Spatial Correlation Robust Inference with Errors in Location or Distance

This paper presents results from a Monte Carlo study concerning inference with spatially dependent data. We investigate the impact of location/distance measurement errors upon the accuracy of parametric and nonparametric estimators of asymptotic variances. Nonparametric estimators are quite robust to such errors, method of moments estimators perform surprisingly well, and MLE estimators are very poor. We also present and evaluate a specification test based on a parametric bootstrap that has good power properties for the types of measurement error we consider.

Social Networks in Ghana

In this chapter we examine social networks among farmers in a developing country. We use detailed data on economic activities and social interactions between people living in four study villages in Ghana. It is clear that economic development in this region is being shaped by the networks of information, capital and influence that permeate these communities. This chapter explores the determinants of these important economic networks. We first describe the patterns of information, capital, labor and land transaction connections that are apparent in these villages. We then discuss the interconnections between the various economic networks. We relate the functional economic networks to more fundamental social relationships between people in a reduced form analysis. Finally, we propose an equilibrium model of multi-dimensional network formation that can provide a foundation for further data collection and empirical research.Endogenous Networks, Informal Credit, Social Learning

Learning about a new technology: pineapple in Ghana

This paper investigates the role of social learning in the diffusion of a new agricultural technology in Ghana. We use unique data on farmers' communication patterns to define each individual's information neighborhood, the set of others from whom he might learn. Our empirical strategy is to test whether farmers adjust their inputs to align with those of their information neighbors who were surprisingly successful in previous periods. We present evidence that farmers adopt surprisingly successful neighbors' practices, conditional on many potentially confounding factors including common growing conditions, credit arrangements, clan membership, and religion. The relationship of these input adjustments to experience further supports their interpretation as resulting from social learning. In addition, we apply our methods to input choices for another crop with known technology and they correctly indicate an absence of social learning effects.

Learning About a New Technology: Pineapple in Ghana

This paper investigates the role of social learning in the diffusion of a new agricultural technology in Ghana. We use unique data on farmers’ communication patterns to define each individual’s information neighborhood, the set of others from whom he might learn. Our empirical strategy is to test whether farmers adjust their inputs to align with those of their information neighbors who were surprisingly successful in previous periods. We present evidence that farmers adopt surprisingly successful neighbors’ practices, conditional on many potentially confounding factors including common growing conditions, credit arrangements, clan membership, and religion. The relationship of these input adjustments to experience further supports their interpretation as resulting from social learning. In addition, we apply our methods to input choices for another crop with known technology and they correctly indicate an absence of social learning effects.Social Learning, Technology, Innovation

Learning About a New Technology: Pineapple in Ghana

This paper investigates the role of social learning in the diffusion of a new agricultural technology in Ghana. We use unique data on farmers communication patterns to define each individuals information neighborhood, the set of others from whom he might learn. Our empirical strategy is to test whether farmers adjust their inputs to align with those of their information neighbors who were surprisingly successful in previous periods. We present evidence that farmers adopt surprisingly successful neighbors practices, conditional on many potentially confounding factors including common growing conditions, credit arrangements, clan membership, and religion. The relationship of these input adjustments to experience further supports their interpretation as resulting from social learning. In addition, we apply our methods to input choices for another crop with known technology and they correctly indicate an absence of social learning effects

Social Networks in Ghana

In this chapter we examine social networks among farmers in a developing country. We use detailed data on economic activities and social interactions between people living in four study villages in Ghana. It is clear that economic development in this region is being shaped by the networks of information, capital and influence that permeate these communities. This chapter explores the determinants of these important economic networks. We first describe the patterns of information, capital, labor and land transaction connections that are apparent in these villages. We then discuss the interconnections between the various economic networks. We relate the functional economic networks to more fundamental social relationships between people in a reduced form analysis. Finally, we propose an equilibrium model of multi-dimensional network formation that can provide a foundation for further data collection and empirical research

Socio-Economic Distance and Spatial Patterns in Unemployment”.

SUMMARY This paper examines the spatial patterns of unemployment in Chicago between 1980 and 1990. We study unemployment clustering with respect to different social and economic distance metrics that reflect the structure of agents' social networks. Specifically, we use physical distance, travel time, and differences in ethnic and occupational distribution between locations. Our goal is to determine whether our estimates of spatial dependence are consistent with models in which agents' employment status is affected by information exchanged locally within their social networks. We present non-parametric estimates of correlation across Census tracts as a function of each distance metric as well as pairs of metrics, both for unemployment rate itself and after conditioning on a set of tract characteristics. Our results indicate that there is a strong positive and statistically significant degree of spatial dependence in the distribution of raw unemployment rates, for all our metrics. However, once we condition on a set of covariates, most of the spatial autocorrelation is eliminated, with the exception of physical and occupational distance. Racial and ethnic composition variables are the single most important factor in explaining the observed correlation patterns

Educating Future Nursing Scientists: Recommendations for Integrating Omics Content in PhD Programs

Preparing the next generation of nursing scientists to conduct high-impact, competitive, sustainable, innovative, and interdisciplinary programs of research requires that the curricula for PhD programs keep pace with emerging areas of knowledge and health care/biomedical science. A field of inquiry that holds great potential to influence our understanding of the underlying biology and mechanisms of health and disease is omics. For the purpose of this article, omics refers to genomics, transcriptomics, proteomics, epigenomics, exposomics, microbiomics, and metabolomics. Traditionally, most PhD programs in schools of nursing do not incorporate this content into their core curricula. As part of the Council for the Advancement of Nursing Science\u27s Idea Festival for Nursing Science Education, a work group charged with addressing omics preparation for the next generation of nursing scientists was convened. The purpose of this article is to describe key findings and recommendations from the work group that unanimously and enthusiastically support the incorporation of omics content into the curricula of PhD programs in nursing. The work group also calls to action faculty in schools of nursing to develop strategies to enable students needing immersion in omics science and methods to execute their research goals

Casting a wider net: Immunosurveillance by nonclassical MHC molecules

Most studies of T lymphocytes focus on recognition of classical major histocompatibility complex (MHC) class I or II molecules presenting oligopeptides, yet there are numerous variations and exceptions of biological significance based on recognition of a wide variety of nonclassical MHC molecules. These include αβ and γδ T cells that recognize different class Ib molecules (CD1, MR-1, HLA-E, G, F, et al.) that are nearly monomorphic within a given species. Collectively, these T cells can be considered “unconventional,” in part because they recognize lipids, metabolites, and modified peptides. Unlike classical MHC-specific cells, unconventional T cells generally exhibit limited T-cell antigen receptor (TCR) repertoires and often produce innate immune cell-like rapid effector responses. Exploiting this system in new generation vaccines for human immunodeficiency virus (HIV), tuberculosis (TB), other infectious agents, and cancer was the focus of a recent workshop, “Immune Surveillance by Non-classical MHC Molecules: Improving Diversity for Antigens,” sponsored by the National Institute of Allergy and Infectious Diseases. Here, we summarize salient points presented regarding the basic immunobiology of unconventional T cells, recent advances in methodologies to measure unconventional T-cell activity in diseases, and approaches to harness their considerable clinical potential