Jhordan Silveira de Borba, Sebastián Gonçalves, Celia Anteneodo · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.24192 · cited by 0 works (OpenAlex) · open-access PDF
이전 연구에서 이안 라이트(Ian Wright)가 제안한 자본주의 경제 모델의 에이전트 기반 시뮬레이션을 사용하여 우리는 부와 수입 분포의 불평등이 주로 한 핵심 매개 변수에 의해 지배된다: 인당 평균 부와 평균 임금 사이의 비율, $R$로 지정. 이 매개 변수는 세계 불평등 데이터베이스(WID)와 경제협력개발기구(OECD)의 실험적 데이터를 사용하여 세계 불평등 데이터베이스(World Inequality Database, WID)의 상대적 중요성을 캡처한다. 이 모델에서는 $R$의 더 큰 값이 Gini 요소에 의해 측정된 더 높은 수준의 불평등과 체계적으로 연관되어 있습니다. 현재의 연구에서 우리는 이 관계가 실제 세계 경제에서
Machine translation (Meta M2M100); the English original follows.
In previous studies, using agent-based simulations of a model of a capitalist economy proposed by Ian Wright, we showed that inequality in both wealth and income distributions is largely governed by a single key parameter: the ratio between average wealth per capita and the mean salary, denoted by $R$. This parameter captures the relative importance of accumulated assets compared with labor income. In the model, larger values of $R$ are systematically associated with higher levels of inequality, as measured by the Gini coefficient. In the present work, we investigate whether this relationship is also observed in real-world economies. Using empirical data from the World Inequality Database (WID) and the Organization for Economic Co-operation and Development (OECD), we construct empirical estimates of $R$ and compare them with standard measures of income and wealth inequality. We find a positive relation between $R$ and inequality across several empirical settings, with the strongest relationships observed for mature and advanced economies. These results provide empirical support for the role of the wealth-to-salary ratio as a simple macroscopic indicator associated with economic inequality, while also highlighting the limitations imposed by differences in data definitions and measurement across countries and periods.
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Topics: Inequality, Econometrics, Gini coefficient, Economics, Economic inequality, Income inequality metrics
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