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

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

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.

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

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.

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

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.

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.

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.

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.

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?

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.

References USAID Feed the Future. 2016, 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. 2015. 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.