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The estimation of Human Capital in structural models with flexible specification

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  • Pietro Giorgio Lovaglio

    ()
    (Dept. of Quantitative Methods, University of Bicocca-Milan)

  • Giuseppe Folloni

    (Department of Economics, University of Trento)

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    Abstract

    The present paper focuses on statistical models for estimating Human Capital (HC) at disaggregated level (worker, household, graduates). The more recent literature on HC as a latent variable states that HC can be reasonably considered a broader multi-dimensional non-observable construct, depending on several and interrelate causes, and indirectly measured by many observed indicators. In this perspective, latent variable models have been assuming a prominent role in the social science literature for the study of the interrelationships among phenomena. However, traditional estimation methods are prone to different limitations, as stringent distributional assumptions, improper solutions, and factor score indeterminacy for Covariance Structure Analysis and the lack of a global optimization procedure for the Partial Least Squares approach. To avoid these limitations, new approaches to structural equation modelling, based on Component Analysis, which estimates latent variables as exact linear combinations of observed variables minimizing a single criterion, were proposed in literature. However, these methods are limited to model particular types of relationship among sets of variables. In this paper, we propose a class of models in such a way that it enables to specify and fit a variety of relationships among latent variables and endogenous indicators. Specifically, we extend this new class of models to allow for covariate effects on the endogenous indicators. Finally, an application aimed to measure, in a realistic structural model, the causal impact of formal Human capital (HC), accumulated during Higher education, on the initial earnings for University of Milan (Italy) graduates is illustrated.

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    File URL: http://www2.almalaurea.it/universita/pubblicazioni/wp/pdf/wp11.pdf
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    Bibliographic Info

    Paper provided by AlmaLaurea Inter-University Consortium in its series Working Papers with number 11.

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    Length: 16
    Date of creation: Sep 2011
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    Handle: RePEc:laa:wpaper:11

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    Web page: http://www.almalaurea.it

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    1. Hartog,Joop & Maassen van den Brink,Henriëtte (ed.), 2007. "Human Capital," Cambridge Books, Cambridge University Press, number 9780521873161, October.
    2. Henk Kiers & Yoshio Takane & Jos Berge, 1996. "The analysis of multitrait-multimethod matrices via constrained components analysis," Psychometrika, Springer, vol. 61(4), pages 601-628, December.
    3. Ludger Woszmann, 2003. "Specifying Human Capital," Journal of Economic Surveys, Wiley Blackwell, vol. 17(3), pages 239-270, 07.
    4. Camilo Dagum & Giorgio Vittadini & Pietro Giorgio Lovaglio, 2007. "Formative Indicators and Effects of a Causal Model for Household Human Capital with Application," Econometric Reviews, Taylor & Francis Journals, vol. 26(5), pages 579-596.
    5. Lovaglio, Pietro Giorgio, 2008. "Process of accumulation of Italian human capital," Structural Change and Economic Dynamics, Elsevier, vol. 19(4), pages 342-356, December.
    6. Izenman, Alan Julian, 1975. "Reduced-rank regression for the multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 5(2), pages 248-264, June.
    7. Dagum, Camilo & Slottje, Daniel J., 2000. "A new method to estimate the level and distribution of household human capital with application," Structural Change and Economic Dynamics, Elsevier, vol. 11(1-2), pages 67-94, July.
    8. Arnold Wollenberg, 1977. "Redundancy analysis an alternative for canonical correlation analysis," Psychometrika, Springer, vol. 42(2), pages 207-219, June.
    9. Takane, Yoshio & Hwang, Heungsun, 2005. "An extended redundancy analysis and its applications to two practical examples," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 785-808, June.
    10. Roger Millsap & William Meredith, 1988. "Component analysis in cross-sectional and longitudinal data," Psychometrika, Springer, vol. 53(1), pages 123-134, March.
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