This paper proposes a methodology to obtain estimates in small domains when the target is a composite indicator. These indicators are of utmost importance for studying multidimensional phenomena, but little research has been done on how to obtain estimates of these indicators under the small area context. Composite indicators are particularly complex for this purpose since their construction requires different data sources, aggregation procedures, and weighting which makes challenging not only the estimation for small domains but also obtaining uncertainty measures. As case study of our proposal, we estimate the incidence of multidimensional poverty at the municipality level in Colombia. Furthermore, we provide uncertainty measures based on a parametric bootstrap algorithm.

翻译：本文提出了一种在目标为复合指标时获取小区域估计值的方法。这类指标对于研究多维现象至关重要，但关于如何在小区域背景下获取这些指标估计值的研究却鲜有涉足。复合指标的构建需要整合不同数据源、聚合步骤和权重分配，这使得小区域估计及其不确定性度量均面临挑战。作为案例研究，我们基于该方法估算了哥伦比亚市级多维贫困发生率，并利用参数自助法算法提供了相应的不确定性度量。