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Effective Schools Do Exist: Low Income Children's Academic Performance in Chile

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The aim of this article is twofold. First, we show that despite students' disadvantaged backgrounds and despite not having more financial resources than similar schools, there are schools in Chile that serve low-income students and that obtain superior academic outcomes. Second, we present qualitative evidence to identify school and classroom processes that might explain these good results. Specifically, we analyse a network of Chilean private voucher schools called Sociedad de Instrucción Primaria (SIP). In the econometric analysis we use a number of propensity score-based estimation methods to find that SIP students' achievement is not due to observables or selection on measured variables. We also perform a number of interviews in SIP schools and other neighbouring schools. Our qualitative analysis suggests that having children's learning as a central and permanent goal, an aim that is shared and that drives the community's efforts, seems to best summarize what makes SIP schools special.
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  • Andrea Repetto & Francisco Henriquez & Bernardo Lara & Alejandro Mizala, 2011. "Effective Schools Do Exist: Low Income Children's Academic Performance in Chile," Working Papers wp_010, Adolfo Ibáñez University, School of Government.
    • Handle: RePEc:uai:wpaper:wp_010
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    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. Hanushek, Eric A, 1995. "Interpreting Recent Research on Schooling in Developing Countries," The World Bank Research Observer, World Bank, vol. 10(2), pages 227-246, August.
    3. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    4. Richard Emsley & Mark Lunt & Andrew Pickles & GraHam Dunn, 2008. "Implementing double-robust estimators of causal effects," Stata Journal, StataCorp LP, vol. 8(3), pages 334-353, September.
    5. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    6. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
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    Cited by:

    1. Andrea Repetto & Alejandra Mizala & Bernardo Lara, 2010. "Una Mirada a la Efectividad de los Profesores en Chile," Working Papers wp_004, Adolfo Ibáñez University, School of Government.
    2. Francesca Marchetta & Tom Dilly, 2019. "Supporting Education in Africa: Opportunities and Challenges for an Impact Investor," Working Papers hal-02288103, HAL.

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