Food Security As Resilience:
Reconciling Definition And Measurement
Empirical Example from Nor thern Kenya
Joanna Upton, Jenn Cissé
and Chris Barrett
The Dyson School, Cornell University
Finding Meaning in
Our Measures:
Overcoming
C h a l l e n g e s to
Q u a n t i t a t i v e Fo o d
Security
U S DA
E c o n o m i c Re s e a r c h
Ser vice
Fe b r u a r y 9 , 2 01 5
Motivation
 Implement the Barrett and Constas (2014)
framework following a decomposable poverty
measure approach
 Prevalence of food (in)security, or population with an
acceptable probability of falling (below)above a given
health/nutrition threshold over time
 For individuals or any aggregate (entire sample, female headed households, specific livelihood group…)
 Satisfies all four axioms of food security measurement
 Can then be used to measure impacts of shocks or
interventions
Measurement
 To implement, need to make (at least) two
normative statements:
 Level – An acceptable standard of well-being, for an
individual or population
 e.g. individual MUAC ≥ 125mm;
and/or < 10% of population with MUAC < 125mm
 Probability – An acceptable ikelihood of meeting that
standard of well-being
 These must be set by prior research, analysis of
context, comparing to other measures, etc.
Empirical Example
Northern Kenya (Marsabit)
 Data collected to assess
the impacts of Index Based
Livestock Insurance (IBLI)
 924 households, tracked
annually for five years
(2009-2013)
 Includes data on several
well-being outcomes:
livestock, expenditure, food
consumption, child
anthropometry
Empirical Example
 Follow the empirical procedure piloted by Cissé and
Barrett (in production)
 Choose an outcome and a threshold(s)
 Mid-upper arm circumference (MUAC)
 Typically, MUAC thresholds are set in the ‘negative,’ i.e.
admittance to treatment at < 115mm, lower risk of
death at > 125mm
 Using WHO growth guidelines: > -1SD for gender and
age appropriate MUAC (with acceptable probability at
⅔)
 Depending on setting and goals, could use different
indicators, thresholds, and/or probabilities
Empirical Example
 First, estimate the conditional mean MUAC
equation, conditioned on:
 Lagged well-being (MUAC; squared and cubed to
capture path dynamics)
 Livelihoods and risk factors, here livestock mortality
and livestock death ‘strike point’ (based on NDVI)
 Demographics (age, sex, and education level of
household head)
 Child sex and supplemental feeding status
Empirical Example
 Regression of MUAC on (selected) covariates:
(1)
MUAC
SE
MUAC lag
-7.031***
(2.314)
MUAC lag2
0.503***
(0.168)
MUAC lag3
-0.011***
(0.004)
Livestock ‘strike’ point
-0.379*
(0.197)
Female hh head
-0.105
(0.066)
0.032***
(0.009)
-0.054*
(0.030)
-0.412***
(0.069)
-0.024
(0.054)
VARIABLES
HH head education
Dependency ratio
Supp. feeding
Girl
Observations
1,714
Empirical Example
 Square residuals and estimate the conditional
variance, as a function of the same regressors
 Here, assume a normal distribution (such that the mean and
variance fully describe the child’s conditional MUAC
distribution)
 Use the mean and variance to estimate resilience
 Individual probabilities of MUAC > -1SD (for age and
gender), conditional on lags & other characteristics
 Individual-level PDFs, with value (above cut-off)
between 0 and 1
Empirical Example
 Explore which characteristics are associated with food
security (MUAC) resilience:
VARIABLES
(1) MUAC
SE
(3) Resilience
SE
MUAC lag
-7.031***
(2.314)
-2.501***
(0.185)
MUAC lag (^2)
0.503***
(0.168)
0.117***
(0.013)
MUAC lag (^3)
-0.011***
(0.004)
-0.004***
(0.0003)
Livestock ‘strike’ point
-0.379*
(0.197)
-0.213***
(0.024)
Female hh head
-0.105
(0.066)
-0.063***
(0.009)
0.032***
(0.009)
.0112***
(0.001)
-0.054*
(0.030)
-0.011*
(0.004)
-0.412***
(0.069)
0.381***
(0.008)
-0.024
(0.054)
0.0211***
(0.007)
HH head education
Dependency ratio
Supp. feeding
Girl
Observations
1,714
Resilience Aggregation
 We can, by construction, aggregate the resilience
measure for different groups, by setting an
accepted probability threshold
 Set to ⅔ (i.e. acceptable threshold is 66.7% chance of
falling above the -1 SD MUAC threshold)
 Note, can set R=0 (headcount), R=1 (gap), R=2 (gap 2 or
depth); here we calculate the resilience ‘headcount’
Resilience Aggregation
 Across periods, divided by gender of household
head:
Resilience Aggregation
 Across periods, divided by education level of
household head:
Summary & Next Steps
 The Barrett and Constas (2014) resilience theory
encapsulates the core dimensions of food security…
 Stability over time, responses to shocks
 Individuals and aggregate groups of interest
 …and it can be empirically implemented
 Condition on access (helps to illuminate mechanisms)
 Choice of specific outcome to best reflect food security
in a given context
 Results may be sensitive to the choice of outcome indicator
 Reflects all four of the axioms for measurement of food
security
Summary & Next Steps
 We can implement this measure with panel data
that is routinely collected
 In some cases with minor adjustments or additions
 Need further attention to data on shocks and stressors
 Significant work ahead, in applying this metric to
different settings and problems
 Ideally also in improving (and institutionalizing)
conducive data collection mechanisms
Thank you
Questions and comments (more than) welcome