AEA Conference 2013 "Measuring Resilience at the Project and Program Level" - Part 2

The American Evaluation Association Conference took place in Washington DC, 16-19 October 2013.

This panel session at the American Evaluation Association Conference in Washington DC took place early morning on 18 October, chaired by Timothy Frankenberger of TANGO International and presented 4 very interesting presentations on measuring resilience.

Measuring Resilience at a program and project level has been evolving quickly in the last year as donors, academics and implementers gain a deeper understanding of this complex and dynamic concept. Practitioners and academics on this panel shared their experiences either conceptualizing models to measure resilience or testing a variety of methods and drawing lessons from their experiences.

Mercy Corps' Contingency Approach to Measuring Resilience (Jon Kurtz)

Mercy Corps is taking a "contingency approach" to generate evidence on what contributes to strengthening resilience in the arid lands of Africa. Methods choices are made based on the purpose, timing, available resources, and other constraints to a given study. The main methods being used are: analysis of post-shock cross-sectional data to identify predictors of resilience; quasi-experimental impact evaluation designs to determine attributable program effects on resilience outcomes; and ex-post program evaluations to understand how target populations recover and manage subsequent shocks. Mercy Corps' Director of Research and Learning shared details on the data collection and analysis techniques employed in their recent studies in Somalia, Ethiopia, and Niger. He discussed the strengths and limitations of the methods, and key remaining measurement gaps around resilience from the perspective of field practitioners.

Building Resilience: Social Capital in Post-Disaster Recovery (Daniel P. Aldrich)

Using micro- and neighborhood-level data from four disasters in three nations over the 20th and 21st centuries, this talk investigated standard definition and theories of resilience. Bivariate, time series cross sectional, and matching analyses show that more than factors such as individual or personal wealth, aid from the government, or damage from the disaster, the depth of social capital best predicts the ability of local communities to reform. Social capital works through three main mechanisms: elevating voice and suppressing exit, overcoming collective action barriers, and providing informal insurance. Should social networks prove the critical engines before, during, and after disaster, this suggests a new approach to disaster mitigation for NGOs, individuals, and governments.

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