The persistent countervailing consequences of urbanization: A longitudinal study of homicide rates

Published date01 November 2023
AuthorMatthew Thomas Clement,Nathan W. Pino,Jarrett Blaustein
Date01 November 2023
Subject MatterArticles
The persistent countervailing
consequences of urbanization:
A longitudinal study of
homicide rates
Matthew Thomas Clement
Texas State University, USA
Nathan W. Pino
Texas State University, USA
Jarrett Blaustein
Monash University, Australia
Quantitative criminologists often use temporally lagged variables to estimate the structural forces
contributing to variation in crime rates. We elucidate the relevance of temporal lags for cross-
national research by looking specically at the lagged longitudinal relationship between urbaniza-
tion and homicide rates. Using cross-national time-series data for (n=83) nations, we run a series
of 10 separate panel models, in which we incrementally increase the time lag between the depend-
ent variable homicide rate and two independent measures of urbanization, controlling for changes
in GDP and age-structure as well as xed effects for time and unit. Results from these panel mod-
els conrm that the two measures of urbanization are oppositely associated with homicide rates.
Moreover, while the magnitudes of the associations for both predictors decline as lag time
increases, they continue to be statistically signicant. These results provide evidence that urban-
ization has countervailing and persistent consequences for homicide rates that ripple through
time. These results also lead us to conclude that a more systematic approach to lag time in lon-
gitudinal research is needed.
Corresponding author:
Matthew Thomas Clement, Department of Sociology, Texas State University, 601 University Dr, San Marcos,
TX 78666, USA.
European Journal of Criminology
2023, Vol. 20(6) 18291851
© The Author(s) 2022
Article reuse guidelines:
DOI: 10.1177/14773708221098990
Homicide, urbanization, cross-national, time lag, persistence effects
Quantitative criminologists have long studied the structural factors contributing to vari-
ation in the homicide rate cross-nationally (for reviews see Koeppel, Rhineberger-Dunn
and Mack, 2015; LaFree, 1999; Nivette, 2011; Trent and Pridemore, 2012). In this vast
literature, there have been several studies examining urbanization, either as a control vari-
able or as a variable of primary theoretical interest (e.g. Clement, Pino and Blaustein,
2019; Koeppel et al., 2015; Levchak, 2016; Nivette, 2011; Rotolo and Tittle, 2006;
Trent and Pridemore, 2012). Although formative theoretical frameworks offered a
uniform perspective on the criminogenic consequences of urbanization (for a review,
see Levchak, 2016), more recent quantitative scholarship, at both the local-level and
cross-nationally, taken as a whole, presents urbanization as a multidimensional process
with independent, countervailing consequences, in particular for the homicide rate
(Chang, Kim and Jeon, 2019; Clement et al., 2019; Levchak, 2016; Sahasranaman and
Bettencourt, 2019). Following this line of inquiry, in a cross-national time-series
study, we delve into the multiple dimensions of urbanization and their consequences
on homicide rates. In doing so, we also reconsider the process of time (e.g. Chamlin
et al., 1992) and ask whether and how variation in homicide rates temporally lags
behind changes in these different measures of urbanization.
Cross-national studies help us understand the impact of global development trajector-
ies and other global phenomena on crime patterns around the world, and homicide data
arguably provide the best basis for cross-national comparisons on crime in part because
they are a relatively reliable measure used to gauge a countrys general criminal violence
rate (Howard, Newman and Pridemore, 2000). In studying time lags, the concept of
urbanization and its operational measures is not described simply as static spatial features
of society but as a spatial-temporal process of consequential social change. This is con-
sistent with the development literature which denes urbanization as a spatial-temporal
process of concentrating large numbers of people, living at high densities (Martine
et al., 2008). Thus, to describe our research question in more detail, we acknowledge
the data and analytic practices when dealing with the issue of time. Previously, in a
meta-analysis of cross-national criminological studies, Nivette (2011: 123) observed,
“‘Timeis nearly nonexistent in cross-national research, as designs predominantly
favor cross-sectional over longitudinal.Other scholars have made similar observations
about the common use of single-wave cross-sectional data in quantitative analyses
focused on urbanization (e.g. Levchak, 2016; Rotolo and Tittle, 2006). While some quan-
titative criminologists continue to conduct single-wave cross-sectional analyses, much
scholarship now incorporates multiwave (i.e. longitudinal or panel) data into their
studies (for a general review, see Koeppel et al., 2015; for a review of urbanization,
see Levchak, 2016).
Although longitudinal data present opportunities to help minimize omitted variable
bias through the use of xed effects, the quantitative cross-national literature as a
whole tends to rely on observational data, which still present potential statistical
1830 European Journal of Criminology 20(6)

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