AI 中文总结
研究通过引入数据驱动框架，用面向对象贝叶斯网络（OOBNs）对欧洲性别平等指数（GEI）建模，解决其概念和方法局限，增强性别平等评估监测，增加预测维度，以支持政策决策，意大利官方统计应用验证了框架实用性。
AI 中文摘要
本文引入了一种新的数据驱动框架，通过补充和强化广泛使用的欧洲性别综合指标——性别平等指数（GEI）来评估性别平等。GEI将性别平等的潜在结构综合为一个单一分数，广泛用于跨国比较和监测。然而，这种做法存在概念和方法上的局限性，包括边际分析未考虑交互作用和条件（非）依赖性，且缺乏预测能力。为解决这些局限，本文提出使用面向对象贝叶斯网络（OOBNs）对GEI进行建模。OOBNs通过保留指数的层次结构扩展了贝叶斯网络，能够对性别平等各组成部分之间的相互依存关系进行多变量和概率表示。该方法通过从计算单一综合分数转向对形成性别不平等的潜在机制进行建模，推进了交叉性别统计。所提出的方法增强了对性别平等的评估和监测，并通过基于情景的评估增加了预测维度，从而支持性别影响评估和政策决策。对意大利官方统计数据的应用说明了该框架的实际相关性及其对其他国家背景和政策需求的适用性。
英文摘要
A novel data-driven framework is introduced to assess gender equality by complementing and empowering a widely used European gender composite indicator, the Gender Equality Index (GEI). The GEI synthetizes the latent construct of gender equality into a single score and is extensively employed for cross-country comparison and monitoring. While effective for communication and benchmarking, this practice is affected by conceptual and methodological limitations, including marginal analysis that leaves interactions and conditional (in)dependencies unmeasured, and a lack of predictive capability. To address these limitations, this paper proposes the use of Object-Oriented Bayesian Networks (OOBNs) to model the GEI. By preserving the hierarchical structure of the index, OOBNs extend Bayesian Networks and enable a multivariate and probabilistic representation of interdependencies among the components of gender equality. This approach advances intersectional gender statistics by shifting the focus from computing a single composite score to modelling the underlying mechanisms that shape gender inequalities. The proposed methodology enhances the assessment and monitoring of gender equality and adds a predictive dimension through scenario-based evaluation, thereby supporting Gender Impact Assessment and policy decision-making. An application to Italian official statistics illustrates the practical relevance of the framework and its applicability to other national contexts and policy needs.
