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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bartholomew, D. J. (1973). Stochastic models for social processes (2nd ed.). New York: Wiley.
Bartolucci, F., Pennoni, F., & Francis, B. (2007). A latent Markov model for detecting patterns of criminal activity. Journal of the Royal Statistical Society, Series A, 170, 151–132.
Bertarelli, G. (2015). Latent Markov models for aggregate data: application to dis-ease mapping and small area estimation. Ph.D thesis. https://boa.unimib.it/handle/10281/96252
Fisher, B., & Naidoo, R. (2016). The geography of gender inequality. PlosOne, 11(3), 0145778.
Germain, S. H. (2010). Bayesian spatio-temporal modelling of rainfall through non-homogenous hidden Markov models. PhD thesis, University of Newcastle Upon Tyne.
Mecatti, F., Crippa, F., & Farina, P. (2012). A special gen(d)re of statistics: Roots, develop-ment and methodological prospects of gender statistics. International Statistical Review, 80(3), 452–467.
Permanyer, I. (2010). The measurement of multidimensional gender inequality: Continuing the debate. Social Indicators Research, 95(2), 181–198.
Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.
Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82, 528–540.
United Nation. What are gender statistics? http://unstats.un.org/unsd/genderstatmanual/What-are-gender-stats.ashx
United Nations Development Programme. (2016). Human development reports. http://hdr.undp.org/en/content/gender-inequality-index-gii/
World Economic Forum. (2015). The global gender index. https://www.weforum.org
World Economic Forum. (2016). The global gender gap report 2016. http://reports.weforum.org/global-gender-gap-report-2016/
Zucchini, W., & MacDonald, I. (2009). Hidden Markov models for time series. New York: Springer.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Bertarelli, G., Crippa, F., Mecatti, F. (2018). Measuring Latent Variables in Space and/or Time: A Gender Statistics Exercise. In: Skiadas, C., Skiadas, C. (eds) Demography and Health Issues. The Springer Series on Demographic Methods and Population Analysis, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-76002-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-76002-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76001-8
Online ISBN: 978-3-319-76002-5
eBook Packages: Social SciencesSocial Sciences (R0)