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Resilience IndexMeasurement and Analysis II

RIMA-II

Marco d’ErricoResilience Analysis and Policies teamAgricultural Development Economics Division

Food and Agriculture Organization of the United NationsFAO-RIMA@fao.org

Ou

tlin

eResilience and RIMA-II

What you can get from RIMA

RIMA-II: the descriptive measure

RIMA-II: the causal measure

Stepping up policy influence

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Resilience and RIMA-II

Photo: FAO

RESILIENCE

Latin: “Resilire”“to jump back”

Engineering“…ability to return to a steady

state after a perturbation”

19th Century

naval architecture“…the ability of materials to

withstand severe conditions”

Ecology “the magnitude of

disturbance a system

can absorb before it

redefines its structure...”

Economics & food

security“the ability of a household to keep

with a certain level of well-being

withstanding shocks and

stresses...” (options and ability)

The concept of resilience

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•Why resilience?

• Increased risks and uncertainty: natural risks

Mo

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50

100

150

200

250

300

350

400

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Climatological events (Extreme temperature, drought, forest fire)

Hydrological events(Flood, mass movement)

Meteorological events(Storm)

Asia, 1980-2008

•Why resilience?

• Increased risks and uncertainty: economic risks

Mo

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SD92-06 = 13,5

SD07-11 = 29,3

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•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

Mo

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•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

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•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) Mo

tiva

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re

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Res

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mea

sure

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t• 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.

Most adopted approach through a multivariable index (Constas et al., 2016):

𝑅𝐸𝑆𝑖,𝑡 = 𝛼1𝐴1𝑖 + 𝛼2𝐴2𝑖,𝑡 + 𝛼3𝑋𝑖 + 𝛼4𝑆𝑖,𝑡 + 𝜀

Resilience:

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• 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-dependentImpact on resilience can be measured as change over time; need baseline/end-line data. It is all about time.

How can resilience be measured?

quantitative vs qualitative

big surveys vs lighter surveys

ad hoc vs pre-existing data

Challenges in Resilience Measurement

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RIMA (Resilience Index Measurement andAnalysis) is an innovative quantitative approachthat estimates resilience to food insecurity andgenerates the evidence for more effectivelyassisting vulnerable populations.

RIMA allows explaining why and how somehouseholds cope with shocks and stressorbetter than others do and provides rigorousframework for humanitarian and long-termdevelopment initiatives to build food secure andresilient livelihoods.

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RIMA suits several definitions of resilience:

• The ability to prevent disasters and crises as well as toanticipate, absorb, accommodate or recover from themin a timely, efficient and sustainable manner (FAO, 2013)

• The capacity of a household to bounce back to aprevious level of well-being (for instance food security)after a shock (Alinovi, Mane & Romano, 2009)

• The capacity that ensures adverse stressors and shocksdo not have long-lasting adverse developmentconsequences (Resilience Measurement TechnicalWorking Group, 2014)

RIMA is focused on householdsR

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• It is the unit within which the most important decisions to manage uncertain events are made

• It is the unit that benefits the positiveeffects of policies and suffers for negativeeffects of shocks

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IRIMA-II provides a comprehensive estimation of resilience and clear policy indications.

It estimates household resilience to food insecuritywith a comprehensive pack which includesdescriptive and causal measure as well as long andshort term measurement approaches

Shocks are considered exogenous and included into aregression model for estimating their impact on foodsecurity and on resilience

Food security variables are considered exogenousindicators of resilience capacity

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Sh

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RIMA-II takes into account several types of shocks that can affect households.

Shocks affecting one household such as livestockdeath, job loss and illness of a household member.These shocks are all directly reported byhouseholds in surveys (idiosyncratic shocks)

Shocks affecting an entire community (covariateshocks) which in turn are divided into:

Climate shocks, such as droughts, floods,rainfalls and other natural hazards,registered through GIS;

Conflict-related shocks, such as war,murders and social disorders

Dat

aset

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

Validated and integrated with qualitative data• Focus group, rapid assessment,

other tools

Qualitative data

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Dat

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Mixed methods approach

RIMA-II: the descriptive measure

Photo: FAO

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• Descriptive measure• It provides information on 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.

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Access to basic services (ABS)

Assets (AST) Adaptive Capacity (AC)

Social Safety Nets (SSN)

Household resilience

Resilience pillars

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Resilience pillars Definition

Adaptive CapacityAdaptive Capacity is the ability of a household to adapt to a new situation and develop new strategies of livelihood

Social Safety Nets

The Social Safety Nets pillar measures the ability of households to access timely and reliable assistance provided by international agencies, charities, and NGOs, as well as help from relatives and friends.

Assets

Assets comprise both productive and non-productive assets. Examples of indicators include land, livestock and durables. Other tangible assets such as house, vehicle, and household amenities reflect living standards and wealth of a household.

Access to Basic Services

Access to Basic Services shows the ability of a household to meet basic needs, and access and effective use of basic services; e.g., access to schools, health facilities; infrastructures and markets.

Des

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1) Factor analysis: from observed variables to pillars

2) Multiple Indicators Multiple Causes: from pillars to RCI

Two-step procedure for RCI estimation:

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RC

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• 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)

Step 1 – Factor analysis

RC

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𝑅𝐶𝐼 = 𝛽1, 𝛽2 , 𝛽3 , 𝛽4 ×

𝐴𝐵𝑆𝐴𝑆𝑇𝑆𝑆𝑁𝐴𝐶

+ 𝜀3 (2)

𝐹𝑜𝑜𝑑 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝐷𝑖𝑒𝑡𝑎𝑟𝑦 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦

= Λ1, Λ2 × 𝑅𝐶𝐼 + 𝜀1, 𝜀2 (1)

Multiple Indicators Multiple Causes (MIMIC) (Bollen et al., 2010)

• 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

• A scale has been defined setting the coefficient of the food expenditure loading (Λ1) as equal to 1

• Fit statistics: Chi2, TLI, CFI.

Step 2 – MIMIC

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Dat

aset

• Pre-existing data (LSMS, MICS, NHBS, … )

• Ad hoc data

Sources of data for covariate shocks:

(1) 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

(2) Geo-climatic variables

Normalized Difference Vegetation Index (NDVI):

www.fao.org/giews/earthobservation

Th

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f sh

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sRIMA takes into account several types of shocks:

Idiosyncratic shocks, such as livestock death, job lossand illness of a household member. These shocks areself-reported by households in surveys.

Covariate shocks, which in turn are divided into:

Climate shocks, such as droughts, floods, rainfalls andother natural hazards, registered through GIS (FAO-GIEWS);

Conflict-related shocks, such as war, murders and socialdisorders (ACLED, UCDP/PRIO, HIIK), damages (OCHA);

Market shocks, such as input/output price fluctuations (WFP)

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Shock code

Z2.2 In the last month, have you or any of your household members experienced [SHOCK]? [what is in Z2.2 cannot be in Z2.5]0. No >> NEXT SHOCK1. Yes

Z2.3. Can you quantify the total loss you suffered?

RMO

Z2.4. What did your household do in response to this [SHOCK] to try to regain your former welfare level? USE COPINGS STRATEGIES CODES ON CODE PAGE.

Z2.5. In the last 12 months, have you or any of your household members experienced [SHOCK]? [what is in Z2.5 cannot be in Z2.2] 0. No >> NEXT SHOCK1. Yes

Z2.6. Can you quantifythe total loss you suffered?

RMO

Z2.7. What did your household do in response to this [SHOCK] to try to regain your former welfare level? USE COPINGS STRATEGIES CODES ON CODE PAGE.

Flood

Drought

Crop disease

Livestock death

Business failure

High food prices

High input prices

Severe water shortage

Crop failure

Loss of land

Accident

Severe illness

Clashes

Death of main earner

Inability to pay loan

Displacement

Storm

Crop damage when stored

Job loss/no salary

Communal/Political crisis

Fire

Fishing Failure

Loss of fising gear

others SPECIFY

Shock module – Triangle of Hope, Mauritania questionnaire

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Agricultural Stress Index (ASI) in Senegal, January 2016 Resilience analysis in Matam, Senegal (2016) report

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RIMA-II: the causal measure

Photo: European Commission DG ECHO / Flircr

• 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.

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Cau

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reFood security trajectory

What you can get from RIMA

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Wh

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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?

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The most important pillars of resilience are Access to Basic Services and Assets (productive and not)

Resilience analysis in the Triangle of Hope (Mauritania)

Regional heterogenity: Brakna is the most resilientregion, followed by Assabaand Tagant. Guidimagha isthe least resilient one.

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Resilience maps

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Urban households have on average higher resilience capacitythan rural households.

Rural vs urban status

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The urban effect is found within each region (by the t-test on the difference RCI), with the exception of Tagant which is predominantlyrural.

Rural vs urban status

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Resilience Structure Matrix: correlation pillars - RCI by urban status

Rural vs urban status

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CONFLICT

FOOD SECURITY RESILIENCE

How are they captured?

• Conflict exposure Self-reported assessment & HH localization

• Resilience capacity & FS __ FAO-RIMA

Eco

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hInstrumental Variable regression:

𝐶𝑖 = 𝛽0 + 𝛽1𝐷𝑖 + 𝛽2𝑿𝒊 + ε

𝐷𝑖 = distance (km) locality-boarder * > 1 Km to buffer zone𝑪𝒊, indicator of conflict exposure is a dummy for residence damaged because of aggression;𝒀𝒊, outcome of interest, in different specification RCI; ABS; AST; SSN; AC, food security indicators and pillars’ components.

1) Omitted factors (time-varying) affecting resilience and conflict exposure;

2) Measurement errors in conflict exposure;3) Self-selection.

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First-stage regression results: instrumenting the residence damage with the distance to the Israeli border

Controlling for HH characteristics and self-reported shocks

1) RelevanceThe 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.

Fin

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gsSecond-stage regression results: impact of residence damage on RCI,

resilience pillars and food security indicators

Controlling for HH characteristics and self-reported shocks

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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)

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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

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 in the Triangle of Hope (Mauritania)

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Rural Urban

Nov-Dec 2015 July-Aug 2016 Nov-Dec 2015 July-Aug 2016

Source: Authors' own calculation

Resilience over Urban/Rural location

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Assaba Brakna Tagant Guidimagha

Nov

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July-A

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016

Nov

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July-A

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016

Nov

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5

July-A

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016

Nov

-Dec

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5

July-A

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016

Source: Authors' own calculation

Resilience over regions

Regional differences

• In the first seven months of 2016, resilience capacity decreased with respect to the previous round

• Guidimagha is still the less resilient region

Urban status differences

• Urban household are still more resilient than rural ones

• In general, the level of resilience decreased

Imp

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• 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 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 a baseline and on a mid-term datasets.

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

Generic Shock Drought

H6.1 Absorptive

Capacity

My household can bounce back from any

challenge that life throws at us

H6.11 Absorptive

Capacity

If an severe drought occurred tomorrow, my

household would be well prepared in

advance

H6.2 Absorptive

Capacity

My household is better able to deal with

hardship compared with others in our

community

H6.12 Absorptive

Capacity

If an severe drought occurred tomorrow,

my household could recover fully within six

months?

H6.3 Adaptive

Capacity

If threats to my household become more

frequent and intense, we would still find a

way to get by

H6.13 Adaptive

Capacity

If severe droughts were to become more

frequent and intense, my household would

still find a way to get by?

H6.4 Transformative

capacity

During times of hardship, my household

can change its primary source of income or

livelihood if needed

H6.5 Financial capitalMy household can afford all of the things

that it needs to survive and thrive

H6.6 Social capitalMy household can rely on the support of

family and friends when we need help

H6.7 Social/Political

capital

My household can rely on the support

politicians and government when we need

help

H6.8 Learning

Capacity

My household has learned important

lessons from past hardships that will help

us to better prepare for the future

H6.9 Anticipatory

Capacity

My household is fully prepared for any

future threats and challenges that life

throws at us

H6.10 Knowledge and

Information

My household frequently receives

information warning us about future

extreme weather events in advance

1) Strongly Agree ; 2) Agree; 3) Neither Agree or Disagree; 4) Disagree 5) Strongly Disagree

Perceived resilience

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nHow well-being perception and social inclusion indicators are associated with resilience capacity?

Perception of well-being and social inclusion in Matam(Senegal) and the Triangle of Hope (Mauritania)

• 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).”

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• Endogeneity issue between well-being indicator and RCI: no possibility of causal inference! (confirmed by Hausman test)

• Well-being indicator is the result of a Factor Analysis aggregating the answers coming from a list of variables that assumes a value from 0 (meaning “never”) to 4 (meaning “always”) was used, reflecting whether the HH during the last week has 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.

Average values by subjective well-being thresholdsSubjective well-being RCI Triangle of Hope RCI Matam

Very Low 48.17 47.02Low 40.88 51.51Middle 42.59 57.55High 48.15 58.37Very High 50.21 65.49

Per

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n• Hausman test rejects the hypothesis of endogeneity between the social

inclusion perception indicators and resilience.

• Note that social inclusion is at communitarian level and not at household level as resilience.

RC Ii =α+δ Si +δ Wi+ϑ Xi+ εi (3)

Where RC I is estimated by RIMA for the household i. Si is the vector of all the shocks experienced and reported by the households, while Wi is the vector of the two indicators of perception of social inclusion. Finally, Xi is the vector of control variables and εi the error term.

Impact of perception of social inclusion on RCITriangle of Hope Matam

Perception of social inclusion:

- services provision 1.666 0.224(0.000) (0.918)

- decision-making process 0.533 2.889(0.003) (0.000)

• Participation in the decision-making process enhances household resilience capacity in both Matam and in the Triangle of Hope;

• The community’s influence on achieving better local services has a positive and statistically significant effect in the Triangle of Hope, while in Matam the positive sign of the coefficient is not statistically significant.

Results:

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• The CGP is an unconditional cash transfer programme, implemented by the Ministry of Social Development (MoSD), targeting the poorest families with children in: Berea, Leribe, Mafeteng, Maseru and Qacha’s Nek

• Over four years, between 2009 and 2013, around 20,000 households received cash transfer on a regular, monthly basis

• The primary goal of the CGP was to increase well-being of children livingin the poorest households in Lesotho. Encouraged the beneficiaries tospend the received cash on the youngest

• The baseline data include information for 3,054 households• In the follow-up round only 2150 of those interviewed in baseline were

captured.• The attrition rate is equal to 6 percent

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In this report Average Treatment Effect (ATE) has been estimated, formally:

ATE = E[𝑌1 − 𝑌0]

Yit = β0 + β1Di + β2Tt + β3 TtDi + εit

RCT DiD

CT project

LT impact on children

ST general impact on hh

Impact on resilience

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• Positive effects on household resilience (+2.2%);• Strong effect on food insecure (+0.8%) and borderline (+1.4%);• Stronger effect on MHH then FHH (+3.9%); and• Strong effect on labor constrained (+4.6%).

Limitation of an IE for resilience

• Results can be driven by dynamic variables; • Counfoundness with other projects;• Difficult interpretation of an increase/decrease or RCI; and• Need more evidence on how this works.

Resilience & food security

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Resili

ence &

food s

ecurityResilience index as a measure of

HH resilience to food insecurity

where,

loss = 1 if the HH suffers a loss in food security between t and t1

RCI is the HH resilience capacity index in year t

FS is the HH food security status in year t

X is a vector of HH characteristics in year t

F is CDF of the normal distribution

𝑃 𝑙𝑜𝑠𝑠𝑡−𝑡1 = 1 = Φ 𝑅𝐶𝐼ℎ,𝑡, 𝐹𝑆𝑡 , 𝐗ℎ,𝑡

𝑃 𝑟𝑒𝑐𝑜𝑣𝑡1−𝑡2 = 1 = Φ 𝑅𝐶𝐼ℎ,𝑡, 𝐹𝑆𝑡1 , 𝐗ℎ,𝑡

where,

recov = 1 if the HH recovers the loss in food security between t1 and t2

FS is the HH food security status in year t1

Resilience index and food expenditure

Probit regression: likelihood of suffering a loss and recovering

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Tanzania Uganda

(1) (2) (3) (4)

Loss btw t and t+1 Recovery btw t+1

and t+2

Loss btw t and t+1 Recovery btw t+1

and t+2

RCI -0.0389*** 0.00366 -0.856*** 0.0227***

(0.00690) (0.00504) (0.150) (0.004)

Per capita food expenditure (log) 2.348*** -1.185*** 16.86*** -0.857***

(0.164) (0.117) (2.842) (0.0665)

Female HH head 0.154** -0.0554 -0.0351 -0.0487

(0.0640) (0.0852) (0.071) (0.089)

Age of HH head 0.000274 -0.00248 0.0012 -0.0067**

(0.00180) (0.00239) (0.0022) (0.00287)

HH size 0.0890*** -0.0419 0.0434 0.0338

(0.0260) (0.0375) (0.0320) (0.0398)

Squared HH size -0.00176 0.000853 -0.000999 -0.00156

(0.00165) (0.00248) (0.00217) (0.00270)

Rural 0.346*** -0.282*** 0.348*** -0.304***

(0.0705) (0.0959) (0.086) (0.106)

Constant -5.680*** 3.344*** -1.114*** 1.201***

(0.310) (0.478) (0.240) (0.310)

Observations 2,866 1,440 2,015 1,341

Log-Likelihood -1551.561 -855.002 -1100.709 -679.7411

Pseudo-R2 0.219 0.115 0.142 0.153

Pearson Chi2

Prob > Chi2

2854.13

0.386

1447.74

0.219

2020.53

0.393

2221.08

0.000

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30

Resilience index and dietary diversity

Probit regression: likelihood of suffering a loss and recovering

Re

sili

en

ce

& fo

od

se

cu

rity

Tanzania Uganda

(1) (2) (3) (4)

Loss btw t and t+1 Recovery btw t+1

and t+2

Loss btw t and t+1 Recovery btw t+1

and t+2

RCI -0.0272*** 0.0108*** -0.0052** 0.0138***

(0.00344) (0.00413) (0.00253) (0.0027)

Dietary diversity 3.031*** -2.466*** 2.096*** -1.940***

(0.144) (0.172) (0.125) (0.118)

Female HH head 0.0330 0.0468 0.177** -0.157*

(0.0628) (0.0880) (0.0761) (0.0832)

Age of HH head -0.000445 0.00208 0.00144 0.00043

(0.00175) (0.00248) (0.0023) (0.0026)

HH size 0.000291 -0.0202 -0.119*** 0.0582

(0.0183) (0.0375) (0.0342) (0.0426)

Squared HH size -0.000306 0.00202 0.0049** -0.00132

(0.000902) (0.00250) (0.0022) (0.00298)

Rural 0.231*** -0.175* 0.107 0.0103

(0.0698) (0.0967) (0.0924) (0.101)

Constant -3.565*** 2.478*** -1.937*** 1.249***

(0.283) (0.476) (0.250) (0.306)

Observations 2,866 1,483 2,015 1,417

Log-Likelihood -1584.558 -842.139 -966.809 -805.850

Pseudo-R2 0.201 0.163 0.2110 0.179

Pearson Chi2

Prob > Chi2

2819.64

0.567

1559.22

0.023

2018.33

0.406

1654.08

0.000

Re

sili

en

ce

& fo

od

se

cu

rityThe role of risks

where,

all variables have the same meaning as before

S is a vector of covariant and idiosyncratic shocks that hit the HH

between t and t1

𝑃 𝑙𝑜𝑠𝑠𝑡−𝑡1 = 1 = Φ 𝑅𝐶𝐼ℎ,𝑡, 𝐹𝑆𝑡 , 𝐗ℎ,𝑡 , 𝐒ℎ,𝑡−𝑡1 , 𝑅𝐶𝐼ℎ,𝑡𝐒ℎ,𝑡−𝑡1

Probit regression with self-reported shocks (LSMS-ISA):

⟹ RCI coefficients same magnitude as in previous models, expected

signs, highly statistically significant

⟹ also other variables show the same behaviour: rural positive and

highly significant, female-headed positive and significant

⟹ self-reported shocks not statistically significant

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31

The role of risks: covariate shocks

Probit regression: likelihood of suffering a loss in food expenditure

Re

sili

en

ce

& fo

od

se

cu

rity

Tanzania Uganda

Loss btw t and t+1 dx/dy Loss btw t and t+1 dx/dy

RCI -0.056*** -0.012*** -0.873*** -0.265***

(0.011) (0.152)

Conflict intensity index -0.233 -0.023 0.00288 0.0005

(0.242) (0.0137)

Rainfall CV -5.468** -0.525** -2.801 0.0118

(2.206) (3.624)

RCI * Conflict intensity index 0.0021 -5.72e-05

(0.003) (0.000209)

RCI * Rainfall CV 0.0544* 0.0695

(0.032) (0.0743)

Per capita food expenditure (log) 2.474*** 16.87***

(0.180) (2.867)

Female HH head 0.176** -0.0351

(0.0692) (0.0713)

Age of HH head 0.0010 0.000929

(0.0019) (0.00221)

HH size 0.10*** 0.0424

(0.029) (0.0323)

Squared HH size -0.0023 -0.000966

(0.0018) (0.00218)

Rural 0.310*** 0.362***

(0.0764) (0.0925)

Constant -4.006*** -0.492

(0.759) (0.839)

The role of risks: covariate shocks

Probit regression: likelihood of suffering a loss in dietary diversityR

esili

en

ce

& fo

od

se

cu

rity

Tanzania Uganda

Loss btw t and t+1 dx/dy Loss btw t and t+1 dx/dy

RCI -0.031*** -0.0075*** -0.008 -0.0015**

(0.0079) (0.0169)

Conflict intensity index 0.193 0.013 0.00843 0.002

(0.223) (0.017)

Rainfall CV -1.214 0.181 -2.944 -0.609

(2.046) (3.761)

RCI * Conflict intensity index -0.0024 1.21e-05

(0.0027) (0.00025)

RCI * Rainfall CV 0.0312 0.0131

(0.0293) (0.0753)

Dietary diversity 2.987*** 2.110***

(0.155) (0.127)

Female HH head 0.0238 0.178**

(0.067) (0.0766)

Age of HH head -0.00042 0.00159

(0.00188) (0.00233)

HH size 0.00194 -0.121***

(0.0192) (0.0344)

Squared HH size -0.0003 0.00501**

(0.0008) (0.00226)

Rural 0.238*** 0.139

(0.0755) (0.0992)

Constant -3.610*** -1.319

(0.722) (0.865)

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32

Wh

at y

ou

can

get

fro

m R

IMA

An infographic visually explains the process step by step and the

results of the analysis.

Evidence-based policy choice

A brief addressed to government policy-makers summarizes the results of the resilience analysis and formulates policy recommendations.

Cas

e st

ud

ies

Resilience analysis Karamoja (Uganda)

• The region of Karamoja, located in the northeast of Uganda, is the poorest and least developed region in the country

• In 2015, UNICEF, FAO and WFP developed a resilience strategy for the region in order to improve food security and nutrition

• RIMA-II was adopted as the tool to measure resilience capacity to food insecurity in the region

• Results and policy recommendations have been published in a resilience analysis report. They can also be visualized through an interactive infographic

In collaboration with IGAD, the Office of the Prime Minister, the Bureau of Statistics, UNICEF &WFP, FAO is conducting a food security and resilience analysis among refugee and host community households in North Uganda.

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RIM

A a

nal

ysi

s in

th

e w

orl

d

RIMA – Finalized AnalysisSenegal, Niger, Burkina Faso, Mali, Sudan, South Sudan, Kenya, Somalia, West Bank and Gaza Strip, Nigeria, Uganda, Tanzania and Malawi

RIMA – Ongoing AnalysisSenegal, Mauritania, Chad, Ethiopia, Lesotho, West Bank and Gaza Strip

Mauritania

Chad

Ethiopia

Tanzania

Malawi

Lesotho

Senegal

MaliNiger

Sudan

South Sudan

Kenya

Nigeria

West Bank & Gaza Strip

Stepping up policy influence

Photo: FAO

24/4/2018

34

Ste

pp

ing

up

po

licy

in

flu

ence

• Resilience markerpilot in West Bank and Gaza Strip

• Integration/harmonization with other toolse.g. USAID-TANGO, UNICEF, WFP, IFAD

• Global resilience indexe.g. future development for global comparison

• C-RIMApilot in Somalia

• Broadening RIMA analytical capacitiese.g. gender (FAO); shocks (IFPRI; Cornell and TUFTS University)

Ste

pp

ing

up

po

licy

in

flu

ence

• Strict collaboration with:1) Regional initiatives (CILSS/IGAD)

2) National Bureau of Statistics and other significative ministries

3) FAO country regional and sub-regional offices

4) Universities for enhancing local capacity building

• Re-thinking resilience analyses and communications tools under a policy-oriented perspective

24/4/2018

35

THANK YOU!

Contact me…

… and sign up to our newsletter!

Marco.Derrico@fao.org

FAO-RIMA@fao.org

www.fao.org/resilience/background/tools/rima