3 resultados para dissemination bias

em University of Connecticut - USA


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Knowles, Persico, and Todd (2001) develop a model of police search and offender behavior. Their model implies that if police are unprejudiced the rate of guilt should not vary across groups. Using data from Interstate 95 in Maryland, they find equal guilt rates for African-Americans and whites and conclude that the data is not consistent with racial prejudice against African-Americans. This paper generalizes the model of Knowles, Persico, and Todd by accounting for the fact that potential offenders are frequently not observed by the police and by including two different levels of offense severity. The paper shows that for African-American males the data is consistent with prejudice against African-American males, no prejudice, and reverse discrimination depending on the form of equilibria that exists in the economy. Additional analyses based on stratification by type of vehicle and time of day were conducted, but did not shed any light on the form of equilibria that best represents the situation in Maryland during the sample period.

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We study how the democratization of the diffusion of research through the Internet could have helped non traditional fields of research. The specific case we approach is Heterodox Economics as its pre-prints are disseminated through NEP, the email alert service of RePEc. Comparing heterodox and mainstream papers, we find that heterodox ones are quite systematically more downloaded, and particularly so when considering downloads per subscriber. We conclude that the Internet definitely helps heterodox research, also because other researcher get exposed to it. But there is still room for more participation by heterodox researchers.