Resilience Measurement

Transcription

1 Resilience Measurement Technical Working Group Resilience Measurement Proceedings of the Technical Working Group Consultative Meeting ROME, ITALY OCTOBER 2013 October 2013

2 Table of Contents Executive Summary... 2 Background... 4 Objectives of the October 2013 Meeting... 4 Review of the Technical Paper on Resilience Measurement Principles... 5 Findings of Clusters... 7 Proposal for the Analytical Framework Paper Discussion of Ongoing and Emerging Initiatives Next Steps for the Resilience Measurement Technical Working Group References Appendix A Agenda for Resilience Measurement Technical Working Group Meeting Appendix B List of Participants in the Resilience Measurement Technical Working Group Meeting Appendix C Reports from RM-TWG Clusters Report from Shocks and Stressors Cluster Report from Scales and Systems Cluster Report from Qualitative and Subjective Measures Cluster Report from the Estimation Models Cluster Report from Existing Data Resources Cluster Proceedings of the Resilience Measurement Technical Working Consultative Meeting 1

3 Executive Summary Resilience has recently emerged as a framework for helping people and communities reduce, cope with and/or adapt to shocks. However, a common understanding of how to assess and predict resilience levels, and to evaluate the impact of resilience programmes, is lacking. In this context, the Resilience Measurement Technical Working Group (RM-TWG) was established under the auspices of the Food Security Information Network (FSIN) to identify and promote means of operationalizing the concept of resilience in humanitarian and development practice, primarily through research and technical oversight related to resilience measurement. Operationalizing resilience measurement will require that practitioners provide credible, data-based insights into the attributes, capacities and processes observed at various scales (e.g., individual, household, community, national) and maximize the use of available data from ongoing resilience initiatives to identify and promote best practice. Therefore, the RM-TWG will promote adoption of best practice in resilience measurement through collaborative development of three primary outputs: 1) a technical paper on resilience measurement principles; 2) a common analytical framework for resilience measurement; and 3) technical guidelines for resilience measurement. The TWG met in Rome in October 2013 with the following objectives: 1) to provide feedback on a draft of the technical paper; 2) reach a consensus on the definition of resilience and principles of resilience measurement; 3) discuss initial work undertaken by five thematic cluster groups; and 4) establish an agenda of work for the analytical framework. A fundamental question to answer from the outset is whether resilience offers a new perspective or whether the term simply uses a different vocabulary to describe vulnerability. Resilience is defined here as 1 The capacity that ensures adverse stressors and shocks do not have long-lasting adverse development consequences. As a phenomenon to be measured, vulnerability draws attention to sensitivity to disturbances whereas resilience is concerned with the various ways a given entity prepares for and responds to shocks and stressors that threaten their well-being. One of the key features of this definition is that resilience is understood and measured according to the instrumental effects it exerts on targeted development outcomes that may be affected by stressors and shocks. Defining resilience as a capacity means that resilience is comprised of a set of ex ante attributes and supports that should positively shift the likelihood function that exists between shocks and food security. While resilience may be viewed as a stand-alone outcome, the end-goal of building and measuring resilience is defined in terms of a particular outcome or set of outcomes. 1 Constas M., Frankenberger T. and Hoddinott J. (2013), Technical Paper on Resilience Measurement Principles Toward an Agenda for Measurement Design. Draft for Internal Review. November 25, Proceedings of the Resilience Measurement Technical Working Consultative Meeting 2

4 The RM-TWG reached consensus on a key principle of resilience measurement, namely that measures of resilience should be developed in relation to the instrumental value various capacities (absorptive, adaptive, and transformative) have for a particular outcome. The outcome of interest (e.g., food and nutrition security, poverty, health) should include a normative boundary that defines a threshold condition below which the well-being of an individual, household, or community is unacceptable. Measurement of resilience should also include subjective perceptions regarding dynamic changes in conditions, the extent to which they are recognized as disturbances and their impact on human welfare. In summary, important outcomes of the RM-TWG meeting are: 1. agreement on a definition of resilience; 2. recognition of connections and distinctions between resilience and vulnerability; 3. articulation of resilience-specific measurement principles and general technical guidelines for measurement; 4. identification of focal areas of work for RM-TWG cluster groups; and 5. recommendations for how to organize the next stage of work of the RM-TWG, including participation in events in which the RM-TWG may present its findings and interact with various groups (e.g., policy makers, implementation staff, researchers, monitoring and evaluation staff) who are interested in resilience. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 3

5 Background Under the auspices of the Food Security Information Network (FSIN), the Food and Agricultural Organization (FAO) and the World Food Programme (WFP) hosted the first meeting of the Resilience Measurement Technical Working Group (RM-TWG) on October 9 and 10, The activities of the RM-TWG are jointly supported by the European Commission (EC) and the United States Agency for International Development (USAID) as part of the larger effort to address the challenge of how to design, implement, and assess the effectiveness of policies and programs to build the resilience of shock-prone, vulnerable populations. The October meeting, and the creation of the RM-TWG itself, were conceived as follow up to an Expert Consultation meeting on resilience measurement that was held in Rome on February 19-21, The February Expert Consultation meeting was held to bring together stakeholders, donors and practitioners to promote a common understanding of the key issues regarding resilience measurement and best related approaches. Given the lack of consensus over a model to measure resilience, the main goals of the initial consultation were to understand the key issues regarding resilience measurement and to explore how best to measure reactions (for households, communities, and possibly larges aggregates) to the shocks and stresses they experience. The establishment of the Resilience RM-TWG is a first step toward reaching an agreement on a common analytical framework to provide direction on the best approach for measuring the effects of programmes aimed at building resilience. The RM-TWG was initially established in June of 2013 by the Food Security Information Network (FSIN). The October meeting, which was the first in person meeting of the RM-TWG, provided an opportunity for focused discussion on the topic of resilience measurement. The agenda for the meeting is shown in Appendix A and a list of participants in Appendix B. Objectives of the October 2013 Meeting The overall objective of RM-TWG is to promote adoption of best practices in resilience measurement through collaborative development of three primary outputs: 1) a technical paper on resilience measurement principles; 2) a common analytical framework for resilience measurement; and 3) technical guidelines for resilience measurement. The specific objectives of the October meeting were to: 1) review the draft technical paper on resilience measurement principles; 2) reach a consensus on the definition of resilience and principles of resilience measurement; 3) discuss initial work undertaken by five thematic cluster groups; and 4) establish an agenda of work for the analytical framework. Individual sessions of the meeting were dedicated to reviewing and providing feedback on the Proceedings of the Resilience Measurement Technical Working Consultative Meeting 4

6 technical paper, presentations of findings and plans of action from each of the RM-TWG clusters, discussion of the purpose and structure of a common analytical framework for resilience measurement, and a review of currently ongoing or planned resilience building initiatives. Review of the Technical Paper on Resilience Measurement Principles The overall aim of the paper is to frame the various tasks involved in developing contextually appropriate measures of resilience for food and nutrition security. This necessarily involves adoption of a concise definition of resilience, identifying the challenges involved in measurement, the specific information and analytical standards for resilience measurement, and means through which the RM-TWG can best utilize its technical expertise to facilitate adoption of best practice. Discussion of the technical paper during the meeting primarily focused on adopting a definition that adequately distinguishes the related notions of resilience and vulnerability, the importance of promoting a pro-poor stance on resilience building, and identifying a set of guiding design principles and variables for resilience measurement. The technical paper defines resilience as 2 : The capacity that ensures adverse stressors and shocks do not have long-lasting adverse development consequences. In adopting this relatively concise definition of a complex concept, RM-TWG members deliberately choose to emphasize the importance of capacity rather than focus more narrowly on pre-determined resilience outcomes. In order to operationalize this definition of resilience, members agreed on the need to further articulate complementary capacities that enable greater resilience and acknowledged that resilience measurement should be capable of providing relevant information at different scales and for program-specific outcomes. Toward this end, members agreed the conceptual framework for resilience incorporates three types of capacities 3 : 2 Constas M., Frankenberger T. and Hoddinott J. (2013), Technical Paper on Resilience Measurement Principles Toward an Agenda for Measurement Design. Draft for Internal Review. November 25, Frankenberger T., Mueller M., Spangler T. and Alexander S. (2013), Community Resilience: Conceptual Framework and Measurement. Feed the Future Learning Agenda. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 5

7 Absorptive capacity the ability to minimize exposure to shocks and stresses through preventative measures and appropriate coping strategies to avoid permanent, negative impacts. Adaptive capacity making proactive and informed choices about alternative livelihood strategies based on an understanding of changing conditions. Transformative capacity the governance mechanisms, policies/regulations, infrastructure, community networks, and formal and informal social protection mechanisms that constitute the enabling environment for systemic change. These capacities are interconnected, mutually reinforcing, and exist at multiple levels (individual, household, community, state, and ecosystem) 4,5. Members agreed that measurement of the impact of resilience building initiatives would be aided by indexing specific capacity indicators against specific well-being outcomes (e.g., food and nutrition security). An important aspect of this approach to resilience measurement will be the identification of thresholds for wellbeing outcomes, beyond which individuals, households or populations could objectively be determined to be on ascending or descending resilience trajectories. Identifying appropriate indicators of resilience is dependent on determining the nature of the shocks and stresses encountered and the unit of analysis. For instance, rather than focusing strictly on the impacts of drought, practitioners should acknowledge that populations affected by drought may be subject to additional shocks and stresses in the form of conflict, environmental degradation, demographic change, gender inequity, and/or price volatility. Likewise, resilience measurement must clarify whether analysis will focus on the individual, household, community, or higher levels, because resilience at one level is not mutually inclusive (i.e., does not necessarily imply resilience at other levels). Agreement was reached that the purpose of the technical paper was to provide a perspective on the range of challenges associated with resilience measurement. The approach taken to express this perspective involved the articulation of measurement design principles specific to resilience measurement and of technical guidelines associated with sound measurement practice. As the result of discussion that took place during the meeting, the RM-TWG settled on ten measurement principles that were seen as specific to resilience. While not meant to be comprehensive, these principles provide an important point of reference for future work of the RM-TWG. The ten principles, shown here as simple headings, are as follows: 4 Béné C., Wood R. G., Newsham A. and Davies M. (2012), Resilience: new utopia or new tyranny? Reflection about the potentials and limits of the concept of resilience in relation to vulnerability reduction programmes. IDS Working Paper, Volume 2012, Number Frankenberger T. R., Langworthy M., Spangler T. and Nelson S. (2012), Enhancing resilience to food security shocks in Africa. Discussion paper. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 6

8 Measurement Principle 1: Resilience as a Normatively Indexed Capacity Measurement Principle 2: Subjective States and Qualitative Data Measurement Principle 3: Systems and Complex Causality Measurement Principle 4: Shock and Stressor Specificity Measurement Principle 5: Desirable and Undesirable Equilibria Measurement Principle 6: Inherent Volatility and Instability Measurement Principle 7: Multiple Scales and Multi-Level Interactions Measurement Principle 8: Rates of Change and Timing of Measurement Measurement Principle 9: Resilience-Vulnerability Connections Measurement Principle 10: Tool for Interpreting Heterogeneity As just noted, the RM-TWG recommended that the ten principles should be supplemented by a general set of technical considerations that include standard measurement topics such as validity and reliability. More detailed descriptions of the ten principles and the general set of technical considerations will be provided in the forthcoming resilience measurement principles technical paper. Findings of Clusters Individual members of the RM-TWG are organized into five clusters to bring important issues into focus and promote concrete action on a number of specific and complex issues related to resilience measurement. These clusters include: Scales and Systems; Shocks and Stressors; Existing Data Resources; Estimation Models; and Qualitative and Subjective Measures. During the meeting, each of the clusters provided initial analyses of technical issues related to resilience measurement within specific clusters and shared their perspectives on priority issues to be resolved. Each cluster produced a brief report in advance of the meeting to summarize ideas shared within each cluster and to generate discussion at the meeting. The reports, which should be treated as draft documents, are provided in Appendix C. A summary of main points for each of the cluster is shown below. Scales and Systems Cluster The complex and dynamic nature of social, economic and environmental factors at multiple scales necessitates a wider systems approach to resilience measurement. Using a systems approach to resilience measurement entails recognition of the influence and impact of larger structural processes on household and community resilience. In light of this reality, the cluster members formulated a single question that reflects their task: Proceedings of the Resilience Measurement Technical Working Consultative Meeting 7

9 What are the different levels at which resilience data should be collected and what is the best way to conceptualize and assess dependencies that exist at multiple scales, within and across interacting systems, over varying time periods? Cluster members explained that a systems approach must account for multi-level and multiscale dynamics, adaptive and change capacities of structures and organizations, and drivers and thresholds of system changes in order to provide insight into resilience trajectories. The Scales and Systems Cluster intends to work closely with the Data Cluster, the Qualitative Measures Cluster, and the Modeling Cluster to optimize the use of existing data to provide opportunities for analytical research related to resilience measurement at multiple levels. Estimation Models Cluster At this relatively early stage, models of resilience measurement tend to follow one of two general approaches. The first is based on construction of indices based on cross-sectional data. This approach is based on the assumption that context-specific characteristics can be correlated with resilience using data reduction techniques (e.g., factor analysis, principal component analysis). A second (proposed) approach constructs a system model and then runs a large number of simulations based on plausible ranges and probability distributions of variables that are inputs into the outcome being considered (e.g., household food security). Data mining techniques are then applied to identify the input variables that had the greatest influence on modeled system outcomes, including measures of resilience. Similar to the Scales and Systems Cluster, many of the technical issues raised by the Estimation Models Cluster could be addressed through answering the following question: What are some of the key features of how resilience [capacity] will be modeled and what are the methodological conditions (e.g., sample design, number of waves of panel data, counterfactuals) that need to be satisfied to generate and test models? Several outstanding issues are yet to be resolved including whether or not to use a single resilience measure or assess resilience separately with several measures for different types of shocks and stresses; assigning the appropriate weight to locality and household characteristics within the model; incorporating qualitative data before and after modeling; and ensuring that models are sensitive to gender, nutrition, and heterogeneity of systems. Existing Data Resources Cluster The objective of the Existing Data Resources Cluster is to assess available data and data sources to better understand the usefulness of available information and identify and fill in data gaps. More specifically: Proceedings of the Resilience Measurement Technical Working Consultative Meeting 8

10 What are sources of data and readily available measures that contain indicators and measurement approaches useful for resilience and what kinds of primary data collection activities should be undertaken? In addition to the availability of data, the cluster recognizes the importance of assessing data accessibility and determining data quality. These issues led to a discussion regarding whether it is preferable to use large-scale (i.e., national) surveys or small-scale, micro-level, in-depth surveys for resilience analysis. The group also discussed the logistical and cost implications of adding resilience modules to existing survey instruments and prioritizing genderdisaggregated data. Finally, the cluster explained challenges related to collecting or obtaining data related to shocks given that gaining timely access to relevant information in the wake of various shocks and stresses can be difficult. Recommendations of the cluster include strategic combination of data from large-scale and small-scale surveys, more timely and frequent data collection to monitor the impact of and response to shocks and establishment of institutional relationships that improve data production and harmonization related to resilience measurement. Shocks and Stresses Cluster The objective of the Shocks and Stresses Cluster is to clarify the relationships between shocks, stresses and resilience in order to establish a foundation for consistent and reliable resilience measurement. This will entail support for comprehensive analysis of potential hazards, evolving trends in the frequency and severity of hazardous events, and ways in which such events impact livelihood systems. It is important to acknowledge that rather than single, isolated shocks, vulnerable populations typically face a diverse array of potential shocks and dynamic social and economic stresses that constrain their ability to adapt. While a considerable amount of research has been carried out on the impact of natural and market-related hazards and stressors, relatively little attention has been made to analyzing the effects of weak governance, conflict and political instability on the resilience to food insecurity. An important step forward in resilience measurement will be the ability to explicitly link measurement of shocks and stressors with food security outcomes and various forms of capacity at the household and community levels. Doing so will require that practitioners develop, test and refine approaches to measuring both the subjective and objective dimensions of shocks and stressors. It will also be important for resilience measurement tools to be sensitive to various types and combinations of shocks and stresses, including those that exhibit longer-term trends. Going forward, priority steps for the Shocks and Stresses Cluster include: - developing guidance on the integrated analysis of multiple shocks and stressors at different scales; Proceedings of the Resilience Measurement Technical Working Consultative Meeting 9

11 - establishing more systematic approaches to linking measurement of dynamic trends in shocks and stressors with specific livelihood outcomes; and - achieving improved understanding of the impact of different shocks on resilience to food insecurity to enable prioritization of research and programming. Qualitative and Subjective Measures Cluster The focus of this cluster is the examination of ways in which qualitative data and subjective measures can increase understanding of resilience dynamics and how subjective aspects of resilience are best measured. Qualitative data provides insight into the dynamic social relations, institutional roles, governance structures, and decision making processes that directly influence household and community resilience. Subjective measures capture judgments, perceptions and aspirations at the individual, household and community levels. Figure 1 (below) illustrates some important differences in approach between quantitative and qualitative research methods. Figure 1: Situating Subjective and Qualitative Information Data Types and Empirical Focus The value of incorporating qualitative data in resilience measurement is to clarify relationships between multiple factors that influence resilience and enable a variety of inferences to be made in relation to more objective data gained through quantitative research methods. For instance, qualitative data is important for interpreting and explaining variations in outcome data based on differing social, economic and environmental contexts. A mixed-methods approach (quantitative and qualitative research methods) is the best approach to examining the complex phenomena of resilience. Cluster members agreed on Proceedings of the Resilience Measurement Technical Working Consultative Meeting 10

12 the need to develop an approach to qualitative research that is specifically focused on resilience and the establishment of peer-reviewed research protocols to guide qualitative monitoring and evaluation of resilience building initiatives. Proposal for the Analytical Framework Paper The RM-TWG also discussed the proposed analytical framework paper which aims to develop a common set of indicators for measuring resilience related to food and nutrition security. The paper will also include an analysis of existing theories, measurement approaches and selected studies. The structure of the common analytical framework developed through the RM-TWG will address the multi-dimensional, multi-level, and multi-scale aspects of resilience and support analysis of data that is multi-scalar, systems-oriented, hierarchical and nested. During the meeting members discussed the need to reflect a definitional focus on identification and measurement of long-lasting adverse effects of shocks and stresses on outcome variables. In their terms, the left hand side (LHS) of the resilience equation would ideally measure a wellbeing outcome index to account for distribution sensitivity (around the mean) among various population groups (e.g., different income levels, gender). On the right hand side (RHS), the equation would have not one indicator of resilience, but rather many indicators capable of identifying causal mechanisms related to outcome performance. These variables should be carefully and purposefully chosen to capture differences in context, types of risk, livelihood profile and capacities. Further discussion of both the LHS and RHS is needed, particularly regarding candidate indicators, the availability of information on these indicators, most appropriate research methods, and the optimal frequency of data collection. An important first step will be to identify sources and availability of existing data and current information gaps. The RM-TWG will then determine means of filling those gaps and will seek to develop research protocols to ensure the validity and reliability of resilience studies and establish priority areas of research. Discussion of Ongoing and Emerging Initiatives Members of the RM-TWG gave brief overviews of the following relevant initiatives: USAID Resilience Leadership team and Resilience Secretariat and TWG USAID has established a multi-bureau Resilience Leadership Team to institutionalize resilience and deliver results. It also has formed a Resilience Secretariat to ensure linkages Proceedings of the Resilience Measurement Technical Working Consultative Meeting 11

13 and joint programming across its various teams and units, and to guide its own Resilience Technical Working Group. Resilience Analysis Unit FAO, UNICEF and WFP have proposed establishing a joint analytical unit to assist IGAD by enhancing the capacity for resilience measurement of joint programming in the Horn of Africa. The RAU is intended to function as a multi-stakeholder initiative and make an important contribution to strengthening collaboration among partners in the region. FAO and partners will identify opportunities for collaborative analysis and advocate for establishment of a common analytical framework. The RAU will be managed jointly by FAO, IGAD, UNICEF, WFP through platforms such as the Regional Drought Disaster Resilience and Sustainability Platform, the Food Security and Nutrition Working Group (FSNWG), and the RM-TWG. Resilience Learning Network The Resilience Learning Network is an NGO-led initiative comprised of Mercy Corps, CARE, Catholic Relief Services, and World Vision. The network s aim is to capture and disseminate lessons from resilience programs to inform future policy, program design and impact evaluation. The Resilience Learning Network has established a partnership with Cornell University intended to ensure quality standards and incorporation of best practice in resilience research. Technical and Operational Programme Support, TOPS Over the last two years, TOPS has taken a leadership role in providing technical guidance for resilience programming through organization and facilitation of professional forums in Washington D.C., Addis Ababa, Ethiopia and Niamey, Niger. These events have provided important opportunities for donors, policy makers, academic research institutions, and implementing agencies to discuss emerging issues related to resilience programming and measurement Vision Initiative, IFPRI An international policy conference will be held on Building Resilience for Food and Nutrition Security May 15-17, 2014, in Addis Ababa, Ethiopia. IFPRI s 2020 Vision Initiative is organizing the international conference as the centerpiece of a two-year consultative process that will address a number of key questions. The conference will bring together policymakers, development practitioners, donors, and others to: - evaluate emerging shocks that pose significant threats to food and nutrition security; - assess experiences and draw lessons for using programs, policies, institutions, and investments to build resilience; - determine key approaches and tools for building resilience to shocks of varying levels; Proceedings of the Resilience Measurement Technical Working Consultative Meeting 12

14 - identify knowledge and action gaps in research, policy, and programming; and - set priorities for action by different actors and in different regions. The participation of the RM-TWG in this event will be discussed with IFPRI. Resilience Conference The Resilience Alliance Network organizes an international science and policy conference every three years. In 2014, the conference will be held in Montpellier, France, May 4-8. FSIN will present a paper on the work of the RM-TWG at the Resilience Conference. The overall objective of Resilience 2014 is to create an arena for scientific exchange that fosters a larger debate on adaptation, transformation and development. To address the multiple links between resilience and development, the conference organizers, along with the scientific and organizing committees, have defined the following themes: - development challenges through a resilience lens; - trade-offs and synergies; - analyzing and promoting change and transformation; - knowledge opportunities; - whose development, whose resilience?; and - new methodology and tools. Next Steps for the Resilience Measurement Technical Working Group Members of the RM-TWG agreed that the following next steps will be taken: completion of the resilience measurement principles technical paper based on feedback provided during the meeting and on written comments received prior to the meeting; preparation of the analytical framework paper; expansion of the briefs already prepared by the clusters into stand-alone papers; articulation of a Research Agenda; identification of research areas where joint efforts should be undertaken (Northern Kenya was identified as a potential area for a case study); and further consideration of specific technical issues. These include: - how to make resilience measurement more gender and nutrition sensitive; Proceedings of the Resilience Measurement Technical Working Consultative Meeting 13

15 - need to include intra-household dimension in the household level analysis; - how resilience relates to other work and initiatives (links with IPC analysis); - engage other actors (e.g., EU) by sharing information on RM-TWG efforts through FSIN and other networks; and - accounting for the diagnostic and program monitoring and evaluation purposes of resilience measurement and how evolving approaches may be best used at the country level (e.g., for Country Policy Profiles). Proceedings of the Resilience Measurement Technical Working Consultative Meeting 14

16 References Béné C., Wood R., Newsham A. and Davies M. (2012), Resilience: new utopia or new tyranny? Reflection about the potentials and limits of the concept of resilience in relation to vulnerability reduction programmes. IDS Working Paper, Volume 2012, Number 405. Constas M., Frankenberger T. and Hoddinott J. (2013), Technical Paper on Resilience Measurement Principles Toward an Agenda for Measurement Design. Draft for Internal Review. November 25, Frankenberger T., Langworthy M., Spangler T. and Nelson S. (2012), Enhancing resilience to food security shocks in Africa. Discussion paper. Frankenberger T., Mueller M., Spangler T. and Alexander S. (2013), Community Resilience: Conceptual Framework and Measurement. Feed the Future Learning Agenda. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 15

17 Appendix A Agenda for Resilience Measurement Technical Working Group Meeting Day 1: Wednesday, October 9, :30-8:45 Welcome and meeting logistics Alexis Hoskins, FSIN Secretariat 8:45-9:15 Introduction and review of TWG goals & meeting objectives Joyce Luma, WFP, and Luca Russo, FAO 9:15-9:45 Review of technical paper: purpose, objectives and main points Mark Constas, Cornell, Tim Frankenberger, TANGO and John Hoddinott, IFPRI 9:45-10:45 Overview of comments received on technical paper and priorities for revision, followed by open discussion Joyce Luma, WFP, and Luca Russo, FAO 10:45-11:00 Break 11:00-12:00 Plans for completing and disseminating the technical paper Mark Constas, Cornell, Tim Frankenberger, TANGO and John Hoddinott, IFPRI 12:00-1:00 Discussion of ongoing and emerging initiatives of potential interest to resilience measurement USAID Resilience Secretariat, Greg Collins, USAID 2020 Vision Initiative, John Hoddinott, IFPRI Resilience Analysis Unit: Dorothee Klaus, UNICEF 1:00-2:00 Lunch 2:00-3:00 Meeting time for clusters in preparation for presentations 3:00-3:15 Break 3:15-4:15 Report from Scales and Systems Cluster and open discussion Nancy Mock, Tulane, Cluster Chair and panel of cluster members 4:15-5:15 Report from Shocks and Stressors Cluster and open discussion Richard Choularton, WFP Cluster Chair and panel of cluster members 5:15-5:30 Overview of main issues from day one: conclusions and day two tasks Tim Frankenberger, TANGO and Greg Collins, USAID 5:30-6:00 Reception Proceedings of the Resilience Measurement Technical Working Consultative Meeting 16

18 Day Two: Thursday, October 10, :30-8:45 Overview of plans for day two Joyce Luma, WFP, and Luca Russo, FAO 8:45-9:45 Report from Existing Data Sources Cluster and open discussion Gero Carletto, World Bank, Cluster Chair and panel of cluster members 9:45-10:45 Report from Explanatory Models Cluster and open discussion John Hoddinott, IFPRI, Cluster Chair and panel of cluster members 10:45-11:00 Break 11:00-12:00 Reports from Qualitative-Subjective Measures Cluster and open discussion Dan Maxwell, Tufts, Cluster Chair and panel of cluster members 12:00-1:00 TWG papers and the need for interim outputs proposal and discussion Joyce Luma, WFP, and Luca Russo, FAO 1:00-2:00 Lunch 2:00-2:30 Expectations for common analytical framework paper and technical guidelines paper as we move forward Joyce Luma, WFP, and Luca Russo, FAO 2:30-3:30 Plans for the common analytical framework paper and open discussion Tim Frankenberger, TANGO, Mark Constas, Cornell, Nancy Mock, Tulane, and Donato Romano, University of Florence 3:30-3:45 Break 3:45 4:45 Discussion of TWG organizational issues: clusters, products, review functions and building a community of practice Mark Constas, Cornell, Nancy Mock, Tulane, Alexis Hoskins, FSIN Secretariat 4:45-5:00 Closing of the meeting and next steps Joyce Luma, WFP, and Luca Russo, FAO Proceedings of the Resilience Measurement Technical Working Consultative Meeting 17

19 Appendix B List of Participants in the Resilience Measurement Technical Working Group Meeting Resilience Measurement Technical Working Group members Christophe Béné Institute of Development Studies (IDS), University of Sussex Tesfaye Beshah Intergovernmental Authority on Development (IGAD) Gero Carletto Development Research Group, World Bank Richard Choularton Climate Change and Disaster Risk Reduction, World Food Programme (WFP) Greg Collins U.S. Agency for International Development (USAID) Mark A. Constas Applied Economics and Management, Cornell University Marco D'Errico Food and Agriculture Organization (FAO) Katie Downie International Livestock Research Institute (ILRI) Tim Frankenberger TANGO International Alessandra Garbero International Fund for Agricultural Development (IFAD) John Hoddinott International Food Policy Research Institute (IFPRI) Dorothee Klaus United Nations Children's Fund (UNICEF) Jon Kurtz Mercy Corps Daniel Maxwell Feinstein International Center, Tufts University Nancy Mock Tulane University School of Public Health and Tropical Medicine Donato Romano University of Florence The FSIN Steering Committee members Joyce Luma WFP Luca Russo FAO The FSIN Secretariat members Alexis Hoskins WFP Kaisu-Leena Rajala WFP Lavinia Antonaci FAO Other participants Laura Mattioli Alberto Zezza Richard Caldwell Erdgin Mane FAO World Bank Gates Foundation FAO Proceedings of the Resilience Measurement Technical Working Consultative Meeting 18

20 Appendix C Reports from RM-TWG Clusters Report from Shocks and Stressors Cluster Participants: Richard Choularton, Chair (WFP), Tim Frankenberger (TANGO), Jon Kurtz (Mercy Corps), Dan Maxwell (Tufts University). Shocks and stressors are central to understanding and measuring resilience. The draft FSIN Technical Paper on Resilience Measurement Principles for Food and Nutrition Security: Toward an Agenda for Measurement Design defines resilience as the capacity that ensures adverse stressors and shocks do not have long-lasting adverse development consequences. With this definition as a starting point, this note outlines the main issues, questions and considerations related to understanding how shocks and stressors relate to resilience, and therefore its measurement, stemming from initial discussions of the Cluster on Shocks and Stressors of the FSIN TWG on Resilience Measurement. Key principle: We need to start with a comprehensive analysis of potential hazards, their trends, and their links to local livelihoods and context. Doing so is complex and needs to take into account the following issues: A single shock, a few hazards, or a risk landscape? We know a good deal about individual hazards, such as drought or floods, and an increasing amount on market shocks. We have a solid understanding of the impacts of these hazards individually, on people s lives and livelihoods. But, we do not always have a clear understanding of the trends and the dynamics underlying recurrent shocks and worsening trends. If we do not understand the full range of risks affecting households, we are likely to fail at helping them to build resilience. Although we know this as a community, we struggle to learn the lesson. Already we are referring to the Somalia famine as a drought. While there was a very bad drought, there was also a global food price spike that was independent of the local production shock, there was an on-going conflict, there were counter terrorism measures that prevented an early response, and the response mechanism itself was missing in action (among other things!). In this and other cases if we only focus on the most visible shocks and stressors, we miss the full picture. If we miss the full picture, we are likely to support robust but fragile systems, rather than resilient systems 6. 6 Robust but fragile is a term from the ecological resilience literature that refers to a system that is strong against its most frequent and identifiable shocks, but not at all against unanticipated or less frequent shocks. For example, an irrigated orchard is robust in the face of drought, but may have no defenses against fire or pest. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 19

21 Shocks, stressors, and more shocks Part 1 Natural, economic, social or man-made, market and natural We typically focus on a well-defined but limited set of hazards or stressors. We have made progress at predicting and preparing for climatic shocks, and are at least thinking about how changes in climate are going to require people to adapt in terms of production and primary livelihoods. Since 2008, we have gotten better at responding to (but not necessarily predicting or preparing for) market shocks. However, the discussion around risk reduction and resilience shies away from conflict or political shocks, although 60 to 70 percent of spending on humanitarian response is in crises that are partially or completely related to conflict and political causes. The Feinstein Center just completed a study on DRR that included a comprehensive desk review that found very little on risk reduction and conflict. Here, there is a lot to learn from local level risk analyses, many of which do include conflict. Some lessons could be drawn from Mercy Corps which has been working on analyzing the role of conflict in resilience to food insecurity. Shocks, stressors, and more shocks Part 2 Covariate vs. idiosyncratic risk and intensive vs. extensive risk As mentioned above, discussions on resilience focus on large scale covariate and intensive risks such as severe drought which affected large populations. However, households must manage both covariate risks and idiosyncratic risks. Measuring resilience will require a better understanding of how households manage both forms of risk. Recent work (e.g., Barrett) strongly suggests that in the medium to long term, idiosyncratic shocks can be more of a threat at the level of individual households than covariate shocks. Our focus on large scale hazard events (intensive risk) means we often miss the multiple recurrent small scale hazards (extensive risk) that affect people. The 2009 Global Assessment Report (GAR) 7 showed that most disaster mortality and asset destruction is intensively concentrated in very small areas exposed to infrequent but extreme hazards. However, lowintensity damage to housing, local infrastructure, crops and livestock, which interrupts and erodes livelihoods, is extensively spread within many countries and occurs very frequently. Such damage represents a significant and largely unaccounted for facet of disaster impacts 8. 7 UN ISDR (2009), 2009 Global Assessment Report on Disaster Risk Reduction Risk and poverty in a changing climate. Invest today for a safer tomorrow. UN ISDR, For example, across the 12 countries, 34% of the economic cost of disasters in the housing sector was associated with low-intensity loss reports, as well as 57% of the damage to schools, 65% of the damage to hospitals and 89% of the damage to roads (GAR 2009). Proceedings of the Resilience Measurement Technical Working Consultative Meeting 20

22 Linking measurement of shocks and stressors with food security outcomes and capacity Among current practices for measuring resilience, shocks and stressors are often overlooked. Most agencies measure changes to food security outcomes and/or a set of characteristics and capacities believed to contribute to resilience to food insecurity. Without an understanding of how these factors have affected or been affected by a disturbance, it is inappropriate to speak of this as resilience. How do shocks and stressors relate to food security outcomes? And how should these relationships be measured? When there is no shock (Part 1)? In many circumstances we find food security and other indicators at levels that imply a crisis, but where there is no specific (or at least visible) shock to which to attribute these indicators. We see this in areas where we find wasting rates well over emergency thresholds (GHA and Sahel) even in good years. These places are often the epicenters of our focus on resilience. While these conditions may be the result of multiple prior shocks and stressors, addressing them requires us to understand the underlying context of vulnerability in more depth. Where there is no shock (Part 2)? The majority of humanitarian and development programs will need to attempt to measure their contributions to resilience during periods when there is no major covariate shock. Characteristics approaches, like Oxfam GB s, attempt to do this, although they are based on a number of untested assumptions. Perhaps these situations are when better measurement of idiosyncratic shocks and longer-term stressors are most important, as these are nearly always present in some form. Implications of different kinds of hazards may be very different even contradictory and the strategies for managing them are sometimes at odds A Feinstein Center analysis of the factors that made households more resilient to the food price crisis of showed that households that relied on subsistence production in Guinea were less exposed and therefore better able to resist the shock. In this case, it was precisely the factors that made households more vulnerable to production shock drought, pests, flooding that made them resilient to the price shock. In order to measure resilience, we have to understand all the hazards, keep in mind the panoply of strategies that make people more resilient, and be aware that what makes people more resilient in the face of some hazards makes them more vulnerable to others 9. 9 In socio-technical disaster theory (see Perrow) this is referred to as coupling. Tightly coupled systems or those systems where the components of the system are strongly connected and interdependent are more vulnerable to failure elsewhere in the system. Loosely coupled or decoupled systems or more insulated when something goes wrong. One example is gas refineries in the United States are concentrated along the Gulf Coast. Hurricane Katrina crippled the refining capacities of the whole country. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 21

23 For example, livelihoods diversification is a common prescription for reducing exposure and vulnerability to certain types of shock, but the evidence on livelihoods diversification in highly risk-prone contexts does not always support the notion that diversification is a good risk management strategy. Often, livelihood diversification is a strategy pursued by households that are too destitute to manage a more specialized strategy. This and other similar points suggest that index approaches which try to distill hazard exposure and coping capacities may be too limiting and not support robust resilience measurement. The full range of shocks and stressors need to be understood over time More trend-based analysis of shocks with food security indicators is a positive step forward, especially when this is combined with local level planning. At WFP, we are linking multihazard risk analysis with food security trends as part of our context analysis for resilience building programs. This is then combined with seasonal livelihoods programming which combines hazard and livelihoods calendars with trend analysis to help identify programming options. Trend analysis that works to integrate secondary sources (e.g., remote sensing, Armed Conflict Location and Events Dataset) is one promising avenue. In general, this area has strong implications for the TWG data cluster. But some practical guidance on how to integrate secondary data on shocks/stressors into, for example HH survey datasets, would be helpful for implementing organizations. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 22

24 Report from Scales and Systems Cluster Participants: Nancy Mock, Chair (Tulane University), Christophe Bene (IDS), Mark Constas (Cornell University), Dramane Coulibaly (FAO/CILLS). What is it about resilience that requires a systems-oriented approach to food security? Proposition: A focus on systems draws attention to interactions between different processes at different levels and scales. Systems and levels are undeveloped in food security literature. Resilience over what time scale? Resilience across different levels? Emphasize expected rates of change and unit of analysis. What do we know about cross-scale and cross-level interactions? Systems as an approach and systems as identifiable entities whose functional properties with respect to food security could be measured. Systems Concepts Many components: Panarchy. Adaptive: can learn and change structure/organization. Drivers and thresholds: system changes are driven by a small number of important factors and change occurs when threshold levels are reached (nonlinear relationships). Multi-level and multi-scale dynamics within systems. Our concern is with the resilience of social-ecological systems (combining measurement of social and ecological system components, scales and drivers). Research Needs Integrating environmental, household and community data sets: secondary analysis of wealth of household data sets with community level measures and rich geospatial data layers available. Identification of best practices in measuring community attributes (in collaboration with the qualitative measures group). Agent-based modeling and other simulation techniques to identify thresholds of key resilience drivers in varying scenarios based upon extant data sets (in collaboration with modeling group). Proceedings of the Resilience Measurement Technical Working Consultative Meeting 23

25 Graphic that Illustrates Relationship between Frequency and Scale. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 24

26 Report from Qualitative and Subjective Measures Cluster Participants: Dan Maxwell (Tufts University), Dorothy Klaus (UNICEF), Tim Frankenberger (TANGO), Nancy Mock (Tulane University), Mark Constas (Cornell University). Task for qualitative and subjective measures: In what ways will qualitative data increase understanding of resilience dynamics and how will subjective aspects of resilience be measured? Qualitative and subjective measures are two separate categories of information. Qualitative information is primarily captured with words or narrative. Subjective measures are those that reflect the perceptions, preferences or self-assessments of specific actors in this case, the people about whom the resilience agenda is intended: people in protracted crises or risk-prone environments. There are subjective measures that result in quantitative data (e.g., Likert scales). And there can be qualitative measures that are objective i.e., reflecting verifiable phenomena external to the individual but which can t be captured meaningfully as numeric information. This may refer to institutional factors or social relations that may shape resilience, but which are not easily captured quantitatively. The columns in Table 1 express the difference between objective and subjective information; the rows express the difference between quantitative and qualitative information. Table 1. Situating Subjective and Qualitative Information Data Types and Empirical Focus Data Type Empirical Focus Subjective Objective Numeric E.g., survey data on perceptions, E.g., survey data on N.A.? (Quantitative) preferences, self-assessment events, behaviors and Trend material conditions analysis? Mixed Textual E.g., interpretation and affective E.g., political allegiances, N.A.? (Qualitative) states, meaning or reason of social relations, decision preference or perception making, institutional forms Time Frame Past Current Future Past Current Future Proceedings of the Resilience Measurement Technical Working Consultative Meeting 25

27 Subjective measures involve some judgment by an individual or household about their own status. These may include: The experience and perceived severity of shocks. The occurrence of shocks can be captured objectively, but not the experience of shocks. Measures of well-being and quality of life dimensions. Self-assessment or ranking. Self-assessment can sometimes be used much more effectively and accurately than complex objective assessments they can also be easier to game (i.e., the right approach depends on the objective). Aspirations or future projections (note time frame of information in Table 1). Qualitative information is important in areas where quantitative measures don t capture important relationships. This may include: The role of social relations or networks in shaping resilience: patriarchy, kinship, gender etc. Quantitative data cannot easily distinguish multi-layered and complex realities, the strength of social bonds, or the way in which these influence resilience (see for example, the tale of the two widows in the theory paper by Chris Barrett and Mark Constas). The role of institutions in shaping resilience. The resilience of institutions themselves. Information that can explain behavior and decision-making processes by revealing motivational or cultural value systems and beliefs. Approaches that provide clear indications of intentions or driving factors for decisionmaking that are important to understand transformational intentions or opportunities that can drive (or undermine) resilience. Some elements of capacity may lend themselves to accurate measurement quantitatively, but many may only lend themselves to qualitative description of complex relationships. Some elements of behavior can best be explored through qualitative methods illicit or illegal activities, violence, anti-social behavior, or shadow economic behavior. Some information may be both qualitative and subjective. For example: Information that allows for an emic explanation of phenomena as opposed to generation of meaning according to external models of logic. Information that supports uncovering or understanding of hidden or irrational realities. Other important dimensions of resilience: preparedness, acceptance and neglect, attitude, values, need, want, identity and belonging including social status, physical, socio-cultural and psychological well-being, optimism/pessimism, confidence/mistrust, satisfaction/dissatisfaction. Information on psychological strength and perceptions of resilience; information on perceived needs and wants; feedback on local acceptance or neglect of proposed solutions. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 26

28 Different kinds of information may be more applicable to different levels of analysis. At a minimum these include: individual-level information; household information; group or community-level information; institutional information and system or population-level information. Combining qualitative and quantitative approaches may be fruitful. Different kinds of information may be used in a stand-alone analysis, but experience suggests that mixed methods or approaches work well in understanding complex phenomena like resilience. Qualitative information can be used to shape quantitative instruments and to help explain or understand the results of quantitative analysis. However, careless combination of methods can be counter-productive, and some possibilities ignored. These include: Qualitative techniques are often equated with methodological looseness when, in fact, qualitative methods typically require more skill on the part of the interviewer, as much more judgment in the conduct of the method is required. Focus group methods are a very good example. Qualitative inquiry and qualitative measures often rely heavily on focus groups but which are loosely defined key informant type approaches. Constructing useful focus group assessments requires careful selection of participants, replications, an experienced facilitator and either an excellent recorder or electronic recording. Another common error is the use of qualitative methods to extrapolate to populationbased estimates of food security status/resilience. The food security literature is heavily quantitative and household oriented. While data is collected frequently at the community level through both quantitative and qualitative techniques, such data are rarely utilized in the analysis of food security. Many other techniques are well documented in the literature. For example, Delphic methods, which are relatively new, use random sampling together with strategies to help the sample better understand the issues being discussed, then conduct a follow up poll of the same sample. In this way, knowledgeable individuals are polled and re-polled until convergence on a finding is reached. These methods are typically documented in terms of their validity and reliability and could easily be integrated into resilience analysis. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 27

29 Report from the Estimation Models Cluster Participants: John Hoddinott, Chair (IFPRI), Katie Downey (ILRI), Marco D Errico (FAO), Alessandra Garbero (IFAD), Donato Romano (University of Florence). Background The Estimation Models Cluster is asked to consider the following: What are some of the key features of how resilience will be modeled and what are the methodological conditions (e.g., sample design, number of waves of panel data, counterfactuals) that need to be satisfied to generate and test models? Topics that we draw on in developing this note include: what do we know ideas that represent established knowledge within a cluster; what do we need to know ideas that represent knowledge gaps within a cluster; challenges immediate and long term challenges for resilience measurement in connection with the focus of a given cluster; and recent accomplishments recent or emerging work that illustrates best practices and/or opportunities related to the kinds of questions raised within a cluster. State of existing knowledge / recent accomplishments Modeling resilience is at an early stage. There are only a handful of extant studies and concept notes that model resilience. They fall into two groups. One group of studies, for example Keil et al. (2007) and Alinovi et al. (2009), construct an index of resilience using cross-sectional data. In these studies, resilience is not directly observed. Instead, these authors take a series of household and locality characteristics which are assumed to be correlated with resilience and using a data reduction technique, construct a measure of resilience. For example, Alinovi et al. see resilience as a function of the following components: stability, social safety nets, access to public services, assets; income and food access, and adaptive capacity. Household or locality data are used to describe each of these e.g., income and food access is seen to be reflected in average per capita daily income, current dietary diversity and a scale measure capturing household s perception of food insecurity. Each component is reduced using factor analysis and in turn these become covariates in the estimation of the resilience index. Keil et al. (2007) construct a measure of resilience using principal components. Variables used are recall data on drought-induced reductions in food consumption and basic necessities. They then examine the relationship between this index and post-drought household assets, off-farm income and assumed crop yields during the drought. Downie et al. are considering a different approach. They are proposing to construct a system model, then run a large number (i.e., thousands) of simulations based on plausible ranges and probability distributions of variables that are inputs into the outcome being considered (e.g., household food security). Data mining techniques Proceedings of the Resilience Measurement Technical Working Consultative Meeting 28

30 are then applied to identify the input variables that had the greatest influence on modeled system outcomes, including measures of resilience. Statistically established relationships among variations in particular input variables and system outcomes can then provide guidance on which input variables are the main determinants of system resilience. What do we need to know? / Challenges going forward One of my colleagues in the cluster noted, One of the challenges to measuring resilience is that even though we have strong ideas as to the components that together constitute the determinants of resilience, we are uncertain about the exact combination of these components and the relative importance of each in contributing to a cumulative resilience. This uncertainty follows from several features of the existing literature: o It attempts to derive a temporal property from cross-sectional data even though we have no real idea of their out-of-sample predictive properties. Apart from simulation models, it is highly unlikely that we will make significant progress on modeling resilience in the absence of longitudinal data (it is worth noting that this limitation doomed much of the early literature on vulnerability which also attempted to extrapolate temporal results from single cross sections). o It is unclear whether we should aim for a single resilience measure or should we assess resilience separately for different types of shocks and stressors. For example, assets such as livestock may provide a means of resilience during a drought shock but because they are easily stolen, may be less useful when the shock emanates from civil strife. At the core of much of this work is an attempt to amalgamate data on assets, local resources, incomes and existing food security status. If we end up with models that say that households with higher asset holdings are more resilient, or households with more schooling are more resilient, then it is not obvious that we are advancing knowledge (the latter point has been made for at least the last 40 years). Interestingly, in other fields that employ resilience concepts such as psychology the emphasis has been on understanding why some individuals are more resilient than others conditional on characteristics such as schooling, wealth or socioeconomic status. Put another way, are we being sufficiently imaginative in our data collection efforts or are we focused on shuffling and re-shuffling a narrow set of variables. How much weight should we put on locality characteristics as opposed to householdlevel characteristics? The extant literature contains no guidance on how this modeling can be made gender sensitive. Should it be? Proceedings of the Resilience Measurement Technical Working Consultative Meeting 29

31 References Alinovi L., Mane E. and Romano D. (2009), Measuring household resilience to food insecurity: Application to Palestinian households. Working Paper. FAO. Keil A., Zeller M., Wida A., Sanim B. and Birner R. (2007), What determines farmers resilience towards ENSO related drought? An empirical assessment in Central Sulawesi, Indonesia. Climatic Change, 86: Proceedings of the Resilience Measurement Technical Working Consultative Meeting 30

32 Report from Existing Data Resources Cluster Participants: Gero Carletto, Chair (World Bank), Tesfaye Besbah (IGAD), Greg Collins (USAID), Marco D Errico (FAO), Katie Downie (ILRI) Multiple objectives of resilience data Validating definitions, measuring, understanding/policy options. Data requirements and preferred source/methodology will vary depending on objective. What are the data requirements and instruments for each of the objectives? What does already exist and what s feasible to collect in the short-term vis a vis medium/long-term? Data sources and requirements to measure resilience Start from definition(s) and map to data requirements. Use existing sources taking into account feasibility of implementation. Do we need a comprehensive inventory of existing data? How to assess the goodness-of-fit to purpose of existing data? Should TWG commission it? Who can do that? Existing data and new data collection Should cluster focus only on existing data sources? Should we take a more forward looking approach and identify data gaps and instruments/initiatives/institutional arrangements to be put in place to collect what s needed based on agreed definition(s)? Existing data sources vs. repurposing vs. new instruments/surveys It would be useful to explore possibilities for repurposing existing/planned surveys vs. proposing new instruments. There might be a lot to gain from repurposing. What s the right spatial resolution? Should system be at national level, with appropriate spatial resolution, or instead focus on hot spots /special areas or groups? What s the right frequency of data collection? How to complement and link less frequent data collection efforts, e.g., household surveys, with more frequent data collection systems (sentinels, mobile phone surveys). Data integration Improving the linkages across different data sources e.g., socio-economic and environmental/spatial. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 31

33 Developing a standard/harmonized module to measure resilience? Consensus on definition and what can/should be measured. Resilience data at global vs. national/local level Comparability issues. Panel vs. cross-sectional data Exploring use of synthetic panels and other imputation techniques to measure and monitor resilience. Institutional arrangements needed to improve data production and harmonization and role of FSIN and other players This is particularly importance for repurposing existing surveys. Proceedings of the Resilience Measurement Technical Working Consultative Meeting 32

34 Photo credits: WFP Resilience Measurement Technical Working Group Resilience has recently emerged as a framework for enhancing people s and communities capacities to reduce their exposure, cope with and/or adapt to shocks. However, a common understanding of how to assess and predict resilience levels, and to evaluate the impact of resilience programmes, is lacking. In this context, the Resilience Measurement Technical Working Group (RM-TWG) was established under the auspices of the Food Security Information Network (FSIN) to identify and promote means of operationalizing the concept of resilience in humanitarian and development practice, primarily through research and technical oversight related to resilience measurement. Operationalizing resilience measurement will require that practitioners provide credible, data-based insights into the attributes, capacities and processes observed at various scales (e.g., individual, household, community, national) and maximize the use of available data from ongoing resilience initiatives. Therefore, the RM-TWG will promote adoption of best practice in resilience measurement through collaborative development of three primary outputs: 1) a paper on resilience measurement principles and definition of resilience; 2) a common analytical framework for resilience measurement; and 3) technical guidelines for resilience measurement. For more information and to join the community of practice: October 2013