finding meaning in our measures: overcoming challenges to quantitative food security usda economic...

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