Search Results

Author: Tobias, Justin L.
Resulting in 10 citations.
1. Heckman, James J.
Tobias, Justin L.
Vytlacil, Edward
Four Parameters of Interest in the Evaluation of Social Programs
Southern Economic Journal 68,2 (October 2001): 211-223.
Also: http://www.jstor.org/stable/1061591
Cohort(s): NLSY79
Publisher: Southern Economic Association
Keyword(s): College Education; Evaluations; Sociability/Socialization/Social Interaction

This paper reviews four treatment parameters that have become commonly used in the program evaluation literature: 1. the average treatment effect, 2. the effect of treatment on the treated, 3. the local average treatment effect, and 4. the marginal treatment effect. The paper derives simply computed closed-form expressions for these treatment parameters in a latent variable framework with Gaussian error terms. These parameters can be estimated using nothing more than output from a standard two-step procedure. It also briefly describes recent work that seeks to go beyond mean effects and estimate the distributions associated with various outcome gains. The techniques presented in the paper are applied to estimate the return to some form of college education for various populations using data from the National Longitudinal Survey of Youth.
Bibliography Citation
Heckman, James J., Justin L. Tobias and Edward Vytlacil. "Four Parameters of Interest in the Evaluation of Social Programs." Southern Economic Journal 68,2 (October 2001): 211-223.
2. Heckman, James J.
Tobias, Justin L.
Vytlacil, Edward
Simple Estimators for Treatment Parameters in a Latent Variable Framework with an Application to Estimating the Returns to Schooling
NBER Working Paper No. W7950, National Bureau of Economic Research, October 2000.
Also: http://nber.nber.org/papers/W7950
Cohort(s): NLSY79
Publisher: National Bureau of Economic Research (NBER)
Keyword(s): Armed Services Vocational Aptitude Battery (ASVAB); College Education; Earnings; Education; Educational Returns; Modeling; Schooling; Selectivity Bias/Selection Bias; Siblings

This paper derives simply computed closed-form expressions for the Average Treatment Effect (ATE), the effect of Treatment on the Treated (TT), Local Average Treatment Effect (LATE) and Marginal Treatment Effect (MTE) in a latent variable framework for both normal and non-normal models. The techniques presented in the paper are applied to estimating a variety of treatment parameters capturing the returns to a college education for various populations using data from the National Longitudinal Survey of Youth (NLSY).
Bibliography Citation
Heckman, James J., Justin L. Tobias and Edward Vytlacil. "Simple Estimators for Treatment Parameters in a Latent Variable Framework with an Application to Estimating the Returns to Schooling." NBER Working Paper No. W7950, National Bureau of Economic Research, October 2000.
3. Koop, Gary
Tobias, Justin L.
Learning about Heterogeneity in Returns to Schooling
Journal of Applied Econometrics 19,7 (November-December 2004): 827-849.
Also: http://www3.interscience.wiley.com/cgi-bin/fulltext/107636928/HTMLSTART
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Bayesian; Educational Returns; Heterogeneity

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

Using data from the National Longitudinal Survey of Youth (NLSY) we introduce and estimate various Bayesian hierarchical models that investigate the nature of unobserved heterogeneity in returns to schooling. We consider a variety of possible forms for the heterogeneity, some motivated by previous theoretical and empirical work and some new ones, and let the data decide among the competing specifications. Empirical results indicate that heterogeneity is present in returns to education. Furthermore, we find strong evidence that the heterogeneity follows a continuous rather than a discrete distribution, and that bivariate normality provides a very reasonable description of individual-level heterogeneity in intercepts and returns to schooling. Copyright (C) 2004 John Wiley Sons, Ltd.
Bibliography Citation
Koop, Gary and Justin L. Tobias. "Learning about Heterogeneity in Returns to Schooling." Journal of Applied Econometrics 19,7 (November-December 2004): 827-849.
4. Koop, Gary
Tobias, Justin L.
Semiparametric Bayesian Inference in Smooth Coefficient Models
Journal of Econometrics 134,1 (September 2006): 283-315.
Also: http://www.sciencedirect.com/science/article/pii/S0304407605001491
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Bayesian; Cognitive Ability; Education; Labor Supply; Modeling; Variables, Independent - Covariate

We describe procedures for Bayesian estimation and testing in cross-sectional, panel data and nonlinear smooth coefficient models. The smooth coefficient model is a generalization of the partially linear or additive model wherein coefficients on linear explanatory variables are treated as unknown functions of an observable covariate. In the approach we describe, points on the regression lines are regarded as unknown parameters and priors are placed on differences between adjacent points to introduce the potential for smoothing the curves. The algorithms we describe are quite simple to implement—-for example, estimation, testing and smoothing parameter selection can be carried out analytically in the cross-sectional smooth coefficient model. We apply our methods using data from the National Longitudinal Survey of Youth (NLSY). Using the NLSY data we first explore the relationship between ability and log wages and flexibly model how returns to schooling vary with measured cognitive ability. We also examine a model of female labor supply and use this example to illustrate how the described techniques can been applied in nonlinear settings. [ABSTRACT FROM AUTHOR; Copyright 2006 Elsevier]
Bibliography Citation
Koop, Gary and Justin L. Tobias. "Semiparametric Bayesian Inference in Smooth Coefficient Models." Journal of Econometrics 134,1 (September 2006): 283-315.
5. Koop, Gary
Tobias, Justin L.
Semiparametric Bayesian Inference in Smooth Coefficient Models
Working Paper No. 04/18, Department of Economics, University of Leicester, October 2003
Cohort(s): NLSY79
Publisher: Department of Economics, University of Leicester
Keyword(s): Bayesian; Cognitive Ability; Educational Returns; Endogeneity; Modeling; Schooling; Variables, Independent - Covariate

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

We describe procedures for Bayesian estimation and testing in both cross sectional and longitudinal data smooth coefficient models (with and without endogeneity problems). The smooth coefficient model is a generalization of the partially linear or additive model wherein coefficients on linear explanatory variables are treated as unknown functions of an observable covariate. In the approach we describe, points on the regression lines are regarded as unknown parameters and priors are placed on differences between adjacent points to introduce the potential for smoothing the curves. The algorithms we describe are quite simple to implement - estimation, testing and smoothing parameter selection can be carried out analytically in the cross-sectional smooth coefficient model, and estimation in the hierarchical models only involves simulation from standard distributions.

We apply our methods by fitting several hierarchical models using data from the National Longitudinal Survey of Youth (NLSY). We explore the relationship between ability and log wages and flexibly model how returns to schooling vary with measured cognitive ability. In a generalization of this model, we also permit endogeneity of schooling and describe simulation-based methods for inference in the presence of the endogeneity problem. We find returns to schooling are approximately constant throughout the ability support and that simpler (and often used) parametric specifications provide an adequate description of these relationships.

Bibliography Citation
Koop, Gary and Justin L. Tobias. "Semiparametric Bayesian Inference in Smooth Coefficient Models." Working Paper No. 04/18, Department of Economics, University of Leicester, October 2003.
6. Koop, Gary
Tobias, Justin L.
Semiparametric Bayesian Inference in Smooth Coefficient Models
Staff General Research Papers 12202, Department of Economics, Iowa State University, July 2004.
Also: http://www.econ.iastate.edu/faculty/tobias/documents/smoothrev2.pdf
Cohort(s): NLSY79
Publisher: Department of Economics, Iowa State University
Keyword(s): Bayesian; Cognitive Ability; Educational Returns; Labor Supply; Modeling; Schooling; Variables, Independent - Covariate

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

We describe procedures for Bayesian estimation and testing in cross sectional, panel data and nonlinear smooth coefficient models. The smooth coefficient model is a generalization of the partially linear or additive model wherein coefficients on linear explanatory variables are treated as unknown functions of an observable covariate. In the approach we describe, points on the regression lines are regarded as unknown parameters and priors are placed on differences between adjacent points to introduce the potential for smoothing the curves. The algorithms we describe are quite simple to implement - for example, estimation, testing and smoothing parameter selection can be carried out analytically in the cross-sectional smooth coefficient model.

We apply our methods using data from the National Longitudinal Survey of Youth (NLSY). Using the NLSY data we first explore the relationship between ability and log wages and flexibly model how returns to schooling vary with measured cognitive ability. We also examine model of female labor supply and use this example to illustrate how the described techniques can been applied in nonlinear settings.

Bibliography Citation
Koop, Gary and Justin L. Tobias. "Semiparametric Bayesian Inference in Smooth Coefficient Models." Staff General Research Papers 12202, Department of Economics, Iowa State University, July 2004.
7. 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.
8. Tobias, Justin L.
Model Uncertainty and Race and Gender Heterogeneity in the College Entry Decision
Economics of Education Review 21,3 (June 2002): 211-219.
Also: http://www.sciencedirect.com/science/article/pii/S0272775701000024#
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Armed Services Vocational Aptitude Battery (ASVAB); Cognitive Ability; College Enrollment; Family Characteristics; Gender Differences; Modeling; Racial Differences; School Quality

This paper uses a flexible modeling strategy to examine the roles of measured ability, family characteristics and proxies for secondary schooling quality as determinants of the decision to enter college. While previous work on this topic has been careful to determine which explanatory variables to include when modeling college entry decisions, few studies have been concerned about appropriate distributional assumptions, (i.e. choice of link function). In this paper, I extend my binary choice analysis to the class of Student-t link functions, which enables me to approximately regard the often-used probit and logit models as special cases. Unconditional estimates which average over competing models and integrate out model uncertainty are also obtained. Using NLSY data, I apply these methods and find that the link functions and estimated impacts of ability and family characteristics on the probabilities of enrolling in college are not constant across race and gender groups.
Bibliography Citation
Tobias, Justin L. "Model Uncertainty and Race and Gender Heterogeneity in the College Entry Decision." Economics of Education Review 21,3 (June 2002): 211-219.
9. 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.
10. 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.