Resilience Index Measurement and Analysis II RIMA-II

1. Resilience IndexMeasurement and Analysis IIRIMA-II Marco d’Errico Resilience Analysis and Policies team Agricultural Development Economics DivisionFood and Agriculture Organization of the United NationsFAO-RIMA@fao.org

2. Resilience and RIMA-II What you can get from RIMA RIMA-II: the descriptive measure RIMA-II: the causal measure Stepping up policy influence 1 2 Outline 3 4 5

3. Resilience and RIMA-II Photo: FAO

4. The concept of resilience Latin: “Resilire” “to jump back” Ecology “the magnitude of disturbance a system can absorb before it redefines its structure...” 19th Century naval architecture “…the ability of materials to withstand severe conditions” • Economics & food security • “the ability of a household to keep with a certain level of well-being withstanding shocks and stresses...” (options and ability) • Engineering • “…ability to return to a steady state after a perturbation” RESILIENCE

5. Motivation & research questions Asia, 1980-2008 • Why resilience? • Increased risks and uncertainty: natural risks Meteorological events(Storm) Hydrological events(Flood, mass movement) Climatological events (Extreme temperature, drought, forest fire)

6. Motivation & research questions • Why resilience? • Increased risks and uncertainty: economic risks SD07-11 = 29,3 SD92-06 = 13,5

7. Motivation & research questions • Why resilience? • Increased risks and uncertainty • Scholarly debate and policy frameworks • WB’s Social Protection and Labor Strategy (2012): “Resilience, Equity, Opportunity” • Davos 2013 World Economic Forum “Resilient Dynamism” • IFPRI 2020 Conference (Addis Ababa, 2014) “Building Resilience for Food and Nutrition Security” • EU Action Plan for Resilience in Crisis Prone Countries 2013-2020 • FAO SO5: Increase the resilience of livelihoods to threats and crises

8. Motivation & research questions • Resilience vs. early warning • Rather than forecasting a crisis (EW), resilience analysis seeks to understand what are the • the determinants of vulnerability • the strategies to gain a livelihood, and • how these strategies are modified to reduce the negative impact of future shocks • health check of the system at hand

9. Motivation & research questions • Resilience vs. vulnerability • Vulnerability, output-based: asset-income-wellbeing (Dercon, 2001) • V = f (exposure to risk, resilience) • risks faced by the HH • options available to the HH • ability to handle risks • Resilience • risk reduction and mitigation (ex-ante actions) • risk coping (ex-post actions) • short term (e.g. coping) vs. long term (e.g. adaptation) • we focus only on resilience

10. Resilience: • Is not observable in nature; • can be applied to various systems (households; community; nations) and sciences (ecological and economic and architectural); • is highly context-specific; • changes characteristics and effects based on the nature and extent of shocks; • is highly time-dependent; and • We need to consider the “dynamics” of resilience. Resilience measurement Most adopted approach through a multivariable index (Constaset al., 2016):

11. Challenges in Resilience Measurement • a context-specific concept with respect to: • specific population of interest • specific outcome of interest • specific shocks • Linked to an outcome • Resilience is on the right hand of the equation • The Y is in the LHS (food security; consumption) • Time-dependent • Impact on resilience can be measured as change over time; need baseline/end-line data. It is all about time. • Resilience and RIMA-II How can resilience be measured? • quantitative vs qualitative • big surveys vs lighter surveys • ad hoc vs pre-existing data

12. RIMA (Resilience Index Measurement and Analysis) is an innovative quantitative approach that estimates resilience to food insecurity and generates the evidence for more effectively assisting vulnerable populations. RIMA allows explaining why and how some households cope with shocks and stressor better than others doand provides rigorous framework for humanitarian and long-term development initiatives to build food secure and resilient livelihoods. Resilience and RIMA-II

13. RIMA suits several definitions of resilience: • The ability to prevent disasters and crises as well as to anticipate, absorb, accommodate or recover from them in a timely, efficient and sustainable manner (FAO, 2013) • The capacity of a household to bounce back to a previous level of well-being (for instance food security) after a shock (Alinovi, Mane & Romano, 2009) • The capacity that ensures adverse stressors and shocks do not have long-lasting adverse development consequences (Resilience Measurement Technical Working Group, 2014) • Resilience and RIMA-II

14. RIMA is focused on households • It is the unit within which the most important decisions to manage uncertain events are made • Resilience and RIMA-II • It is the unit that benefits the positive effects of policies and suffers for negative effects of shocks

15. Conceptual framework

16. RIMA-II provides a comprehensive estimation of resilience and clear policy indications. • It estimates household resilience to food insecurity with a comprehensive pack which includes descriptive and causal measure as well as long and short term measurement approaches • Shocks are considered exogenous and included into a regression model for estimating their impact on food security and on resilience • Food security variables are considered exogenous indicators of resilience capacity • Resilience and RIMA-II

17. RIMA-II takes into account several types of shocks that can affect households. • Shocks affecting one household such as livestock death, job loss and illness of a household member. These shocks are all directly reported by households in surveys (idiosyncratic shocks) • Shocks affecting an entire community (covariate shocks) which in turn are divided into: • Shocks • Climate shocks, such as droughts, floods, rainfalls and other natural hazards, registered through GIS; • Conflict-related shocks, such as war, murders and social disorders

18. Quantitative data • Existing data (LSMS, MICS, other HH budget survey) • LSMS-ISA (Niger, Nigeria, Ethiopia, Malawi, Mali, Uganda, Tanzania) • Kenya Integrated Household Budget Survey 2005 • Ad hoc data (LSMS-type, primary data collection through surveys) • Baseline/final survey for impact evaluation (South Sudan, Sudan, Somalia) • Sampling; design; training; data collection, entry, cleaning & analysis • Dataset Qualitative data Validated and integrated with qualitative data • Focus group, rapid assessment, other tools

19. Mixed methods approach • Dataset

20. RIMA-II: the descriptive measure Photo: FAO

21. Descriptive measure Itprovides informationon household resilience capacity. RIMA-II employs latent variable models to estimate the Resilience Capacity Index (RCI) and the Resilience Structure Matrix (RSM). It is a valuable policy analysis tool to inform funding and policy decisions, as it allow to target and rank households from most to less resilient.

22. Descriptive measure Resilience pillars Assets (AST) Adaptive Capacity (AC) Social Safety Nets (SSN) Access to basic services (ABS) Household resilience

23. Descriptive measure

24. Two-step procedure for RCI estimation: Factor analysis: from observed variables to pillars Multiple Indicators Multiple Causes: from pillars to RCI • Descriptive measure

25. Step 1 – Factor analysis • Pre: • Outliers (avoiding multiple imputation and employing “imputeout” Stata command by variable) • “Positive” direction of the variables (e.g. inverse distances to services) • Test of variables correlation and collinearity(corr and alpha command) • Post: • Use of the iterated principal-factor method for analyzing the correlation matrix • Use of the Bartlett method for predicting as many factor scores explain the 90% of varibles’ variance • Generate the 4 pillars ABS, AST, SSN and AC as a linear combination of predicted factors (the weights are the percentages of explained variance) RCI estimation

26. Step 2 – MIMIC Multiple Indicators Multiple Causes (MIMIC) (Bollenet al., 2010) (1) RCI estimation (2) • The measurement equation (1) reflects that the observed indicators of food security are imperfect indicators of resilience capacity – and the structural equation (2) correlates the estimated attributes to resilience capacity • Ascale has been defined setting the coefficient of the food expenditure loading (Λ1) as equal to 1 • Fit statistics: Chi2, TLI, CFI.

27. Pre-existing data (LSMS, MICS, NHBS, … ) • Ad hoc data Sources of data for covariate shocks: • Dataset Episodes of violence collected by Armed Conflict Location & Event Data Project (ACLED): www.acleddata.com and Peace Research Institute Oslo (PRIO): www.prio.org/Data/Armed-Conflict/UCDP-PRIO Geo-climatic variables Normalized Difference Vegetation Index (NDVI): www.fao.org/giews/earthobservation

28. RIMA takes into account several types of shocks: • Idiosyncratic shocks, such as livestock death, job loss and illness of a household member. These shocks are self-reportedby households in surveys. • Covariate shocks, which in turn are divided into: • The role of shocks • Climate shocks,such as droughts, floods, rainfalls and other natural hazards, registered through GIS (FAO-GIEWS); • Conflict-related shocks, such as war, murders and social disorders (ACLED, UCDP/PRIO, HIIK), damages (OCHA); • Market shocks, such as input/output price fluctuations (WFP)

29. Shock module – Triangle of Hope, Mauritania questionnaire • Shock types and sources

30. Agricultural Stress Index (ASI) in Senegal, January 2016 Resilience analysis in Matam, Senegal (2016) report • Shock types and sources

31. RIMA-II: the causal measure Photo: European Commission DG ECHO / Flircr

32. Causal measure RIMA-II estimates the main determinants of food recovery and it moves the resilience analysis in the long term perspective. The causal measure can be adopted as a predictor tool for interventions that build and strengthen resilience to food insecurity. It provides new depth and breadth to resilience analysis and permits decision makers and other stakeholders to better understand the dynamics of positive trends in resilience and thus develop strategies that will yield positive results.

33. Food security trajectory • Causal measure

34. What you can get from RIMA

35. Who is most in need? • Where should investment focus in terms of geographical location? • Which dimensions of resilience need to be supported? • To what extent have interventions increased target populations’ resilience? Was our money well-spent? • What are the main determinants of food security recover? What questions answer?

36. Resilienceanalysis in the Triangle of Hope (Mauritania) Descriptive analysis Regionalheterogenity: Braknais the mostresilientregion, followed by Assaba and Tagant. Guidimaghais the least resilient one. The most important pillars of resilience are Access to Basic Services and Assets (productive and not)

37. Resilience maps • What you can get from RIMA

38. Rural vs urbanstatus Descriptive analysis Urbanhouseholds have on averagehigherresiliencecapacitythan rural households.

39. Rural vs urbanstatus Descriptive analysis The urbaneffectisfoundwithineachregion (by the t-test on the differenceRCI), with the exception of Tagantwhichispredominantly rural.

40. Rural vs urbanstatus Descriptive analysis Resilience Structure Matrix: correlationpillars - RCI by urbanstatus

41. CONFLICT FOOD SECURITY RESILIENCE Research question How are they captured? Conflict exposure  Self-reported assessment & HH localization Resilience capacity & FS __ FAO-RIMA

42. Instrumental Variable regression: = distance (km) locality-boarder * > 1 Km to buffer zone , indicator of conflict exposure is a dummy for residence damaged because of aggression; outcome of interest, in different specification RCI; ABS; AST; SSN; AC, food security indicators and pillars’ components. • Econometric approach Omitted factors (time-varying) affecting resilience and conflict exposure; Measurement errors in conflict exposure; Self-selection.

43. First-stage regression results: instrumenting the residence damage with the distance to the Israeli border Controlling for HH characteristics and self-reported shocks • Findings • Relevance • The distance to the border is a statistically significant (negative) predictor of the likelihood of being affected by residence damages • 2) Exogeneity • Instrumenting conflict with the area of maximum violence intensity is widespread in the empirical literature (Akresh & de Walkw, 008; Voors et al., 2012; Rohner et al., 2013; Serneels & Verpooten, 2015); • For the small size and Israeli restrictions living conditions (job opportunities, food availability, market access, etc.) are homogenous as food security levels; • Differences in the observable varibles (balance test) between affected and non-affected households are not significant on sub-samples of HHS located less than 1 Km from the buffer zone, 1Km to the border, 2Km to the border, up to 9km to the border.

44. Second-stage regression results: impact of residence damage on RCI, resilience pillars and food security indicators • Findings Controlling for HH characteristics and self-reported shocks

45. Key message: Food security of households in Gaza was not directly affected by the conflict; Household resilience capacity that is necessary to resist food insecurity declined as a result of the conflict, mainly due to a reduction of adaptive capacity, driven by a deterioration of income stability and income diversification. Conflict increased the use of social safety nets (cash, in-kind and other transfers) and access to basic services (mainly sanitation and school). Extensions: New waves of the panel dataset to study the persistency of the effects;\ Additional sources of data (e.g. child malnutrition) • Conclusions

46. Seasonality in the Triangle of Hope (Mauritania) • Why seasonality is relevant? • Differences between cropping seasons (post harvest vs growing season); • Differences between consumption habits (fresh vs stored food) and asset smoothing; • Differences in subjective well-being (happiness vs sadness); • Seasonality 1) First data collection: Nov-Dec 2015 (post harvest season)Reference period: last 12 months 2) Second data collection: Jul-Aug 2016 (post hot dry season)Reference period: last 7 months

47. Regional differences • In the first seven months of 2016, resilience capacity decreased with respect to the previous round • Guidimaghais still the less resilient region • Seasonality • Urban status differences • Urban household are still more resilient than rural ones • In general, the level of resilience decreased

48. Impact evaluation in Dolow (Somalia) • Impact evaluation in Dolow, Somalia, is being implemented in the framework of the Joint Resilience Strategy programme launched in 2012 by FAO, UNICEF and WFP. • It is based on abaseline and on a mid-term datasets. • Results show an increase in resilience capacity (23%), obtained through a positive impact on agricultural production, income deriving from livestock, transfers, diversification of income sources and access to infrastructures. Impact evaluation

49. Perceived resilience Module developed with Overseas Development Institute (ODI) and implemented in Mauritania, Senegal, Somalia and Uganda (Karamoja). Perception

50. Perception of well-being and social inclusion in Matam (Senegal) and the Triangle of Hope (Mauritania) How well-being perception and social inclusion indicators are associated with resilience capacity? The perception of well-being: “Has the HH during the last week felt (i) cheerful and in good spirit; (ii) calm and relaxed; (iii) active and vigorous; (iv) fresh and rested; (v) that his/her life has been filled with interesting things” The perception of social inclusion in the decision-making process: “Is the current process of decision-making in your community: based on mutual agreement among all men and women (4); based on mutual agreement but with lesser participation of women (3); based on participation but without agreement (2); elite or leader driven (1); don’t know (0)” The perception of social inclusion in local services provision: “To what extent can members of this community influence the public sector to provide better local services: a great deal (4); some (3); a small amount (2); none (1); don’t know (0).” Perception