Children’s Multidimensional Health and Medium-Run

Cognitive Skills in Low- and Middle-Income Countries

Elisabetta AurinoImperial College, London

Francesco BurchiGerman Development Institute, Bonn

Background & Motivation• Child development intrinsic and instrumental

objective of development policy• Literature on Early Child Development (ECD)

– Children’s health in the first 1000 days on lifecourse outcomes

• Limitations– Height as key indicator of health– Enrolment as most used proxy education– Often cross-sectional data

Objectives• To examine the effect of child’s height, the most commonly

used indicator of health, on cognitive abilities at preschool and primary school age in Ethiopia, India, Peru and Vietnam.

• To investigate whether cognitive abilities are better explained by a “suite of health indicators”.

• To analyse whether a composite index of health deprivation in early childhood can synthesise adequately the overall effect of early childhood deprivation in health on children’s later cognitive outcomes

• To explore a few possible channels through which child health may affect cognitive skills.

Child health and mid-run educational achievements: direct effects

• Early childhood health can affect cognitive development in different ways (Jukes 2005).

• Child health typically proxied by low height-for-age

• Associations with:– School completion (e.g. Moock and Leslie 1986 in Nepal; Clark

et al. 1990 in Jamaica; Glewwe and Jacoby 1995 in Ghana; Shariff et al. 2000 in Malaysia; Alderman et al. 2006 in Zimbabwe etc).

– Cognitive abilities (Hoddinott et al. 2008; Behrman et al. 2008)

• Some studies analysed the relationship between child height/stunting and cognitive outcomes of pre-school children (Sanchez 2009; Outes-Leon et al 2011) or of children at age 8 (Crookston et al. 2010, 2013) using Young Lives data.

Child health and mid-run educational achievements: indirect effects

• Child health could have an indirect effect on learning abilities, by increasing educational aspirations.– Dercon and Sanchez (2013) find a positive and large

effect of height-for-age at the age 7-8 on educational aspirations for children of age 11-12

• Ailments or undernutrition may affect long-run cognitive functioning by impacting on short- middle-run health outcomes (i.e. brain development) or affecting other psychosocial competencies such as agency or self-efficacy (Jukes 2005)

Conceptual framework• Child health is viewed as a complex, multi-faceted phenomenon

– About 15% of “healthy years of life” among children aged 0-4 in less developed countries is lost due to mortality and morbidity, and half of the burden of disease is due to communicable diseases (Glewwe & Miguel 2008)

– Most of the infectious diseases affecting children in developing countries are unlikely to affect height-for-age

– Malnutrition itself is a multidimensional phenomenon (i.e. chronic, concurrent, micronutrient deficiencies etc)

• We move from one single measure (height) to a multidimensional assessment.

– Given the complexity of health, and the complexity of its relationship with other human development outcomes, a set of health indicators is likely to be more informative (Strauss & Thomas 2008).

Econometric strategy• We model cognitive outcomes at preschool

and primary school age as a function of children’s early childhood health, and a set of other child- and household- characteristics

• OLS estimation: long-term associations, not causal impact

• Omitted variables bias alleviated by the use of cluster fixed effects and many relevant controls available in the YL study

• Several different models for every dependent variable (2), country (4) and round of survey (2):

1. With only height-for-age

2. With all health indicators separately

3. With composite health deprivation index• Robustness check: addition of variables

related to child’s concurrent nutrition and schooling, and to her cognitive test scores (round 2) were added to explore possible channels.

Econometric strategy (ctd.)

The Young Lives data• Longitudinal study of childhood poverty in

Ethiopia, India (Andhra Pradesh), Peru and Vietnam.

• Younger cohort: 1 y.o. in round 1 (2002), 5 y.o. in round 2 (2006) and 8 y.o. in round 3 (2009).

• Sampling: non-random selection of “sentinel sites”, and then random selection of children in these sites.

• Attrition in the sample is extraordinarily low thanks to a particular effort in tracking children when they move (Outes-Leon and Dercon 2008).

Outcome indicators of learning• At preschool age (5 years, round 2):

– Peabody Picture Vocabulary Test (PPVT): test of vocabulary acquisition.

– Cognitive Developmental Assessment (CDA) test: measures children’s grasp about quantity-related concepts.

• At primary school age (8 years, round 3):– PPVT– Mathematics Achievement Test: measures basic

numeracy skills. 29 simple arithmetical problems.

Indicators of early childhood health

• 2 indicators of nutrition (in z-scores):– Height-for-age (HAZ), proxy for chronic nutritional status,– Weight-for-height (WHZ), proxy for acute nutritional status.

• 1 indicator of morbidity (binary): whether the child has experienced a life-threatening illness or injury since birth (reported by the main caregiver).

• Composite index “Multidimensional Health Poverty Index” (MHPI), a multidimensional score of children’s joint deprivations in the health dimension.

• 3 dimensional cut-offs, equal weights: ranges from 0 (child is not deprived in any dimension) to 1 (deprived in all 3 dimensions)

• ...a possible proxy for child multidimensional poverty!

Descriptive stats of EC health

Height-for-age z-scores

Weight-for-height z-scores

Life-threatening

illness MHPI

EthiopiaMean -1.48 -0.73 0.30 0.29

SD -1.83 1.39 0.46 0.27

IndiaMean -1.3 -1.21 0.22 0.24

SD 1.47 1.06 0.42 0.26

PeruMean -1.28 0.6 0.32 0.2

SD 1.27 1.11 0.47 0.23

VietnamMean -1.12 -0.62 0.13 0.13

SD 1.25 0.95 0.34 0.21

Controls• Child’s gender; age (in months); ethnicity (or caste in

India); child’s mother tongue; disability status; whether the child is first-born; born in a health facility; vaccinations; preschool attendance;

• Caregiver’s sex, age, level of education; • HH size; sex, age and education of the head of the

household; mother’s and father’s presence, 3 composite indicators for HH economic status;

• Robustness check: (1) child’s contemporaneous nutritional status (round 3), as measured by BMI; (2) child school grade completed (round 3); (3) child cognitive attainments (round 2).

Results (I)

Results (II)• As expected, child HAZ has a positive and significant

effect on all cognitive skills, throughout the 4 countries• Only exception is for PPVT scores in round 3 in

Vietnam• The magnitude of the coefficient varies across

countries, outcomes and child developmental stages• WHZ is significantly associated with all the cognitive

outcomes in Andhra Pradesh and with Maths scores in Peru. When significant, the magnitudes of the WHZ coefficients are similar, or slightly smaller, to the ones of the HAZ indicator.

• Especially in India, both forms of malnutrition work in concert to hinder children’s cognitive development

Results (III)• The indicator related to early childhood life-threatening

disease is not significantly associated to any outcomes in any countries, with the exception of Vietnam in Maths.

• MHPI is significant in all the outcomes and rounds considered in Andhra Pradesh and Peru, while it is significant in the case PPVT round 2, CDA and Maths in Vietnam, and only in the case of Maths scores in Ethiopia.

• With MHPI the adjusted R2 falls and we lose information about the specific health dimension that has an influence on cognition

• The suite of indicators is more informative

Robustness checks (I)

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Table 5. Results from the extended models, Ethiopia Round 3 (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES PPVT PPVT PPVT PPVT MATHS MATHS MATHS MATHS Height-for-age z-scores 0.030 0.030 0.018 0.025 0.037 0.037 0.014 0.036 (2.470)** (2.461)** (1.753)* (2.110)** (3.553)*** (3.540)*** (1.540) (3.477)*** Weight-for-height z-scores 0.009 0.016 0.005 0.007 0.027 0.030 0.019 0.027 (0.587) (1.048) (0.344) (0.455) (1.688) (1.790)* (1.327) (1.624) Illness (z-scores) -0.009 -0.008 -0.016 -0.010 0.014 0.014 -0.002 0.012 (-0.888) (-0.791) (-1.579) (-0.833) (0.842) (0.832) (-0.141) (0.671) Enrolled but not completed Grade 1

0.063 0.118

(1.038) (2.583)** Enrolled & completed Grade1 0.283 0.678 (3.972)*** (10.085)*** Enrolled & completed Grades 2 or 3

0.537 0.970

(5.742)*** (7.913)*** BMI-for-age z-score -0.038 -0.016 (-2.155)** (-0.860) CDA raw score R2 z scores 0.118 0.078 (5.663)*** (2.759)** PPVT raw score R2 z scores 0.105 0.079 (2.918)*** (2.478)** Constant -3.613 -3.645 -3.077 -2.989 -2.569 -2.591 -1.723 -2.111 (-7.579)*** (-7.834)*** (-7.231)*** (-6.758)*** (-5.102)*** (-5.110)*** (-4.700)*** (-4.550)*** Observations 1,639 1,638 1,628 1,599 1,596 1,595 1,584 1,560 Number of clusters 20 20 20 20 20 20 20 20 Adj. R-squared 0.140 0.141 0.174 0.179 0.126 0.126 0.263 0.144 R3 core controls YES YES YES YES YES YES YES YES Robust t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 7. Results from the extended models, Peru Round 3 (1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES PPVT PPVT PPVT PPVT MATHS MATHS MATHS MATHS

Height-for-age z-scores

0.037 0.036 0.033 0.026 0.057 0.055 0.043 0.052

(3.789)*** (3.583)*** (3.449)*** (3.490)*** (5.919)*** (5.832)*** (4.232)*** (5.349)***

Weight-for-height z-scores

0.008 0.005 0.005 0.005 0.044 0.037 0.034 0.039

(0.818) (0.605) (0.537) (0.591) (3.423)*** (3.030)*** (2.678)** (2.757)**

Illness (z-scores) -0.004 -0.005 -0.004 -0.001 -0.010 -0.010 -0.009 -0.009

(-0.434) (-0.450) (-0.390) (-0.148) (-0.896) (-0.886) (-0.785) (-0.713)

Completed Grade 2 0.293 0.631

(7.146)*** (7.492)***

Completed Grade 3 or 4

0.368 1.061

(7.318)*** (11.411)***

Bmi-for-age z-scores 0.009 0.022

(0.836) (1.435)

CDA raw score R2 z-scores

0.041 0.106

(4.576)*** (4.474)***

PPVT raw score R2 z-scores

0.198 0.170

(10.046)*** (7.813)***

Constant -3.223 -3.217 -2.761 -2.131 -5.919 -5.901 -2.849 -4.849

(-10.882)*** (-10.709)*** (-6.097)*** (-6.626)*** (-11.809)*** (-11.847)*** (-5.516)*** (-9.466)***

Observations 1,762 1,760 1,752 1,720 1,800 1,798 1,790 1,756

Number of clusters 20 20 20 20 20 20 20 20

R3 core controls YES YES YES YES YES YES YES YES

Adj. R-squared 0.265 0.265 0.286 0.374 0.251 0.251 0.322 0.287

Robust t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

Robustness checks (II)

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Conclusions (1)

1. Does child height affect cognitive skills? With the exception of PPVT scores in round 3 in

Vietnam, the estimates show a positive, highly significant effect

In India a 40% increase of a standard deviation in HAZ would translate into equalising the performances of rural and urban children in CDA scores, while an increase of a standard deviation in HAZ would be equivalent to closing half of the gender gap in Maths scores in Peru in round 3.

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Conclusions (2)2. Does a suite of indicators help understanding the

health effect on learning? Evidence of the relevance of WHZ, proxy for acute

malnutrition, particularly in India, where its coefficient is always significant, and in the case of Maths scores in Peru. However, large heterogeneity.

The morbidity indicator, instead, contributes to explain only Maths scores in Vietnam.

While height remains the key indicator, recognizing the multidimensional nature of health provides additional policy-relevant information, not necessarily at higher costs.

Improve the quality of morbidity indicators

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Conclusions (3)3. Can the whole contribution of children’s health be

summarized by a composite health deprivation index? “Suite of indicators” approach provides substantially

more information. Though, the index can provide a “quick and dirty”

measure for assessing overall health status

4. What are the possible channels? A large part of the early childhood health-cognition

nexus is mediated by variation in grade attainment, especially in Ethiopia, India and for maths skills in Vietnam. However, other channels may play an additional role.

Peru is an outlier

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• Thank you for your attention!– e.aurino@imperial.ac.uk

– And thanks to the thousand Young Lives children, families and communities that rendered possible this study

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