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The role of social capital, personal networks, and emergency responders in post-disaster recovery and resilience: a study of rural communities in Indiana

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Abstract

The factors that explain the speed of recovery after disaster remain contested. While many have argued that physical infrastructure, social capital, and disaster damage influence the arc of recovery, empirical studies that test these various factors within a unified modeling framework are few. We conducted a mail survey to collect data on household recovery in four small towns in southern Indiana that were hit by deadly tornadoes in March 2012. The recovery effort is ongoing; while many of the homes, businesses, and community facilities were rebuilt in 2013, some are still under construction. We investigate how households in these communities are recovering from damage that they experienced and the role of social capital, personal networks, and assistance from emergency responders on the overall recovery experience. We used an ordered probit modeling framework to test the combined as well as relative effects of (a) damage to physical infrastructures (houses, vehicles, etc.); (b) recovery assistance from emergency responders (FEMA) as well as friends and neighbors; (c) personal network characteristics (size, network density, proximity, length of relationship); (d) social capital (civic engagement, contact with neighbors, trust); and (e) household characteristics. Results show that while households with higher levels of damage experienced slower recovery, those with recovery assistance from neighbors, stronger personal networks, and higher levels of social capital experienced faster recovery. The insights gained in this study will enable emergency managers and disaster response personnel to implement targeted strategies in facilitating post-disaster recovery and community resilience.

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Acknowledgements

This study was funded by an Andrew W. Mellon Foundation grant to Purdue University to address grand challenges through supporting interdisciplinary collaborations, especially between the liberal arts and STEM disciplines. The second and third authors’ work is partially funded by the NSF grant 1638311. The authors are grateful for this support. The authors would also like to acknowledge two students from Courtney Page of Purdue University, Indiana, and Pedro Henrique dos Reis Rezende of FUMEC University, Brazil, for their participation in the earlier part of the project. The authors are also grateful to the reviewers’ valuable comments, which improved the manuscript. However, the authors are solely responsible for the findings presented in this study.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Rose-Hulman Institute of Technology, Terre Haute, IN, 47803, USA

    Arif Mohaimin Sadri

  2. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Satish V. Ukkusuri

  3. Brian Lamb School of Communication, Purdue University, West Lafayette, IN, 47907, USA

    Seungyoon Lee

  4. Department of Political Science, Purdue University, West Lafayette, IN, 47907, USA

    Rosalee Clawson

  5. Department of Political Science, Northeastern University, Boston, MA, 02115, USA

    Daniel Aldrich

  6. Library Sciences, Purdue University, West Lafayette, IN, 47907, USA

    Megan Sapp Nelson

  7. Purdue Polytechnic Institute, Purdue University, West Lafayette, IN, 47907, USA

    Justin Seipel

  8. Department of Philosophy, Purdue University, West Lafayette, IN, 47907, USA

    Daniel Kelly

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  1. Arif Mohaimin Sadri
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Correspondence to Arif Mohaimin Sadri.

Appendix

Appendix

See Figs. 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 and Tables 5, 6, 7, 8, 9.

Fig. 5
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Recovery time as a function of total number of physical items damaged or destroyed

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Fig. 6
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Recovery time as a function of component 1 (contact with neighbors)

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Fig. 7
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Recovery time as a function of component 2 (government trust)

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Fig. 8
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Recovery time as a function of component 3 (civic engagement)

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Fig. 9
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Recovery time as a function of network density

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Fig. 10
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Recovery time as a function of geographic distance in miles

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Fig. 11
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Recovery time as a function of length of relationship in years

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Fig. 12
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Recovery time as a function of total assistance received

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Fig. 13
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Recovery time as a function of household size

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Fig. 14
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Recovery time as a function of time spent in the current home

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Table 5 Physical infrastructure items damaged or destroyed by tornado as experienced by different households
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Table 6 Items used to measure social capital
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Table 7 Principal components (oblique rotation) (N = 279)
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Table 8 Scoring coefficients (oblique rotation) (N = 279)
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Table 9 Correlation matrix (N = 279)
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Sadri, A.M., Ukkusuri, S.V., Lee, S. et al. The role of social capital, personal networks, and emergency responders in post-disaster recovery and resilience: a study of rural communities in Indiana. Nat Hazards 90, 1377–1406 (2018). https://doi.org/10.1007/s11069-017-3103-0

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