Cross-National Predictors of Crime: A Meta-Analysis

Abstract

Cross-national research has increased in the past few decades, resulting in a large body of empirical research. In particular, cross-national studies are often limited in data sources, which restrict variable selection to debatable proxy indicators. This study therefore uses meta-analytic techniques to examine major cross-national predictors of homicide to determine strengths and weaknesses in theory and design. The findings indicate several critical limitations to cross-national research, including biased sample composition, a lack of theoretical clarity in predictor operationalizations, and an overwhelming reliance on cross-sectional design. The predictors that showed the strongest mean effects were Latin American regional dummy variables, income inequality indicators and the Decommodification Index. Conversely, static population indicators, democracy indices, and measures of economic development had the weakest effects on homicide.

Societal Violence and Collective Consciousness

“…This would predict increased rather than decreased violent crime. Also, the proportion of youth aged 18 to 25 positively correlated with higher homicide or violent crime (Nivette, 2011) was increasing through the 2000s (Howden & Meyer, 2011) and, thus, would not predict reduced rates of homicide or violent crime. Recent attention has been given to the role of temperature on interpersonal violence (Hsiang, Burke, & Miguel, 2013), with an estimated 4% increase in violence for one standard deviation of warmer temperature; but again, any recent trend to warmer temperature would not predict a resulting decrease in violence, and the seasonal variations in violence associated with annual cycles of temperature are modeled by the seasonal parameters of the analyses performed here.…”

Intra- and inter-neighborhood income inequality and crime

“…Others (see Hipp 2007) argue that income inequality can also increase crime, though empirical findings have been mixed (for discussions see Ousey and Lee 2013;Patterson 1991). In addition, many studies of income inequality and crime have been conducted at large levels of aggregation such as nations (Chamlin and Cochran 2006;Nivette 2011;Pratt and Godsey 2003), cities (Harer and Steffensmeier 1992;Kovandzic, Vieraitis, and Yeisley 1998;Parker and McCall 1997;Shihadeh and Steffensmeier 1994) or SMSAs (Blau and Blau 1982) (see Ousey and Lee 2013 for an overview). Several studies (e.g.…”

Beyond Crime Statistics: The Construction and Application of a Criminogenity Monitor in Amsterdam

“…The first step was to extract risk factors from metastudies conducted in criminology (e.g. Bol et al 1998;Buettner and Spengler 2003;Gendreau et al 1996;Hawkins 1995;Hawkins et al 2000;Huang and Wellford 1989;Jones and Connelly 2002;Nivette 2011;Pratt and Cullen 2005;Van der Laan and Blom 2006). On top of that, we made use of risk factors applied in the Communities that Care programme mentioned above and risk factors available from an evaluation of recidivism probability.…”