Chapter Gender INequality Indicator for Academia (GINIA)

• Main Author: Silan, Margherita (auth)
• Other Authors: Boccuzzo, Giovanna (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: Florence Firenze University Press, Genova University Press 2023
• Series: Proceedings e report
• Subjects: Society & social sciences; Gender equality; Composite indicator; Sensitivity analysis
• Online Access: DOAB: download the publication; DOAB: description of the publication

The main aim of this work consists on a methodological proposal to represent and measure gender inequality in academia, focusing on the University of Padua. In order to reach our goal, we ended up with two different and complementary tools: a system of indicators and a composite indicator, that we called Gender INequality Indicator for Academia (GINIA). Data used to build and compute GINIA in the University of Padua come both from administrative datasets and from an ad-hoc survey, whose data were adjusted by post-stratification weights. Starting from existing indexes described in the literature, the GINIA is articulated into seven domains: work, money, knowledge, time, power, health, and space. These seven domains are better specified and declined through twelve subdomains that are measured by seventeen variables. The composite indicator is the result of the three-step aggregation and weighting procedure: 1) variables are aggregated into subdomains with an arithmetic mean and equal weights; 2) subdomains are aggregated into domains by arithmetic mean with equal weights; 3) domains are aggregated into GINIA indicator by a weighted geometric mean. Indeed, we think that variables related to the same domain can compensate each other, while this consideration is not plausible for domains. Additionally, the weights in the last step are calculated through a preference matrix based on the responses of the respondents about the importance they give to each domain. The indicator can change substantially if we change the methods of weighting or aggregation. Therefore, an uncertainty and sensitivity analysis was undertaken to assess the robustness of the composite indicator as the final step of the analysis with the computation of bootstrap confidence intervals.