The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

David Mcdowall - One of the best experts on this subject based on the ideXlab platform.

  • time series properties of Crime Rate changes comments related to david greenberg s paper
    Justice Quarterly, 2014
    Co-Authors: David Mcdowall
    Abstract:

    David Greenberg has done a masterful job of considering the methodological and data requirements for a study of the New York Crime drop. Anything that I could say about his presentation would amoun...

  • are u s Crime Rate trends historically contingent
    Journal of Research in Crime and Delinquency, 2005
    Co-Authors: David Mcdowall, Colin Loftin
    Abstract:

    Conventional explanations of Crime Rate trends assume that changes in the Rates follow a process that is linear and constant, and that draws its inputs from a normal distribution. These features ensure that the present is linearly predictable from the past and that the future will be linearly predictable from the present. Questioning the conventional assumptions, an emerging class of historical contingency theories stresses variation in the Crime-generating mechanism. According to contingency explanations, the process underlying the Rates is nonlinear or non-normal, or has a structure that shifts over time. Future Rate changes can then differ greatly depending on current conditions, and unanticipated developments will limit the accuracy of linear predictions. This article examines U.S. Crime Rates during the past two-thirds of the twentieth century, and finds little evidence of historical contingency. This supports the standard explanations, but it also raises deeper questions about the forces that produc...

David Bjerk - One of the best experts on this subject based on the ideXlab platform.

  • Racial Profiling, Statistical Discrimination, and the Effect of a Colorblind Policy on the Crime Rate
    Journal of Public Economic Theory, 2007
    Co-Authors: David Bjerk
    Abstract:

    This paper develops a model of racial profiling by law enforcement officers when officers observe both an individual's race as well as a noisy signal of his or her guilt that depends on whether or not a Crime has been committed. The model shows that given officers observe such a guilt signal, data regarding the guilt Rate among those investigated from each race will not be sufficient for determining whether racially unequal investigation Rates are due to statistical discrimination or racial bias on the part of officers. The model also reveals that when racially unequal investigation Rates are due to statistical discrimination, imposing a colorblind policy on officers can increase, decrease, or have little effect on the Crime Rate, depending on specific characteristics of the jurisdiction and the Crime in question.

  • racial profiling statistical discrimination and the effect of a colorblind policy on the Crime Rate
    2004
    Co-Authors: David Bjerk
    Abstract:

    Using a model similar to labor market models of statistical discrimination, I de- scribe how and why racial profiling can arise even when law enforcement officers are racially unbiased. Specifically, if one racial group has a higher fraction of individuals who are at risk of committing the relevant type of Crime than another, and if law enforcement officers can observe a noisy signal of guilt in addition to an individual's race, then it will be optimal for officers to treat observationally equivalent individu- als of different races differently. Moreover, this model can be used to show how the effect of a racially colorblind policy on the overall Crime Rate for a particular type of Crime will depend on the racial make-up of the relevant jurisdiction, the relative proportions of each racial group that are at risk of choosing to commit that Crime, the proportion of the relevant population that officers can observe, the magnitude of the punishment for that particular type of Crime, and distribution of the benefits to committing that particular Crime. The implications coming from this analysis are then applied and analyzed with respect to two specific contexts--highway patrol vehicle searches for drugs or weapons, and border patrol investigations of foreign entrants for terrorist connections.

K Van Montfort - One of the best experts on this subject based on the ideXlab platform.

  • an analysis of the Crime Rate in the netherlands 1950 93
    Social Science Research Network, 1999
    Co-Authors: C Beki, Kees Zeelenberg, K Van Montfort
    Abstract:

    This study describes an analysis of trends in Crime in the Netherlands during the period 1950-93, using time-series analysis to estimate relationships between recorded Crime and demographic, economic and policy developments in the community. We are especially interested in the relationship between Crime and economic welfare. We use the Crime Rates recorded by the police, which, because of variations in the propensity to report by the victims and the recording policy of the police, may differ from actual Crime rats. Therefore we include variables that measure the propensities to report and to record in our model. We estimate the relationship between several Crime categories and the independent variables. The most important independent variables are: welfare measured by personal consumption of households per capita, number of unemployed people, male population in four age categories, police strength, and clear-up Rate of offences. Three important hypotheses are tested: (1) a higher growth in consumption leads to lower growth in the number of thefts because it leads to less incentive with potential criminals (motivation effect); (2) a higher growth in consumption leads to higher growth in the number of thefts because more goods are available (opportunity effect); (3) a higher growth in consumption leads to higher growth in the number of violent offences because it leads to more outdoor activities (routine-activity effect). We find the following results. The motivation effect is significant with total theft, qualified theft, burglary, theft from shops and pickpocket theft. The opportunity effect is significant with care thefts. The routine-activity is significant with criminal damage. Interpretations for these findings and the problems that occur with the time-series analysis are fully discussed.

Kenneth C Land - One of the best experts on this subject based on the ideXlab platform.

  • the age structure Crime Rate relationship solving a long standing puzzle
    Journal of Quantitative Criminology, 2013
    Co-Authors: Patricia L Mccall, Kenneth C Land, Cindy Brooks Dollar, Karen F Parker
    Abstract:

    Develop the concept of differential institutional engagement and test its ability to explain discrepant findings regarding the relationship between the age structure and homicide Rates across ecological studies of Crime. We hypothesize that differential degrees of institutional engagement—youths with ties to mainstream social institutions such as school, work or the military on one end of the spectrum and youths without such bonds on the other end—account for the direction of the relationship between homicide Rates and age structure (high Crime prone ages, such as 15–29). Cross sectional, Ordinary Least Squares regression analyses using robust standard errors are conducted using large samples of cities characterized by varying degrees of youths’ differential institutional engagement for the years 1980, 1990 and 2000. The concept is operationalized with the percent of the population enrolled in college and the percent of 16–19 year olds who are simultaneously not enrolled in school, not in the labor market (not in the labor force or unemployed), and not in the military. Consistent and invariant results emerged. Positive effects of age structure on homicide Rates are found in cities that have high percentages of disengaged youth and negative effects are found among cities characterized with high percentages of youth participating in mainstream social institutions. This conceptualization of differential institutional engagement explains the discrepant findings in prior studies, and the findings demonstRate the influence of these contextual effects and the nature of the age structure-Crime relationship.

  • unemployment and Crime Rate fluctuations a comment on greenberg
    Journal of Quantitative Criminology, 2001
    Co-Authors: David Cantor, Kenneth C Land
    Abstract:

    Several ways in which the specification of the Cantor and Land (1985) conceptual model of transient relationships between aggregate unemployment and Crime Rate fluctuations differs from that of Greenberg (2001) are noted. It follows that we do not accept Greenberg's Eq. (1) as a valid theoretical representation of the processes of interest. We briefly review the substantive context from which our investigation began in the mid-1980s. We also review the time series properties of our model and of the aggregate unemployment and Crime Rates used in its estimation. We note how the time series behavior of various Crime Rates determines which parts of the Cantor and Land model are and are not likely to be estimated as statistically significant for those series. We conclude with some comments on the limitations of aggregate time series research designs for testing the behavioral hypotheses used to geneRate expected relationships between aggregate unemployment and Crime Rates and suggest some alternative research designs.

Michael R Smith - One of the best experts on this subject based on the ideXlab platform.

  • conflict theory and racial profiling an empirical analysis of police traffic stop data
    Journal of Criminal Justice, 2003
    Co-Authors: Matthew Petrocelli, Alex R Piquero, Michael R Smith
    Abstract:

    Using data collected by the Richmond, Virginia Police Department, this article applies conflict theory to police traffic stop practices. In particular, it explores whether police traffic stop, search, and arrest practices differ according to racial or socioeconomic factors among neighborhoods. Three principal findings emanate from this research. First, the total number of stops by Richmond police was determined solely by the Crime Rate of the neighborhood. Second, the percentage of stops that resulted in a search was determined by the percentage of Black population. Third, when examining the percentage of stops that ended in an arrest/summons, the analyses suggest that both the percentage of Black population and the area Crime Rate served to decrease the percentage of police stops that ended in an arrest/summons. Implications for conflict theory and police decision-making are addressed.