Abstract
The pursuance of gender equality has embraced a long-standing statistical engendering process to reflect women’s and men’s lives. In pursuing the 2030 Sustainability Development Goals (SDGs), the availability of high-quality gender-sensitive data has generated the current informative outburst. In the process, gender-sensitive data collection has departed from a mere disaggregation between men and women towards an unprecedented multifaceted informational spectrum. Methods for fully exploiting gender-sensitive statistics, both standard and big data, though, face some levels of criticality. The traditional descriptive linear combinations of a collection of simple indicators yields contradictory order results, whereas inference has so far privileged latent modelling only, holding several constraints. A novel statistical perspective stems from recent developments in multivariate latent Markov models (MLMMs), suitable to express a latent characteristic in both time and space. In addition to introducing covariates, on any measurement scale, not only in the measurement model but also the latent one, MLMMs are innovative such that they can handle a vast mass of data from very different sources. Thus, they lead the way to an extensive investigation of the gender gap, accounting for apparent contradictions in rankings and hence highlighting different paths, or transitions, towards a more equitable society.
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Crippa, F., Bertarelli, G., Mecatti, F. (2021). A Spatio-Temporal Approach to Latent Variables: Modelling Gender (im)balance in the Big Data Era. In: Rudas, T., Péli, G. (eds) Pathways Between Social Science and Computational Social Science. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-54936-7_7
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