Search results

To date, there is a gap in the literature exploring the perceptions and experiences of law enforcement regarding enforcement of online and offline prostitution. The purpose of this paper is to investigate the perceptions of law enforcement in the USA regarding the safety and mobility of individuals who prostitute online compared to those who sell sexual services offline. The next section will explain the methodology of the exploratory study, including the method of original data collection.

All police departments in the USA located in a jurisdiction of 50,000 people or more were requested participation in the study (n=689). Respondents were sent an initial mailing of a cover letter and survey, followed by an e-mail reminder and a second mailing of a cover letter and survey. Individual respondents were asked questions about their own perceptions of behaviors and lifestyles of offline vs online prostitutes.

The majority of law enforcement respondents did not feel as if online prostitutes were safer compared to offline prostitutes. However, the majority of respondents did believe that online prostitutes are afforded a better lifestyle and are more mobile.

The majority of the respondents were from the Northeast and Southwest may mean that their perceptions could be different from those that are not located within either region. In addition, since almost 80 percent of the agencies were in a jurisdiction with a population between 50,000 and 249,000, this too may have influenced their perceptions. Law enforcement in a smaller or larger area may have felt differently or have had different experiences to report.

This study is very unique as to date, another study with the same methodology and question content was not found.

As suggested in the lyrics from the popular Beach Boys song, students may be expected, by themselves and/or others, to ‘be true’ to their school; to be loyal to their alma mater. It is likely that the reader can readily recall their own school ‘fight’ song and rousing cheers at sporting events touting the superiority of the home team.

We describe a new Bayesian estimation algorithm for fitting a binary treatment, ordered outcome selection model in a potential outcomes framework. We show how recent advances in simulation methods, namely data augmentation, the Gibbs sampler and the Metropolis-Hastings algorithm can be used to fit this model efficiently, and also introduce a reparameterization to help accelerate the convergence of our posterior simulator. Conventional “treatment effects” such as the Average Treatment Effect (ATE), the effect of treatment on the treated (TT) and the Local Average Treatment Effect (LATE) are adapted for this specific model, and Bayesian strategies for calculating these treatment effects are introduced. Finally, we review how one can potentially learn (or at least bound) the non-identified cross-regime correlation parameter and use this learning to calculate (or bound) parameters of interest beyond mean treatment effects.