University of Maine

About: University of Maine is a education organization based out in Orono, Maine, United States. It is known for research contribution in the topics: Population & Ice sheet. The organization has 8637 authors who have published 16932 publications receiving 590124 citations. The organization is also known as: University of Maine at Orono.

Productivity and the density of human capital

Abstract: We estimate a model of urban productivity in which the agglomeration effect of density is enhanced by a metropolitan area's stock of human capital. Estimation accounts for potential biases due to the endogeneity of density and industrial composition effects. Using new information on output per worker for U.S. metropolitan areas along with a measure of density that accounts for the spatial distribution of population, we find that a doubling of density increases productivity by 2–4 percent. Consistent with theories of learning and knowledge spillovers in cities, we demonstrate that the elasticity of average labor productivity with respect to density increases with human capital. Metropolitan areas with a human capital stock one standard deviation below the mean realize no productivity gain, while doubling density in metropolitan areas with a human capital stock one standard deviation above the mean yields productivity benefits that are about twice the average. These patterns are particularly pronounced in industries where the exchange of information and sharing of ideas are important parts of the production process.

Depression Among Older Adults: A 20-Year Update on Five Common Myths and Misconceptions.

Abstract: Is depression among older adults symptomatically different than younger adults? Is it more common or chronic or difficult to treat? Is depression in late life more likely to be attributed to psychological problems? Twenty-years ago, Dan Blazer, a pioneer known for his groundbreaking work on depression in older adulthood, conducted an important review of the existing literature to refute five commonly held beliefs about depression in late life. Now, two decades later, we call upon selected articles that are representative of our current knowledge to provide an update and identify research priorities. The research consensus spanning the past 20 years suggests that when compared with their younger counterparts, depression in older adults is not more common and is not more often caused by psychological factors. Although some studies have suggested that depression in late life may be symptomatically different and characterized by a more somatic presentation, there is insufficient empirical evidence to conclude that depression presents differently across adulthood. Overall, older adults respond to psychological interventions as well as younger adults, although evidence suggests that antidepressants are less efficacious in late life. Finally, compared with middle-aged adults, depression in older adults is associated with a more chronic course (i.e., higher rate of relapse), which is likely moderated by medical comorbidity. This special article summarizes our current understanding of the nature and treatment of late-life depression and highlights areas of inquiry in need of further study.

From Objects to Events: GEM, the Geospatial Event Model

Abstract: This paper discusses the construction of a modeling approach for dynamic geospatial domains based on the concepts of object and event. The paper shows how such a model extends traditional object-based geospatial models. The focus of the research is the introduction of events into the object-based paradigm, and consequent work on the classification of object-event and event-event relationships. The specific geospatial nature of this model is captured in the concept of a geosetting. The paper also introduces an extension of UML diagrams to incorporate events and their relationship to each other, and to objects. The paper briefly considers an example to show the working of some of the modeling constructs, and concludes with a discussion of further research needed on event aggregation and event-based query languages.