3 aug 2-sunaina_dhingra
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Child Health Outcomes and Changes Over Time
By: Sunaina DhingraPh.D. Scholar
Delhi School of Economics
Motivation
• Are improvements in stunting and underweight the same across the distribution?
Stunted Underweight
52%
42%43%36%
Change in Stunting and Underweight Pre-valence Among Rural Girls Over Time
1992-93 2005-06
Distribution of Height for Age and Change Over Time Rural Girls
Anthropometric Z-scores Height for Age, Rural Girls, ages 0-4, 1992-93 and 2005-06 , between -6 Std. dev. and +2 std. dev. of the reference median
Change = Q2005-06 – Q1992-93
-6-5
-4-3
-2-1
01
2Z
scor
es
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
1992/93 2005/06
Height for Age
-.2
-.1
0.1
.2.3
.4.5
.6C
ha
ng
e i
n Z
sc
ore
s o
ver
tim
e
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Change over time across quantiles
Objectives
• What are the principal covariates associated with anthropometric outcomes for young rural girls, and do the magnitudes of the coefficients vary across the distribution?
• To what extent can improvements over time be attributed to (a) changes in covariates and (b) differences in coefficients? Do these relative magnitudes also vary over the distribution of the outcomes?
Data Source and Anthropometric Measures
• Target population- Rural girls in India, under the age of four years, born to women between age 15-49 and who are not over nourished ( -6 std dev to +2 std. dev)
• Two rounds of National Family Health Survey – 1992-93 and 2005-06
• Nutritional status indicator expressed in standard deviation units (Z-scores) with reference to the revised WHO-2006 growth charts– Height-for-age z-score (stunting)
Empirical Literature
• Evidence of gender differences based on average nutrient intakes and nutritional status is mixed– Mishra et al. (2004) – Griffiths et al. (2002)
• Evidence based on changes in distributions is limited and is suggestive of gender bias. – Tarozzi and Mahajan (2007)
6
Quantile regression to model height for age
Covariates include:
– Child’s age in months (age in months) and Age squared– Child’s birth order (dummy; reference being birth order ≥ 3)– Mother’s education (dummy; omitted being atmost primary
education)– Mother’s age at the time of birth of the child (age in years) – Prenatal care- (dummy; base category is mother’s who did not
receive any care)– Vaccination received (dummy; reference is children who are
reported with no vaccination received)– Sanitation- (two indicators- type of water and toilet facility,
Reference is access to poor source of drinking water and having no toilet facility)
– Caste (dummy; reference is the OBC and others category)– Economic status (Wealth score generated through principal
component analysis)
7
Machado and Mata (MM) decomposition to quantify magnitudes of contribution of changes in covariates and changes in coefficients in explaining improved outcomes
• Extends Oaxaca-Blinder decomposition to account for changes across distribution
• Uses Quantile regressions and simulated counterfactuals to construct decomposition
• Improvement in outcome (Q) between 1992-3 and 2005-6 can be decomposed as:
Qθ2005-06- Qθ
1992-93 = (Qθ2005-06 - Qθ
cf) + (Qθcf – Qθ
1992-93)
∆’s in + ∆’s in covariates coefficients • Aggregate decomposition: Doesn’t quantify magnitudes of
contribution of each covariate or returns to it in total change
8
Effect of more than primary education of mother on the HAZ score of rural girls, relative to at most primary education
-.4
-.3
-.2
-.1
0.1
.2.3
.4.5
.6.7
.8H
AZ
sc
ore
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06
-.5
-.4
-.3
-.2
-.1
0.1
.2.3
.4.5
.6.7
.8.9
1c
ha
ng
e i
n Z
sc
ore
s o
ver
tim
e
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
Effect of household wealth on the HAZ score of rural girls0
.05
.1.1
5.2
HA
Z s
co
re
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06
-.05
-.03
-.01
.01
.03
.05
.07
.09
.11
chan
ge i
n Z
sco
res
ove
r ti
me
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
Coefficient of being first born on girl’s HAZ score-.
2-.
10
.1.2
.3.4
.5.6
HA
Z s
core
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06
-.6
-.5
-.4
-.3
-.2
-.1
0.1
.2.3
.4ch
ange
in
Z s
core
s ov
er
tim
e
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
Effects of having access to improved sources of drinking water on girls HAZ score
-.4-.3
-.2-.1
0.1
.2H
AZ
sco
re
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06
-.6-.5
-.4-.3
-.2-.1
0.1
.2ch
ange
in Z
sco
res
over
tim
e
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
The MM decomposition for height for age, using coefficients of 2005/06
Quantiles CovariateCoefficient
0.1 7.097183 92.902820.2 8.581061 91.418940.3 5.481875 94.518130.7 -6.41174 106.41170.8 -3.90792 103.90790.9 -13.8879 113.8879
OAXACA -1.67 101.67
-.10
.1.2
.3.4
.5.6
chan
ge in
HA
Z s
core
ove
r ti
me
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Total differential Characteristics Coefficients
1992/93 CF using coefficients of 2005/06
Deciles
Covar- iate
(%) Coeff. (%)
0.1 7.10 92.90
0.2 8.58 91.42
0.3 5.48 94.52
0.7 -6.41 106.41
0.8 -3.91 103.91
0.9 -13.89 113.89
O-B -1.67 101.67
The MM decomposition for height for age, using coefficients of 1992/93
DecilesCovar- iate (%)
Coeff. (%)
0.1 -49.75 149.75
0.2 -6.15 106.15
0.3 7.73 92.27
0.7 37.29 62.71
0.8 43.64 56.36
0.9 57.69 42.31
O-B 8.38 91.62
-.5-.3
-.1.1
.3.5
.7.9
chan
ge in
HA
Z s
core
s ov
er t
ime
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Total differential Characteristics Coefficients
2005/06 CF using coefficients of 1992/93
ConclusionQuantile Regression:• Mothers’ education and being born first are the principal
factors that explain improvements in both periods. The coefficients are higher at the lower quantiles—that is, an increase in education matters much more for the undernourished than the better nourished. In these lower deciles, the coefficient has increased over time, as might be expected. In the rest of the quantiles, however, the change in the value of the coefficients over time is not significant.
• Wealth helps in explaining the improvements at the higher quantiles, implying that returns to wealth have significantly increased for a healthy child.
• Although having access to flush and pit toilet is significant within a given cross section, the change in coefficient value over time is insignificant.
Machado and Mata Decomposition Results:• The covariate effects contribute insignificantly to the change
in the health outcomes of rural girls. Although the contribution appears negative in the lower quantiles it is not statistically significant. Note that there have been improvements in the distribution of covariates over time in general.
• Thus, virtually the entire improvement in outcomes for girls may be attributed to a coefficient effect—this is across all quantiles, though magnitudes vary: they are more pronounced among the under nourished than the better nourished.
• The trend of declining nutrition differentials across quantiles is driven by declining coefficient effects.
Thank You !!!
Summary statistics of the explanatory variablesExplanatory Variables
1992/93 (NFHS-1)
2005/06(NFHS-3)
Mother's Education
Illiterate (dummy)At most Primary (omitted)Above primary (dummy)
64.6416.1419.22
50.2414.4435.32
Sanitation
Improved water facility (dummy)Poor water facility (omitted)Flush toilets (dummy)Pit toilets (dummy)Other toilets (dummy)No toilets (omitted)
51.80 48.10 8.31 12.46 0.06 79.11
76,54 23.42 23.21 11.41 1.85 63.67
Prenatal care
Received some kind of prenatal care (dummy) Received no care (omitted)
45.92 53.69
53.8921.61
Wealth Score (index using Principal component Analysis) (mean) .0014 -.192
Explanatory Variables1992/93
(NFHS-1)2005/06(NFHS-3)
Caste
SC/ST (dummy)OBC and Others (omitted)
27.53 72.47
40.15 59.85
Mother’s age at birth (in years) 24.8 24.9
Vaccination
At least one vaccination (dummy)No vaccination (omitted)
34.49 41.31
57.68 10.65
Child’s Birth Order
Birth Order =1(dummy) Birth Order =2(dummy)Birth Order >=3(omitted)
25.51 24.27 50.22
28.69 25.84 45.48
Child’s age ( in months ) 22.7 23.8
Quantile Regression
• The θth quantile of the outcome variable, Yi, can be written as
Qθ(y|X) = x’ β(θ ) , given any θ in (0,1)• β(θ ) are interpreted as the estimated returns to characteristics
at the θth conditional quantile of the nutritional outcome distribution
• For any given θ in (0,1), β(θ) can be estimated by minimizing in β, (Koenker & Basset, 1978)
n-1 ∑ pθ(yi- Xi’β(θ))
Where, pθ(u) = θ*u if u ≥ 0
= (1- θ)*u if u < 0
Individual covariate and coefficient effects overtime on girls HAZ score using coefficients of 2005/06
Covariate Effect Coefficient Effect
.1.2
.3.4
.5.6
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantile
-.2-.1
0.1
.2
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantile
Individual covariate and coefficient effects overtime on girls HAZ score using coefficients of 1992/93
Covariate Effect Coefficient Effect
-.6-.4
-.20
.2.4
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantile
0.2
.4.6
.81
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantile
Effect of illiterate mother’s on the HAZ score of rural girls, relative to at most primary education
-.6-.5
-.4-.3
-.2-.1
0.1
.2H
AZ
sco
re
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06
-.4-.3
-.2-.1
0.1
.2.3
.4ch
ange
in Z
sco
res
over
tim
e
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
Effect of receiving atleast one vaccination on girls HAZ score-.
4-.
3-.
2-.
10
.1.2
.3.4
HA
Z s
core
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06
-.4
-.3
-.2
-.1
0.1
.2.3
.4ch
ange
in
Z s
core
s ov
er
tim
e
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
Effect of receiving some form of prenatal care on girl’s HAZ score
-.4-.3
-.2-.1
0.1
.2.3
.4H
AZ
sco
re
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06
-.6-.5
-.4-.3
-.2-.1
0.1
.2.3
.4ch
ange
in Z
sco
res
over
tim
e
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
Effects of having access to better toilet facilities on girls HAZ score, relative to having no toilet facility
Flush Toilet Pit Toilet
-.6-.5
-.4-.3
-.2-.1
0.1
.2.3
.4HA
Z sco
re
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06-.5
-.4-.3
-.2-.1
0.1
.2.3
.4.5
chan
ge in
Z sc
ores
over
time
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
-.2-.1
0.1
.2.3
.4.5
.6HA
Z sco
re
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06
-.2-.1
0.1
.2.3
.4.5
.6ch
ange
in Z
scor
es ov
er ti
me
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
Effect of being SC/ST on girl’s HAZ score, relative to OBC and others
-.2-.1
0.1
.2.3
.4.5
.6H
AZ
sco
re
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
2005-06
-.6-.5
-.4-.3
-.2-.1
0.1
.2ch
ange
in Z
sco
res
over
tim
e
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles
Coeff.(05-06) - Coeff.(92-93)
Height for Age
Empirical framework (Objective 2)
Because of significant differences across the distribution, we use quantile regressions to model child anthropometric outcomes. Covariates include:
– Child’s age in months (age in months)– Child’s birth order (reference being birth order ≥ 3)– Mother’s education (three dummiest primary education)– Mother’s age at the time of birth of the child (age in years) – Prenatal care- (base category is mother’s who did not receive any
care)– Vaccination received – (reference is children who are reported with
no vaccination received)– Sanitation- (two indicators- type of water and toilet facility,
Reference is access to un improved water and having no toilet facility)
– Caste ( reference is the OBC and others category)– Economic status (Wealth score generated through principal
component analysis)
28
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