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1. Food Security As Resilience:
Reconciling Definition And Measurement
Joanna B. Upton, Jennifer Denno Cissé & Christopher B. Barrett
Charles H. Dyson School of Applied Economics & Management
Cornell University
International Conference of Agricultural Economists conference
Milan, Italy
August 12, 2015

2. Measurement matters!
– But must be founded on agreed definition of subject
– The internationally agreed (1996) definition of food security:
“Food security exists when all people at all times have physical, social, and
economic access to sufficient, safe and nutritious food that meet their dietary
needs and food preferences for an active and healthy life.”
– Challenging to measure because intrinsically unobservable
– Nonetheless, definition implies some axioms of measures
Motivation

3. • Decades of grappling with measurement…
– Different metrics have different goals (to meet different needs)
– Metrics each reflect one or more observable dimension of food
security
– Sometimes try to combine dimensions using indices … but that
introduces many well-known problems
– No existing measure well captures “food insecurity” per
internationally agreed definition and derivative axioms
Motivation

4. • The emergent concept of development resilience (Barrett &
Constas PNAS 2014) may offer a way forward (in time, not
immediately) ….
– Barrett and Constas (PNAS 2014) offer a theoretical
foundation for development resilience that we show can
fit the 4 axioms implied by 1996 definition of food
security.
– Econometric implementation of BC is now feasible with
adequate individual/household panel data
– We illustrate empirically how this can prove useful
• This is a suggestive exercise only, meant to prompt
continued pursuit of better measurement.
Punch line

5. The 1996 FAO definition of FS implies 4 core measurement axioms:
“all people” – the scale axiom (address both individuals and groups at
various scales of aggregation)
“at all times” – the time axiom (assess stability, given both predictable
and unpredictable variation)
“physical, social, and economic access” – the access axiom (must control
for poverty, institutions, infrastructure)
“an active and healthy life” – the outcomes axiom (nutrition/ health
outcome indicators are the ultimate targets)
Claim: Measures should adhere, as better as possible, to these axioms.
Axioms of measurement

6. Measures necessarily depend on data. And data quality issues abound.
• Shortcomings in national-level data
– Often must rely on national governments
• Disagreement on what to collect, and/or how
• Resource and capacity constraints make for unreliable
quality
• …also in household-level data
– Analytical challenges (sampling and survey design)
– Reliability (proxy reporting, recall, accounting for income…)
– Nutrient composition tables not universal
– Limited comparability between data sets
– Attrition
– Etc…
Data challenges

7. Data challenges
All levels of data collection face challenges of:
• Consistency over time
– Funding streams usually have short-term time scales
– Methods & priorities change with actors and institutions
• Cost
– Especially large scale, repeated collection
• Challenges typically greatest where need is most acute
• But, some new opportunities are emerging
– New data sources and technologies (e.g., ICT, RS)

8. Existing metrics
1
3
5
4
Larger size indicates better representation of the access axiom
Darker color indicates better reflection of the outcomes axiom
2
6
8
7
We can rate metrics for how they perform in addressing the 4 axioms
that follow from the agreed 1996 FS definition
• For the most part, the choice
of metric involves trade-offs…
1 – One-off, aggreg.availability
2 – Annual, aggreg. availability
3 – One-off, hh-level (e.g.,DD)
4 – High freq, aggreg. avail./access
5 – Annual, aggreg. composite
6 – Annual, hh-level poverty
7 – Annual, hh-level DD
8 – High freq,indiv/hh health outcomes

9. Existing metrics
• Other criteria besides axiom satisfaction are also important:
– Cost; difficulty (analytical and logistical); comparability between
countries and other groups
• And, different metrics address different needs:
– For example, a health metric may capture the end outcome, but
without other metrics we don’t understand mechanisms in order
to design/evaluate appropriate interventions
– Food security is ultimately about individuals, but national- and
multinational-level information is needed for policy
• So tradeoffs abound among current food security metrics

10. As applied to humans, development resilience is both a
capacity and a state (Barrett and Constas PNAS 2014):
• Capacity: The likelihood over time of a person, household or
other aggregate unit enjoying adequate well-being in the face
of various stressors and in the wake of myriad shocks.
• State: If and only if that likelihood is and remains high, then
the unit is resilient.
One can adapt this for food security measurement when using
health/nutrition indicators of well-being
Adapting development resilience

11. A moments-based approach
Describe stochastic well-being dynamics (in reduced form) with
moment functions:
mk(Wt+s | Wt, Xt, εt)
where mk represents the kth moment (e.g., mean (k=1), variance
(k=2), etc.)
Wt is well-being at time t
Xt is vector of conditioning variables at time t
εt is an exogenous disturbance (scalar or vector) at time t

12. Adapting resilience for food security
We can adapt the concept of development resilience for food security
by using an indicator for an ‘active and healthy life’.
This measure can address all 4 food security axioms:
• Satisfies the time and scale axioms (short and long term dynamics;
estimate for individuals/ households but aggregable to larger
groups)
• The access outcome can be addressed by conditioning the
moments on any host of economic, physical, or social
characteristics
• We take as outcomes either proxy or direct indicators of
health/nutrition status

13. Adapting resilience for food security
• Key limitation remains data
– Some possibilities, and proposals for easing this constraint
(see Headey & Barrett PNAS in press on sentinel sites)
• Data on shocks not previously systematically
considered…but increasingly possible (satellite imagery,
etc.)
• We have illustrative applications of the metric to evaluate
food insecurity among rural households in northern Kenya

14. An empirical example
Northern Kenya (Marsabit County)
• 924 households, tracked annually for 5 years (2009-2013)
• Data collected by ILRI and partners to assess the impacts of
Index Based Livestock Insurance (IBLI)
• Period encompasses a massive drought (2011)
• Data include several well-being outcomes: hh livestock holdings,
expenditures, food consumption, individual child
anthropometry, etc. and can control for exogenous
environmental conditions (esp. NDVI of rangelands)

15. • Outcome variables: Household dietary diversity score (HDDS) based
on 7-day recall and child mid-upper arm circumference (MUAC) …
these satisfy the outcomes axiom.
• Conditioning variables: a host of access-related variables at
community, agro-ecological and household level … satisfy the
access axiom
• Household and individual panel data satisfy the time and scale
axioms
An empirical example

16. • Follow procedure developed by Cissé and Barrett (2015)
• Implementation demands (at least) two normative judgements:
– Level – Minimum acceptable standard of ‘adequate well-being’,
for an individual or household. For this example we use:
• Individual child MUAC ≥ -1 SD by WHO SDs
• HDDS ≥ mean of upper 1/3 of sample (per FANTA III)
– Probability – Minimum acceptable likelihood of meeting level:
• We set 𝑃 ≥ 0.25 but then test alternative levels
• Note: Like poverty lines, these are intrinsically arbitrary cut-offs
Resilience categorization

17. • Step 1: Estimate the conditional mean MUAC and HDDS
equations, conditioned on:
– Lagged well-being (MUAC/HDDS) in cubic polynomial to
allow for nonlinear path dynamics
– A range of access indicators – wealth(TLUs), location,
demographics, etc. OLS w/robust standard errors.
• Step 2: Capture residuals and estimate conditional variance
similarly.
• Assume normality for simplicity in illustration, so 1 and 2 suffice.
• Step 3: Use predicted conditional mean and conditional variance to
estimate conditional cdf for each child (MUAC) or HH (HDDS),
categorize as resilient if 𝑃(𝑌 ≥ W) ≥ 0.25.
Resilience categorization

18. Resilience aggregation
• We can, by construction, aggregate the resilience measure for
different groups, just like FGT for poverty:
• Different measures yield strikingly different aggregate results: just
before 2011 drought struck, widespread resilience with moderate
MUAC threshold, limited resilience with strict HDDS threshold.
𝑅𝐻𝐷𝐷𝑆,0,2 𝝆; 𝑊 = 7.9, 𝑃 = .25 ≡ 1 −
1
𝑛
𝑔𝑖
𝑃
0
𝑞
𝑖=1
= 0.371
𝑅𝑀𝑈𝐴𝐶,0,2 𝝆; 𝑊 = −1 𝑠. 𝑑. , 𝑃 = .25 ≡ 1 −
1
𝑛
𝑔𝑖
𝑃
0
𝑞
𝑖=1
= 0.824,

19. Resilience aggregation
Captures time-varying, group-specific estimates of food security:
Key implication: Can identify targeting characteristics and project out
of sample to generate predictions for targeting purposes.

20. Policy advantages
By varying 𝑃 can choose to minimize errors of exclusion, inclusion,
or their sum, depending on operational priorities.
Relative to current practice of using most recent observation
(implicitly, random walk assumption), can outperform in forecasting.
Estimates of Targeting Accuracy - HDDS
P
Correctly
Not Targeted
Correctly
Targeted
TI Error TII Error
Sum of
Errors
0.15 0.266 0.503 0.088 0.143 0.231
0.20 0.198 0.566 0.156 0.080 0.236
0.25 0.122 0.609 0.231 0.037 0.268
0.30 0.056 0.644 0.298 0.002 0.300
Standard 0.209 0.536 0.145 0.110 0.255

21. Summary and next steps
• Food security measurement is important.
• The world is making slow but steady progress in improving
these measures.
• But need to maintain fidelity to agreed definitions and the
axioms they imply.
• An adaptation of emergent development resilience measures
shows promise as a next-generation food security measure.
• Its implementability is sharply limited by data availability.
• Opportunity to develop robust, axiomatic measures of food
security is considerable
• So is the opportunity to improve food security programming
through the use of improved measures for diagnosis,
inference, prediction and targeting.

22. Thank you for your time, interest and comments!