Indiana University

About: Indiana University is a education organization based out in Bloomington, Indiana, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 64480 authors who have published 150058 publications receiving 6392902 citations. The organization is also known as: Indiana University system & indiana.edu.

The ability of banks to lend to informationally opaque small businesses

Abstract: Consolidation of the banking industry is shifting assets into larger institutions that often operate in many nations. Large international financial institutions are geared toward serving large wholesale customers. How does this affect the banking system's ability to lend to informationally opaque small businesses? The authors test hypotheses about the effects of bank size, foreign ownership, and distress on lending to informationally opaque small firms, using a rich new data set on Argentinean banks, firms, and loans. They also test hypotheses about borrowing from a single bank versus borrowing from several banks. Their results suggest that large and foreign-owned institutions may have difficulty extending relationship loans to opaque small firms, especially if small businesses are delinquent in repaying their loans. Bank distress resulting from lax prudential supervision and regulation appears to have no greater effect on small borrowers than on large borrowers, although even small firms may react to bank distress by borrowing from multiple banks, despite raising borrowing costs and destroying some of the benefits of exclusive lending relationships.

Motifs in Brain Networks

Abstract: Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks (structural and functional motifs) in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information.

The extended evolutionary synthesis: its structure, assumptions and predictions.

Abstract: Scientific activities take place within the structured sets of ideas and assumptions that define a field and its practices. The conceptual framework of evolutionary biology emerged with the Modern Synthesis in the early twentieth century and has since expanded into a highly successful research program to explore the processes of diversification and adaptation. Nonetheless, the ability of that framework satisfactorily to accommodate the rapid advances in developmental biology, genomics and ecology has been questioned. We review some of these arguments, focusing on literatures (evo-devo, developmental plasticity, inclusive inheritance and niche construction) whose implications for evolution can be interpreted in two ways—one that preserves the internal structure of contemporary evolutionary theory and one that points towards an alternative conceptual framework. The latter, which we label the 'extended evolutionary synthesis' (EES), retains the fundaments of evolutionary theory, but differs in its emphasis on the role of constructive processes in development and evolution, and reciprocal portrayals of causation. In the EES, developmental processes, operating through developmental bias, inclusive inheritance and niche construction, share responsibility for the direction and rate of evolution, the origin of character variation and organism-environment complementarity. We spell out the structure, core assumptions and novel predictions of the EES, and show how it can be deployed to stimulate and advance research in those fields that study or use evolutionary biology.

The New Governance: Practices and Processes for Stakeholder and Citizen Participation in the Work of Government

Abstract: Leaders in public affairs identify tools and instruments for the new governance through networks of public, private, and nonprofit organizations. We argue the new governance also involves people—the tool makers and tool users—and the processes through which they participate in the work of government. Practitioners are using new quasi-legislative and quasi-judicial governance processes, including deliberative democracy, e-democracy, public conversations, participatory budgeting, citizen juries, study circles, collaborative policy making, and alternative dispute resolution, to permit citizens and stakeholders to actively participate in the work of government. We assess the existing legal infrastructure authorizing public managers to use new governance processes and discuss a selection of quasi-legislative and quasi-judicial new governance processes in international, federal, state, and local public institutions. We conclude that public administration needs to address these processes in teaching and research to help the public sector develop and use informed best practices.

Designing Difference in Difference Studies: Best Practices for Public Health Policy Research

Abstract: The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal inference poses many challenges in DID designs. In this article, we review key features of DID designs with an emphasis on public health policy research. Contemporary researchers should take an active approach to the design of DID studies, seeking to construct comparison groups, sensitivity analyses, and robustness checks that help validate the method's assumptions. We explain the key assumptions of the design and discuss analytic tactics, supplementary analysis, and approaches to statistical inference that are often important in applied research. The DID design is not a perfect substitute for randomized experiments, but it often represents a feasible way to learn about casual relationships. We conclude by noting that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach.