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

Listed author(s):
  • Ling Zhou

    (Southwestern University of Finance and Economics)

  • Huazhen Lin

    (Southwestern University of Finance and Economics)

  • Yi-Chen Lin

    ()

    (Tamkang University)

Registered author(s):

    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.

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    File URL: http://link.springer.com/10.1007/s11205-015-0865-1
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    Article provided by Springer in its journal Social Indicators Research.

    Volume (Year): 125 (2016)
    Issue (Month): 3 (February)
    Pages: 1011-1033

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    Handle: RePEc:spr:soinre:v:125:y:2016:i:3:d:10.1007_s11205-015-0865-1
    DOI: 10.1007/s11205-015-0865-1
    Contact details of provider: Web page: http://www.springer.com

    Order Information: Web: http://www.springer.com/economics/journal/11135

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