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In the United States, the ratio of consumption to GDP has risen steadily over the past half century. In trying to understand why this ratio has increased so much, it is argued that standard models of the consumption function, built up from the neoclassical theory of constrained optimization, cannot offer a satisfactory answer. An alternative perspective is offered whereby aggregate consumption expenditure is seen as primarily the outcome of the population adopting widely upheld rules ('meso-rules') in a complex economic system. Aggregate consumption is viewed as the outcome of two contrasting historical processes: one mainly involving pre-committed, rule-bound choices and the other involving open-ended choices, made knowingly in the face of uncertainty, to adopt new meso-rules concerning the consumption of novel kinds of goods and services. The former process provides the degree of order that must be present in any complex system and the latter facilitates evolutionary change to occur. Using over half a century of data, the US consumption function is modelled successfully on the presumption that the economy is a complex system. The evidence supports the hypothesis that the ratio of consumption to GDP has risen because of the diffusion of a 'culture of consumerism' in the post-war era and that the limit of this process is now being approached, with important macroeconomic and social implications. [ABSTRACT FROM AUTHOR]

Objective: To assess the impact of nonphysician providers on measures of spatial access to primary care in Iowa, a state where physician assistants and advanced practice registered nurses are considered primary care providers.Data Sources: 2017 Iowa Health Professions Inventory (Carver College of Medicine), and minor civil division (MCD) level population data for Iowa from the American Community Survey.Study Design: We used a constrained optimization model to probabilistically allocate patient populations to nearby (within a 30-minute drive) primary care providers. We compared the results (across 10 000 scenarios) using only primary care physicians with those including nonphysician providers (NPPs). We analyze results by rurality and compare findings with current health professional shortage areas.Data Collection/extraction Methods: Physicians and NPPs practicing in primary care in 2017 were extracted from the Iowa Health Professions Inventory.Principal Findings: Considering only primary care physicians, the average unallocated population for primary care was 222 109 (7 percent of Iowa's population). Most of the unallocated population (86 percent) was in rural areas with low population density (< 50/square mile). The addition of NPPs to the primary care workforce reduced unallocated population by 65 percent to 78 252 (2.5 percent of Iowa's population). Despite the majority of NPPs being located in urban areas, most of the improvement in spatial accessibility (78 percent) is associated with sparsely populated rural areas.Conclusions: The inclusion of nonphysician providers greatly reduces but does not eliminate all areas of inadequate spatial access to primary care. [ABSTRACT FROM AUTHOR]

Abstract: Technological advances have created new opportunities for defining and mapping ecological and biogeographical regions on the basis of quantitative criteria while generating a need for studies that evaluate the sensitivity of ecoregionalizations to clustering methods and approaches. In this study, we used a novel regionalization algorithm, regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP), to identify hierarchical regions based on measures of forest extent, connectivity, and change for 2109 watersheds in the continental U.S. Unlike regionalizations developed using non-spatial clustering techniques, REDCAP directly incorporates a spatial contiguity constraint into a traditional hierarchical clustering method, resulting in contiguous regions that optimize a homogeneity measure. Results of our analyses identified nine- and eighteen-class Forest Pattern Regions that reflected the influence of natural and anthropogenic factors structuring forest extent and fragmentation. Because these regions are defined by the forest pattern metrics themselves, rather than pre-defined political or ecological units, they provide a valuable means for visualizing forest pattern information and quantifying forest patterns across a large, diverse geographic area. In contrast, regionalizations of the same data using two non-spatial methods (k-means clustering and non-spatial average linkage clustering) resulted in more homogeneous classes composed of many discontiguous units. While it should not be viewed as a replacement for non-spatial clustering techniques, REDCAP provides an alternative approach to developing ecological regionalizations by placing greater emphasis on maintaining the spatial contiguity of units, a property that may be desirable in many broad-scale regionalizations because it reduces data complexity and facilitates the visualization and interpretation of ecological or biogeographic data. [Copyright &y& Elsevier]

The recently implemented court decision to break up Bell (= American Telephone & Telegraph Co.) to accord with U.S. anti-trust laws represents a highly significant policy decision which is proving to be influential in other countries as well as the U.S. The telecommunication industry is of such size and importance that even relatively small economies that might be lost with Bell's breakup as a "natural monopoly" could involve substantial welfare losses to consumers and producers. Studies commissioned by the U.S. Justice Department that approached this topic by econometric methods reported that the evidence failed to support the contention that Bell was a natural monopoly. Here a goal programming/constrained regression, as developed in the Management Science literature, uses the same functional form and the same data but nevertheless reverses the main findings of the econometric studies in every one of the 20 years covered. This kind of difference in results obtained by two different methods of analysis points up a need for drawing on persons from different disciplines who are capable of checking each other's methodologies when important policy decisions may be influenced by results that depend on the methodologies that these disciplines customarily use. Advantages of doing this are further illustrated by data deficiencies that escape detection by the econometric methods employed in the Justice Department-commissioned studies. [ABSTRACT FROM AUTHOR]