Resilience, Poverty and Malnutrition in Mali

Resilience, Poverty and Malnutrition in

Mali

Rebecca PietrelliEconomistESA Division-FAO

Marco d’ErricoEconomistESA Division-FAO

#sdafrica2015

26-27 November 2015, Dakar

Francesca GrazioliEconomistESA Division-FAO

Outli

ne

Contribution

Resilience measurement

Identification strategy

Conclusions

Data

Findings

(1) To estimate the resilience capacity index (RCI) by using the FAO - Resilience Index Measurement and Analysis (RIMA) model in Mali (2009/10).(2) Is resilience capacity a determinant of household expenditure?(3) Does resilience capacity affect child malnutrition?

Cont

ribut

ion

The RIMA model adopts a 2-step procedure:1. The pillars (Assets – AST; Access to basic Services

– ABS; Adaptive Capacity – AC; Sensitivity - S) are estimated through Factor Analysis from observed variables.

2. A Structural Equation Model (SEM) predicts the Resilience index by identifying the relation between the pillars:

Resil

ienc

e m

easu

rem

ent

Figure 1. Resilience index and pillars

1. Multiple Indicator Cluster Survey - Enquête Légère Intégrée aprés des Ménages (MICS-ELIM) -> 8,660 Households interviewed in 2009/10.

Data

2. Communes survey: 703 communes surveyed in 2008.

Data

Figure 2. HH expenditure by low, medium and high resilience

Data

Figure 3. Percentage of households with malnourished children by low, medium and high resilience

Iden

tifica

tion

stra

tegyTwo-stage least square regressions (2SLS):

(1)(i) household expenditure per capita;

(ii) three dummies for having at least one stunted, wasted or underweight child;

(iii)three continuous variables expressing the number of stunted, wasted and under-weight children in the household.

(2)

Iden

tifica

tion

stra

tegyValidity of the instrumental variable:

1. The instrument must be exogenous

2. The instrument must be correlated with the endogenous explanatory variable

Table 1. First-stage: Instrumenting regression results for Resilience

Findi

ngs

  Resilience

Number of technical services of the State (per 100 inhabitants) 3.102***

(0.337)HH size 0.00175

(0.00259)Squared HH size 2.31e-05

(5.52e-05)Age of HH head -0.00560***

(0.000605)Male of HH head -0.0411

(0.0274)Constant 1.474***

(0.0409)

Observations 8,548R-squared 0.351

Regional dummies are included.Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 2 OLS and 2SLS models of household expenditure

Findi

ngs

  (a) (b)OLS 2SLS

Resilience 0.272*** 0.504***(0.00594) (0.0650)

HH size -0.0902*** -0.0906***(0.00143) (0.00155)

Squared HH size 0.00117*** 0.00116***(3.05e-05) (3.31e-05)

Age of HH head -0.00155*** -0.000235(0.000335) (0.000516)

Male of HH head 0.0764*** 0.0865***(0.0151) (0.0167)

Kayes -0.0329* 0.271***(0.0198) (0.0874)

Koulikoro 0.0163 0.318***(0.0196) (0.0867)

Sikasso -0.436*** -0.143*(0.0189) (0.0841)

Segou -0.189*** 0.126(0.0198) (0.0904)

Mopti -0.116*** 0.230**(0.0199) (0.0988)

Tomboctou -0.0793*** 0.289***(0.0215) (0.105)

Gao -0.0583*** 0.299***(0.0224) (0.103)

Kidal 0.0548** 0.489***(0.0241) (0.124)

Constant 13.16*** 12.81***(0.0242) (0.100)

Observations 8,548 8,548R-squared 0.602 0.531Cragg-Donald Wald F statistic 84.75

Angrist-Pischke F test 84.75

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Findi

ngs

  Stunting Wasting Underweight(a) (b) (a) (b) (a) (b)

  Probit IVProbit Probit IVProbit Probit IVProbitResilience -0.367*** -0.868*** -0.284*** -0.817*** -0.140*** -0.785***

(0.0216) (0.137) (0.0224) (0.144) (0.0240) (0.147)

Table 4 Probit and IV Probit models of the N. of malnourished children  Stunting Wasting Underweight

(a) (b) (a) (b) (a) (b)  OLS 2SLS OLS 2SLS OLS 2SLSResilience -0.178*** -0.392*** -0.101*** -0.322*** -0.031*** -0.211***

(0.0101) (0.104) (0.00838) (0.0877) (0.00581) (0.0618)

Table 3 Probit and IV Probit models of having malnourished children

Controlling for HH characteristics and regional dummies.

Controlling for HH characteristics and regional dummies.

Conc

lusio

ns

Household resilience capacity has the potential to:• increase household expenditure • decrease the probability of having

malnourished children• and decrease the number of malnourished

children.-> More evidence from other countries and different HH profiles is needed.

THANK YOU!

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