Measuring Latent Variables in Space and/or Time: A Gender Statistics Exercise

Gaia Bertarelli (), Franca Crippa () and Fulvia Mecatti ()
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Gaia Bertarelli: University of Perugia
Franca Crippa: University of Milano-Bicocca
Fulvia Mecatti: University of Milano-Bicocca

Chapter Chapter 12 in Demography and Health Issues, 2018, pp 133-142 from Springer

Abstract: Abstract This paper concerns a Multivariate Latent Markov Model recently introduced in the literature for estimating latent traits in social sciences. Based on its ability of simultaneously dealing with longitudinal and spacial data, the model is proposed when the latent response variable is expected to have a time and space dynamic of its own, as an innovative alternative to popular methodologies such as the construction of composite indicators and structural equation modeling. The potentials of the proposed model and the added value with respect to the traditional weighted composition methodology, are illustrated via an empirical Gender Statistics exercise, focused on gender gap as the latent status to be measured and based on supranational o cial statistics for 30 European countries in the period 2010–2015.

Keywords: Latent clustering; Longitudinal data; Spatial ordering; Gender gap (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-3-319-76002-5_12

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DOI: 10.1007/978-3-319-76002-5_12

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