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Changes in agglomeration and productivity are poor predictors of inequality across the archaeological record

Abstract:
We address three basic issues regarding the long-term dynamics of inequality in society. First, we consider the interpretation of residence sizes in socioeconomic terms by comparing statistical patterns extracted from the Global Dynamics of Inequality (GINI) Project database with those from the 21st-century United States. Second, we examine the degree to which the size and productivity of human networks is systematically related to inequality. Finally, we investigate relationships between productivity and productivity growth distributions for patterns of inequality and development across preindustrial societies. We find that across preindustrial societies residence size distributions provide a reasonable proxy for the distribution of productivity (income, a flow of physical and social resources to the group) and a minimum estimator for the distribution of wealth (a stock of such resources accumulated over time); that scale and productivity affect levels of inequality but account for only a small fraction of the observed variance across societies; and that inequality growth is independent of productivity growth, on average and over time. These findings have important implications for efforts to promote more equitable economic development in the present.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1073/pnas.2400693122

Authors


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Role:
Author
ORCID:
0000-0003-0709-5287
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Institution:
University of Oxford
Division:
SSD
Department:
School of Archaeology
Role:
Author
ORCID:
0000-0002-6716-8890
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Role:
Author
ORCID:
0000-0002-2362-3804
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Role:
Author
ORCID:
0000-0001-5613-1243
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Role:
Author
ORCID:
0000-0002-3324-5165


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Funder identifier:
https://doi.org/10.13039/100000001


Publisher:
National Academy of Sciences
Journal:
Proceedings of the National Academy of Sciences More from this journal
Volume:
122
Issue:
16
Article number:
e2400693122
Publication date:
2025-04-14
Acceptance date:
2025-01-29
DOI:
EISSN:
1091-6490
ISSN:
0027-8424


Language:
English
Keywords:
Source identifiers:
2858833
Deposit date:
2025-04-15
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