Building resilient students: Integrating resiliency into what you already know and do. Corwin: Corwin Press.

    Google Scholar 

  60. Veselska, Z., Madarasova, A., Gajdosova, B., Orosova, O., van Dijk, J. P., & Reijneveld, S. A. (2009). Socio-economic differences in self-esteem of adolescents influenced by personality, mental health and social support. The European Journal of Public Health, 20(6), 647–652. https://doi.org/10.1093/eurpub/ckp210.

    Article  Google Scholar 

  61. Zief, S. G., Lauver, S., & Maynard, R. A. (2006). Impacts of after-school programs on student outcomes. Campbell Systematic Reviews, 2(1), 1–51. https://doi.org/10.4073/csr.2006.3.

    Article  Google Scholar 

Download references

Acknowledgements

The data used in this research has been provided by Agencia Andaluza de Evaluación Educativa, Consejería de Educación, Junta de Andalucía. This work has been partly supported by the Ministerio de Economía, Industria y Competitividad under Research Project ECO2017-88883-R, the FEDER funding under Research Project UMA18FEDERJA024 and Fundación Centro de Estudios Andaluces under Research Project PRY085/19.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Oscar David Marcenaro-Gutierrez.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables

Table 8 Descriptive statistics of the socio-economic characteristics of the Andalusian student population in 5th grade, the sample of students who can be followed in 8th grade and the sample of students who can be followed in 8th grade and is employed in the estimations

8,

Table 9 Mean standardised scores in reading, mathematics and socio-economic status index in 8th grade, conditioned on the covariates that define the profile of advantaged low-achieving students

9 and

Table 10 Determinants of the likelihood of becoming a resilient student

10.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Marcenaro-Gutierrez, O.D., Lopez-Agudo, L.A. The back of the coin in resilience: on the characteristics of advantaged low-achieving students. Econ Polit (2021). https://doi.org/10.1007/s40888-021-00220-0

Download citation

Keywords

  • Advantaged low-achieving students
  • Census data
  • Longitudinal data
  • Andalusia

JEL Classification

  • I20
  • I21
  • I28