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