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US homicide rates increase when resources are scarce and unequally distributed

Published online by Cambridge University Press:  11 December 2023

Weston C. McCool*
Affiliation:
Department of Anthropology, University of Utah, Salt Lake City, UT 84102, USA Society, Water, and Climate Interdisciplinary Research Group, University of Utah, Salt Lake City, UT 84102, USA
Brian F. Codding
Affiliation:
Department of Anthropology, University of Utah, Salt Lake City, UT 84102, USA
*
Corresponding author: Weston C. McCool; E-mail: weston.mccool@anthro.utah.edu

Abstract

As homicide rates spike across the United States, researchers nominate diverse causes such as temperature, city greenness, structural racism, inequality, poverty and more. While variation in homicide rates clearly results from multiple causes, many correlation studies lack the systematic theory needed to identify the underlying factors that structure individual motivations. Building on pioneering work in evolutionary human sciences, we propose that when resources are unequally distributed, individuals may have incentives to undertake high-risk activities, including lethal violence, in order to secure material and social capital. Here we evaluate this theory by analysing federal data on homicide rates, poverty and income inequality across all 50 US states for the years 1990, 2000 and 2005–2020. Supporting predictions derived from evolutionary social sciences, we find that the interaction of poverty (scarcity) and inequality (unequal distribution) best explains variation in US homicide rates. Results suggest that the increase in homicide rates during the height of the COVID-19 pandemic are driven in part by these same underlying causes that structure homicide rates across the US over the last 30 years. We suggest that these results provide compelling evidence to expand strategies for reducing homicide rates by dismantling structures that generate and concentrate sustained poverty and economic inequality.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press
Figure 0

Figure 1. Distribution of homicide rates (per 100,000), Gini Index values and poverty proportions for each US state per decade in our study interval. Scales are not fixed to best highlight variation across time intervals.

Figure 1

Table 1. Model coefficients for parametric and smoothed terms included in the best performing model. These include the estimate (Est.), standard error (SE), t-statistic (t) and p-value (p) for parametric terms, and estimated degrees of freedom (EDF), reference degrees of freedom (RDF), F-statistic (F) and p for smoothed terms

Figure 2

Figure 2. Partial response of homicide rate as a function of (left) the proportion of individuals in poverty for 0% (min), 25% (lower quartile), 50% (median), 75% (upper quartile) and 100% (max) quantiles of the Gini Index during the focal year, and of (right) the Gini Index for each quantile of the proportion of individuals in poverty during the focal year, both predicted for the years 1990, 2000, 2010 and 2020 (from top to bottom). Grey points show the observed value for each state in that focal year. Predicted model fits (lines) only cover the range of observed values.

Supplementary material: File

McCool and Codding supplementary material 1
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Supplementary material: File

McCool and Codding supplementary material 2
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