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Abstract This paper sets forth an interdisciplinary interpretation of social capital that will permit the concept to be used with precision in scholarly and scientific work. If the interpretation is accepted, the social capital concept cannot be regarded as a social theory, nor as a statement of normative goals. The interdisciplinary concept proposed here is applied to rural studies.

AbstractThis article reviews the methodological and theoretical foundations of natural resource and environmental economics prior to an examination of three examples of topics of current interest in the field. The three examples are nonmarket values, benefit—cost analysis, and the new institutional economics. The article draws on the review of contemporary work to identify a number of topics deserving of attention in subsequent work in this field. The items listed would have the effect of making future research more particular and less doctrinaire.

Rural America is an exceedingly diverse place, and this diversity constitutes a national asset. Diversity can be documented by an examination of any social or economic attribute one wishes to choose—educational attainment, per capita income, occupation, and ethnic background provide examples. This diversity shapes both the public policies applicable to the countryside and the opportunities of rural communities and rural people. Highly centralized public programs and policies, unless they provide for local adaptation, are unlikely to be successful. Many existing federal programs have outlived their usefulness. Federal entitlement programs continue to be enormously important to rural people and can be made even far more effective by recognizing rural differences. Not all rural areas will flourish in the future; the supply of rural places that would like to attract economic activity far exceeds the demand for such places. Exogenous forces that will improve the prospects for some rural areas include an increase in the number of people of retirement age and with a retirement income as well as growth of outdoor recreation and tourism activities. Entrepreneurship will continue to be of great importance as diverse rural places discover ways they can serve an increasingly urban and global society. The cost of distance and the benefits of space are key parameters in establishing the economic framework within which economic development will occur.

Diversity in the rural US can be documented by an examination of many social or economic attributes: eg, educational attainment, per capita income, occupation, & ethnic background. Highly centralized public programs & policies, unless they provide for local adaptation, are unlikely to be successful. Federal entitlement programs continue to be enormously important to rural people & can be made even far more effective by recognizing rural differences. Exogenous forces that will improve the economic prospects for some rural areas include an increase in the number of people of retirement age as well as growth of outdoor recreation & tourism activities. Entrepreneurship will continue to be of great importance as diverse rural places discover ways they can serve an increasingly urban & global society. The cost of distance & the benefits of space are key parameters in establishing the economic framework within which economic development will occur. Adapted from the source document.

AbstractAn entire economic system cannot be understood unless there is reliable knowledge about both rural and urban sectors, including their interactions. In particular, there are two anomalies that cannot be rationalized satisfactorily by existing economic theory: the decentralizing economic activities that proceed from the city to the countryside; and the unequal per capita economic rewards between rural and urban people over time. This article presents empirical evidence on the two anomalies, proposes an integrated framework of rural–urban space, discusses the historic interdependence of rural and urban places from the perspective of the integrated framework, and looks at the implications for research and public policy.

One approach for understanding the encoding of information by spike trains is to fit statistical models and then test their goodness of fit. The time-rescaling theorem provides a goodness-of-fit test consistent with the point process nature of spike trains. The interspike intervals (ISIs) are rescaled (as a function of the model's spike probability) to be independent and exponentially distributed if the model is accurate. A Kolmogorov-Smirnov (KS) test between the rescaled ISIs and the exponential distribution is then used to check goodness of fit. This rescaling relies on assumptions of continuously defined time and instantaneous events. However, spikes have finite width, and statistical models of spike trains almost always discretize time into bins. Here we demonstrate that finite temporal resolution of discrete time models prevents their rescaled ISIs from being exponentially distributed. Poor goodness of fit may be erroneously indicated even if the model is exactly correct. We present two adaptations of the time-rescaling theorem to discrete time models. In the first we propose that instead of assuming the rescaled times to be exponential, the reference distribution be estimated through direct simulation by the fitted model. In the second, we prove a discrete time version of the time-rescaling theorem that analytically corrects for the effects of finite resolution. This allows us to define a rescaled time that is exponentially distributed, even at arbitrary temporal discretizations. We demonstrate the efficacy of both techniques by fitting generalized linear models to both simulated spike trains and spike trains recorded experimentally in monkey V1 cortex. Both techniques give nearly identical results, reducing the false-positive rate of the KS test and greatly increasing the reliability of model evaluation based on the time-rescaling theorem. ; National Institutes of Health (U.S.) (grant K25 NS052422-02) ; National Institutes of Health (U.S.) (grant DP1 OD003646-01) ; National Institutes of Health (U.S.) (grant MH59733-07) ; Hertie Foundation ; Max Planck Society for the Advancement of Science ; European Union (grant FP6-2005-NEST-Path-043309.)