The Socioeconomic Determinations of the Distribution of School Learning. The TERCE Data
Keywords:
TERCE, Primary education, Multilevel, School achievement, Educational inequality.Abstract
Based on the data from the Third Regional Comparative and Explanatory Study (Tercer Estudio Regional Comparativo y Explicativo, TERCE), the degree of educational inequality, defined as the strength of the association between the socioeconomic and cultural levels of the students’ families and the performance on math and reading standard tests, is analyzed in this study. The correlations between the results in math and reading tests of students in 6th grade, and the indicators of both the socioeconomic level of the students’ families and the socioeconomic composition of the schools, are analyzed. Multilevel models of three levels (country, school and student) are used. The analysis also allows to estimate the extent to which the school factors may influence the unequal distribution of learning. The study concludes that the socioeconomic composition of student population in school is the main factor explaining the inequalities in performance among schools. After discounting the effect of socioeconomic variables, the proportion of learning inequalities that could be affected by the proper educational variables is drastically reduced in the countries participating in the TERCE as a whole. The image for the countries as a whole is also observed for most of the participating countries.
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Ammermueller, A. y Pischke, J. (2006). Peer effects in european primary schools: evidence from PIRLS. Londres: Centre for the Economics of Education London School of Economics
Baker, D. y Stevenson, D. (1986). Mothers' strategies for children's school achievement: managing the transition to high school. Sociology of Education, 59(3), 156–166. doi:10.2307/2112340
Bishop, J. H. (1992). The impact of academic competencies on wages, unemployment, and job performance. Carnegie-Rochester Conference Series on Public Policy, 37, 127-194. doi:10.1016/0167-2231(92)90006-5
Blanden, J. y Gregg, P. (2004). Family income and educational attainment: a review of approaches and evidence for Britain. Oxford Review of Economic Policy, 20(2), 245-263. doi:10.1093/oxrep/grh014
Boissiere, M., Knight, J. y Sabot, R. (1985). Earnings, schooling, ability, and cognitive skills. The American Economic Review, 75(5), 1016-1030.
Bourdieu, P. y Passeron, J. C. (1990). Reproduction in education, society and culture. Londres: Sage.
Bosker, R. J. y Witziers, B. (1996, abril). The magnitude of school effects, or: does it really matter which school a student attends? Ponencia presentada en el Annual Meeting of the American Educational Research Association, Nueva York.
Bradley, R. H. y Corwyn, R. F. (2002). Socioeconomic status y child development. Annual Review of Psychology, 53(1), 371-399.
Cervini, R. (2012). El “efecto escuela” en países de América Latina: re-analizando los datos del SERCE. Archivos Analíticos de Políticas Educativas, 20(39), 1-24. doi:10.14507/epaa.v20n39.2012
Cordero Ferrera, J., Manchón López, C. y Simancas Rodríguez, R. (2012). Análisis de los condicionantes del rendimiento educativo de los alumnos españoles en PISA 2009 mediante técnicas multinivel. Presupuesto y Gasto Público, 67, 71-96.
Cordero Ferrera, J., Crespo Cebada, E. y Pedraja Chaparro, F. (2013). Rendimiento educativo y determinantes según PISA: una revisión de la literatura en España. Revista de Educación, 362, 273-297.
Currie, J. y Duncan, T. (2001). Early test scores, school quality and SES: long run effects on wage and employment outcomes. Research in Labor Economics, 20, 103-132. doi:10.1016/s0147-9121(01)20039-9
De Jorge Moreno, J. y Santín González, D. (2010). Los determinantes de la eficiencia educativa en la Unión Europea. Hacienda Pública Española, 193(2), 131-156.
Ewijk, R. (2006). The effect of ethnicity and SES in the class on achievement: a meta-analysis. Amsterdam: SCO-Kohnstamm Institute.
Fernández, A. y Del Valle, R. (2013). Desigualdad educativa en Costa Rica: la brecha entre estudiantes de colegios públicos y privados. Análisis con los resultados de la evaluación internacional PISA. Revista CEPAL, 111, 37-57. doi:10.18356/38309458-es
Fusch, T. y Wöβmann, L. (2007). What accounts for international differences in student performance? A re-examination using PISA data. Empirical Economics, 32(2), 433-464. doi:10.1007/978-3-7908-2022-5_10
Goldstein, H. (1989). Multilevel models in educational and social research. American Educational Research Association, 13(2), 187-191. doi:10.2307/2347744
Goldstein, H. (1995). Multilevel statistical models. Londres: Edward Arnold.
Guerin, N., Reinberg A., Testu, F., Boulenguiez, S., Mechkouri, M. y Touitou, Y. (2001). Role of school schedule, age and parental socio-economic status on sleep duration and sleepiness of parisian children. Chronobiology International, 18(6), 1005-1017. doi:10.1081/cbi-100107974
Hauser, R. M. (1970). Context and consex: a cautionary tale. American Journal of Sociology, 75(4), 645-664. doi:10.1086/224894
Jednoróg, K., Altarelli, I., Monzalvo, K., Fluss, J., Dubois, J., Billard, C., Dehaene-Lambertz, G. y Ramuset, F. (2012). The influence of socioeconomic status on children’s brain structure. PLoS ONE, 7(8), 424-486. doi:10.1371/journal.pone.0042486
Ministerio de Educación, Cultura y Deporte. (2013). PISA 2012. Informe español: resultados y contexto. Madrid: Ministerio de Educación, Cultura y Deporte.
Murillo, F. J. (Coord.). (2003). La investigación sobre eficacia escolar en Iberoamérica. Revisión internacional del estado de la cuestión. Bogotá: Convenio Andrés Bello.
Murnane, R., Willett, J. y Levy, F. (1995). The growing importance of cognitive skills in wage determination. Review of Economics and Statistics, 77(2), 251-266. doi:10.3386/w5076
Poston, D. L. y Falbo, T. (1990). Academic performance and personality traits of chinese children: “onlies” versus others. American Journal of Sociology, 96(2), 433-451. doi:10.1086/229535
Reay, D. (2004). It´s all becoming a habitus: beyond the habitual use of habitus in education research. British Journal of Sociology, 25(4), 431-444. doi:10.1080/0142569042000236934
Ridell, A. (1993). The evidence on public/private educational trade-offs in developing countries. International Journal of Educational Development, 13(4), 373-386. doi:10.1016/0738-0593(93)90048-5
Ridell, A. (1997). Assessing designs for school effectiveness research and school improvement in developing countries. Comparative Education Review, 41(2), 178-204. doi:10.1086/447429
Rivera-Batiz, F. (1992). Quantitative literacy and the likelihood of employment among young adults in the United States. Journal of Human Resources, 27(2), 313-328. doi:10.2307/145737
Sirin, S. R. (2005). Socioeconomic status and academic achievement: a meta-analytic review of research. Review of Educational Research, 75(3), 417–453. doi:10.3102/00346543075003417
Slee, R., Weiner, G. y Tomlinson, S. (1998). School effectiveness for whom? Challenges to the school effectiveness and school improvement movements. Londres: Falmer Press.
Smith, L., Fagan, J. y Ulvund, S. (2002). The relation of recognition memory in infancy and parental socioeconomic status to later intellectual competence. Intelligence, 30(3), 247-259. doi:10.1016/s0160-2896(01)00099-x
Teddlie, C., Reynolds, D. y Sammons, P. (2000). The methodology and scientific properties of school effectiveness research. En C. Teddlie y D. Reynolds (Eds.), The international handbook of school effectiveness research (pp. 55-133). Londres: Falmer Press.
Thrupp, M. (2001). Recent school effectiveness counter-critiques: problems and possibilities. British Educational Research Journal, 27(4), 443-457. doi:10.1080/01411920124475
Yeung, W., Linver, M. y Brooks-Gunn, J. (2002). How money matters for young children’s development: parental investment and family processes. Child Development, 73(6), 1861-1879. doi:10.1111/1467-8624.t01-1-00511