Composite indicators are a common choice for synthesizing complex phenomena. Over the years, they have grown in popularity and are now applied in many social and environmental sciences. Among others, a subject of increasing interest is gender equality analysis. Gender composite indicators, even if easy to read, may provide a limited picture of the problem. Here we discuss the potentiality to integrate the use of composite indicators for gender gaps with Bayesian networks, powerful tools for explaining the complex association structure in the dataset and developing scenarios to orient policy-making. An example is carried out on Italian province-level data.
Musella, F., Giammei, L., Romio, S., Mecatti, F., Vicard, P. (2022). Bayesian networks for monitoring the gender gap. In A. Balzanella, M. Bini, C. Cavicchia, R. Verde (a cura di), SIS 2022 Book of the Short Papers (pp. 958-963). Pearson.
Bayesian networks for monitoring the gender gap
Romio, S;Mecatti,F;
2022
Abstract
Composite indicators are a common choice for synthesizing complex phenomena. Over the years, they have grown in popularity and are now applied in many social and environmental sciences. Among others, a subject of increasing interest is gender equality analysis. Gender composite indicators, even if easy to read, may provide a limited picture of the problem. Here we discuss the potentiality to integrate the use of composite indicators for gender gaps with Bayesian networks, powerful tools for explaining the complex association structure in the dataset and developing scenarios to orient policy-making. An example is carried out on Italian province-level data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.