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The Effects of Cognitive Ability and High School Quality on College Entry Decisions: Nonparametric Estimation of Parameters of Interest

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  • Tobias, Justin
Abstract
The decisions to attend college are analysed and nonparametric predictions compared to those obtained from the widely used logit model. The impacts of measured cognitive ability and proxies for high school quality on the decisions to attend college are examined for a sample of white and black males and females from the USA. Two different parameters of interest which isolate the effects of ability and high school quality on college entry decisions are described and estimated by "integrating out" the effect of other covariates. It is found that measured cognitive ability is an extremely important determinant of college entry for all race and gender groups. At the same point in the ability distribution, blacks are more likely to select into college than whites, and females more likely than males of the same racial group. Proxies for high school quality such as teacher education, student teacher ratios, school enrolment and library size are shown to have little or no effects on the likelihood of college entry for all race and gender groups. Further, predictions obtained from the flexible nonparametric analysis are found to be quite similar to those obtained from the logit model, suggesting that simpler fully parametric binary choice models perform quite well as modelling college entry decisions.

Suggested Citation

  • Tobias, Justin, 2003. "The Effects of Cognitive Ability and High School Quality on College Entry Decisions: Nonparametric Estimation of Parameters of Interest," Staff General Research Papers Archive 12017, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:12017
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    References listed on IDEAS

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    Cited by:

    1. Yuhong Xu & Shih-Fen Cheng & Xinyu Chen, 2023. "Improving Quantal Cognitive Hierarchy Model Through Iterative Population Learning," Papers 2302.06033, arXiv.org, revised Feb 2023.
    2. Anil Kumar, 2006. "Nonparametric conditional density estimation of labour force participation," Applied Economics Letters, Taylor & Francis Journals, vol. 13(13), pages 835-841.

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