注意收入差距：小样本中不平等估计量的偏差校正
Mind the Income Gap: Bias Correction of Inequality Estimators in Small-Sized Samples
AI总结：
本文提出了一种针对小样本中不平等估计量的偏差校正方法，通过复杂调查设计提升估计精度，减少偏差并提高小面积估计的可靠性。
摘要：
收入不平等估计量在小样本中存在偏差，通常导致低估。在估计小领域不平等和进行区域层面的小面积估计时，这一点值得特别关注。我们提出了一种针对包含基尼指数、广义熵和阿特金森指数家族的大量不平等度量的偏差校正框架，通过考虑复杂的调查设计。所提出的方法不需要对收入分布进行任何参数假设，非常灵活。通过欧盟SILC数据对我们的方法进行了基于设计的性能评估，结果表明对于所有度量而言都有明显的偏差减少。最后，一个在小面积估计中的应用示例证实，忽略事前偏差校正会导致模型不恰当。
英文摘要：
Income inequality estimators are biased in small samples, leading generally to an underestimation. This aspect deserves particular attention when estimating inequality in small domains and performing small area estimation at the area level. We propose a bias correction framework for a large class of inequality measures comprising the Gini Index, the Generalized Entropy and the Atkinson index families by accounting for complex survey designs. The proposed methodology does not require any parametric assumption on income distribution, being very flexible. Design-based performance evaluation of our proposal has been carried out using EU-SILC data, their results show a noticeable bias reduction for all the measures. Lastly, an illustrative example of application in small area estimation confirms that ignoring ex-ante bias correction determines model misspecification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
