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Evaluation of the P900 Program: A Targeted Education Program for Underperforming Schools

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  • Andrea Tokman

Abstract

Education policies targeted at the worst performing schools are controversial. Its positive discrimination nature has been debated, while the lack of serious empirical evaluation has frozen the discussion at a highly theoretical level. The biggest problem has been to identify unbiased effects, given non-random participation even within underperforming schools. This paper contributes to the debate by evaluating the P900 program - that provides material support to low-achieving Chilean schools- estimating effects that are free of bias due to unmeasured fixed school-specific effects, which are correlated with participation. It explicitly considers the changing nature of both the program and the selection process, computing yearly effects and biases. The simplifying assumptions usually found in the literature (e.g. constant program effects, bias, and school effects) are tested and rejected, thus shedding doubts on previous findings. The paper finds that schools were selected for the P900 in an unobserved compensatory manner, thus downward biasing uncontrolled estimates. Moreover, the bias and the estimated effects of the Program are increasing in time. The 1992 program is not significantly different from zero, while the 1994 and 1996 program´s are significantly positive. The latter is significantly higher than the rest, thus it would be effectively helping the schools that participate in it.

Suggested Citation

  • Andrea Tokman, 2002. "Evaluation of the P900 Program: A Targeted Education Program for Underperforming Schools," Working Papers Central Bank of Chile 170, Central Bank of Chile.
  • Handle: RePEc:chb:bcchwp:170
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    References listed on IDEAS

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    1. Goldberger, Arthur S., 1981. "Linear regression after selection," Journal of Econometrics, Elsevier, vol. 15(3), pages 357-366, April.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    3. Lee, Lung-Fei, 1983. "Generalized Econometric Models with Selectivity," Econometrica, Econometric Society, vol. 51(2), pages 507-512, March.
    4. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    5. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
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    Cited by:

    1. World Bank, 2008. "Colombia - The Quality of Education in Colombia : An Analysis and Options for a Policy Agenda," World Bank Other Operational Studies 7875, The World Bank.
    2. Emanuela di Gropello, 2006. "Meeting the Challenges of Secondary Education in Latin America and East Asia : Improving Efficiency and Resource Mobilization," World Bank Publications, The World Bank, number 7173, January.
    3. Alejandra Mizala & Pilar Romaguera, 2004. "Teachers’ Salary Structure and Incentives in Chile," Documentos de Trabajo 193, Centro de Economía Aplicada, Universidad de Chile.
    4. Emiliana Vegas & Ilana Umansky, 2005. "Improving Teaching and Learning through Effective Incentives : What Can We Learn from Education Reforms in Latin America?," World Bank Other Operational Studies 8694, The World Bank.
    5. Levin, Henry M., 2012. "Some economic guidelines for design of a charter school district," Economics of Education Review, Elsevier, vol. 31(2), pages 331-343.

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