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The Social Side of Community Resilience: Human Capital Modeling

Authors:
Clayton J. Hutto
Alexandra Trani
Elizabeth Williams
Hi Shin Shim
Tom McDermott
Dennis Folds
Molly Nadolski

Keywords: human capital modeling, social capital modeling, structural equation modeling, transformation techniques

Abstract:
Present measures of community resilience – that is, how communities respond or adapt to changes as well as recover from disasters – are often too shallow and fail to account for the gamut of variables contributing to community health. We argue that this problem stems from attempting to measure community resilience with an overly simplistic assessment. It is understandably difficult to construct a predictive model of community resilience. Such a model would need to be composed of variables that represent a range of elements which capture the community’s ability to respond to and/or overcome natural or man-made disasters/disruptions, including factors spanning the resilience or (in)vulnerability of houses and buildings, roads and bridges, emergency services, electrical grids, computer and information exchange networks, potable water distribution systems, sanitation systems, and so on. Furthermore, the resilience associated with the aggregate human/social spirit of a community is often marginalized or, in some cases, ignored completely. The disparate nature of such a broad range of variables is that they are measured on different scales, with incongruent units, collected from diverse sources, at dissimilar time intervals. The current paper addresses all three of the challenges associated with (1) incorporating human and social elements of community resilience, (2) representing the complexity of community (social) resilience variables in a single common latent variable construct model that addresses concerns about disparate scales, units, sources, and types of data, and (3) creating useful models for both characterizing and predicting the resilience of a given community. We achieve this by demonstrating a novel technique for translating extant data such that the entire gamut of relevant variables are expressed in terms of their impact on human capital. Our technique then utilizes structural equation modeling techniques to construct causal (and thus, descriptive and predictive) models of community resilience.

Pages: 45 to 50

Copyright: Copyright (c) IARIA, 2017

Publication date: July 23, 2017

Published in: conference

ISSN: 2519-8351

ISBN: 978-1-61208-578-4

Location: Nice, France

Dates: from July 23, 2017 to July 27, 2017