A Jewel in Our Midst: a photo essay of the Connecticut River

Its unique tidal marshes, ecology, geology, scenic areas, and fascinating history make the Connecticut River a treasure to residents and visitors alike. It is one of the 1,713 “Wetlands of International Importance” designated throughout the world by the International Ramsar Convention. This photo essay also describes the education efforts underway by Connecticut Sea Grant and its partners to assist educators with resource materials.

Carnot Cycles: Traditional and Stefan Boltzmann, and the First Planck Argument

The Stefan Boltzmann equation is obtained using a non-traditional Carnot Engine. In addition, the original Planck argument for radiation density is given.

L'Hopital's Rule in Chemistry: Irreversible Morphing into Reversible Isothermal Expansions

L'Hopital's Rule is discussed in the cvase of an irreversible isothermal expansion.

MO visualization of Pi Orbitals

In the spirit of trying to convert people to understanding atomic orbitals centered elsewhere than the origin, we continue the discussion of visualizing molecular orbitals, so called LCAO-MO, using various plotting tricks in Maple.

Free Riders, Holdouts, and Public Use: A Tale of Two Externalities

Free riders and holdouts are market failures that potentially impede the completion of otherwise beneficial transactions. The key difference is that the free rider problem is a demand side externality that requires taxation to compel payment for a public good, while the holdout problem is a supply side externality that requires eminent domain to force the sale of land for large scale projects. This paper highlights that distinction between these two problems and uses the resulting insights to clarify the meaning of the public use requirement of the Fifth Amendment takings clause.

Penalty Enhancement for Hate Crimes: An Economic Analysis

The issue of bias-motivated crimes has attracted consderable attention in recent years. In this paper, we develop an economic framework to analyze penalty enhancements for bias-motivated crimes. We extend the standard model by introducing two different groups of potential victims of crime, and assume that a potential offender's benefits from a crime depend on the group to which the victim belongs. 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 targeting an identifiable group leads to greater social harm. This conclusion follows both from a model where disparities in groups' victimization probabilities lead to social losses due to fairness concerns, as well as a model where potential victims have the opportunity to undertake socially costly victimization avoidance activities. In particular, penalty enhancements can reduce the incentives for avoidance activity, and thereby protect the networks of profitable interactions that link members 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. Finally, we consider a number of other issues related to hate crimes, including teh choice of sanctions from behind a Rawlsian 'veil of ignorance' concerning group identity.

Performance Evaluation of the New Connecticut Leading Employment Index Using Lead Profiles and BVAR Models

Dua and Miller (1996) created leading and coincident employment indexes for the state of Connecticut, following Moore's (1981) work at the national level. The performance of the Dua-Miller indexes following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new indexes show improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates the truth in the Granger and Newbold (1986) caution that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support.