Beyond crime statistics: the construction and application of a criminogenity monitor in Amsterdam

Criminologists have devoted a great deal of attention to risk factors - also called criminogenic factors - leading to criminal offending. This paper presents a criminogenity monitor which includes 19 risk factors that underlie crime. These factors do not themselves cause criminal behaviour; rather, they must be seen as signals that crimes may be committed. After discussing how the criminogenity monitor was constructed, we apply the risk factors we examined to the situation in Amsterdam, capital city of the Netherlands. The monitor is intended to function particularly as an instrument to rationalise policy-makers' work in targeting and preventing symptoms of crime at three geographical levels: the entire city, its boroughs and its neighbourhoods. © 2012 The Author(s)

Women convicted for violent offenses: Adverse childhood experiences, low level of education and poor mental health

<p>Abstract</p> <p>Background</p> <p>In past years, the female offender population has grown, leading to an increased interest in the characteristics of female offenders. The aim of this study was to assess the prevalence of female violent offending in a Swiss offender population and to compare possible socio-demographic and offense-related gender differences.</p> <p>Methods</p> <p>Descriptive and bivariate logistic regression analyses were performed for a representative sample of N = 203 violent offenders convicted in Zurich, Switzerland.</p> <p>Results</p> <p>7.9% (N = 16) of the sample were female. Significant gender differences were found: Female offenders were more likely to be married, less educated, to have suffered from adverse childhood experiences and to be in poor mental health. Female violent offending was less heterogeneous than male violent offending, in fact there were only three types of violent offenses females were convicted for in our sample: One third were convicted of murder, one third for arson and only one woman was convicted of a sex offense.</p> <p>Conclusions</p> <p>The results of our study point toward a gender-specific theory of female offending, as well as toward the importance of developing models for explaining female criminal behavior, which need to be implemented in treatment plans and intervention strategies regarding female offenders.</p

Ideological Labels in America

This paper extends Ellis and Stimson’s (Ideology in America. New York: Cambridge UniversityPress, 2012) study of the operational-symbolic paradox using issue-level measures of ideological incongruence based on respondent positions and symbolic labels for these positions across 14 issues. Like Ellis and Stimson, we find that substantial numbers—over 30 %—of Americans experience conflicted conservatism. Our issue-level data reveal, furthermore, that conflicted conservatism is most common on the issues of education and welfare spending. In addition, we also find that 20 % of Americans exhibit conflicted liberalism. We then replicate Ellis and Stimson’s finding that conflicted conservatism is associated with low sophistication and religiosity, but also find that it is associated with being socialized in a post-1960s generation and using Fox News as a main news source. Finally, we show the important role played by identities, with both conflicted conservatism and conflicted liberalism linked with partisan and ideological identities, and conflicted liberalism additionally associated with ethnic identities

Modelling survival : exposure pattern, species sensitivity and uncertainty

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans

Additive and multiplicative hazards modeling for recurrent event data analysis

<p>Abstract</p> <p>Background</p> <p>Sequentially ordered multivariate failure time or recurrent event duration data are commonly observed in biomedical longitudinal studies. In general, standard hazard regression methods cannot be applied because of correlation between recurrent failure times within a subject and induced dependent censoring. Multiplicative and additive hazards models provide the two principal frameworks for studying the association between risk factors and recurrent event durations for the analysis of multivariate failure time data.</p> <p>Methods</p> <p>Using emergency department visits data, we illustrated and compared the additive and multiplicative hazards models for analysis of recurrent event durations under (i) a varying baseline with a common coefficient effect and (ii) a varying baseline with an order-specific coefficient effect.</p> <p>Results</p> <p>The analysis showed that both additive and multiplicative hazards models, with varying baseline and common coefficient effects, gave similar results with regard to covariates selected to remain in the model of our real dataset. The confidence intervals of the multiplicative hazards model were wider than the additive hazards model for each of the recurrent events. In addition, in both models, the confidence interval gets wider as the revisit order increased because the risk set decreased as the order of visit increased.</p> <p>Conclusions</p> <p>Due to the frequency of multiple failure times or recurrent event duration data in clinical and epidemiologic studies, the multiplicative and additive hazards models are widely applicable and present different information. Hence, it seems desirable to use them, not as alternatives to each other, but together as complementary methods, to provide a more comprehensive understanding of data.</p

Re-Arrest Among Juvenile Justice-Involved Youth: An Examination Of The Static And Dynamic Risk Factors

The purpose of this study is to investigate the static and dynamic risk factors for re-arrest among detained youth by examining gender, race/ethnicity, age, special education and mental health variables (i.e., anger/irritability, depression/anxiety, somatic complaints, suicide ideation, thought disturbances, and traumatic experiences). The demographic profiles of detained youth with one admit were also compared with those with multiple admits to the juvenile detention center. With regards to static risk factors, older, white, and special education were significantly at risk of re-arrest. Concerning dynamic risk factors, only anger/irritability predicted re-arrest. Practice implications are also discussed

Some Evidence on the Importance of Sticky Wages

Nominal wage stickiness is an important component of recent medium-scale structural macroeconomic models, but to date there has been little microeconomic evidence supporting the assumption of sluggish nominal wage adjustment. We present evidence on the frequency of nominal wage adjustment using data from the Survey of Income and Program Participation (SIPP) for the period 1996-1999. The SIPP provides high-frequency information on wages, employment and demographic characteristics for a large and representative sample of the US population. The main results of the analysis are as follows. 1) After correcting for measurement error, wages appear to be very sticky. In the average quarter, the probability that an individual will experience a nominal wage change is between 5 and 18 percent, depending on the samples and assumptions used. 2) The frequency of wage adjustment does not display significant seasonal patterns. 3) There is little heterogeneity in the frequency of wage adjustment across industries and occupations 4) The hazard of a nominal wage change first increases and then decreases, with a peak at 12 months. 5) The probability of a wage change is positively correlated with the unemployment rate and with the consumer price inflation rate

An Information Theory Approach to Hypothesis Testing in Criminological Research

Background: This research demonstrates how the Akaike information criterion (AIC) can be an alternative to null hypothesis significance testing in selecting best fitting models. It presents an example to illustrate how AIC can be used in this way. Methods: Using data from Milwaukee, Wisconsin, we test models of place-based predictor variables on street robbery and commercial robbery. We build models to balance explanatory power and parsimony. Measures include the presence of different kinds of businesses, together with selected age groups and social disadvantage. Results: Models including place-based measures of land use emerged as the best models among the set of tested models. These were superior to models that included measures of age and socioeconomic status. The best models for commercial and street robbery include three measures of ordinary businesses, liquor stores, and spatial lag. Conclusions: Models based on information theory offer a useful alternative to significance testing when a strong theoretical framework guides the selection of model sets. Theoretically relevant ‘ordinary businesses’ have a greater influence on robbery than socioeconomic variables and most measures of discretionary businesses