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Gender statistics concerns differences and inequalities in the situation of women and men in all areas of life. Gender inequality - both in space and time - is a latent trait, namely only indirectly measurable through a collection of observablevariables and indicators purposively selected. The most common gender statistical measures are classified in statistical literature as composite indicators. Even if they are normally used by social-scientists, such gender-gap measures are known to havecase-specific technical limitations. In this paper we propose an innovative approach to gender statistics based on a multivariate Latent Markov model (LMM) for the analysis of gender inequalities as measured by the aforementioned indicators.