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Bayesian Proportional Hazard Analysis of the Timing of High School Dropout Decisions

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  • Mingliang Li

Abstract

In this paper, I study the timing of high school dropout decisions using data from High School and Beyond. I propose a Bayesian proportional hazard analysis framework that takes into account the specification of piecewise constant baseline hazard, the time-varying covariate of dropout eligibility, and individual, school, and state level random effects in the dropout hazard. I find that students who have reached their state compulsory school attendance ages are more likely to drop out of high school than those who have not reached compulsory school attendance ages. Regarding the school quality effects, a student is more likely to drop out of high school if the school she attends is associated with a higher pupil-teacher ratio or lower district expenditure per pupil. An interesting finding of the paper that comes along with the empirical results is that failure to account for the time-varying heterogeneity in the hazard, in this application, results in upward biases in the duration dependence estimates. Moreover, these upward biases are comparable in magnitude to the well-known downward biases in the duration dependence estimates when the modeling of the time-invariant heterogeneity in the hazard is absent.

Suggested Citation

  • Mingliang Li, 2007. "Bayesian Proportional Hazard Analysis of the Timing of High School Dropout Decisions," Econometric Reviews, Taylor & Francis Journals, vol. 26(5), pages 529-556.
  • Handle: RePEc:taf:emetrv:v:26:y:2007:i:5:p:529-556
    DOI: 10.1080/07474930701509416
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    References listed on IDEAS

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    1. Steven G. Rivkin & Eric A. Hanushek & John F. Kain, 2005. "Teachers, Schools, and Academic Achievement," Econometrica, Econometric Society, vol. 73(2), pages 417-458, March.
    2. Caroline M. Hoxby, 1998. "The Effects of Class Size and Composition on Student Achievement: New Evidence from Natural Population Variation," NBER Working Papers 6869, National Bureau of Economic Research, Inc.
    3. Caroline Hoxby, 2000. "Peer Effects in the Classroom: Learning from Gender and Race Variation," NBER Working Papers 7867, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Li, Mingliang & Tobias, Justin L., 2011. "Bayesian inference in a correlated random coefficients model: Modeling causal effect heterogeneity with an application to heterogeneous returns to schooling," Journal of Econometrics, Elsevier, vol. 162(2), pages 345-361, June.
    2. Li, Mingliang, 2009. "Is there "white flight" into private schools? New evidence from High School and Beyond," Economics of Education Review, Elsevier, vol. 28(3), pages 382-392, June.
    3. Fernando Núñez-Regueiro & Pascal Bressoux, 2022. "Évaluer l’action éducative des lycées à travers les indicateurs de valeur ajoutée des lycées : quand le « bruit » s’immisce dans l’administration de la preuve," Post-Print hal-03896378, HAL.

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