1. Resilience Concepts and Measurement Workshop
1.2 Introduction to the Resilience Analytical Framework
Tim Frankenberger
TANGO International
Washington, D.C.
January 11, 2017

2. Session 1.2 Overview Introduction to resilience measurement
Example indicators for resilience capacities (Ethiopia)
Resilience Analytical Framework
Plenary discussion and small group work:
What information is important for resilience measurement?
What do we want to gather from resilience measurement in each area/country/region?
Reflect on possible indicators and accompanying interventions

3. Introduction to Resilience Measurement
Indicators may be single or composite that represent some level or state of well-being/condition.
Indicators may be measured at the household, inter- household, community and higher systems levels.
These same indicators may be part of a performance monitoring system.
Data may come from surveys, interviews/focus groups, monitoring activities and other secondary sources.

4. Example Resilience Indicators (Ethiopia)
Absorptive Capacity
Bonding social capital
Informal safety nets
Shock preparedness & mitigation
Hazard insurance
Household savings
Asset ownership
Conflict mitigation
Adaptive Capacity
Bridging social capital
Human capital
Access to financial services
Livelihood diversity
Exposure to information
Aspirations & confidence to adapt
Transformative Capacity
Linking social capital
Formal safety nets
Access to markets
Access to infrastructure
Access to basic services
Access to communal natural resources
Please note, this is an example/not all-inclusive. Indicators may fall into multiple capacities

5. Analytical Framework
To measure improvements in resilience, there is a need for empirical evidence of what factors contribute to resilience, under what contexts, and for what types of shocks.
Analytical frameworks are useful because:
They focus measurement activities, and
They provide a potential link between the logic of interventions and the organization of data analysis that follows measurement.

6. Analytical Framework Participants can see printed handout of framework
Source: Béné, C., T. Frankenberger and S. Nelson. 2015

7. Analytical Framework
Ex ante component – generates data to describe the initial state at time one (t1), before the occurrence of a shock, using categories of indicators that represent:
The development outcome of interest in a manner that is sensitive to the fact that well-being states are not static ;
Resilience capacity as a set of skills, abilities, relationships and resources held by a household, community, or larger unit ;
Variables that influence the likelihood and consequences of risk exposure (e.g., vulnerability); and
Initial well-being using indicators related to, for example, food security, nutrition, poverty, or safety.

8. Analytical Framework
Disturbance component – generates data to describe the intensity and effects of various types of shocks and stressors, including:
Natural disasters/stressors;
Pest and disease outbreaks that threaten agriculture;
Political conflicts that directly threaten well-being and/or disrupt the systems on which well-being depends; and
Economic shocks and stressors that affect asset holdings, asset consumption patterns, market functions, pricing of food and commodities.

9. Analytical Framework
Ex post component – generates data to describe the end state at time two (t2) using categories of indicators that represent:
Resilience capacity as a set of skills, abilities, relationships and resources held by a household, community, or larger unit;
It is important to measure both ex ante and ex post indicators because the resources that comprise resilience capacity are often sacrificed to meet short-term needs
Variables that influence the likelihood and consequences of risk exposure (e.g., vulnerability); and
Development outcomes using indicators related to, for example, food security, poverty, nutrition or safety.

10. Analytical Framework Notes on ex post data collection timing:
Data collection for M&E often include a simple pre-post design with a single measurement taken at some point following the completion of intervention.
The point at which a post (or ex ante) measure is taken should not be arbitrary (e.g., based on funding cycles).
It should be informed by a theory of expected rate of change associated with an outcome of interest.
It is also important to administer measures at more than one point time to ensure that observed patterns of adaptation and transformation are not short-lived.

11. Plenary Discussion
What information is important for resilience measurement that can contribute to more informed programming in various contexts?
What do we want to gather from resilience measurement in each area/country/region and across different organizations/offices?

12. Small Group Work
Using the handout for Session 1.2, reflect on indicators that may be useful in capturing resilience M&E.
In column A reflect on the following:
Based on your experience, what do you want to gather from resilience measurement? What types of indicators that build resilience would be useful to capture?
Consider different types of indicators (e.g., economic, social, technological, environmental, infrastructure- related, safety, and institutional) and various contexts.
In column B reflect on the following:
Consider what types of interventions could be developed based on the indicators in Column A.

13. References
USAID Feed the Future , draft. Resilience in the Sahel- Enhanced (RISE) Project Impact Evaluation Volume 1. Baseline Survey. Resilience Analysis. April. Prepared by Lisa Smith, Tim Frankenberger, Sabrina Aguiari, and Carrie Presnall for the Feed the Future FEEDBACK project.
Béné, Christophe, Tim Frankenberger and Suzanne Nelson Design, Monitoring and Evaluation of Resilience Interventions: Conceptual and Empirical Considerations. Supported by USAID and TOPS. IDS Working Paper. Volume 2015 No. 459, Brighton, UK. July.