The impact of job stability on monetary poverty in Italy: causal small area estimation

Job stability – encompassing secure contracts, adequate wages, social benefits, and career opportunities – is a critical determinant in reducing monetary poverty, as it provides households with reliable income and enhances economic well-being. This study draws on EU-SILC survey and census data to estimate the causal effect of job stability on monetary poverty across Italian provinces, quantifying its influence, and analyzing regional disparities. We introduce a novel causal small area estimation (CSAE) framework that integrates global and local estimation strategies for heterogeneous treatment effect estimation, effectively addressing data sparsity at the provincial level. Furthermore, we develop a general bootstrap scheme to construct reliable confidence intervals, applicable regardless of the method used for estimating nuisance parameters. Extensive simulation studies demonstrate that our proposed estimators outperform classical causal inference methods in terms of stability while maintaining computational scalability for large datasets. Applying this methodology to real-world data, we uncover significant relationships between job stability and poverty in six Italian regions, offering critical insights into regional disparities and their implications for evidence-based policy design.

就业稳定性——涵盖稳定用工合同、合理薪酬、社会福利与职业发展机遇——是降低货币贫困的关键决定因素，它能为家庭提供稳定收入并提升经济福祉。本研究依托欧盟收入与生活条件统计（EU-SILC）调查数据与人口普查数据，对意大利各省份就业稳定性对货币贫困的因果效应进行估计，量化其影响程度并分析区域差异。我们提出了一种全新的因果小域估计（Causal Small Area Estimation, CSAE）框架，该框架整合了全局与局部估计策略以实现异质性处理效应估计，有效解决了省级层面的数据稀疏性问题。此外，我们开发了一种通用的Bootstrap抽样方案，用于构建可靠的置信区间，该方案适用于任何估计冗余参数的方法。大量模拟研究表明，我们提出的估计量在稳定性上优于经典因果推断方法，同时在大型数据集上仍保持计算可扩展性。将该方法应用于真实数据后，我们在意大利六个地区发现了就业稳定性与贫困之间的显著关联，为区域差异及其对循证政策设计的启示提供了重要洞见。