⭐⭐⭐⭐⭐ Vulnerable Victim Enhancement Analysis

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Vulnerable Victim Enhancement Analysis



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Click here to sign up. Download Free PDF. Nuno Garoupa. A short summary of this paper. Economics Working Papers. Paper For more information, please contact digitalcommons uconn. In this paper, we develop an economic framework to analyze penalty enhancements for bias-motivated crimes. We begin with the assumption that the harm to an individual victim from a bias-motivated crime is identical to that from an equivalent non-hate crime.

Nonetheless, we derive the result that a pattern of crimes disproportionately tar- geting an identifiable group leads to greater social harm. In particular, penalty enhancements can reduce the incentives for avoidance activity, and thereby protect the networks of profitable interactions that link mem- bers of different groups. We also argue that those groups that are covered by hate crime statutes tend to be those whose characteristics make it especially likely that penalty enhancement is socially optimal. However, the economic model of crime and law enforcement e. The central issue in the study of HCLs is how and whether to justify sen- tencing enhancements.

Thus, if it could be assumed that a hate crime causes more harm to the victim than does a physically identical non- hate crime, then it would follow straightforwardly that penalty enhancements are socially optimal. Indeed, McDevitt et al. Byrd, Jr. In particular, in the summer of , Benjamin Nathaniel Smith, a member of the hate-based World Church of the Creator, carried out a series of shootings in Illinois and Indiana. In the spring of , a lawyer named Richard Scott Baumhammers also targeted minorities in a series of shootings in the Pittsburgh area.

See e. Mitchell, U. The latter punishes the selection of victims on the basis of group characteristics, independently of any animus towards the group. Gellman, Instead, they advocate a victim-centered perspective discussed further in Section 4 below. We begin with the as- sumption that the harm to an individual victim from a hate crime is identical to that from an equivalent non-hate crime.

However, we then seek to endogenously derive disparities in the social harms that result from a pattern of discrimina- tory selection of victims. That is, we show that even if a particular hate crime causes the same harm to its victim as an identical non-hate crime, a pattern of crimes disproportionately targeting an identifiable group causes greater harm than does a pattern of crimes where targets are randomly chosen. The first relates to fairness concerns. The second mechanism assumes that individuals can undertake avoidance activities; thus, a B can avoid contact with members of the dominant group labeled A , and thereby reduce her probability of victimization.

The social waste occurs because avoidance involves foregoing opportunities for profitable transactions with As in addition to any direct costs. Of course, non-hate crimes may also lead to avoidance activities. However, it seems reasonable to assume that the behavioral elasticity in response to hate crimes is much larger, because potential victims can easily identify and avoid members of group A, rather than having to avoid all individuals. A more empirically oriented view is provided by Iganski For a model with avoidance activities by criminals, see Malik The threat of such crimes induces members of the disfavored group to avoid the dominant group, forego- ing profitable interactions.

This social waste can be ameliorated by instituting penalty enhancements. It should be noted that the Supreme Court, in upholding the Wis- consin bias crime statute at issue in Wisconsin v. Our second goal is to propose an explanation for why some types of bias- motivated crimes are subject to penalty enhancements, while others are not. However, the scope of HCLs is typically restricted to certain groups. Recently, Posner , pp. In Sections 3 and 4, fairness concerns and the possibility of avoidance activities are incorpo- rated to yield analogous conditions.

When these conditions are satisfied for a particular group B, then it is socially optimal to enact penalty enhancements that punish bias-motivated crimes against Bs more severely. However, it is not optimal to enact penalty enhancements for a group for which these conditions are not satisfied. Moreover, we argue in Section 5 below that the observed pattern of the types of groups included in HCLs closely tracks what our theo- retical results would suggest. In addition, HCLs usually apply to groups that are identifiable by immutable characteristics, or who face large costs of changing or disguising their group identity. Potential victims from such groups can only 9 Supra note 2 at In Section 2, the basic model is introduced.

Fairness concerns are introduced in Section 3, and the extension with avoidance activities is developed in Section 4. Section 5 discusses some implications of the results, while Section 6 concludes the paper. Individuals are assumed to be risk-neutral, with the population size normalized to 1. In their model, individuals can commit an act that causes harm h to society. The probability density functions are denoted by fA b and fB b , respectively. As discussed in the Introduction, the harm from the crime is assumed to be h, regardless of whether it is committed against an A or a B. As in Shavell and Polinsky and Shavell , punishment is assumed to be costly in this model.

If punishment were costless, the issue of penalty enhancements would be moot, as penalties would always be set at the maximal levels that will deter all crime. The cost of punishment is denoted by c s , where s is the punishment, and c s is assumed to be increasing and convex. There is an exogenous probability of detection p that is fixed regardless of whether the crime is against an A or a B. Following the standard framework Polinsky and Shavell, a , social wel- fare should be defined as the sum of benefits minus harm from crimes, minus punishment costs.

In this particular context, it may be argued by some that the inclusion of that component of illegal gains directly attributable to hatred of Bs i. In particular, if the gain to a perpetrator from a hate crime is higher than the gain from an otherwise identical crime against an A, the expected sanction should be higher, even though sanctions are never actually applied. Hylton, ; however, the focus here is not on this issue. Thus, 1 is a particular case of 2. The social objective is to choose sA and sB to maximize the above expression. The marginal cost right-hand-side is the same for both A and B. In the legal profession, information is the key to success. Law provides the intelligence you need to remain an expert and beat the competition.

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DowellF. Get instant Vulnerable Victim Enhancement Analysis to the one-stop news source for business lawyers Vulnerable Victim Enhancement Analysis Now! The defendants further maintain Vulnerable Victim Enhancement Analysis targeting the elderly did not constitute a major portion of their fraudulent activity. We begin with the assumption that the harm to an individual victim from a bias-motivated Essay On The Spanish American Revolution is identical Vulnerable Victim Enhancement Analysis that from an equivalent non-hate crime. The Vulnerable Victim Enhancement Analysis was sufficient there to find that the defendants targeted the victim in particular and exploited Persuasive Speech On Poverty unusual vulnerabilities. The government further argues that the defendants operated at least Vulnerable Victim Enhancement Analysis programs to entice Vulnerable Victim Enhancement Analysis "elderly" to Vulnerable Victim Enhancement Analysis in their real Vulnerable Victim Enhancement Analysis partnerships. Similarly, in United States v.

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