Seminar for Statistics, ETH Zurich, Zurich, Switzerland.
Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany.
Nat Hum Behav. 2022 Nov;6(11):1525-1536. doi: 10.1038/s41562-022-01430-7. Epub 2022 Aug 29.
Prior research has found mixed results on how economic inequality is related to various outcomes. These contradicting findings may in part stem from a predominant focus on the Gini coefficient, which only narrowly captures inequality. Here, we conceptualize the measurement of inequality as a data reduction task of income distributions. Using a uniquely fine-grained dataset of N = 3,056 US county-level income distributions, we estimate the fit of 17 previously proposed models and find that multi-parameter models consistently outperform single-parameter models (i.e., models that represent single-parameter measures like the Gini coefficient). Subsequent simulations reveal that the best-fitting model-the two-parameter Ortega model-distinguishes between inequality concentrated at lower- versus top-income percentiles. When applied to 100 policy outcomes from a range of fields (including health, crime and social mobility), the two Ortega parameters frequently provide directionally and magnitudinally different correlations than the Gini coefficient. Our findings highlight the importance of multi-parameter models and data-driven methods to study inequality.
先前的研究发现，经济不平等与各种结果之间的关系存在多种结果。这些相互矛盾的发现可能部分源于对基尼系数的主要关注，而基尼系数仅狭隘地捕捉到了不平等。在这里，我们将不平等的衡量概念化为收入分布的数据简化任务。使用独特的、细粒度的美国 3056 个县一级收入分布数据集，我们估计了 17 个先前提出的模型的拟合度，发现多参数模型始终优于单参数模型（即，代表基尼系数等单一参数度量的模型）。随后的模拟表明，最佳拟合模型——双参数 Ortega 模型——能够区分低收入和高收入百分位数之间的不平等。当应用于来自多个领域（包括健康、犯罪和社会流动性）的 100 项政策结果时，Ortega 双参数通常比基尼系数提供了方向和幅度上不同的相关性。我们的研究结果强调了多参数模型和数据驱动方法在研究不平等方面的重要性。
