resilience, poverty and malnutrition in mali
DESCRIPTION
Outline Contribution Resilience measurement Data Identification strategy Conclusions Data Findings OutlineTRANSCRIPT
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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
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Outli
ne
Contribution
Resilience measurement
Identification strategy
Conclusions
Data
Findings
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(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
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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
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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.
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Data
Figure 2. HH expenditure by low, medium and high resilience
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Data
Figure 3. Percentage of households with malnourished children by low, medium and high resilience
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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)
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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
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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
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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
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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.
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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.