Toward multiscalar measures of inequality in archaeology

• 成果类型: Article
• 署名作者: Crema, Enrico R.; Fochesato, Mattia; Ramon, Andres G. Mejia; Munson, Jessica; Ortman, Scott G.
• 署名单位: University of Cambridge; University of Cambridge; Bocconi University; Bocconi University; Bocconi University; Centre for Economic Policy Research - UK; Okinawa Institute of Science & Technology Graduate University; Autonomous University of Barcelona; University of Colorado System; University of Colorado Boulder
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12465
• DOI: 10.1073/pnas.2400700121
• 发表日期: 2025-04-22
• 关键词: wealth; income; BIAS

摘要

The Gini coefficient is a statistical measure commonly used to characterize distributions of socioeconomic quantities. Archaeologists and social scientists have recently adopted this method to analyze ancient inequality by targeting specific proxy variables (e.g., residential unit size, burial data, etc.). Variations in the Gini are then examined in relation to key factors such as time, geography, and subsistence. Yet, Gini coefficients could be obtained across different scales of aggregation, from small neighborhoods within a larger settlement to an ensemble of multiple settlements that are part of the same polity. These different scales of aggregation represent considerable methodological and theoretical challenges, as larger scales might, for example, imply greater social and economic variation within groups and thus affect the Gini coefficients. Furthermore, these issues can also be exacerbated by the idiosyncrasies and limitations of historical and archaeological datasets. This paper discusses the potential and challenges of measuring Gini coefficients at and above the scale of individual archaeological sites, contrasting different approaches and discussing how each can reveal insights into different patterns of past wealth inequality, addressing methodological, empirical, and theoretical implications arising from the multiscalar nature of human interactions.