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Author: Li, Mingliang
Resulting in 3 citations.
1. Li, Mingliang
Tobias, Justin L.
Bayesian Analysis of Structural Effects in an Ordered Equation System
Studies in Nonlinear Dynamics and Econometrics 10,4 (December 2006): 1363-1363.
Also: http://www.bepress.com/snde/vol10/iss4/art7
Cohort(s): NLSY79
Publisher: Berkeley Electronic Press (bpress)
Keyword(s): Alcohol Use; Bayesian; Child Health; Endogeneity; Modeling, Probit; Mothers; Pregnancy and Pregnancy Outcomes

Permission to reprint the abstract has not been received from the publisher.

We describe a new simulation-based algorithm for Bayesian estimation of structural effects in models where the outcome of interest and an endogenous treatment variable are ordered. Our algorithm makes use of a reparameterization, suggested by Nandram and Chen (1996) in the context of a single equation ordered-probit model, which significantly improves the mixing of the standard Gibbs sampler. We illustrate the improvements afforded by this new algorithm (relative to the standard Gibbs sampler) in a generated data experiment and also make use of our methods in an empirical application. Specifically, we take data from the National Longitudinal Survey of Youth (NLSY) and investigate the impact of maternal alcohol consumption on early infant health. Our results show clear evidence that the health outcomes of infants whose mothers drink while pregnant are worse than the outcomes of infants whose mothers never consumed alcohol while pregnant. In addition, the estimated parameters clearly suggest the need to control for the endogeneity of maternal alcohol consumption. [ABSTRACT FROM AUTHOR]

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Bibliography Citation
Li, Mingliang and Justin L. Tobias. "Bayesian Analysis of Structural Effects in an Ordered Equation System." Studies in Nonlinear Dynamics and Econometrics 10,4 (December 2006): 1363-1363.
2. Tobias, Justin L.
Li, Mingliang
A Finite-Sample Hierarchical Analysis of Wage Variation Across Public High Schools: Evidence from the NLSY and High School and Beyond
Journal of Applied Econometrics 18,3 (May/June 2003):315-347.
Also: http://onlinelibrary.wiley.com/doi/10.1002/jae.696/pdf
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Earnings; Educational Returns; Family Income; High School; High School and Beyond (HSB); Wage Differentials; Wage Dynamics

Permission to reprint the abstract has not been received from the publisher.

Using data from both the National Longitudinal Survey of Youth (NLSY) and High School and Beyond (HSB), we investigate if public high schools differ in the "production" of earnings and if rates of return to future education vary with public high school attended. Given evidence of such variation, we seek to explain why schools differ by proposing that standard measures of school "quality" as well as proxies for community characteristics can explain the observed parameter variation across high schools. Since analysis of widely-used data sets such as the NLSY and HSB necessarily involves observing only a few students per high school, we employ an exact finite sample estimation approach. We find evidence that schools differ and that most proxies for high school quality play modest roles in explaining the variation in outcomes across public high schools. We do find evidence that the education of the teachers in the high school as well as the average family income associated with students in the school play a small part in explaining variation at the school-level. [ABSTRACT FROM AUTHOR]
Bibliography Citation
Tobias, Justin L. and Mingliang Li. "A Finite-Sample Hierarchical Analysis of Wage Variation Across Public High Schools: Evidence from the NLSY and High School and Beyond." Journal of Applied Econometrics 18,3 (May/June 2003):315-347.
3. Tobias, Justin L.
Li, Mingliang
Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures
Journal of Economic Surveys 18,2 (April 2004): 153-181.
Also: http://onlinelibrary.wiley.com/doi/10.1111/j.0950-0804.2004.00003.x/pdf
Cohort(s): NLSY79
Publisher: Blackwell Publishing, Inc. => Wiley Online
Keyword(s): Bayesian; Educational Returns; Modeling; Schooling

In this paper, we review and unite the literatures on returns to schooling and Bayesian model averaging. We observe that most studies seeking to estimate the returns to education have done so using particular (and often different across researchers) model specifications. Given this, we review Bayesian methods which formally account for uncertainty in the specification of the model itself, and apply these techniques to estimate the economic return to a college education. The approach described in this paper enables us to determine those model specifications which are most favored by the given data, and also enables us to use the predictions obtained from all of the competing regression models to estimate the returns to schooling. The reported precision of such estimates also account for the uncertainty inherent in the model specification. Using U.S. data from the National Longitudinal Survey of Youth (NLSY), we also revisit several "stylized facts" in the returns to education literature and examine if they continue to hold after formally accounting for model uncertainty. [ABSTRACT FROM AUTHOR]
Bibliography Citation
Tobias, Justin L. and Mingliang Li. "Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures." Journal of Economic Surveys 18,2 (April 2004): 153-181.