Dreaming Beyond Gini:

Gini coefficient at La Corona: The impacts of variation in analytical unit and aggregation scale

“…However, because volume provides a critical dimension of information that cannot be ignored, we are not willing to discard it entirely. For this reason, we suggest calculating both area and volume, and then reporting a Gini coefficient range (see Oka et al 2018, for alternative approaches to developing composite coefficients). A second source of significant Gini coefficient variation is the aggregation of different features.…”

Population pressure and prehistoric violence in the Yayoi period of Japan

“…The frequency of violence might be influenced by the development of social hierarchy or political organization (e.g., Oka et al, 2018), presumed to be reflected in highly stratified burial systems in which certain members of the ruling elite, conventionally referred to as 'kings' in Japanese archaeology, have tombs with large quantities of prestige goods such as bronzes (weapons and mirrors), followed by a class of 'warriors' interred with lesser amounts of bronze or iron weapons, while 'commoners' were buried in communal cemeteries with few grave goods. Typical cases are found at the type KIc phase site of Yoshitake-Takagi in the Sawara Plain, and at the type KIIIc phase sites of Mikumo-Minamishoji in the Itoshima Plain and Sugu-Okamoto in the Fukuoka Plain (Kasuga City Board of Education 1994; Fukuoka city Museum of History, 1986).…”

Understanding and calculating household size, wealth, and inequality in the Maya Lowlands

“…These archaeological concepts integrate well with existing social network analysis perspectives more broadly, and specifically with definitions of “network centrality” that provide several interpretations and measurements of the comparative positioning of specific nodes in networks (see Borgatti 2005; Rivers et al 2013). For a pertinent modern example, Oka and colleagues (2018:71–73) present the idea that wealth inequalities in the Soviet Union remained more visible in access to services, a form of relational wealth, rather than in variation among house sizes, a form of material wealth. This suggests that relational wealth may also be considered in terms of time, space, and access to resources on physical landscapes in addition to social connections, statuses, and positions of individuals.…”

“…The first matters because different forms of wealth have different expected Gini for distributions of labor income (0.2–0.4), capital ownership (0.6–0.9), and total inequality (0.3–0.5), as mentioned by Piketty (2014:266–267) in modern and historic datasets. The latter matters because inequality can be rendered less, or more, visible in one type of measurement than in another (e.g., Borgerhoff Mulder et al 2009; Munson et al 2023; Oka et al 2018:71–73). In essence, multiple scholars have updated and modified the Gini index, but their results have simply shifted the underlying analytical issues.…”

“…Analysis of inequality—and use of the Gini coefficient—has remained a pertinent topic of scholarly interest for well over a century (Gini 1912); however, a multitude of ways to measure and consider disparities among peoples, households, and places exist, from various quality of life and capability metrics (e.g., Arponen et al 2016; Borgerhoff Mulder et al 2009; Bourdieu 1986; Bowles et al 2010; Guyer 1993, 1995; Guyer and Belinga 1995; Munson and Scholnick 2022; Nussbaum and Sen 1993; Phillips 2006; Sen 1973, 1992, 1999) to aggregated indices and modified methodological approaches (see, e.g., Blesch et al 2022; Bowles and Carlin 2020; Neves Costa and Pérez-Duarte 2019; Oka et al 2018; Sitthiyot and Holasut 2020). Separate critiques of composite measures like Gini also exist, focusing on how such metrics mix inequalities resulting from income as resource flows and capital as assets (Piketty 2014)—at least in the modern and historic datasets used.…”