Education, Intelligence, and Well-Being: Evidence from a Semiparametric Latent Variable Transformation Model for Multiple Outcomes of Mixed Types

Ling Zhou, Huazhen Lin and Yi-Chen Lin ()

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2016, vol. 125, issue 3, 1033 pages

Abstract: This paper uses a semiparametric latent variable transformation model for multiple outcomes to examine the effect of education and maternal education on female multidimensional well-being and proposes a procedure to build a well-being index that is less susceptible to functional form misspecification. We model multidimensional well-being as an unobserved common factor underlying the observed well-being outcomes. The semiparametric methodology allows us to alleviate misspecification bias by combining multiple indicators into a latent construct in an unspecified, data-driven way. Using data from female participants of the 1974–2010 waves of the US General Social Survey, we find that education, intelligence, and maternal education contribute positively to multidimensional well-being. However, the effects of education and maternal education on female multidimensional well-being declined steadily between the mid-1970s and the 1990s, and have not rebounded since. Copyright Springer Science+Business Media Dordrecht 2016

Keywords: Well-being; Education; Intelligence; Maternal education; Latent variable model; Semiparametric method; I25; I31 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11205-015-0865-1

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