Evidence map›Paper›PMID 36038775›Full record

ArticleNature human behaviour2022

Measuring inequality beyond the Gini coefficient may clarify conflicting findings.

Kristin Blesch, Oliver P Hauser, Jon M Jachimowicz

Abstract read
In one paragraph

Article in Nature human behaviour, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kristin BleschSeminar for Statistics, ETH Zurich, Zurich, Switzerland. blesch@leibniz-bips.de.ORCID 0000-0001-6241-3079
Oliver P HauserDepartment of Economics, University of Exeter Business School, University of Exeter, Exeter, UK. o.hauser@exeter.ac.uk.ORCID 0000-0002-9282-0801
Jon M JachimowiczOrganizational Behavior Unit, Harvard Business School, Harvard University, Boston, MA, USA. jjachimowicz@hbs.edu.ORCID 0000-0002-1197-8958

Funding

Medical Research Council MR/T020253/1
6 · The paper itself

Abstract

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.

Indexed as

CrimeIncomeHumansPolicySocial Mobility

Identifiers

PMID36038775
PMCPMC7614289

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.