finding meaning in our measures: overcoming challenges to quantitative food security usda economic...
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Finding Meaning in Our Measures: Overcoming Chal lenges to Quantitat ive Food Security
USDAEconomic Research ServiceFebruary 9, 2015
Food Security As Resilience: Reconciling Definition And MeasurementEmpirical Example from Northern Kenya
Joanna Upton, Jenn Cisséand Chris Barrett
The Dyson School, Cornell University
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 measurementCan then be used to measure impacts of
shocks or interventions
Motivation
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.
Measurement
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:
Empirical Example
VARIABLES (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)
HH head education 0.032*** (0.009)
Dependency ratio -0.054* (0.030)
Supp. feeding -0.412*** (0.069)
Girl -0.024 (0.054)
Observations 1,714
Square residuals and estimate the conditional variance, as a function of the same regressorsHere, 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:
Empirical Example
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)
HH head education 0.032*** (0.009) .0112*** (0.001)
Dependency ratio -0.054* (0.030) -0.011* (0.004)
Supp. feeding -0.412*** (0.069) 0.381*** (0.008)
Girl -0.024 (0.054) 0.0211*** (0.007)
Observations 1,714
We can, by construction, aggregate the resilience measure for different groups, by setting an accepted probability thresholdSet 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 (gap2 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:
Resilience Aggregation
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 implementedCondition 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
indicatorReflects 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
stressorsSignificant work ahead, in applying this metric
to different settings and problemsIdeally also in improving (and institutionalizing)
conducive data collection mechanisms
Summary & Next Steps
Questions and comments (more than) welcome
Thank you