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Measuring inequality beyond the Gini coefficient may clarify conflicting findings


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.

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Fig. 1: Plotting the distributions of income for Putnam County, Ohio, and Chambers County, Texas.
Fig. 2: The strength of evidence in favour of the two-parameter Ortega model.
Fig. 3: Using simulations to systematically vary the two Ortega parameters to identify their impacts on the shape of the income distribution.
Fig. 4: Different representations of inequality across counties in the United States.
Fig. 5: A two-parameter Ortega approach reveals significant correlations between inequality and policy outcomes across N = 3,049 US counties that the Gini coefficient misses in our dataset.

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We thank S. Bhatia, S. Davidai, T. Graeber and J. Tan for helpful discussions and comments that substantially improved this paper; I. Zahn for technical support; and M. Kalisch for his advice on statistics. We also acknowledge funding from the German Academic Scholarship Foundation (to K.B.), Harvard Business School (to J.M.J.), University of Exeter Business School (to O.P.H.) and the UKRI Future Leaders Fellowship (to O.P.H.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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K.B. led the data collection and statistical analysis under the supervision of J.M.J. and O.P.H. All authors wrote and edited the paper.

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Correspondence to Kristin Blesch, Oliver P. Hauser or Jon M. Jachimowicz.

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Supplementary Figs. 1–24, Tables 1–11, Methods and Results.

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Blesch, K., Hauser, O.P. & Jachimowicz, J.M. Measuring inequality beyond the Gini coefficient may clarify conflicting findings. Nat Hum Behav 6, 1525–1536 (2022).

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