malnutrition classification and severe malnutrition management
Resilience, Poverty and Malnutrition in Mali
-
Upload
branden-collins -
Category
Documents
-
view
226 -
download
5
description
Transcript of 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.