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Child Health Outcomes and
Changes Over Time
By: Sunaina Dhingra
Ph.D. Scholar
Delhi School of Economics
Motivation
• Are improvements in stunting and underweight the same
across the distribution?
52%
42%43%
36%
Stunted Underwe...
Distribution of Height for Age and Change Over Time
Rural Girls
Anthropometric Z-scores Height for Age, Rural Girls, ages ...
Objectives
• What are the principal covariates associated with
anthropometric outcomes for young rural girls, and do
the m...
Data Source and Anthropometric Measures
• Target population- Rural girls in India, under the age of four
years, born to wo...
Empirical Literature
• Evidence of gender differences based on
average nutrient intakes and nutritional status
is mixed
– ...
Quantile regression to model height for age
Covariates include:
– Child’s age in months (age in months) and Age squared
– ...
Machado and Mata (MM) decomposition to quantify
magnitudes of contribution of changes in covariates
and changes in coeffic...
Effect of more than primary education of mother on the HAZ
score of rural girls, relative to at most primary education
-.4...
Effect of household wealth on the HAZ score of rural girls0
.05
.1
.15
.2
HAZscore
.1 .2 .3 .4 .5 .6 .7 .8 .9 1
Quantiles
...
Coefficient of being first born on girl’s HAZ score-.2-.1
0
.1.2.3.4.5.6
HAZscore
.1 .2 .3 .4 .5 .6 .7 .8 .9 1
Quantiles
2...
Effects of having access to improved sources of drinking water
on girls HAZ score
-.4-.3-.2-.1
0
.1.2
HAZscore
.1 .2 .3 .4...
The MM decomposition for height for age, using
coefficients of 2005/06
Quantiles Covariate
Coefficien
t
0.1 7.097183 92.90...
The MM decomposition for height for age, using
coefficients of 1992/93
Deciles
Covar-
iate (%)
Coeff.
(%)
0.1 -49.75 149.7...
Conclusion
Quantile Regression:
• Mothers’ education and being born first are the principal
factors that explain improveme...
Machado and Mata Decomposition Results:
• The covariate effects contribute insignificantly to the change
in the health out...
Thank You !!!
Summary statistics of the explanatory variables
Explanatory Variables
1992/93
(NFHS-1)
2005/06
(NFHS-3)
Mother's Education...
Explanatory Variables
1992/93
(NFHS-1)
2005/06
(NFHS-3)
Caste
SC/ST (dummy)
OBC and Others (omitted)
27.53
72.47
40.15
59....
Quantile Regression
• The θth quantile of the outcome variable, Yi, can be written as
Qθ(y|X) = x’ β(θ ) , given any θ in ...
Individual covariate and coefficient effects overtime on girls
HAZ score using coefficients of 2005/06
Covariate Effect Co...
Individual covariate and coefficient effects overtime on girls
HAZ score using coefficients of 1992/93
Covariate Effect Co...
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
....
Effect of receiving atleast one vaccination on girls HAZ score
-.4-.3-.2-.1
0
.1.2.3.4
HAZscore
.1 .2 .3 .4 .5 .6 .7 .8 .9...
Effect of receiving some form of prenatal care on girl’s HAZ
score
-.4-.3-.2-.1
0
.1.2.3.4
HAZscore
.1 .2 .3 .4 .5 .6 .7 ....
Effects of having access to better toilet facilities on girls HAZ
score, relative to having no toilet facility
Flush Toile...
Effect of being SC/ST on girl’s HAZ score, relative to OBC and
others
-.2-.1
0
.1.2.3.4.5.6
HAZscore
.1 .2 .3 .4 .5 .6 .7 ...
Empirical framework (Objective 2)
Because of significant differences across the distribution, we use
quantile regressions ...
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3 aug 2-sunaina_dhingra

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3 aug 2-sunaina_dhingra

  1. 1. Child Health Outcomes and Changes Over Time By: Sunaina Dhingra Ph.D. Scholar Delhi School of Economics
  2. 2. Motivation • Are improvements in stunting and underweight the same across the distribution? 52% 42%43% 36% Stunted Underweight Change in Stunting and Underweight Prevalence Among Rural Girls Over Time 1992-93 2005-06
  3. 3. 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 -6-5-4-3-2-1 012 Zscores .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 1992/93 2005/06 Height for Age -.2-.1 0 .1.2.3.4.5.6 ChangeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Change over time across quantiles
  4. 4. 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?
  5. 5. 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)
  6. 6. 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
  7. 7. 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
  8. 8. 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: Qθ 2005-06- Qθ 1992-93 = (Qθ 2005-06 - Qθ cf) + (Qθ cf – 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
  9. 9. 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 .1.2.3.4.5.6.7.8 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.5-.4-.3-.2-.1 0 .1.2.3.4.5.6.7.8.9 1 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age
  10. 10. Effect of household wealth on the HAZ score of rural girls0 .05 .1 .15 .2 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.05-.03-.01.01.03.05.07.09.11 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age
  11. 11. Coefficient of being first born on girl’s HAZ score-.2-.1 0 .1.2.3.4.5.6 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.6-.5-.4-.3-.2-.1 0 .1.2.3.4 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age
  12. 12. Effects of having access to improved sources of drinking water on girls HAZ score -.4-.3-.2-.1 0 .1.2 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.6-.5-.4-.3-.2-.1 0 .1.2 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age
  13. 13. The MM decomposition for height for age, using coefficients of 2005/06 Quantiles Covariate Coefficien t 0.1 7.097183 92.90282 0.2 8.581061 91.41894 0.3 5.481875 94.51813 0.7 -6.41174 106.4117 0.8 -3.90792 103.9079 0.9 -13.8879 113.8879 OAXACA -1.67 101.67 -.1 0 .1.2.3.4.5.6 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 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
  14. 14. The MM decomposition for height for age, using coefficients of 1992/93 Deciles Covar- 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 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Total differential Characteristics Coefficients 2005/06 CF using coefficients of 1992/93
  15. 15. Conclusion Quantile 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.
  16. 16. 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.
  17. 17. Thank You !!!
  18. 18. Summary statistics of the explanatory variables Explanatory Variables 1992/93 (NFHS-1) 2005/06 (NFHS-3) Mother's Education Illiterate (dummy) At most Primary (omitted) Above primary (dummy) 64.64 16.14 19.22 50.24 14.44 35.32 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 53.89 21.61 Wealth Score (index using Principal component Analysis) (mean) .0014 -.192
  19. 19. Explanatory Variables 1992/93 (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
  20. 20. 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
  21. 21. 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 1 Quantile -.2-.1 0 .1.2 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantile
  22. 22. Individual covariate and coefficient effects overtime on girls HAZ score using coefficients of 1992/93 Covariate Effect Coefficient Effect -.6-.4-.2 0 .2.4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantile 0 .2.4.6.8 1 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantile
  23. 23. 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 .1.2 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.4-.3-.2-.1 0 .1.2.3.4 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age
  24. 24. Effect of receiving atleast one vaccination on girls HAZ score -.4-.3-.2-.1 0 .1.2.3.4 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.4-.3-.2-.1 0 .1.2.3.4 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age
  25. 25. Effect of receiving some form of prenatal care on girl’s HAZ score -.4-.3-.2-.1 0 .1.2.3.4 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.6-.5-.4-.3-.2-.1 0 .1.2.3.4 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age
  26. 26. 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 .1.2.3.4 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.5-.4-.3-.2-.1 0 .1.2.3.4.5 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age -.2-.1 0 .1.2.3.4.5.6 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.2-.1 0 .1.2.3.4.5.6 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age
  27. 27. Effect of being SC/ST on girl’s HAZ score, relative to OBC and others -.2-.1 0 .1.2.3.4.5.6 HAZscore .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 2005-06 -.6-.5-.4-.3-.2-.1 0 .1.2 changeinZscoresovertime .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles Coeff.(05-06) - Coeff.(92-93) Height for Age
  28. 28. 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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