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.
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The differences in scores between Andalusia and Spain and also Andalusia and the OECD for reading, mathematics and science, are significant at 1%.
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).
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.
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.
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).
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).
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.
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.
This variable is derived from administrative records regarding the biological sex of the student, i.e. male or female.
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.
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.
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).
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.
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.
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.
Estimations have been replicated using only non-repeater students and results do not change. These estimations are available upon request to the authors.
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”.
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.
These estimations will be provided upon request to the authors.
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.
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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.
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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 38, 323–383 (2021). https://doi.org/10.1007/s40888-021-00220-0
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DOI: https://doi.org/10.1007/s40888-021-00220-0