Abstract:House size provides a comparative measure of household wealth that enables archaeologists to track global trends in inequality across a range of sedentary societies. Such approaches hold particular promise for Maya archaeology given its long history of settlement pattern research and recent applications of lidar to map large areas surrounding ancient Maya cities. Estimating dwelling size, however, is not a trivial exercise. This paper addresses potential confounds associated with geometric-based estimates (vol… Show more
“…These measures provide differing data and resulting Gini metrics, thus influencing interpretations of inequality. We note that not all metrics are equally calculable for all research areas, depending on local taphonomic conditions (e.g., modern agricultural activities; see Munson et al 2023; Walden et al 2023), the form of residential settlement (e.g., Marken 2023), and legacy survey mapping (e.g., Richards-Rissetto 2023). Figure 9.Residential units of analysis: (1) individual house mounds shown in different shades of green; (2) all structures per plazuela as the summed part of all the green colored structures; and (3) entire plazuela group as all the green and all the yellow parts of this image.…”
Section: Measuring House Size As Wealthmentioning
confidence: 99%
“…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.…”
Section: The Gini Coefficient and Lorenz Curvementioning
Inequality is present in all human societies, but building a robust understanding of how that inequality developed and persisted for centuries requires historical and archaeological data. Identifying the degree of inequality (or disparity) in ancient communities can be addressed through a variety of methods. One method becoming standard practice in archaeology evaluates inequality through quantitative analysis of robust settlement data. In this Compact Special Section, we assess household size as a potential reflection of wealth inequality among Classic period (a.d. 250–900) Maya settlements. First, we generate house-size data from both pedestrian and remotely sensed LiDAR surveys. Then we use those data to calculate Gini coefficients and Lorenz curves, which provide measures of variation. Gini coefficients range from 0 to 1, where 0 reflects perfect equality and 1 indicates perfect inequality, regardless of the actual values in the distribution. Both area (m2) and volume (m3) provide different, complementary metrics to investigate residential size as a metric for wealth inequality among Classic Maya Lowland settlements. Proposed mechanisms that generate inequality include the intergenerational transmission of wealth and differential access to resources; however, addressing these and other pathways for how inequality develops and persists, and how it was maintained in the past provides insight into similar processes of systemic inequality worldwide.
“…These measures provide differing data and resulting Gini metrics, thus influencing interpretations of inequality. We note that not all metrics are equally calculable for all research areas, depending on local taphonomic conditions (e.g., modern agricultural activities; see Munson et al 2023; Walden et al 2023), the form of residential settlement (e.g., Marken 2023), and legacy survey mapping (e.g., Richards-Rissetto 2023). Figure 9.Residential units of analysis: (1) individual house mounds shown in different shades of green; (2) all structures per plazuela as the summed part of all the green colored structures; and (3) entire plazuela group as all the green and all the yellow parts of this image.…”
Section: Measuring House Size As Wealthmentioning
confidence: 99%
“…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.…”
Section: The Gini Coefficient and Lorenz Curvementioning
Inequality is present in all human societies, but building a robust understanding of how that inequality developed and persisted for centuries requires historical and archaeological data. Identifying the degree of inequality (or disparity) in ancient communities can be addressed through a variety of methods. One method becoming standard practice in archaeology evaluates inequality through quantitative analysis of robust settlement data. In this Compact Special Section, we assess household size as a potential reflection of wealth inequality among Classic period (a.d. 250–900) Maya settlements. First, we generate house-size data from both pedestrian and remotely sensed LiDAR surveys. Then we use those data to calculate Gini coefficients and Lorenz curves, which provide measures of variation. Gini coefficients range from 0 to 1, where 0 reflects perfect equality and 1 indicates perfect inequality, regardless of the actual values in the distribution. Both area (m2) and volume (m3) provide different, complementary metrics to investigate residential size as a metric for wealth inequality among Classic Maya Lowland settlements. Proposed mechanisms that generate inequality include the intergenerational transmission of wealth and differential access to resources; however, addressing these and other pathways for how inequality develops and persists, and how it was maintained in the past provides insight into similar processes of systemic inequality worldwide.
“…Despite extensive use of architecture as a proxy for wealth in the Maya area and the recent trend of compressing these data into a Gini coefficient (Abrams 1994:77; Arnold and Ford 1980; Ashmore 1988:161; Becker 1973; Brown et al 2012; Carmean 1991; Folan et al 2009; Haviland and Moholy-Nagy 1992; Hutson et al 2006; Kurjack 1974), the methods harbor both technical and conceptual problems (Basri and Lawrence 2020; Hutson 2016:150; see also Munson et al 2023). Regarding the latter, the ancient Maya had choices regarding where to invest their resources, and while some households chose to invest in architecture, it is clear that others did not (Hutson 2020; Sheets 2020).…”
Section: Measuring Inequalitymentioning
confidence: 99%
“…Thus, a singular focus on architecture can reduce some of the richness and unpredictability of social life, providing only one metric (a Gini coefficient) to assess wealth inequality in the past. Multiproxy approaches to understanding wealth differentials are ideal (Chase et al 2023; Munson et al 2023), and architecture provides a foundation for future comparative analyses.…”
Gini coefficients for residential groups at Coba for roofed surface area, volume of architecture, and houselot space range from 0.423 to 0.551, fitting well within the range of many ancient and modern state-level cities and societies and other Mesoamerican centers. These values are also similar to other large, Classic period, Northern Lowland cities, such as Dzibilchaltun and Chunchucmil. These data do not support the idea that autocratic regimes exhibit greater wealth inequality. We also failed to find a pattern in which inequality grew over the course of the Classic period. The Lorenz curves for Coba and other sites do not indicate any breaks that would allow households to be sorted into wealth classes. Thus, wealth differences were fluid, continuous, and out in the open, giving these settlements the dynamism and attractiveness that helped them grow into some of the largest and most remarkable ancient Maya cities.
“…As highlighted in this article and in this Compact Special Section of Ancient Mesoamerica , we find that assessments of wealth inequality using house size provide a robust empirical foundation for comprehensively evaluating ancient societies (Ames and Grier 2020; Barnard 2021; Basri and Lawrence 2020; Betzenhauser 2018; Bogaard et al 2019; Brown et al 2012; Chase 2017; Ellyson et al 2019; Feinman et al 2018; Hutson 2016; Hutson and Welch 2021; Kohler and Ellyson 2018; Kohler and Higgins 2016; Kohler et al 2017; Pailes 2018; Porčić 2012; Smith et al 2014; Stone 2018; Strawinska-Zanko et al 2018; Thompson et al 2021a, 2021b). However, because houses vary in form and complexity, exploring how the variations in analytical parameters of house size metrics and what is defined as a household impact our interpretations of wealth inequality in archaeological contexts are important considerations that can broaden understandings of residential inequality in the past (see also Munson et al 2023; Richards-Rissetto 2023) and frame these data (and our findings), contextualizing them in ways that heighten their relevance for the present.…”
To variable degrees, inequality is present in all human societies, but how archaeologists measure inequality varies greatly. In recent research, we used the same unit of analysis, house size, to evaluate residential wealth inequality among the Classic (a.d. 250/300–800) Maya of southern Belize. Using a Gini coefficient, we found that even in this peripheral region, high degrees of inequality were present. However, nuances in inequality metrics vary based on the analytical parameters or units of measurement (area versus volume) and the unit of analysis (individual residential structures, all structures within a household group, or the entire household group, including the built environment). Generally, Gini coefficients calculated from volume are greater than those from area, and the unit of analysis affects the Gini coefficient and, thus, our interpretations of the degree of inequality present. We discuss the impact of the unit of analysis for house sizes, and how it affects our interpretations of residential wealth inequality in the past in conjunction with previous archaeological research. The findings are instrumental for comparative analyses of wealth inequality through the study of house size variation in ancient and modern societies, highlighting the value of clear definitions of the unit of analysis.
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