Finding Meaning in Our Measures: Overcoming Challenges to Quantitative Food Security USDA Economic Research Service February 9, 2015 Food Security As Resilience: Reconciling Definition And Measurement Empirical Example from Northern Kenya Joanna Upton, Jenn Cissé and 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 measurement  Can 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 ( )  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 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 lag ***(0.168) MUAC lag ***(0.004) Livestock ‘strike’ point-0.379*(0.197) Female hh head-0.105(0.066) HH head education0.032***(0.009) Dependency ratio-0.054*(0.030) Supp. feeding-0.412***(0.069) Girl-0.024(0.054) Observations1,714

 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: Empirical Example VARIABLES(1)MUACSE(3) ResilienceSE 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 education0.032***(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) Observations1,714

 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: 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 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 Summary & Next Steps

Questions and comments (more than) welcome Thank you