Does socioeconomic inequality affect education more in less wealthy municipalities?

Authors

Keywords:

Data Envelopment Analysis – DEA, Educational Efficiency, Basic Education, Social Inequalities

Abstract

This study attempts to capture the full picture of educational development in an emerging country that is characterized by both high economic development and high socioeconomic inequality. A two-step model is used in this study. The first step uses the variables that are directly related to education to capture the educational efficiency of each municipality; the second step uses a statistical Tobit model to estimate the influence of the non-discretionary variables on the educational efficiency found in the first step. A previous categorization by clusters is also implemented to ensure a fair comparison among homogeneous municipalities. The results show significant discrepancies in the influence of socioeconomic variables on educational outcome, which depends on the welfare of the cluster.  

 

Downloads

Download data is not yet available.

References

AFONSO, A.; AUBYN, M. St. Cross-country efficiency of secondary education provision: a semi--parametric analysis with non-discretionary inputs. Economic Modelling, v. 23, n. 3, p. 476-491, 2006.

AGASISTI, T. The efficiency of Italian secondary schools and the potential role of competition: a data envelopment analysis using OECD-PISA 2006 data. Education Economics, v. 21, n. 5, p. 520-544, 2013. Disponível em: <http://www.oecd.org/edu/Brazil_EAG2013%20Country%20Note.pdf>. Acesso em: 03 maio 2017.

AGENCY FOR INTERNATIONAL DEVELOPMENT IN COOPERATION. Health Systems 20/20. The health system assessment approach: a how-to manual, 2012. Disponível em: <http://apps.who.int/medicinedocs/documents/s19838en/s19838en.pdf>. Acesso em: 03 maio 2017.

BRADLEY, S.; JOHNES, G.; MILLINGTON, J. The effect of competition on the efficiency of

secondary schools in England. European Journal of Operational Research, Amsterdã, v. 135, n. 3, p. 545-568, 2001.

BRYK, A. S.; THUM, Y. M. The effects of high school organization on dropping out: an exploratory investigation. American Educational Research Journal, Thousand Oaks, CA, v. 26, n. 3, p. 353-383, 1989.

CHARNES, A.; COOPER, W. W.; RHODES, E. Measuring the efficiency of decision-making units. European Journal of Operational Research, Amsterdã, v. 2, n. 6, p. 429-442, 1978.

CHUDGAR, A.; LUSCHEI, T. National income, income inequality and the importance of schools: a hierarchical cross-national comparison. American Educational Research Journal, Thousand Oaks, CA, v. 46, n. 3, p. 626-58, 2009.

CHUDGAR, A.; SHAFIK, M. N. Family, community, and educational outcomes in South Asia. Prospects, Manchester, UK, v. 40, n. 4, p. 517-534, 2010.

COELLI, T. J.; RAO, D. S. P.; O’DONNELL, C. J.; BATTESSE, G. E. An introduction to efficiency and productivity analysis. 2. ed. New York: Springer Science, 2005.

COMMANDER, S.; DAVOODI, H. R.; LEE, U. J. The causes of government and the consequences for growth and well-being. Washington, DC: World Bank, 1997. (Policy, Research working paper, n. WPS 1785). Disponível em: <http://documents.worldbank.org/curated/en/375251468766769196/The-causes-of-government-and-the-consequences-for-growth-and-wellbeing>. Acesso em: 03 maio 2017.

DRINEAS, P.; KANNAN, R.; MAHONEY, M. W. Fast Monte Carlo algorithms for matrices II: computing a low-rank approximation to a matrix. SIAM Journal on Computing, Philadelphia, PA, v. 36, n. 1, p. 158-183, 2006.

DYSON, R. G.; ALLEN, R.; CAMANHO, A. S.; PODINOVSKI, V. V.; SARRICO, C. S.; SHALE, E. A. Pitfalls and protocols in DEA. European Journal of Operational Research, Amsterdã, v. 132, n. 2, p. 245-259, 2001.

FERNANDES, R. Índice de desenvolvimento da educação básica (Ideb): metas intermediárias para a sua trajetória no Brasil, estados, municípios e escolas. Brasília, DF: MEC/Inep, 2007. Disponível em: <http://download.inep.gov.br/educacao_basica/portal_ideb/o_que_sao_as_metas/Artigo_projecoes.pdf>. Acesso em: 03 maio 2017.

FUNDO DAS NAÇÕES UNIDAS PARA A INFÂNCIA – UNICEF. Situação da infância e da adolescência brasileira em 2009. O direito de aprender: potencializar avanços e reduzir desigualdades. Brasília, DF: Unicef, 2009. Disponível em: <http://www.unicef.org/brazil/pt/siab_capitulos.pdf>. Acesso em: 03 maio 2017.

GOLDSCHMIDT, P.; WANG, J. When can schools affect dropout behavior? A longitudinal multilevel analysis. American Educational Research Journal, Thousand Oaks, CA, v. 36, n. 4, p. 715-738, 1999.

HANUSHEK, E. A. The economics of schooling: production and efficiency in public schools. Journal of Economic Literature, Washington, D.C, v. 24, n. 3, p. 1141-1177, 1986.

HANUSHEK, E. A.; LUQUE, J. A. Efficiency and equity in schools around the world. Economics of Education Review, Denver, v. 22, n. 5, p. 481-502, 2003.

HAUNER, D. Explaining differences in public sector efficiency: evidence from Russia’s regions. World Development, Michigan, v. 36, n. 10, p. 1745-1765, 2008.

HEDGES, L. V.; LAINE, R.; GREENWALD, R. Does money matter? A meta-analysis of studies of the effect of different school inputs on student outcomes. Educational Researcher, Thousand Oaks, CA, v. 23, n. 3, p. 5-14, 1994.

HENRIQUEZ, F.; LARA, B.; MIZALA, A.; REPETTO, A. Effective schools do exist: low-income children’s academic performance in Chile. Applied Economics Letters, Londres, v. 19, n. 5, p. 445-451, 2012.

HOLMLUND, H.; MCNALLY, S.; VIARENGO, M. Does money matter for schools? Economics of Education Review, Denver, v. 29, n. 6, p. 1154-1164, 2010.

JAIN, A. K. Data clustering: 50 years beyond K-means. Pattern Recognition Letters, v. 31, n. 8, p. 651-666, 2010.

JAIN, A. K.; DUBES, R. Algorithms for clustering data. Englewood Cliffs, NJ: Prentice-Hall, 1988.

KIRJAVAINEN, T.; LOIKKANEN, H. A. Efficiency differences of Finnish senior secondary schools: an application of DEA and Tobit analysis. Economics of Education Review, Denver, v. 17, n. 4, p. 377-394, 1998.

LEE, V. E.; BURKAM, D. T. Dropping out of high school: the role of school organization and structure. American Educational Research Journal, Thousand Oaks, CA, v. 40, n. 2, p. 353-393, 2003.

LIU, J. S.; LU, L. Y. Y.; LU, W. M.; LIN, B. J. Y. A survey of DEA applications. Omega, v. 41, n. 5, p. 893-902, 2013.

MANCEBON, M. J.; BANDRÉS, E. Efficiency evaluation in secondary schools: the key role of model specification and of ex post analysis of results. Education Economics, Londres, v. 7, n. 2, p. 131-52, 1999.

MIRANDA, R. N.; MENDES, M. Municípios em extrema pobreza: só dinheiro não resolve. Brasília: Consultoria Legislativa do Senado Federal, 2004. (Textos para discussão, n. 15). Disponível em:

<https://www12.senado.leg.br/publicacoes/estudos-legislativos/tipos-de-estudos/textos-para-discussao/td-15-municipios-em-extrema-pobreza-so-dinheiro-nao-resolve>. Acesso em: 03 maio 2017.

MURILLO, F. J.; ROMÁN, M. School infrastructure and resources do matter: analysis of the incidence of school resources on the performance of Latin American students. School Effectiveness and School Improvement, Londres, v. 22, n. 1, p. 29-50, 2011.

NEAL, D. The effects of Catholic secondary schooling on educational achievement. Journal of Labor Economics, Chicago, v. 15, n. 1, p. 98-123, 1997.

ORGANIZAÇÃO PARA COOPERAÇÃO E DESENVOLVIMENTO ECONÔMICO – OECD. Education at a glance, 2012. Disponível em: <https://www.oecd.org/edu/EAG%202012_e-book_EN_200912. pdf>. Acesso em: 03 maio 2017.

ORGANIZAÇÃO PARA COOPERAÇÃO E DESENVOLVIMENTO ECONÔMICO – OECD. Education at a glance, 2013. Disponível em: <http://www.oecd-ilibrary.org/education/education-at-aglance-2013_eag-2013-en>. Acesso em: 03 maio 2017.

PRIMONT, D. F.; DOMAZLICKY, B. Student achievement and efficiency in Missouri schools and the No Child Left Behind Act. Economics of Education Review, Denver, v. 25, n. 1, p. 77-90, 2006.

RASSOULI-CURRIER, S. Assessing the efficiency of Oklahoma public schools: a data envelopment analysis. Southwestern Economic Review, Texas, v. 34, n. 1, p. 131-144, 2007.

RAY, S. C. Resource use efficiency in public schools: a study of Connecticut data. Management Science, Chicago, v. 37, n. 12, p. 1620-1628, 1991.

RODRÍGUEZ-POSE, A.; TSELIOS, V. Mapping the European regional educational distribution. European Urban and Regional Studies, Pennsylvania, v. 18, n. 4, p. 358-374, 2011. Disponível em:

<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1014.1569&rep=rep1&type=pdf>. Acesso em: 03 maio 2017.

RUMBERGER, R. W. Dropping out of middle school: a multilevel analysis of students and schools. American Educational Research Journal, Thousand Oaks, CA, v. 32, n. 3, p. 583-625, 1995.

RUMBERGER, R. W.; PALARDY, G. J. High school performance: test scores, dropout rates, and transfer rates as alternative indicators of high school performance. American Educational Research Journal, Thousand Oaks, CA, v. 42, n. 3, p. 3-42, 2005.

RUMBERGER, R. W.; THOMAS, S. L. The distribution of dropout and turnover rates among urban and suburban high schools. Sociology of Education, v. 73, n. 1, p. 39-67, 2000.

SHAH, A. A practitioner’s guide to intergovernmental fiscal transfers. Washington, DC: World Bank, 2006. (World Bank Policy Research Working Paper, 4039).

SIMAR, L.; WILSON, P. W. Performance of the bootstrap for DEA estimators and iterating the principle. In: COOPER, W.W.; SEIFORD, L. M.; ZHU, J. (Ed.). Handbook on Data Envelopment Analysis. Boston: Kluwer Academic, 2004. p. 265-298.

TODOS PELA EDUCAÇÃO. Anuário Brasileiro da Educação Básica. São Paulo: Moderna, 2012. Disponível em: <http://www.todospelaeducacao.org.br/biblioteca/1450/anuario-brasileiro-daeducacao-basica>. Acesso em: 03 maio 2017.

UNITED KINGDOM. Parliament. Department for Education. What impact does school spending have on pupil attainment? A review of the recent literature. Londres: Strategic Analysis and Research Division & Infrastructure Funding and Longitudinal Analysis Division, 2014. Disponível em: <http://www.parliament.uk/documents/commons-committees/Education/Impact-of-schoolspending-on-pupil-attainment.pdf>. Acesso em: 03 maio 2017.

ULYSSEA, G.; FERNANDES, R.; GREMAUD, A. P. O impacto do Fundef na alocação de recursos para a educação básica. Pesquisa e Planejamento Econômico, Brasília, DF, v. 36, n. 1, p. 109-136, 2006.

UNITED NATIONS. Diverging growth and development. New York: World Economic and Social Survey, 2006.

YANG, Z. Cross system bank branch evaluation using clustering and data envelopment analysis. In: HUANG, D. S. et al. (Ed.). Advanced intelligent computing theories and applications. Berlin, Heidelberg: Springer, 2010. p. 238-245. (Lecture Notes in Computer Science, v. 6215)

Downloads

Published

2017-05-30

How to Cite

Gramani, M. C. (2017). Does socioeconomic inequality affect education more in less wealthy municipalities?. Cadernos De Pesquisa, 47(164), 470–494. Retrieved from https://publicacoes.fcc.org.br/cp/article/view/4220

Issue

Vol. 47 No. 164: abr./jun.2017

Section

Articles

License

Copyright (c) 2017 Cadernos de Pesquisa

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Authors who publish in this journal agree to the following terms:

a. Authors retain the copyright and grant the journal the right to first publication, with the paper simultaneously licensed under the Creative Commons Attribution license that allows the sharing of the paper with acknowledgment of authorship and initial publication in this journal.

b. Authors are authorized to assume additional contracts separately, for non-exclusive distribution of the version of the paper published in this journal (for example publishing in institutional repository or as a book chapter), with acknowledgment of authorship and initial publication in this journal.

c. Authors are allowed and encouraged to publish and distribute their paper on-line (for example in institutional repositories or on their personal page) at any moment before or during the editorial process, as this can generate productive changes, as well as increase the impact and citation of the published paper (See The Effect of Open Access).