A Comparative Analysis of Coordination, Participation, and Training in Post-Disaster Shelter Projects

Abstract

The delivery of post-disaster shelter assistance continues to be fraught with challenges derived from the coordination of resources, involvement of project stakeholders, and training of households and builders. There is a need to better understand what project elements in the delivery of post-disaster shelter projects most influence resilience and sustainability. To address this need, we examined nineteen post-disaster shelter projects in the Philippines following Typhoon Haiyan. We first characterized coordination, participation, and training employed across the planning, design, and construction phases of shelter projects and then used fuzzy-set qualitative comparative analysis (fsQCA) to assess the influence of these elements, alone and in combination, on building resilient and sustainable community infrastructure systems. Findings show that early involvement of households in planning efforts, combined with subsequent training, was important in evolving recovery outcomes. Our results point to the importance of: (1) supporting household sheltering processes over delivering hard products; (2) strategically linking project processes across phases; and (3) aligning humanitarian actions with long-term development. Conclusions from this study contribute to theory of sheltering in developing communities and more broadly to theory of recovery processes that link to community resilience and sustainability.

Factors Influencing Revenue Collection for Preventative Maintenance of Community Water Systems: A Fuzzy-Set Qualitative Comparative Analysis

“…Recently, QCA has seen increasing application within the international water and sanitation sector research to reveal and characterize the complex drivers of water and sanitation service delivery outcomes, including water and sanitation in schools [28,29], water points [9], sanitation infrastructure [30], and overall service sustainability [31][32][33]. QCA is particularly useful for describing complex causal relationships by analyzing the ways in which conditions combine together to yield an outcome, rather than quantifying the effects of independent variables [34][35][36]. QCA retains the power of rich, in-depth knowledge of cases, while providing more generalizable results from the analysis [37].…”

Qualifying the Sustainability of Novel Designs and Existing Solutions for Post-Disaster and Post-Conflict Sheltering

“…Thirty-six years ago, the UNDRO [17] concluded that the key to success in reconstruction is the local community's participation. Opdyke, Javernick-Will and Koschmann [23] found that early user involvement in PDPC shelter projects, support the resilience and the sustainability of the project outcome. The local input could empower the affected people, encourage the social connectivity and promote solidarity between the beneficiaries themselves [24].…”

Pathways to Livable Relocation Settlements Following Disaster

“…Civil infrastructure services include potable and domestic water, safe sewage disposal, electricity, and passable road networks, all with enough capacity and reliability to sustain the community. Housing measures include perceived satisfaction and quality [23]. With growing recognition that relocation requires more than housing, communal infrastructure (e.g., schools and hospitals) have accompanied recent projects [24].…”

“…Other critical decisions include house design, material selection, community layout, and procedures for implementing the transfer [55,56]. Despite the value of up-front participation in early decisions, participatory approaches are often limited to the inclusion of households in construction [23,57]. Participation in construction generally occurs through sweat equity, which may be required for a new house in lieu of financial equity.…”

“…Incorporating these simplifying assumptions, we followed standard QCA practice in analyzing the intermediate solution provided by the fsQCA software's truth table minimization process [23,96]. We required a consistency cutoff of 0.8 during minimization [94].…”